In this paper, we propose an unmanned aerial vehicle (UAV)-enabled massive multiple-input multiple-out (MIMO) non-orthogonal multiple access (NOMA) two-way relay (TWR) system, where multiple pairs of ground users (GUs) aim to exchange their information via an amplify-and-forward (AF) UAV relay. We consider that deploy a hovering rotary-wing UAV equipped with multiple antennas and operating in full-duplex (FD) mode, to provide reliable links to the GUs with emergency communications needs during periods of temporary damage to ground base stations. Minimum mean-square error (MMSE) channel estimation is employed by the UAV to obtain the air-ground channel state information (CSI), while maximum ratio combining/maximum ratio transmission (MRC/MRT) is adopted to process the signals of GUs. Analytical general closed-form expressions are derived for the sum spectrum efficiency (SE) and total energy efficiency (EE) of the proposed system in the case of imperfect CSI, as well as their respective asymptotic expressions. Based on the obtained expressions, the power scaling laws are presented. The numerical results suggest that the proposed system can achieve higher SE performance than terrestrial massive MIMO relay system due to strong line-of-sight (LoS) links, and the advantage is more obvious with more number of antennas. Furthermore, the optimal hovering height of the UAV is explored, which increases as the number of antennas increases. Also, to achieve a trade-off between SE and EE, it is desirable to increase the transmit power of the GUs when increasing the number of antennas at the UAV. Finally, we confirm the proposed scheme can better enhance the SE of multiple GU pairs communication system by comparing NOMA with orthogonal multiple access (OMA), FD and half duplex (HD) schemes.

This study addresses the challenge of achieving high-precision indoor positioning in non-line-of-sight (NLoS) environments through the development of an innovative visible light positioning (VLP) system that utilizes optical intelligent reflecting surfaces (OIRS). Unlike current hybrid methodologies that combine both line-of-sight (LoS) and NLoS techniques tailored for Internet of Things (IoT) environments, our novel single-LED architecture relies solely on signals reflected by an OIRS to facilitate accurate positioning in intricate indoor settings where direct light paths are often obstructed. This system employs a two-stage maximum likelihood estimation framework that effectively integrates received signal strength (RSS) and time-of-arrival (ToA) characteristics, thereby addressing the shortcomings of traditional single-feature methods and ensuring reliable performance in densely populated IoT scenarios. To tackle the non-convex optimization problem, we propose an improved grey wolf optimization (IGWO) algorithm, which exhibits superior positioning accuracy and convergence properties when compared to particle swarm optimization and genetic algorithms. Simulation results substantiate the framework’s efficacy, demonstrating improved positioning accuracy. The proposed system presents a cost-effective solution for complex indoor environments where direct light paths are frequently obstructed, thereby advancing the practical application of VLP technologies.

Conventional visible light positioning (VLP) systems are limited by inadequate positioning accuracy and vulnerability to obstacle occlusion, thereby hindering their deployment in precision-critical applications. To address these challenges, this paper proposes a fusion algorithm that synergistically combines received signal strength (RSS) and angle of arrival (AoA) information. Furthermore, the proposed approach incorporates an intelligent reflecting surface (IRS) framework into the system model, thereby improving system robustness and simultaneously enhancing positioning accuracy under sparse light-emitting diode (LED) deployment, blockage, or non-line-of-sight (NLoS) conditions. Specifically, this paper employs a multi-photodetector (PD) array at the receiver to formulate a system of linear equations based on RSS measurements, which facilitates accurate angle estimation. This derived AoA information is subsequently fused with the RSS data to establish a joint positioning objective function, thereby mitigating the limitations associated with single-parameter approaches. Crucially, an optical IRS is integrated to produce robust NLoS propagation paths, significantly enhancing accuracy in scenarios characterized by a scarcity of LEDs or obstructed line-of-sight (LoS) links, which are common challenges in practical deployments. To address the resulting non-convex optimization problem, a dimension learning-based hunting enhanced grey wolf optimizer (GWO-DLH) is developed, ensuring efficient convergence to the global optimum. Comprehensive simulations conducted under realistic channel models demonstrate that the proposed algorithm achieves a lower root-mean-square error compared to conventional RSS-only or AoA-only methods, while maintaining a computational complexity that is comparable to state-of-the-art techniques. These findings substantiate the algorithm’s effectiveness in balancing accuracy and robustness, thereby providing a foundational framework for the advancement of high-precision indoor optical positioning systems.

The channel state information (CSI) acquisition plays a pivotal role in cell-free (CF) massive multiple-input-multi-output (mMIMO) systems. However, conventional pilot-based channel estimation incurs prohibitive overhead costs as user density and mobility increase. To address this, we propose a multi-slot alternating estimation–prediction (MAEP) framework, which leverages temporal correlation to predict future CSI directly and thereby drastically reduce pilot overhead. The efficacy of the proposed framework hinges on prediction accuracy. Inspired by the remarkable modeling capabilities of large language models (LLMs) and their demonstrated efficacy in cross-modal applications, we introduce an LLM-driven channel predictor termed frequency-temporal alignment with LLM (FTAlign-LLM). FTAlign-LLM bridges the modality gap between CSI and the LLM’s feature space through three key components:(i) a multi-scale CSI attention (MSCA) network for extracting rich spatiotemporal features across frequency and delay domains, (ii) a frequency–temporal feature fusion (FTFF) network that fuses these features and aligns them with the LLM’s feature space, and (iii) the utilization of parameter-efficient fine-tuning for LLM adaptation. Extensive results demonstrate that FTAlign-LLM significantly outperforms benchmarks in prediction accuracy. Concurrently, the MAEP framework achieves substantial improvements in sum spectral efficiency, particularly when a large number of access points are deployed in CF mMIMO systems.

Energy efficiency is of paramount importance for covert integrated sensing and communication (ISAC) networks to ensure sustained operation. In light of the imperfect channel state information (CSI) encountered in practical scenarios, we investigate the energy efficiency of these networks. Taking into account a variety of CSI estimation errors, our algorithm optimizes both sensing and information beamforming design while ensuring a low detection probability by multiple untrusted wardens. The energy-efficient beamforming design is formulated as a non-convex fractional programming problem. First, we establish that the covariance matrices of communication beamforming vectors are rank-one. Subsequently, we exploit this property to transform the original problem into a semi-definite relaxed version. For Gaussian CSI estimation errors, we adopt Bernstein-type inequalities to handle the probability constraints of interception and exploit Dinkelbach’s algorithm to address the nonlinear fractional objective function. For bounded CSI estimation errors, we employ an S-procedure to tackle the non-convex constraints associated with covert communications, followed by a successive convex optimization algorithm to provide an effective solution to the original problem. Extensive simulations confirm the superiority of our proposed algorithms, demonstrating a remarkable performance gain compared with baseline schemes adopting existing approaches. Specifically, deploying a larger number of antenna elements can enhance the energy efficiency of covert ISAC networks, while simultaneously reducing the system’s total power consumption. Furthermore, the sensing beam power threshold and the outage probability of covertness serve as important trade-off parameters in covert ISAC networks.

As a representative backbone architecture of large artificial intelligence models, a Transformer-based architecture is proposed for learning beamforming in pinching antenna systems (PASS), which is calledPASSformer. A joint tri-beamforming optimization problem is developed, i.e., the digital, analog, and pinching beamforming, for fully realizing the benefits of mitigating long-distance path loss. The PASSformer incorporates a graph-based architecture with permutation properties, enabling it to adapt to different system sizes (e.g., varying numbers of users, waveguides, or pinching antennas) without re-training. To improve the learning performance, the PASSformer is with a staged architecture to learn the highly-coupled tri-beamforming. A training algorithm is also proposed to cope with the highly non-smooth loss function. Simulation results demonstrate the flexibility and generalizability of the PASSformer to different system settings and problem scales, enabling it to be deployed across diverse communication scenarios, including single-/multi-cell fully digital and hybrid transmit beamforming. The performance gain over conventional multi-input-multi-output systems brought by the reduced path loss positions is also validated.

A sensing-aided covert communication network empowered by pinching antenna systems (PASS) is proposed in this work. Unlike conventional fixed-position multiple-input multiple-output (MIMO) arrays, PASS reconfigures wireless channels by repositioning pinching antennas (PAs) to enhance transmission covertness. To further obtain the adversary’s channel state information (CSI), a sensing function is leveraged to track the malicious warden’s movements. In particular, this paper first proposes an extended Kalman filter (EKF) based approach to fulfilling the tracking function. Building on this, a covert communication problem is formulated as a joint design of beamforming, artificial noise (AN) signals, and PA positions. Then, the beamforming and AN design subproblems are resolved using a subspace approach, while the PA position optimization subproblem is handled by a deep reinforcement learning (DRL) approach by treating the evolution of the warden’s mobility status as a temporally correlated process. Numerical results are presented and demonstrate that: i) the EKF approach can accurately track the warden’s CSI with low complexity, ii) the effectiveness of the proposed solution is verified by its outperformance over the greedy and searching-based benchmarks, and iii) with new design degrees of freedom (DoFs), the performance of PASS is superior to the conventional fully-digital MIMO systems.

Beam alignment for multiple-input and multiple-output fluid antenna systems (MIMO-FAS) is studied, where two-sided beamforming and port activation are optimized without channel estimation to enhance transmission rate. In contrast to conventional position-fixed MIMO setups, MIMO-FAS leverages flexible beamforming to achieve higher gains with a smaller number of antennas. However, realizing these gains typically requires high-complexity channel estimation methods, especially in MIMO scenarios. To overcome this challenge, a channel estimation-free active-sensing framework for beam alignment in MIMO-FAS is proposed, which consists of three components: 1) A new ping-pong transmission protocol is conceived, enabling full-dimensional pilot reception through sequential sub-array activation. 2) Based on this protocol, two learning-based active-sensing algorithms are proposed for full-dimensional beam alignment via online and offline learning, respectively. 3) A greedy-policy-based method is developed to design the port activation matrices and associated beamforming vectors based on the active-sensing results. Numerical results demonstrate that: i) the proposed active-sensing framework can effectively utilize the advantages of FAS over conventional MIMO systems without channel estimations; and ii) the online-learning method enhances generalizability by eliminating the need for extensive centralized offline training, while the offline-learning method ensures robustness and low-complexity beam alignment by leveraging prior knowledge from the training phase.

A model-driven deep learning framework is proposed for channel estimation in Rydberg atomic quantum receivers (RAQRs) based on the measurement of holographic snapshots. Specifically, we develop a Transformer-based unrolling architecture, termed URformer, to solve the non-linear biased phase retrieval problem, which is derived by unrolling a stabilized variant of the expectation-maximization Gerchberg-Saxton (EM-GS) algorithm. Each layer of the proposed URformer incorporates three trainable modules: 1) a learnable filter network that replaces the fixed Bessel kernel in the classic EM-GS algorithm; 2) a trainable gating mechanism that adaptively combines classic updates to ensure training stability; and 3) an efficient channel Transformer module that learns to correct residual errors by capturing non-local channel dependencies. Numerical results demonstrate that the proposed URformer significantly outperforms classic iterative algorithms and conventional black-box neural networks with less pilot overhead.

Pinching antenna (PA) is a flexible antenna composed of a waveguide and multiple dielectric particles, which is capable of reconfiguring wireless channels intelligently in line-of-sight links. By leveraging the unique features of PAs, we exploit the uplink (UL) transmission in pinching antenna systems (PASS). To comprehensively evaluate the performance gains of PASS in UL transmissions, three scenarios, multiple PAs for a single user (MPSU), a single PA for a single user (SPSU), and a single PA for multiple users (SPMU) are considered. The positions of PAs are optimized to obtain the maximal channel gains in the considered scenarios. For the MPSU and SPSU scenarios, by applying the optimized position of PAs, closed-form expressions for analytical, asymptotic and approximated ergodic rate are derived. As the further advance, closed-form expressions of approximated ergodic rate is derived when a single PA is fixed in the SPMU scenario. Our results demonstrate the following key insights: i) The proposed PASS significantly outperforms conventional Multiple-input Single-output networks by exploiting the flexibility of PAs; ii) The PA distribution follows an asymmetric non-uniform distribution in the MPSU scenario; iii) Optimizing PA positions significantly enhances the ergodic sum rate performance.

With the increasing carrier frequency in next-generation wireless networks, conventional discrete aperture arrays (DAPA) are unable to fully meet the growing demands of sixth-generation wireless networks. To provide a higher degree of freedom, the continuous-aperture array (CAPA) technique emerges as a promising solution for next-generation networks. In this article, a CAPA-aided network is proposed to deliver access services to multiple users. The transmission power of a two-user pair in the downlink is optimized by using the Fourier transformation to derive tractable results. With the aid of Karush-Kuhn-Tucker (KKT) conditions, closed-form expressions for both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are derived. Time division multiple access (TDMA) and spatial division multiple access (SDMA) are also considered as OMA schemes. Furthermore, DAPA is included as a benchmark for comparison. Our analytical and numerical results demonstrate the following: i) The proposed KKT-based approach achieves optimal performance in transmission power; ii) The minimal required transmission power for NOMA is lower than that for the OMA benchmark schemes; iii) A performance gap is observed between CAPA and DAPA in both NOMA and OMA, emphasizing the advantages of CAPA-aided networks.

Edge computing enables distributed inference for computation-intensive applications. However, the autoregressive nature and large model size of large language models (LLMs) pose challenges for their deployment in wireless edge networks. Existing edge inference methods mainly assume an equivalent delay for each token generation step, which fails to capture the dynamic computational and memory overhead incurred during the decoding process. This paper proposes a latency-sensitive wireless edge inference framework for LLMs, where tasks are grouped into multiple batches and processed in parallel by partitioning the LLM into multiple pipeline stages across heterogeneous edge GPUs. An accurate latency model is established, where the latency of each token generation step increases during the autoregressive generation process. Based on this model, the end-to-end inference latency is minimized, which is formulated as a joint optimization problem of bandwidth allocation, model partitioning, and batch scheduling subject to heterogeneous GPU memory constraints. To solve this NP-hard problem with coupled variables, we develop a polynomial-time alternating optimization algorithm that iteratively optimizes model partitioning and batch scheduling via dynamic programming. The closed-form solutions of wireless bandwidth allocation are derived. Extensive simulations show that our approach reduces latency by up to 42.1% versus state-of-the-art baselines across diverse edge scenarios.

The fundamental sensing limit of pinching-antenna systems (PASS) is studied from a Bayesian Cramér-Rao bound (BCRB) perspective. Compared to conventional CRB, the BCRB is independent of the exact values of sensing parameters and is not restricted by the unbiasedness of estimators, thus offering a global lower bound for evaluating sensing performance. A system where multiple targets transmit uplink pilots to a single-waveguide PASS under a time-division multiple access (TDMA) scheme is analyzed. In the single-target scenario, our analysis reveals a unique mismatch between the sensing centroid (i.e., the PA position that minimizes the BCRB) and the distribution centroid (i.e., the center of the target’s prior distribution), underscoring the necessity of pinching beamforming, i.e., repositioning PAs along the waveguide. In the multi-target scenario, two scheduling protocols are proposed: 1) pinch switching (PS), which performs separate pinching beamforming for each time slot, and 2) pinch multiplexing (PM), which applies a single pinching beamforming across all slots. Based on these protocols, both the total power minimization problem under a BCRB threshold and the min-max BCRB problem under a total power constraint are formulated. By leveraging Karush-Kuhn-Tucker (KKT) conditions, these problems are equivalently converted into a search over PA positions and solved using an element-wise algorithm. Numerical results show that: i) PASS, endowed with large-scale reconfigurability, can significantly enhance the sensing performance compared with conventional fixed-position arrays, and ii) PS provides more robust performance than PM at the cost of higher computational complexity.

In this paper, we investigate the channel estimation problem in near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems, explicitly accounting for both spherical-wave propagation characteristics and spatial non-stationary effects. Building on these properties, we propose a novel model-based deep learning framework that delivers high-accuracy channel estimation with low computational complexity by tightly integrating domain knowledge and data-driven learning. Specifically, the proposed framework comprises three key unfolding networks: a sparse channel recovery network, a codebook update network, and an error cancellation network. The first network, referred to as variational Bayesian inference (VBI)-Net, is derived by unfolding the inverse-free VBI (IF-VBI) algorithm. It enables high-precision sparse channel reconstruction without requiring explicit prior assumptions, by learning the underlying precision distribution directly from data. The second network, gradient (Grad)-Net, is developed by unfolding the gradient ascent procedure, where learnable step sizes are introduced to adaptively refine the parameters of the polar-domain grids. Moreover, Grad-Net captures spatial non-stationary characteristics associated with the polar-domain representation by jointly exploiting gradient information and estimated path parameters. The third network, termed projected gradient descent (PGD)-Net, is constructed by unfolding the PGD algorithm. It iteratively refines the channel estimates and effectively suppresses residual estimation errors induced by spherical-wave propagation and spatial non-stationarity. Extensive numerical simulations demonstrate that the proposed framework significantly outperforms existing methods in both estimation accuracy and computational efficiency. Furthermore, the proposed framework achieves a superior accuracy-complexity tradeoff for practical XL-MIMO systems, delivering enhanced performance while maintaining very low computational complexity.

Semantic communication (SemCom) has recently emerged as a promising approach to enhance communication efficiency by transmitting only important semantic information. In eavesdropping environments, the semantic information may be overheard by the eavesdropper even under poor channel conditions with a powerful semantic decoder. Some advanced secure communication deployments such as autoencoder-enhanced Sem-Com (AE-SemCom) and generative model-enhanced SemCom (GEN-SemCom) have been introduced to efficiently transmit the semantic information. However, these secure solutions require retraining the semantic encoder and decoder, which limits the flexibility and practical deployment. To address this issue, we propose a secure SemCom framework for AE-SemCom and GEN-SemCom without altering the parameters of the semantic encoder and decoder. Specifically, we propose to deploy a cooperative jammer to transmit optimized jamming signal to deteriorate the decoding ability of eavesdropper, while a residual refinement module (RRM) is used at the legitimate receiver to mitigate the impact of these jamming signal. Moreover, to achieve an efficient trade-off between transmission security and reliability, we then introduce a cooperative training strategy by formulating this optimization problem as a two-player game between the jammer and Bob. Finally, we conduct extensive simulations to evaluate the effectiveness of the proposed framework across face recognition dataset. In particular, with a jamming power of 0.3, the eavesdropper experiences up to a 12dB reduction in the peak signal-to-noise ratio (PSNR) of reconstructed images compared to the baselines without the proposed security framework while maintaining high-quality image reconstruction for legitimate users.

This paper proposes a robust received signal strength (RSS)-based localization framework for non-cooperative unmanned aerial vehicles. Conventional RSS methods face three fundamental obstacles: susceptibility to heavy-tailed measurement noise, intractable non-convexity, and severe accuracy degradation when target transmission power is unknown. These vulnerabilities present critical security risks to emerging low-altitude economy networks. To overcome these limitations, we propose an integrated joint-estimation architecture. First, a cascaded preprocessing pipeline, combining Gaussian outlier suppression and statistical median weighting, is developed to mitigate multipath-induced biases and minimize variance. Second, an information-theoretic base station (BS) selection mechanism is designed to identify geometrically optimal BSs, thereby exponentially reducing computational overhead in both uniform and random deployment scenarios. Third, the power-unknown problem is reformulated via semidefinite programming, absorbing the unknown parameter into a higher-dimensional convex cone to guarantee global convergence without relying on initial guesses. Extensive Monte Carlo simulations demonstrate that under uniform BS deployment, our strategy achieves sub-10-meter accuracy (approximately 5 m root mean square error) using only 5 selected BSs in typical urban conditions with a path loss exponent of 3. Consequently, this approach delivers a highly accurate and computationally efficient solution for real-time target tracking in complex environments.

The fundamental capacity limits of pinching-antenna systems (PASS)-based multi-user communication network are investigated. Two practical pinching-antenna configuration strategies are considered, namelymultiple-time discrete activationandone-off continuous sliding. For each strategy, the capacity and rate regions are characterized under the non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes, respectively. 1) For NOMA, the capacity region achieved by the discrete activation is first characterized. In particular, the ideal case with the infinite number of activation times is considered, where the optimal PA activation and resource allocation scheme is derived. It is shown that different user groups or decoding orders are served via time-sharing. Inspired by this result, an inner bound of capacity region is obtained for the practical case with a finite number of activation times. Then, for the continuous sliding case, the inner bound of capacity region is characterized by alternately optimizing the resource allocation and PAs’ continuous positions. 2) For OMA, the rate region is first obtained for the discrete activation case. It is unveiled that multiple users are successively served. Then, by alternately optimizing the resource allocation and PAs’ continuous positions, the inner bound of rate region is obtained for the continuous sliding case. Numerical results demonstrate that i) PASS can significantly improve the capacity performance compared with the conventional fixed-antenna systems; ii) the capacity gain can be further enhanced by using the proposed PA activation and sliding and resource allocation schemes; and iii) the continuous sliding has the potential to outperform the discrete activation in NOMA, whereas the latter performs better in OMA.

The escalating complexity of deep neural networks introduces substantial challenges to deploying federated learning (FL) in resource-limited edge environments. To address these limitations, split federated learning (SFL) has emerged as a promising paradigm, alleviating client-side computational and communication burdens via strategic model splitting, and periodically aggregating client-side and server-side models consistent with the principles of FL. Nevertheless, existing SFL frameworks encounter significant performance degradation arising from data heterogeneity and imbalance, client heterogeneity, as well as constrained wireless resources. To overcome these issues, this paper introduces a novel data distribution deviation-aware split federated learning (DA-SFL) framework. DA-SFL dynamically adjusts aggregation weights according to the deviation of clients’ data distributions from a global distribution, effectively mitigating biases induced by data imbalance and heterogeneity. Furthermore, we theoretically establish the convergence bound of DA-SFL under a non-convex loss function setting, demonstrating that minimizing the data deviation in each training round enhances learning efficacy. Motivated by this, we formulate a mixed-integer nonlinear programming to optimize learning performance under long-term energy constraints. Leveraging the Lyapunov optimization framework, we decompose the problem into a series of tractable subproblems in each learning round, and propose efficient algorithms to find the client scheduling, adaptive cut layer selection, bandwidth allocation, and aggregation weighting policies. Extensive experimental evaluations conducted on Fashion-MNIST, CIFAR-10, and CINIC-10 datasets across diverse scenarios of data heterogeneity and imbalance demonstrate that DA-SFL significantly outperforms baselines regarding test accuracy, time and energy efficiency, while exhibiting notable robustness and scalability.

Pinching-antenna systems (PASS) improve wireless links by configuring the locations of activated pinching antennas along dielectric waveguides, namely pinching beamforming. In this paper, a novel adjustable power radiation model is proposed for PASS, where power radiation ratios of pinching antennas can be flexibly controlled by tuning coupling spacing between pinching antennas and waveguides. The closed-form coupling spacings are derived to achieve flexible and equal-power radiation. Based on the commonly-assumed equal-power radiation, a practical PASS framework relying on discrete activation is considered, where pinching antennas can only be activated among a set of predefined locations. A transmit power minimization problem is formulated, which jointly optimizes the transmit beamforming, pinching beamforming, and the numbers of activated pinching antennas, subject to each user’s minimum rate requirement. (1) To obtain globally optimal solutions of the resulting highly coupled mixed-integer nonlinear programming (MINLP) problem, branch-and-bound (BnB)-based algorithms are proposed for both single-user and multi-user scenarios. (2) A low-complexity many-to-many matching algorithm is further developed. Combined with the Karush-Kuhn-Tucker (KKT) theory, locally optimal and pairwise-stable solutions are obtained within polynomial-time complexity. Simulation results demonstrate that: (i) PASS significantly outperforms conventional multi-antenna architectures, particularly when the number of users and the spatial range increase; and (ii) The proposed matching-based algorithm achieves near-optimal performance, resulting in only a slight performance loss while significantly reducing computational overheads. Code is available at https://github.com/xiaoxiaxusummer/PASS_Discrete.

This paper studies the physical-layer security (PLS) for an unmanned aerial vehicle (UAV)-aided non-orthogonal multiple access (NOMA) network, where transmissions from a base station (BS) to a trusted near user (TNU) and an untrusted far user (UFU) are assisted by an UAV relay in the face of multiple non-colluding external eavesdroppers (Es). The traditional strategies such as artificial noise-aided friendly jammer (TAN-FJ), artificial noise with non-friendly jammer (TAN-NFJ), and reconfigurable intelligent surface (RIS)-aided schemes fail to simultaneously guarantee the secrecy for both TNU and UFU, thus how to guarantee the PLS for NOMA users becomes an urgent issue to be solved. In this context, we propose an artificial noise (PAN) scheme aided with digital network coding (DNC). Specifically, the artificial noise signals generated by UAV are resorted to encrypt confidential signals with the assistance of DNC, which realizes one-time pad, thus the PLS of TNU and UFU can be ensured. We resort security-reliability tradeoff (SRT) as a metric to evaluate the PLS for our proposed PAN scheme. Hence, the exact and asymptotic expressions of outage probability (OP) and intercept probability (IP) are derived to quantify the reliability and security. Moreover, numerical simulations validate the correctness of our theoretical results and reveal that: 1) the PAN scheme enhances the SRT performance of near user compared to the TAN-FJ, TAN-NFJ, and RIS-aided schemes, while far user by the PAN scheme achieves almost the same SRT performance as the TAN-FJ scheme and significantly better performance than the TAN-NFJ and RIS-aided schemes, which indicates that the PAN scheme realizes the fairness of secrecy transmissions for NOMA users; 2) the SRT performance of TNU benefits from multiple antennas at BS and UAV, which has ignorable effect on the SRT performance of UFU; 3) the antenna number at UAV has more obvious impact on the SRT of TUN than the antenna number at BS.

In this paper, we contribute to the beamforming design problem with imperfect channel state information in a monostatic integrated sensing and communication (ISAC) system. We propose a robust waveform design framework tailored for monostatic ISAC systems, incorporating a channel random error vector to account for imperfections in the communication channels, and addressing clutter interference in radar sensing received waveforms. Next, we derive expressions for target estimation performance via utilizing the minimum mean squared error criterion and the Cramer-Rao bound, which serve as objective functions for our beamforming design. Additionally, we introduce signal-to-interference-plus-noise ratio outage probability constraints and power constraints, formulating two different beamforming optimization problems. Utilizing semi-definite programming techniques, we reformulate these optimization problems into convex optimization problems and resolve them via a convex toolbox. Finally, our simulation results achieves 45% sensing gain and 37% communication gain at an SINR threshold of 20 dB compared with the baseline.

The rapid advancement of autonomous driving technologies has imposed stringent requirements on low-latency and high-reliability computation, which often exceed the capabilities of onboard processors. Vehicular edge computing (VEC) provides a promising solution by offloading computation to external servers; however, terrestrial infrastructure suffers from fragmented coverage and limited scalability, particularly in highway and rural scenarios. To address these limitations, this paper considers a multi-layer aerial VEC network integrating a high-altitude platform and multiple unmanned aerial vehicles (UAVs) to jointly provide wide-area coverage and proximity services. Different from existing works that primarily focus on latency minimization under homogeneous resources, this paper explicitly models the heterogeneous leasing pricing of aerial platforms and investigates its impact on task offloading decisions. A joint task offloading and resource allocation problem is formulated to minimize the total system cost, defined as a weighted combination of latency and economic expenditure. To ensure the feasibility of UAV-assisted offloading under high mobility, a dwell-time constraint is incorporated to restrict task execution within the effective service duration. The resulting problem is formulated as a mixed-integer nonlinear programming problem, which is solved via a low-complexity iterative algorithm based on Lagrangian duality, linear relaxation, and the alternating direction method of multipliers. Simulation results demonstrate that the proposed scheme achieves significant cost reduction compared with benchmark strategies, especially under high-mobility conditions.

Mobile Edge Generation (MEG) is presented as a distributed framework in which an identical diffusion model (DM) is deployed on both an edge server (ES) and user equipment (UE). In MEG, most computations and generation steps of UEs are offloaded to the ES. However, heterogeneous user preferences cannot be captured by a uniform DM. To address this, a Personalized Mobile Edge Generation (P-MEG) framework is proposed, where a lightweight personalized U Net is trained on the UE in collaboration with the pre-trained DM from the ES. During inference, pre-trained ES features are fused with UE features through scaling coefficients that encode user-specific preferences. The training stability of P MEG and the robustness of feature fusion under noisy wireless channels are theoretically investigated, where bounds are derived on forward and backward feature oscillations, backpropagation gradients, and feature fusion errors in the presence of additive white Gaussian noise (AWGN) noise. These bounds are shown to depend on the fusion scale, and robustness under AWGN follows the same dependence. A multi-U-Net training model with AWGN perturbations is introduced to emulate over-the air training. Inspired by these insights, a constant scaling connection (CSC) method is proposed to stabilize training by exponentially scaling the fusion coefficients, and a random mask training (RMT) strategy is introduced to reduce computational requirements by adjusting transmission ratios of personalized features. Experimental evaluations on MNIST, EMNIST and PACS demonstrate that: 1) P-MEG enables effective personalized image generation, 2) RMT alleviates computational demands with only slight training overhead, and 3) CSC stabilizes feature oscillations under noisy channels, yielding a 1.4-fold acceleration in training.

As a complementary solution for Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAVs) can temporarily provide reliable and flexible offloading services when edge servers are damaged or unavailable. However, existing UAV-assisted MEC systems suffer from issues such as uneven resource allocation, low utilization efficiency, load imbalance, and poor dynamic adaptability, affecting service quality. Moreover, sensitive user equipment (UE) information faces leakage during the computational process of UAVs. How to jointly optimize the scheduling of servers and UAVs for task offloading and resource allocation without compromising UEs' privacy remains a significant challenge. Thus, this paper proposed a privacy-preserving auction framework (namely Prizty) by considering the trajectory of UAVs, their constrained energy and computational capabilities, and the variability in UE distribution. Prizty employs a combinatorial obfuscation method to protect UEs' privacy and links bidding prices to computational resources and energy characteristics. It calls the sub-algorithm WPA to determine the winners by balancing social costs and utility. Theoretical analysis demonstrates that Prizty satisfies truthfulness and individual rationality while maintaining scalability for large-scale resource allocation problems. Extensive experiments on real-world datasets validate Prizty's effectiveness in critical metrics, including offload rate, average service latency, energy consumption, and social cost.

Satellite network is envisioned as a key enabler for wide-area coverage and seamless connectivity. Nonetheless, satellite-terrestrial communications are confronted with critical challenges, including significant signal attenuation and complex inter-satellite interference, particularly in ensuring uplink performance for power-constrained ground users. Reconfigurable intelligent surface (RIS) is considered as a promising solution to compensate for the severe path loss, owing to their high-gain and dynamic beamforming capabilities. In addition, with the deployment of ultra-dense satellite constellations, multi-satellite cooperation is expected to effectively mitigate inter-satellite interference and enhance channel gains. In this paper, a novel RIS-aided multi-satellite cooperative reception scheme is proposed for multi-user uplink transmission. We first analyze five types of multi-satellite cooperative reception framework within cell-free paradigm and formulate a multi-user total energy efficiency (EE) maximization problem. Based on block coordinate descent method, this nonconvex optimization problem is decomposed into three subproblems: receiver detection vector design, RIS phase shift optimization, and multi-user transmit power control. Maximum ratio combining, zero forcing, and minimum mean square error are adopted for linear detection vector designs, respectively. Then the RIS phase shift is optimized based on Riemannian conjugate gradient algorithm. Furthermore, multi-user transmit power control is designed using Dinkelbach’s algorithm. Finally, the total EE optimization problem is solved in an iterative manner. Extensive simulation results validate the effectiveness of the proposed reception scheme and algorithm.

A DeepCAPA (Deep Learning for Continuous Aperture Array (CAPA)) framework is proposed to learn beamforming in CAPA systems. The beamforming optimization problem is first formulated, and it is mathematically proved that the optimal beamforming lies in the subspace spanned by users’ conjugate channel responses. Two challenges are encountered when directly applying deep neural networks (DNNs) for solving the formulated problem, i) both the input and output spaces are infinite-dimensional, which are not compatible with DNNs. The finite-dimensional representations of inputs and outputs are derived to address this challenge. ii) A closed-form loss function is unavailable for training the DNN. To tackle this challenge, two additional DNNs are trained to approximate the operations without closed-form expressions for expediting gradient back-propagation. To improve learning performance and reduce training complexity, the permutation equivariance properties of the mappings to be learned are proved. As a further advance, the DNNs are designed as graph neural networks to leverage the properties. Numerical results demonstrate that: i) the proposed DeepCAPA framework achieves higher spectral efficiency and lower inference complexity compared to existing numerical algorithms, ii) DeepCAPA approaches the performance upper bound of optimizing beamforming in the spatially discrete array systems as the number of antennas in a fixed-sized area tends toward infinity, and iii) DeepCAPA can be well-generalized to different system settings.

Fluid antenna systems (FAS) are among the most promising technologies for the sixth generation (6G) mobile communication networks. Unlike traditional fixed-position multiple-input multiple-output (MIMO) systems, a FAS possesses position reconfigurability to switch on-demand amongNpredefined ports over a prescribed space. This paper explores the performance of a single-input single-output (SISO) model with a fixed-position antenna transmitter and a single-antenna FAS receiver, referred to as the Rx-SISO-FAS model, under spatially-correlated Rician fading channels. Our contributions include exact expressions and closed-form bounds for the outage probability of the Rx-SISO-FAS model, as well as exact and closed-form lower bounds for the ergodic rate. Importantly, we also analyze the performance considering both uniform linear array (ULA) and uniform planar array (UPA) configurations for the ports of the FAS. To gain insights, we evaluate the diversity order of the proposed model and our analytical results indicate that with a fixed overall system size, increasing the number of ports,N, significantly decreases the outage performance of FAS under different Rician fading factors. Our numerical results further demonstrate that:i) the Rx-SISO-FAS model can enhance performance under spatially-correlated Rician fading channels over the fixed-position antenna counterpart;ii) the Rician factor negatively impacts performance in the low signal-to-noise ratio (SNR) regime;iii) FAS can outperform anLbranches maximum ratio combining (MRC) system under Rician fading channels; andiv) when the number of ports is identical, UPA outperforms ULA.

A Cramér-Rao bound (CRB) optimization framework for near-field sensing (NISE) with continuous-aperture arrays (CAPAs) is proposed. In contrast to conventional spatially discrete arrays (SPDAs), CAPAs emit electromagnetic (EM) probing signals through continuous source currents for target sensing, thereby exploiting the full spatial degrees of freedom (DoFs). The maximum likelihood estimation (MLE) method for estimating target locations in the near-field region is developed. To evaluate the NISE performance with CAPAs, the CRB for estimating target locations is derived based on continuous transmit and receive array responses of CAPAs. Subsequently, a CRB minimization problem is formulated to optimize the continuous source current of CAPAs. This results in a non-convex, integral-based functional optimization problem. To address this challenge, the optimal structure of the source current is derived and proven to be spanned by a series of basis functions determined by the system geometry. To solve the CRB minimization problem, a low-complexity subspace manifold gradient descent (SMGD) method is proposed, leveraging the derived optimal structure of the source current. Our simulation results validate the effectiveness of the proposed SMGD method and further demonstrate that i) the proposed SMGD method can effectively solve the CRB minimization problem with reduced computational complexity, and ii) CAPA achieves a tenfold improvement in sensing performance compared to its SPDA counterpart, due to full exploitation of spatial DoFs.

Integrated sensing and communication (ISAC) technology presents promising prospects for improving spectral efficiency, enabling hardware resource sharing, and facilitating novel application scenarios. Nevertheless, the mutual interference between communication and sensing remains a critical barrier to overcome. As an effective interference management solution, both non-orthogonal multiple access (NOMA) and rate splitting multiple access (RSMA) demonstrate unique advantages in interference suppression, which are expected to substantially enhance the overall performance of ISAC systems. To this end, the design options of NOMA versus RSMA for the ISAC systems are investigated in this paper. Furthermore, due to the inherent complexities in both signal propagation characteristics and receiver processing architectures, channel estimation errors (CEEs) and imperfect successive interference cancellation (ipSIC) are incorporated during system modeling. Against the above background, the communication and sensing performance of the NOMA and RSMA ISAC systems is analyzed by respectively deriving the exact and asymptotic outage probabilities (OPs) and ergodic rates (ERs) for the users, the probability of detection (PoD) for the base station and Cramér-Rao bound (CRB). The numerical results demonstrate that RSMA outperforms NOMA in terms of OPs, ERs, PoD, and CRB.

The distributed deployment of communication infrastructure is a promising evolutionary trend in the next-generation wireless communication systems, as exemplified by the novel cell-free massive multiple-input multiple-output (CF mMIMO) technology. In user-centric CF mMIMO systems, synchronization among access points (APs) is a critical challenge that significantly impacts the effectiveness of coherent joint processing gains. In this paper, we investigate a CF mMIMO system featuring distributed AP deployments and low-resolution analog-to-digital converters (ADCs). To guarantee precise phase synchronization, we first propose two double-layer AP clustering approaches for rapid synchronization using the Leader-Follower paradigm: one based on the K-means algorithm and the other utilizing classical graph theory with geographical distance metrics in AP deployment. Specifically, in the first layer, a designated Leader AP keeps synchronization with its serving secondary Follower-1 APs, while in the second layer, each Follower-1 AP communicates with its neighboring Follower-2 APs. Next, we propose novel phase synchronization and carrier frequency synchronization strategies among APs based on an over-the-air synchronization signal transmission mechanism, which enables mutual calibration without transmitting any measurements to the central processing unit via fronthaul links. Furthermore, we consider the effect of quantization accuracy of radio frequency hardware on synchronization performance, thereby facilitating the adoption of low-cost components. Finally, simulation results demonstrate that synchronization precision can be significantly improved, reaching values on the order of$10^{-5}$. Additionally, even with moderately coarse ADC quantization, near-optimal performance can be achieved in practical scenarios.

A novel mobile edge generation (MEG) framework is proposed to efficiently operate large models at edge networks for low-latency image generation. The generation of large-scale image content is split into two parts, namely primary and secondary regions, with an adjustable generation splitting ratio. The primary region is generated by a large generative model (LGM) at the edge cloud and then transmitted to the mobile device, whereas the remaining secondary regions is created by a tiny generative model (TinyGM) at the mobile device, thus reducing transmission and computation overheads. Both single-user and multi-user cases are considered to characterize the tradeoff between mobile energy consumption and generation delay. For the single-user case, a multi-objective programming (MOP) is formulated for the joint optimization of generation splitting and mobile power control, which simultaneously minimizes the generation delay and mobile energy consumption. This MOP is transferred into single-objective optimization using the ϵ-constraint method. The closed-form optimal solution is derived to obtain Pareto-optimal energy-delay (E-D) region. It is revealed that MEG achieves significant performance gains then conventional fully edge generation (FEG) when signal-to-noise ratio (SNR) or mobile generative cost is low. For the multi-user case, a joint generation splitting and resource allocation problem is formulated, which minimizes the maximum generation delay subject to ϵ-bounded mobile energy consumption and resource constraints. An McCormick-relaxation branch-and-bound (M-BnB) algorithm is proposed to obtain the globally optimal solution. Simulation results demonstrate the Pareto-optimal E-D region in single-user and multi-user cases. Furthermore, MEG flexibly reduces delay compared to conventional FEG and model split schemes while maintaining generative quality.

A novel pinching antenna system (PASS)-enabled downlink multi-user multiple-input single-output (MISO) framework is proposed. PASS consists of multiple waveguides spanning over thousands of wavelength, which equip numerous low-cost dielectric particles, named pinching antennas (PAs), to radiate signals into free space. The positions of PAs can be reconfigured to change both the large-scale path losses and phases of signals, thus facilitating the novelpinching beamformingdesign. A sum rate maximization problem is formulated, which jointly optimizes the transmit and pinching beamforming to adaptively achieve constructive signal enhancement and destructive interference mitigation. To solve this highly coupled and nonconvex problem, both optimization-based and learning-based methods are proposed. 1) For the optimization-based method, a majorization-minimization and penalty dual decomposition (MM-PDD) algorithm is developed, which handles the nonconvex complex exponential component using a Lipschitz surrogate function and then invokes PDD for problem decoupling. 2) For the learning-based method, a novel Karush-Kuhn-Tucker (KKT)-guided dual learning (KDL) approach is proposed, which enables KKT solutions to be reconstructed in a data-driven manner by learning dual variables. Following this idea, a KDL-Transformer algorithm is developed, which captures both inter-PA/inter-user dependencies and channel-state-information (CSI)-beamforming dependencies by attention mechanisms. Simulation results demonstrate that: i) The proposed PASS framework significantly outperforms conventional massive multiple input multiple output (MIMO) system even with a few PAs. ii) The proposed KDL-Transformer can improve over 20% system performance than MM-PDD algorithm, while achieving a millisecond-level response on modern GPUs.

Cell-free massive multiple-input multiple-output (MIMO) systems have emerged as a transformative architecture for sixth generation (6G) communication networks, where distributed access points (APs) collaborate to simultaneously serve all user equipments (UEs). However, in time division duplex (TDD) systems, the reciprocity of uplink channel and downlink channel is disrupted by hardware imperfections in radio frequency (RF) chains, leading to significant degradation in system performance. This paper begins with a theoretical analysis of the downlink performance under a conjugate beamforming scheme, considering scenarios with and without channel calibration. A key theoretical insight highlights the limitation of conventional least squares (LS) calibration method, which fails to achieve high calibration accuracy even with an unlimited number of pilot observations. To overcome this limitation, we propose a novel channel calibration approach based on a diffusion model, designed to successively refine the calibration vector obtained from the LS calibration method. Furthermore, to address the shortcomings of conventional denoising diffusion probabilistic model (DDPM) training architectures, we introduce an innovative bridge-based diffusion model that maps the distribution of LS calibration vectors to their perfect counterparts. The proposed diffusion neural network architecture employs a conditional generative process, integrating a message passing neural network (MPNN) to incorporate domain-specific calibration insights. Numerical results demonstrate the superior performance of our proposed calibration method compared to existing methods, with supplementary experiments and in-depth analyses confirming the efficacy of the proposed successive refinement design.

In this paper, we design a novel two-stage transmission framework in millimeter wave-ISAC systems with multiple communication users (CUs) and multiple target scenarios. In stage I, the dual-functional base station (DFBS) performs beam scanning with pilot signals, estimating target direction of arrival angles (DoAs) through the maximum likelihood estimation and multiple signal classification techniques, while the CU estimate DoAs via minimum mean square error and MUSIC techniques. Further, we derive the closed-form Cramér-Rao Bound (CRB) expressions for estimated CU/target DoAs and establish the relationship between channel station information (CSI) error and CRB. In stage II, the DFBS transmits ISAC signals and maximizes the minimum effective signal-to-interference-plus-noise ratio (SINR) of CU by jointly optimizing two stage resources, while meeting sensing performance requirements and accounting for the impact of imperfect CSI. Since the complex interactions and strong coupling among variables, the formulated problem is non-convex and difficult to be solved directly. To address this issue, we begin by employing one-dimensional search to determine the sensing duration of Stage I. Then, based on this result, the DFBS beamforming optimization design is carried out with S-procedure method, penalty-based and successive convex approximation algorithms to convert the original problem into a tractable convex optimization problem. Finally, simulations are executed to confirm the advantages and effectiveness of our developed scheme.

In this paper, we propose a Satellite-Terrestrial Integrated Network (STIN) assisted vehicular multi-tier distributed computing (VMDC) system leveraging hybrid terahertz (THz) and radio frequency (RF) communication technologies. Task offloading for satellite edge computing is enabled by THz communication using the orthogonal frequency division multiple access (OFDMA) technique. For terrestrial edge computing, we employ non-orthogonal multiple access (NOMA) and vehicle clustering to realize task offloading. We formulate a non-convex optimization problem aimed at maximizing computation efficiency by jointly optimizing bandwidth allocation, task allocation, subchannel-vehicle matching and power allocation. To address this non-convex optimization problem, we decompose the original problem into four sub-problems and solve them using an alternating iterative optimization approach. For the subproblem of task allocation, we solve it by linear programming. To solve the subproblem of sub-channel allocation, we exploit many-to-one matching theory to obtain the result. The subproblem of bandwidth allocation of OFDMA and the subproblem of power allocation of NOMA are solved by quadratic transformation method. Finally, the simulation results show that our proposed scheme significantly enhances the computation efficiency of the STIN-based VMDC system compared with the benchmark schemes.

The long propagation delay makes the delay characteristics of data transmission in low Earth orbit (LEO) satellite networks limited by retransmission. Therefore, this paper focuses on the retransmission delay characteristics through analyzing the local delay, defined as the mean times required for the serving satellite successfully transmitting the message to the ground user. We propose a general analytical framework to evaluate the local delay in massive LEO satellite-to-ground downlink networks. Specifically, binomial point process is used to model the locations of satellites. Considering Nakagami fading and directional transmission, we derive the conditional success probability under a given network topology. On this basis, we first give an exact expression for the local delay and further provide an asymptotic analysis when the signal-to-interference-plus-noise ratio tends to zero and infinity. Additionally, we analyze the local delay in three special cases: noise-limited, Rayleigh fading and infinite antenna array of satellite. Numerical results verify our analysis and show that Rayleigh fading model and the asymptotic analysis can simplify and effectively approximate the exact result of the local delay under Nakagami fading.

With the proliferation of large language models (LLMs), cloud-based LLM serving mechanisms may cause network congestion and high serving delay. Edge computing offers a solution to alleviate backhaul pressure and reduce serving delay by deploying LLMs on edge servers and providing LLM inference services in users’ proximity. However, user accuracy requirements vary over time, and mismatches between these requirements and the deployed LLMs at the edge may lead to inefficient resource usage and increased serving delay. To address this, we formulate a joint LLM caching, inference task scheduling, and network resource allocation problem to minimize LLM serving delay under unknown time-varying user accuracy requirements. To solve the problem, we first derive closed-form solutions for optimal computation and communication resource allocation under any LLM caching and task scheduling policies. Then, we employ an improved branch-and-bound algorithm to obtain optimal task scheduling policies under any LLM caching strategies. Finally, we propose an improved double deep Q-network (DDQN)-based algorithm to determine the LLM caching decisions. It incorporates a state coding and action aggregation (SCAA) mechanism within the deep neural networks (DNNs) of the traditional DDQN. The SCAA-DNNs involve an input-layer gating mechanism to encode users’ request states for LLMs and a two-layer output architecture that dynamically aggregates LLM caching actions to generate the corresponding state-action values, thereby improving learning efficiency and accelerating convergence in large discrete action spaces. Experimental results show that the proposed scheme could rapidly converge and reduce average user delay by up to 20.8% compared to benchmarks.

In massive MIMO systems, traditional channel estimation techniques often suffer from noise sensitivity and high computational complexity. Recently proposed deep supervised learning–based estimators have improved accuracy yet require large labeled datasets and exhibit poor generalization in dynamic channel conditions. Consequently, self-supervised methods have emerged, avoiding extensive label collection and enabling immediate online deployment. However, existing self-supervised frameworks typically rely on large networks with long run times, demanding substantial computational resources. In this work, we present a lightweight self-supervised channel estimation framework, Meta-ZSN2N. It first leverages a traditional estimator, then applies a specialized downsampling step, and finally refines the results via a lightweight two-layer neural network, resulting in a significantly simplified model and a substantially reduced runtime. To further accelerate online inference and boost generalization, we integrate a Meta-SGD module into our design. Simulation results indicate that our proposed lightweight method not only surpasses traditional estimators but also outperforms large learning networks with millions of parameters in terms of efficiency and adaptability.

Pinching antenna (PA) is a flexible antenna composed of a waveguide and multiple dielectric particles, which is capable of reconfiguring wireless channels intelligently in line-of-sight links. By leveraging the unique features of PAs, we exploit the uplink (UL) transmission in pinching antenna systems (PASS). To comprehensively evaluate the performance gains of PASS in UL transmissions, multiple PAs are deployed for a single user, namely MPSU. The positions of PAs are optimized to obtain the maximal channel gains in the considered scenarios. For the MPSU and SPSU scenarios, by applying the optimized position of PAs, closed-form expressions for analytical, asymptotic and approximated ergodic rate are derived. Our results demonstrate the following key insights: i) The PA distribution follows an asymmetric non-uniform distribution in the MPSU scenario; ii) Optimizing PA positions significantly enhances the ergodic sum rate performance.

Flexible intelligent metasurface (FIM) technology has emerged as a promising solution for enhancing wireless communication performance. In contrast to traditional rigid reconfigurable intelligent surfaces (RIS), an FIM consists of an array of electromagnetic (EM) elements, each capable of flexibly adjusting its position along the direction perpendicular to the surface to collaboratively morph the surface shape. In this paper, an optimization problem for maximizing the weighted sum-rate (WSR) in an FIM-aided multicell multi-user multiple-input single-output (MU-MISO) system is investigated. We jointly optimize the beamforming at the base station (BS), the phase shift matrix, and the FIM surface shape. To address this problem, we propose an efficient alternating optimization framework, where we employ the weighted minimum mean square error (WMMSE) method to reformulate the problem and the block coordinate descent (BCD) algorithm to iteratively update the variables. Specifically, we utilize the Riemannian Conjugate Gradient (RCG) algorithm to optimize the phase shift matrix, and the projected gradient descent (PGD) method to optimize the FIM surface shape. Additionally, the optimal beamforming vectors are obtained in closed form. Finally, simulation results demonstrate the superiority of FIM over conventional RIS in various scenarios.

A non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission framework is formulated to enable channel access for grant-free users (GFUs) by leveraging residual resources from grant-based users (GBUs). The goal of reducing age-of-information (AoI) while improving throughput is formulated by a joint optimization problem of transmission scheduling and beamforming design. In an effort to solve the pertinent problem, a hierarchical learning is proposed, which is based on deep reinforcement learning to obtain the channel state information of GBUs and the transmission status of GFUs. Specifically, a high-level policy is trained to perform beamforming from a global perspective, while a lower-level policy adapts to keep the AoI minimized. Numerical results demonstrate that the proposed approach outperforms existing adaptive and state-dependent baselines in AoI reduction, while achieving a throughput improvement of approximately 31.82%.

Fluid antenna systems (FAS) are among the most promising technologies for the sixth generation (6G) mobile communication networks. A FAS possesses position reconfigurability to switch on-demand among$N$predefined ports over a prescribed space. This paper explores the performance of a singleinput single-output (SISO) model with a fixed-position antenna transmitter and a single-antenna FAS receiver, referred to as the Rx-SISO-FAS model, under spatially-correlated Rician fading channels. Our contributions include exact expressions and closedform bounds for the outage probability of the Rx-SISO-FAS model with uniform linear array port configuration. To gain insights, we evaluate the diversity order of the proposed model and our analytical results indicate that with a fixed overall system size, increasing the number of ports,$N$, significantly decreases the outage performance of FAS under different Rician fading factors. Our numerical results further demonstrate that:$i$) the Rx-SISO-FAS model can enhance performance under spatiallycorrelated Rician fading channels over the fixed-position antenna counterpart;$i i)$the Rician factor negatively impacts performance in the low signal-to-noise ratio (SNR) regime.

The application of continuous aperture array (CAPA) for mono-static sensing is studied in this paper. Specifically, the transmit CAPA emits a probing signal to a sensing target (ST) and then positions this ST using the reflected echo signal from the ST. To evaluate the sensing performance, the CramérRao Bound (CRB) is derived according to the proposed roundtrip channel model based on electromagnetic theory. Moreover, a maximum likelihood detection scheme is proposed to position the ST under the maximum likelihood criteria. Simulation results demonstrate the high accuracy of CAPA-enabled mono-static sensing.

In mobile wireless networks, data heterogeneity and resource constraints cause performance degradation in machine learning tasks on edge clients. To alleviate these issues, we propose a novel deviation-aware split federated learning (DevSFL) framework, which adopts an adaptive aggregation weight determination method for mitigating the effects of data heterogeneity across local datasets and improving overall learning performance. Leveraging Lyapunov optimization, we formulate a comprehensive optimization problem including client scheduling, cut layer selection, bandwidth allocation, and weight decisionmaking to enhance resource utilization and energy efficiency. To tackle this problem, we employ a sample average approximation based algorithm and a dichotomy method for optimizing cut layer selection and bandwidth allocation policies, respectively. Furthermore, a set expansion algorithm is employed to find the optimal client subset. Additionally, we introduce a deviationaware algorithm specifically designed to refine the weighting policy. Comparative analysis with benchmark schemes reveals that our proposed DevSFL framework not only achieves higher accuracy within fewer rounds but also significantly reduces the time required to reach a predefined accuracy level, thereby demonstrating the effectiveness of our proposed algorithms.

A novel mobile edge generation (MEG) framework is proposed to enable large generative model (LGM) capabilities at the edge, which offers low-latency, power-saving, and languageguided generation on mobile device. Specifically, our framework splits the generation of large-scale content (e.g., high-definition image) into two parts, namely primary and secondary regions. Only the primary region is generated by the LGM at the edge cloud and transmitted via downlink, while the secondary region is generated by the tiny generative model (TinyGM) at the mobile device. By configuring generation splitting ratio between edge and mobile devices, the transmission and computation overheads can be reduced. We formulate a joint generation splitting and mobile power control optimization problem. The formulated problem is a multi-objective optimization programming, which simultaneously minimizes the generation latency and the mobile energy consumption. To explore the performance limits, we first transfer the multi-objective programming into a single-objective programming based on the$\epsilon$-constraint method. Then, we derive the closed-form Pareto-optimal solution of generation splitting and mobile power control. Thereby, the performance boundary of energy-delay (E-D) tradeoff region is obtained. Furthermore, we also identify the conditions under which the proposed MEG strictly outperforms the fully edge generation (FEG) scheme, and demonstrates that performance gains increase as signal-tonoise ratio (SNR) and mobile generation cost decrease. Numerical results demonstrate the optimal E-D tradeoff of the proposed MEG and verify that it can significantly reduce the latency compared to FEG while achieving satisfactory generation.

To reduce the serving delay of large language model (LLM)-based applications, the edge-based LLM serving mechanism offers a promising solution by caching LLMs at the edge to provide LLM inference services closer to users. Motivated by this, we propose an edge-based LLM caching and inference framework to support low-delay LLM-based services. Based on the framework, we formulate a joint LLM caching, inference task scheduling, and computation resource allocation optimization problem to minimize LLM serving delay, where time-varying LLM popularity is considered. Given an LLM caching policy, we first obtain the optimal solution for the computation resource allocation and task scheduling by using traditional optimization methods. Then, we propose an improved double deep Q-network (IDDQN) algorithm that effectively learns the optimal LLM caching strategy under unknown LLM popularity. The IDDQN algorithm integrates a state coding and action aggregation (SCAA) mechanism in the deep neural network structure, enabling it to efficiently capture users' preferences for LLMs and mitigate the slow convergence issues due to the large action space. Simulation results indicate that the proposed scheme achieves both lower average user delay and faster convergence than other benchmarks.

This paper investigates covert communication in a hybrid-functional (HF) reconfigurable intelligent surface (RIS) assisted symbiotic radio (SR) system, where each element of RIS can adaptively switch between passive reflection and covert signal modulation mode. Since covert communication fundamentally relies on low transmission power to ensure undetectability, we try to minimize the transmit power while ensuring the covert constraint, and the joint optimization of active beamforming (BF) at access point, passive BF and mode selection at HF-RIS can be formulated as a mixed-integer fractional programming (MIFP) problem. By decomposing the original problem into the continuous beamformer design and the discrete mode selection problems, we further develop a binary search-assisted alternative beamformer optimization algorithm to obtain a stable solution of the MIFP problem. Numerical results demonstrate the superiority of the adaptive RIS mode selection covert-enhanced design over existing benchmark schemes and reveal useful insights to guide RIS mode selection design for different SR paradigms.

Low-cost and low-power consumptions have become the trend for the future communication systems, where one-bit quantization is a promising candidate. However, due to the low resolutions, the performance loss of the one-bit system is serious. To alleviate this issue, we propose leveraging the fluid reconfigurable intelligent surface (fRIS) to compensate for the loss in downlink communication systems with one-bit digital-to-analog converters (DACs). Specifically, we formulate a symbol estimation error minimization problem by jointly optimizing the symbol estimator, the one-bit transmit signal, the fRIS phase shifts, and the element positions. To handle the resultant problem, an alternating algorithm is developed, where four subproblems are solved iteratively by using semidefinite-relaxation, discrete optimization, and quadratic programming. In optimizing fRIS element positions, the positions affect both the incident and reflective channels, leading to the high-order complex objective functions. We show that these terms can be simplified by using the characteristics of channels. Numerical results validate that the introduction of fRIS can alleviate the performance loss caused by one-bit quantization.

We investigate an energy-efficient hybrid multiple access design for backscatter communication. A dynamic backscatter device scheduling and resource allocation framework is proposed, targeted at minimizing the total energy consumption. To solve the challenging optimization problem, we devise an efficient two-sided matching-based optimization algorithm to jointly optimize the device scheduling, transmit beamforming, reflecting coefficients, and time allocation. Simulation results show that in high-density Internet of Things networks, the proposed scheme achieves nearly 60% performance improvement compared to existing baseline schemes, while revealing that energy consumption achieves its local minimum with respect to the number of time slots.

We investigate multiple access offloading designs toward weighted peak age of information (PAoI) and energy consumption optimization in mobile edge computing (MEC) systems. Two partial offloading strategies based on orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are proposed, which balance the delay and spectral efficiency for weighted PAoI-energy minimization. Efficient algorithms are then devised to jointly optimize the transmit power, the task splitting factor, and the time allocation. Numerical results show that the proposed algorithm outperforms conventional benchmark strategies.

This study investigates the application of a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided medium-Earth-orbit (MEO) satellite network for providing both global positioning services and communication services in the urban canyons, where the direct satellite-user links are obstructed. Superposition coding (SC) and successive interference cancellation (SIC) techniques are utilized for the integrated navigation and communication (INAC) networks, and the composed navigation and communication signals are reflected or transmitted to ground users or indoor users located in urban canyons. To meet diverse application needs, navigation-oriented (NO)-INAC and communicationoriented (CO)-INAC have been developed, each tailored according to distinct power allocation factors. We then proposed two algorithms, namely navigation-prioritized-algorithm (NPA) and communication-prioritized-algorithm (CPA), to improve the navigation or communication performance by selecting the satellite with the optimized position dilution of precision (PDoP) or with the best channel gain.

As the landscape of sixth-generation (6G) wireless networks advances, low-Earth-orbit (LEO) satellite communication emerges as a promising solution for offering comprehensive global communication services, though its potential is challenged by severe large-scale path loss that significantly impairs the received channel capacity available to terrestrial users. To address this limitation, this paper investigates a federated learning aided distributed beamforming network for LEO satellite communications, namely the FederSat network. In order to enhance the average achievable rate of the LEO satellite networks, we propose a novel code-book-based distributed beamforming strategy in the FederSat networks. Through numerical analysis, we demonstrate that the proposed FederSat networks substantially outperform in average achievable rate. Additionally, more LEO satellites are encouraged for further enhancing the received signal power, which indicates that a mega-constellation LEO satellite network is preferable.

With the rapid advancement of physical-layer security technology, the covert and secure communication has become crucial in safeguarding wireless communication systems. In this article, we propose a joint covert and secure transmission scheme for simultaneous wireless information and power transfer (SWIPT) assisted cooperative nonorthogonal multiple access (CNOMA) systems. In the CNOMA system, a greedy relay transmits the confidential information to the far user (Carol), with the assistance of the near user (Bob). Meanwhile, as a SWIPT node, Bob is self-sustained by harvesting energy from relay. What is more, a warden (Alice) and noncolluding eavesdroppers (Eves) always attempt to detect and capture the confidential information, respectively. To counteract the attacks from Alice and Eves, a jamming-assisted scheme is employed. For the proposed system model, we derive closed-form expressions for the detection error probability (DEP) and the average minimum detection error probability (AMDEP) of Alice. Additionally, closed-form expressions for the outage probability (OP) of users and the intercept probability (IP) of Eves are obtained. Furthermore, to maximize the effective covert rate (ECR) of Carol, an optimization problem is formulated, subject to covertness and security constraints. Numerical results are provided to demonstrate the impact of the system parameters on covert and secure performance, with the results showing perfect agreement with the theoretical analysis.

Orthogonal time frequency space (OTFS) modulation, combined with massive multiple-input-multiple-output (MIMO) technology, offers robust performance in high-mobility environments and high-user densities by capturing the full diversity of the wireless channel and effectively utilizing spatial multiplexing. This article introduces an adaptive block sparse backtracking (ABSB) algorithm designed to enhance channel estimation in OTFS with massive MIMO (massive MIMO-OTFS) systems. The proposed ABSB algorithm features dynamic block size adjustment based on the residual signal, improving its adaptability to the varying sparsity structure of the channel. Additionally, the algorithm extends the selection range of related block atoms to increase redundancy, reducing the risk of underfitting. Comprehensive simulation results demonstrate that the ABSB algorithm significantly outperforms traditional pilot-based methods in terms of channel estimation accuracy. It also surpasses the block orthogonal matching pursuit (BOMP) method as well as other classical compressed sensing methods. Specifically, the ABSB algorithm achieves up to a 20% reduction in estimation error compared to some of these traditional methods. The enhanced adaptability and robustness of the ABSB algorithm make it a promising solution for channel estimation in massive MIMO-OTFS systems, paving the way for more reliable and efficient next-generation wireless communications.

In the evolving landscape of wireless communications, extremely large-scale multiple-input-multiple-output (XL-MIMO) systems offer promising enhancements in capacity and spectral efficiency, particularly in near-field scenarios. This article investigates polar-domain channel estimation methods for near-field wideband XL-MIMO systems, proposing a novel approach based on the bilinear pattern detection (BPD) method. We introduce the multicandidate BPD (MBPD) algorithm, which improves detection accuracy by incorporating adaptive weight matrix adjustments and evaluating multiple candidate modes per iteration. Comprehensive simulations validate the superiority of MBPD over traditional BPD in terms of estimation accuracy and robustness. Furthermore, a detailed complexity analysis demonstrates the computational feasibility of the proposed algorithm. The MBPD algorithm greatly improves polar-domain channel estimation, facilitating more efficient implementations of near-field wideband XL-MIMO systems.

In this article, we design an end-to-end digital semantic communication system to transmit semantic symbols that simultaneously facilitate image classification tasks and reconstruction tasks. By training a mutual information-assisted joint source-channel coding (MIJSCC) framework, the learned semantic representation can incorporate both pixel-level generative information for reconstruction and structural discriminative information for classification, which are obtained label-free via global and local mutual information estimation and maximization, as well as mean-square error (MSE) minimization. Then, the high-resolution semantic representation is quantized into finite constellation symbols to satisfy the hardware constraint on discrete control in practical radio frequency systems. Considering dynamic channel conditions in practical communication systems, we further design an adaptive MIJSCC (A-MIJSCC) framework with attention-based semantic enhancement (A-MIJSCC), which allows for the sequential activation of varying dimensions of the semantic representation according to channel signal-to-noise ratio. Compared to existing semantic communication frameworks that are dominated by end target and labels, the MIJSCC addresses the semi-supervised learning of intermediate semantics. Simulation results show that the proposed MIJSCC supports both image classification and reconstruction via task-agnostic semantic extraction, whose performance surpasses the benchmark frameworks. It is also demonstrated that the A-MIJSCC method facilitates the adaptive semantic transmission under varying channel conditions, which effectively reduces the transmission overhead while preserving task performance.

This paper investigates an active intelligent reflecting surface (IRS) assisted integrated sensing and communications (ISAC) system in a cloud radio access network (C-RAN). In particular, we focus on the IRS assisted target sensing for the blind area, wherein multiple IRSs are deployed to establish controllable reflective links between the targets and the sensing remote radio heads (RRHs). A novel IRS-assisted location-aware direction-of-arrival (DOA) estimation scheme is proposed, where the BBU (baseband unit) pool simultaneously recovers the DOA from each target to each IRS using the RRH received signals. In general, the channel knowledge between the IRSs and RRHs is hard to acquire, hence we utilize the location information of IRSs and RRHs instead. Specifically, we transform the DOA estimation problem into a mixed one-dimensional and two-dimensional atomic norm minimization (ANM) problem, by which the DOAs can be efficiently extracted. Moreover, a theoretical Cramér-Rao lower bound (CRLB) on the DOA estimation error is also derived to evaluate the performance of the proposed scheme. Finally, simulation results illustrate the effectiveness for DOA estimation by the proposed scheme.

With the rapid advancement of Internet of Things applications, the demand for integrated sensing, communication, computing, and caching (ISC3) functions has surged. However, existing systems optimize these functions independently, leading to suboptimal resource utilization and performance bottlenecks. In this paper, we propose a multi-layer aerial ISC3 architecture where a versatile unmanned aerial vehicle (UAV) provides edge computing and caching services to ground wireless devices (WDs) alongside its radar sensing capabilities. A high-altitude platform maintains the complete service library, delivering required services to the UAV when cache misses occur. Partial data compression is employed to reduce uplink communication overhead, where WDs partially compress their offloaded task data before transmitting to the UAV. The objective is to minimize total system energy consumption by jointly optimizing time scheduling ratios, task offloading ratios, compression selection ratios, service caching decisions, and UAV trajectory, subject to task latency, sensing quality, energy budgets, and cache capacity constraints. An efficient iterative algorithm utilizing specialized optimization techniques such as Lagrangian duality and successive convex approximation is developed to solve the resulting mixed-integer nonlinear programming problem. Extensive simulations demonstrate fast convergence under diverse network configurations, with the proposed scheme consistently outperforming all baselines by 22.5%-67.0% in total energy consumption.

As a killer app of edge computing, real-time video analytics has found its wide usage in diverse applications, such as security surveillance and manufacturing automation. Unlike state-of-the-art efforts in edge video analytics, which primarily focus on single-task scenarios, we address multi-task video analytics, enabling concurrent execution of multiple tasks on a single video stream. Specifically, our approach aims to minimize monetary costs for the edge service provider through efficient query and resource scheduling, while meeting accuracy and latency requirements of diverse video analytics tasks. A crucial prerequisite for this is to determine the relationship between video analytics accuracy and system configuration parameters. We design a Transformer-aided configuration-accuracy predictor to capture both the current video content and inter-frame temporal dependencies, generating precise configuration-accuracy profiles in real-time. To better exploit the scarce communication and computing resources, a query merging technique is employed, which allows queries from the same camera to share the network bandwidth and neural network models, leading to reduced resource consumption. A heuristic algorithm is then readily proposed to schedule video queries and resources, which dynamically adapts video configurations, query merging, video analytics model selection, task placement, and GPU provisioning. Experimental results show that our system achieves near-optimal performance and outperforms state-of-the-art methods, demonstrating the superiority of our collaboration scheme.

This paper proposes a novel integrated sensing and communication (ISAC) receiver design framework for OTFS (orthogonal time frequency space)-based MIMO (multi-input-multi-output) systems from a unified perspective of variational inference. We first construct a factor graph representation for the OTFS-based MIMO system according to the factorization of the a posteriori probability (APP). This representation establishes a direct probabilistic link between sensing and communication, allowing both functionalities to benefit from their integration. On this basis, we develop a low computational complexity message passing algorithm by minimizing the variational free energy associated with the global APP. In particular, belief propagation, mean field, and expectation maximization algorithms for data detection, channel coefficient estimation, and kinematic parameter sensing are derived, respectively. To reduce the communication overhead for the implementation of ISAC algorithm, we propose a federated learning scheme for distributed kinematic parameter sensing. Specifically, by solving the sensing problem in different fashions, three federated learning modes are devised. Simulation results validate the superior performance of the proposed scheme.

Massive connections of diverse mission-critical devices have sparked people’s envisioning for next-generation ultra-reliable and low-latency communications (xURLLC), prompting the design of customized next-generation advanced transceivers (NGAT). Rate-splitting multiple access (RSMA) has emerged as a pivotal technology for NGAT design, given its robustness to imperfect channel state information (CSI) and resilience to quality of service (QoS). Additionally, xURLLC urgently necessitates large-scale access techniques, thus massive multiple-input multiple-output (mMIMO) is anticipated to integrate with RSMA to enhance xURLLC. In this paper, we develop an innovative RSMA-assisted massive-MIMO xURLLC (RSMA-mMIMO-xURLLC) framework tailored to accommodate xURLLC’s critical QoS constraints in finite blocklength (FBL) regimes. Leveraging uplink pilot training under imperfect CSI at the transmitter, we estimate channel gains and customize linear precoders for efficient downlink short-packet data transmission. Subsequently, we formulate a joint rate-splitting, beamforming, and transmit antenna selection optimization problem to maximize the total effective transmission rate (ETR). Addressing this multi-variable coupled non-convex problem, we decompose it into three corresponding subproblems and propose a low-complexity alternating optimization algorithm for efficient optimization. Extensive simulations demonstrate that compared with non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), the developed architecture accommodates larger-scale access and improves total ETR by 15.3% and 41.91%, respectively.

Efficient and accurate channel estimation with low pilot overhead is essential for massive multiple-input multiple-output (MIMO) wireless communication systems to achieve high spectral and energy efficiency. This work proposes a novel variational inference method for channel estimation by utilizing the generative diffusion model as a prior. Specifically, we first train a generative diffusion model to learn the score, i.e., the gradient of the log-prior distribution, of MIMO channels to serve as a prior in the channel estimation process. The training process is unsupervised and does not rely on specific pilot structures and signal-to-noise ratios (SNRs). Thus, the learned prior is generalizable and can be directly used for channel estimation under different pilot signals and SNRs without requiring re-training. Then, we propose a variational inference method to infer the posterior distribution of the MIMO channel under given pilots and received measurements by incorporating the learned prior. Finally, we estimate the MIMO channels by sampling from the derived posterior distribution. Our simulations under various wireless propagation environments and antenna architectures demonstrate that the proposed approach achieves over 5 dB reduction in normalized mean square error and faster channel recovery compared to state-of-the-art channel estimators. Additionally, the proposed approach exhibits robust estimation performance when the test channel distribution shifts from the training distribution, even outperforming the benchmarks without distribution shifts.

Owing to the heterogeneous computation and communication capabilities among clients, the synchronous model aggregation in wireless federated learning (FL) is susceptible to the straggler effect and exhibits low learning efficiency, while asynchronous aggregation encounters delayed gradients that lead to convergence errors and learning performance degradation. To address these obstacles, this work proposes an adaptive semi-asynchronous FL (ASAFL) approach to incorporate the strengths of synchronous and asynchronous FL while mitigating their inherent drawbacks. Specifically, the edge server dynamically adjusts the synchronous degree, i.e., the number of local gradients aggregated in each round, to strike a balance between learning latency and accuracy. Recognizing that data heterogeneity among clients may induce biased global model updating, we propose calibrating the global update by leveraging historical gradients received at the edge server from clients. Following that, we theoretically investigate the impact of synchronous degrees in different rounds on the convergence bound of ASAFL. The results imply that allocating more learning time to the later learning stages to increase the synchronous degree contributes to better learning performance. Based on this, we develop an adaptive synchronous degree control and resource allocation algorithm to enhance the learning performance of FL while adhering to the overall learning latency and wireless resources constraint. Numerical results on the MNIST and CIFAR-10 datasets demonstrate that the proposed approach is capable of attaining faster convergence speed and higher learning accuracy compared to the benchmark FL algorithms.

In this paper, we present the strengths of WiFi-enhanced SLAM over traditional SLAM systems. We minimize the Root Mean Square Error (RMSE) in localization and mapping by incorporating WiFi signal information. The work elegantly embeds WiFi signals in the well-studied SLAM optimization architecture without degrading the efficiency on the computational side, providing improved accuracy and reliability. We utilize a deep neural network (DNN) to learn the optimal fusion of WiFi and traditional sensors, which enhances system performance significantly. The presented LoC-WiFi-SLAM system is of moderate computational as well as spatial complexity. Simulation results show that the Loc-WiFi-SLAM system is able to use available WiFi infrastructure to complement traditional sensors, thus achieving an improvement in mapping and location accuracy. It not only increases localization accuracy but also makes it suitable for robotic systems, thus providing a robust and scalable solution for environments.

Wi-Fi is a very common means for providing wireless access to the Internet, e.g., using the 2.4GHz Industrial, Scientific, and Medical (ISM) band and more recently also the 6 GHz band via Wi-Fi 6E. Thanks to a chip recently launched by Semtech, in the same 2.4GHz band now can also operate Long Range (LoRa), which is widely used in Internet of Things (IoT) applications due to its low power consumption and wide coverage range. To allow for data interchange among these technologies, multi-radio gateways are needed, which introduce additional costs, complexities, and potential points of failure. To address this challenge, we propose the concept of Wireless to LoRa (WiLo) to make directional communication from Wi-Fi to LoRa. WiLo uses physical-layer (PHY) communication and dedicated input chips in the 2.4 GHz band to transmit information. To overcome the modulation technique differences between Wi-Fi and LoRa, WiLo leverages narrow-band communication, a technique that generates ultra-narrowband signals using single-tone sinusoidal signals by manipulating the payload of Wi-Fi devices. These signals can be detected by LoRa Wide Area Network base stations due to their high receiver sensitivity for long-range communication. Our experiments, which make use of both Universal Software Radio Peripheral (USRP) and commodity devices, demonstrate that WiLo can achieve concurrent wireless communication over a distance of 500 m, from commercial Wi-Fi chips to a LoRaWAN, with more than 96% frame reception rate. These findings show the effectiveness of WiLo in enabling reliable and efficient wireless communication over long distances, making it particularly relevant for applications such as remote monitoring systems, sensor networks, and smart cities.

This paper investigates the covert communication network assisted by the multi-antenna relay, by taking into account two typical scenarios of eavesdropping channel state information (ECSI). In this network, the relay operates in half-duplex amplify-and-forward (AF) relaying protocol, where the relay either forwards the Alice’s confidential signal to the Bob by designing the precoding matrix to enhance the communication performance or sends artificial noise (AN) to disrupt the warden’s detection. For this covert communication system with either instantaneous ECSI (I-ECSI) or statistical ECSI (S-ECSI), an optimization problem is formulated with the aim of maximizing the received signal-to-noise ratio (SNR) at the Bob while ensuring the constraints on the transmit power and covert performance. To solve the non-convex optimization problem, an alternating optimization method is proposed through jointly optimizing the transmit power at Alice and precoding matrix at the relay. Numerical results are provided to demonstrate the effectiveness of our proposed method. In particular, our method is superior to the conventional ones up to 49.6% with I-ECSI and 57.9% with S-ECSI.

Incorporating mobile edge computing (MEC) and integrated sensing and communication (ISAC) has emerged as a promising technology to enable integrated sensing, communication, and computing (ISCC) in the sixth generation (6G) networks. ISCC is particularly attractive for vehicle-to-everything (V2X) applications, where vehicles perform ISAC to sense the environment and simultaneously offload the sensing data to roadside base stations (BSs) for remote processing. In this paper, we investigate a particular ISCC-enabled V2X system consisting of multiple multi-antenna BSs serving a set of single-antenna vehicles, in which the vehicles perform their respective ISAC operations (for simultaneous sensing and offloading to the associated BS) over orthogonal sub-bands. With the focus on fairly minimizing the sensing completion latency for vehicles while ensuring the detection probability constraints, we jointly optimize the allocations of radio resources (i.e., the sub-band allocation, transmit power control at vehicles, and receive beamforming at BSs) as well as computation resources at BS MEC servers. To solve the formulated complex mixed-integer nonlinear programming (MINLP) problem, we propose an alternating optimization algorithm. In this algorithm, we determine the sub-band allocation via the branch-and-bound method, optimize the transmit power control via successive convex approximation (SCA), and derive the receive beamforming and computation resource allocation at BSs in closed form based on generalized Rayleigh entropy and fairness criteria, respectively. Simulation results demonstrate that the proposed joint resource allocation design significantly reduces the maximum task completion latency among all vehicles. Furthermore, we also demonstrate several interesting trade-offs between the system performance and resource utilizations.

In this paper, we investigate the downlink distributed transmit beamforming problem in partially connected cell-free massive multiple-input multiple-output (CF mMIMO) systems, specifically designed to satisfy the stringent requirements of ultra-reliable and low-latency communication (URLLC) services. First, we propose a scalable framework that incorporates partial access points (APs) to serve active user equipment (UE), with a reduced energy consumption and computational complexity. To this end, a min-max optimization problem is formulated for minimizing the decoding error probability (DEP) among URLLC services. Then, a graph neural network (GNN)-based strategy called G4PCF is proposed for partially connected CF mMIMO, which takes into account the underlying characteristics of the problem. Furthermore, by leveraging the temporal correlation in channel state information acquired from the previous frame, we develop a parallel G4PCF (P-G4PCF) scheme that significantly reduces both the signaling overhead and computation delay for minimizing DEP of the worst UE. Simulation results demonstrate that the proposed G4PCF and P-G4PCF architectures exhibit excellent scalability for CF mMIMO networks, offering superior performance over existing methods in terms of quality of service outage probability. Notably, P-G4PCF excels in supporting URLLC services with short frame durations and highly correlated channels, while G4PCF performs better under lower channel correlation. Moreover, the proposed algorithms can significantly enhance the application of GNNs into CF mMIMO systems with a reduced complexity compared with the classical weighted minimum mean-squared error algorithm, especially with delay sensitive services.

Existing channel estimation and data detection methods for faster-than-Nyquist (FTN) transmission over frequency-selective fading channels primarily face three key challenges: high pilot and guard interval overhead, low channel estimation accuracy, and long distances in satellite communication systems. To address the first two issues, we design a low-overhead frame structure based on circular convolution and, accordingly, propose a low-overhead precoding-driven channel estimation (PD-CE) algorithm. The proposed algorithm leverages circular convolution to suppress inter-block interference (IBI) from the channel with minimal guard intervals and eliminate FTN-induced IBI without guard intervals, significantly reducing pilot and guard overhead. Meanwhile, the limited guard interval mitigates noise enhancement, enabling PD-CE to achieve superior channel estimation accuracy over existing estimation methods. The third challenge arises from the imperfect channel state information obtained at the transmitter. To enhance the robustness in satellite communication systems, we design a precoding matrix based on the minimum mean square error (MMSE) criterion, introducing a low-overhead precoding-driven channel estimation and data detection (MMSE-PD-CEDD) algorithm for interference suppression. Simulation results indicate that, even under channel estimation error, the proposed MMSE-PD-CEDD algorithm exhibits superior interference resistance compared to existing algorithms, while its bit error rate performance loss remains within an acceptable range relative to the Nyquist criterion.

Satellite communication is a crucial component for achieving global communications in sixth generation (6G). Due to the large distance between the satellite and the terrestrial users, the multiplicative fading effects of traditional passive simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) are more severe in satellite communication. Recently, active simultaneous transmitting and reflecting surfaces (ASTARSs) have been proposed to mitigate multiplicative fading by amplifying the incident signal. Motivated by the above, a novel ASTARS-aided non-orthogonal multiple access (NOMA) satellite communication network is proposed in this paper, which includes one low earth orbit (LEO) satellite, one ASTARS and several far-field users (FFUs) and near-field users (NFUs). We propose an adaptive NOMA pairing strategy that allows the satellite to flexibly select pairing schemes. Depending on the users’ quality of service requirements, either the NFU-FFU (NF) pairing scheme or the NFU-NFU (NN) pairing scheme can be used. Then, we formulate an optimization problem to maximize the channel capacity. A two-layer optimization algorithm is proposed, where the outer layer selects the NOMA user pairing schemes, while the inner layer alternately optimizes the NOMA power allocation factors, the satellite precoding matrix and the ASTARS phase shift matrices. To solve the non-convex optimization problem, successive convex approximation and a penalty-based method are employed. The numerical results reveal: 1) The channel capacity of the NF pairing scheme is always higher than that of the NN pairing scheme; 2) Compared to the “Without RIS" and “Passive STARS" schemes, ASTARS can significantly improve the channel capacity of satellite communication.

Irregularly spaced arrays are appearing in diverse ares, such as wearable devices, stealth aircrafts. This paper studies the two-dimensional (2D) direction-of-arrival (DOA) estimation issue for an irregularly spaced electromagnetic vector sensor (EMVS) array. An estimation method of signal parameters via rotational invariance technique (ESPRIT) approach is developed. Unlike existing ESPRIT-like algorithms, the proposed approach in this paper not only estimates the rough directional cosine waveform via the rotational invariance of the polarized response matrix, but also finds the refined directional cosine waveform via the rotational invariance of the spatial response matrix. This proposed algorithm is capable of offering closed-form analytics, thus greatly facilitating 2D-DOA estimation. Numerical results shown in this paper verify that the proposed approach outperforms existing ESPRIT-like algorithms at a sightly increased costs of computation. In addition, numerical results presented in this paper for the proposed 2D-DOA estimation approach also corroborate the theoretical derivations.

Unmanned aerial vehicle (UAV) assisted communication is becoming a promising technology for future networks. Leveraging this benefit, the ambient backscatter communication can utilize the UAV’s emitted signal as the radio frequency carrier to transmit its own information. However, this transmission behavior is easily to be detected by the UAV due to the high possibility of line-of-sight (LoS) air-ground channel. Thus, in this paper, we propose a covert ambient backscatter communication scheme by exploiting the UAV relay as the radio frequency source. Specifically, the UAV relays the information for two legitimate ground nodes, and monitors the potential ambient backscatter communication. Our goal is to maximize the covert ambient backscatter communication rate under the worst case that the UAV performs with the optimal detection threshold, transmit power and hovering location. First, the UAV’s optimal detection threshold is analyzed, and the corresponding closed-form expression of error detection probability is derived. Then, we propose an iterative algorithm to achieve the minimum error detection probability by optimizing the transmit power and hovering location of UAV. To fight against the detection of UAV, we formulate a convex optimization problem to maximize the worst-case covert ambient backscatter communication rate by adjusting the reflection coefficient. Simulation results show that the proposed scheme can effectively improve the covert ambient backscatter communication rate.

In this paper, we investigate channel estimation and tracking for wideband millimeter wave (mmWave) satellite communications (SatComs) with reconfigurable intelligent surface (RIS). Different from the existing schemes that separately estimate the uplink channels from the user to the RIS and from the RIS to the satellite, we directly estimate the cascaded channel by proposing two schemes. In the first scheme with two stages, we power off different antennas in each stage and estimate the channels based on the estimating-signal-parameter-via-rotational-invariance-techniques (ESPRIT). In the second scheme based on the null space projection (NSP), we estimate the equivalent channel matrix through projecting the dictionary steering vectors to the null space of the received signal covariance matrices. The NSP-based scheme does not power off any antenna and needs only one stage. In addition, we propose a gradient descent (GD)-based channel tracking scheme for the moving user. We first obtain a rough estimation of the user channel based on geometry relationships and then make channel refinement using the GD. Simulation results show that the NSP-based scheme needs fewer pilots than the ESPRIT-based scheme but at the cost of some performance sacrifice. The GD-based channel tracking scheme outperforms the existing schemes.

The emerging field of federated learning (FL) holds significant promise for advancing edge intelligence while preserving data privacy. However, as FL systems scale or become more heterogeneous, challenges such as spectrum scarcity and the straggler problem arise. To address these issues, this paper proposes SA-AirFed, a semi-asynchronous FL architecture compatible with Over-the-Air Computation (AirComp). We develop an efficient scheduling scheme that meets AirComp’s requirements and analyze the factors affecting convergence under the Lipschitz-Smooth condition. Building on insights from the convergence analysis, we design an adaptive algorithm that mitigates staleness from semi-asynchronous aggregation and noise from AirComp by dynamically adjusting aggregation weights, formulated as a convex quadratic programming problem. Experimental results on MNIST and CIFAR-10 demonstrate that SA-AirFed significantly reduces wall-clock training time while achieving greater robustness compared to baseline models.

Characterized by its ease of low-latency response, edge computing is capable of supporting real-time video analytics applications, constituting an edge video analytics paradigm, where the joint knob configuration and network scheduling design has drawn ever-escalating research attention. However, the potential of edge video analytics has not been fully exploited, owing to the limitations of the state-of-the-art as follows. i) The eminent impact of video content on accuracy performance has been ignored. ii) The variables that can be tuned are not fully considered in scheduling. iii) The heuristic algorithm-based solutions are far from the optimal. To fill in this gap, in this paper, we conceive a content-aware joint knob configuration and resource allocation scheme for edge video analytics. Concretely, fed with the features extracted from the video content, a deep neural network (DNN)-based predictor is proposed to predict the configuration-accuracy performance in a real-time manner. With an aid of the predictive results, we formulate an accuracy-maximization problem as an integer programming problem, by optimizing the variables, including resolution, frame rate, video analytic model, network bandwidth, and computational resource subject to the latency constraints. To solve this problem in an efficient manner, we devise a novel low-complexity dynamic programming method. Simulation results verify the efficiency of our content-aware joint knob configuration and resource allocation scheme. Quantitatively, a 3.3% gap is attained towards the upper bound in terms of the accuracy in an object detection scenario, relying on the scheme proposed.

In this paper, we investigate joint trajectory and beamforming design for unmanned aerial vehicle (UAV)-relayed integrated sensing and communication (ISAC) systems with mobile edge eomputing (MEC) under the clutter environment. Due to the limited on-board computing capability, the UAV has to offload sensing echoes to the base station (BS) for efficient processing. A novel relay-based ISAC-then-offload frame structure is considered. We aim to maximize the throughput of the BS-UAV-user relaying link while ensuring sensing accuracy and efficient sensing data offloading. The non-convex problem is solved using an alternating optimization algorithm based on successive convex approximation (SCA). Simulation results illustrate that our proposed algorithm achieves near-optimal communication performance while guaranteeing sensing accuracy, addressing the balance between the communication and sensing performance. Furthermore, we evaluate the impact of critical system parameters including sensing constraints, power control factor, and UAV flight duration on communication performance, and explore the trade-offs between energy efficiency and spectral efficiency under varying sensing data intensity and offloading duration.

In this paper, we investigate the joint design of beamforming vectors and trajectory for unmanned aerial vehicles (UAVs) in integrated sensing and communications networks, aiming to maximize the achievable covert rate (ACR) for legitimate users against multiple passive wardens. Considering the worst-case scenario, where the wardens strategically select optimal decision thresholds, we derive the minimum detection error probability and incorporate covertness constraints within the beamforming scheme. Our approach entails formulating the design as a non-convex optimization problem for maximizing the average ACR along the UAV trajectory. The formulation takes into account various practical constraints, such as the maximum transmit power, UAV flight speed limitations, minimum beamforming gain towards sensing targets, and the detection probability threshold for wardens. To address this intricate problem, we propose a block coordinate descent-based optimization algorithm. This algorithm alternates between updating beamforming vectors and UAV trajectories, offering a high-quality suboptimal solution to the original problem. Theoretical analyses reveal that when the detection probability threshold is sufficiently small, a linear correlation emerges between the maximum relative variation ratio in the average received signal power at wardens under two hypotheses and the detection probability. Furthermore, to enhance covertness against the wardens, it is necessary to either decrease the projection of information beamforming covariance matrix or increase the projection of sensing beamforming covariance matrix onto the subspace spanned by the eavesdropping channel vectors. Finally, extensive simulations are presented to validate the covert performance enhancements of our proposed methodology, compared with various baseline schemes adopting existing approaches.

Satellite-Ground Integrated Networks (SGINs) are promising network architectures that can help reduce the load on terrestrial networks, provide mega-access capabilities and intensive task offloading functions. However, traditional resource management methods are difficult to apply directly into SGINs due to their multi-layered, heterogeneous and dynamic three-dimensional characteristics. In addition, massive multi-modal and multi-task information hinders better service performance in SGINs. Therefore, we design a multi-task integrated computation offloading model to process complex multi-modal network information, such as time-varying channel gains and dynamic Low Earth Orbit (LEO) locations, which can efficiently improve data transmission rate and privacy level. Furthermore, we propose three multi-modal based learning methods, such as centralized actor-critic (C-AC) algorithm, distributed multi-agent deep deterministic policy gradient (D-MADDPG) algorithm, and quantization-based federated learning (Q-FL) algorithm for computation-intensive, latency-critical and privacy-preserving tasks, which can further optimize the local execution or LEO offloading ratio, CPU cycle frequency and transmission power. Meanwhile, we demonstrate the quantization error upper bound between the optimal solution and the quantization scheme through massive mathematical derivations. Finally, extensive simulation results show that the proposed multi-modal based learning methods have better performance gains in terms of model convergence performance, quantization metrics, data transmission rate and number of bits processed.

Reconfigurable intelligent surface (RIS) and ambient backscatter communication (AmBC) technologies are recognized for their programmability and high energy efficiency respectively, which will be the key parts of the future sixth generation (6G) mobile communication technology. The combination of the two technologies can improve communication security by reducing the probability of detection and decoding through enhanced transmission and backscatter transmission in different communication slots. In this paper, a dual-function RIS that supports cooperative relaying for covert communications is proposed. It operates in different communication slots (enhanced transmission slot and backscatter slot), but the performance is affected by phase errors due to function switching. A source covertly communicates with an intended destination via the help of RIS and cooperative relay. There is an illegal monitor aims to detect and eavesdrop the covert message. For this system, the outage probability (OP), intercept probability (IP), and detection error probability (DEP) in different communication slots are derived to examine the system reliability and security. Moreover, the system security probability (SSP) is proposed, and a block coordinated ascent (BCA)-based iterative algorithm is used to jointly optimize the power allocation coefficients to maximize the SSP. Simulation results show that increasing the number of elements can improve the security performance and mitigate the negative impact of RIS phase errors.

As one of the promising technologies, reconfigurable intelligent surface (RIS) and simultaneous transmitting and reflecting RIS (STAR-RIS) have attracted great interest. However, the existing RISs offer broadband tuning capability without filtering function due to the absence of radio frequency (RF) units, which easily leads to the unexpected tuning of the RIS undesired signals, especially in large-scale deployments. For the target network, it is difficult to obtain the parameter settings of RISs to serve other networks, which causes the unpredictability of the wireless environment. In this paper, we consider the covert communication in a STAR-RIS assisted random wireless network with randomly distributed blockages. We investigate the impact of STAR-RIS large-scale deployment on covert communication and leverage its inherent unpredictability for improving the covertness. We derive the average detection error probability for warden within the random wireless networks. Furthermore, we optimize the passive beamforming of STAR-RIS to maximize the covert communication rate, considering both direct and indirect line-of-sight (LoS) links. To address this, we employ an alternating optimization (AO) algorithm based on the semi-definite programming (SDP) method. Finally, numerical results demonstrate significant enhancements and increase covert capability achieved through the large-scale deployment of STAR-RIS.

To enhance the capacity and reliability of next-generation wireless communication systems, the novel cell-free massive multiple-input multiple-output (mMIMO) has emerged as a pivotal technology in satisfying the stringent quality of service requirements of massive network-connected devices. In this paper, we propose a partially coherent joint transmission (PCJT) approach that draws insights from both coherent and non-coherent joint transmission (NCJT) strategies. Specifically, we design the downlink transmit beamformers to maximize the weighted sum rate (WSR) and compare the performance in three distinct joint transmission modes, ranging from coherent and partially coherent, to non-coherent joint transmission. Specifically, a non-convex optimization problem is formulated that incorporates multiple data stream transmission and transmit power constraints. Given the intractability of the problem, the weighted minimum mean square error (WMMSE) approach is introduced to transform it into an equivalent form, which facilitates the development of a low-complexity and low-interaction reduced WMMSE (R-WMMSE) beamforming algorithm design to acquire an effective solution. For further reducing communication overhead and improving convergence rates, we propose a novel graph convolution network-based unfolding technique for R-WMMSE algorithm. It significantly reduces the number of iterations required while achieving similar performance to the original WMMSE algorithm, thus alleviating the signaling overhead burdens in distributive implementation. Simulation results demonstrate the significant performance gains achieved by the proposed algorithm in terms of superior WSR and rapid convergence performance. Furthermore, it is evident that the performance of PCJT can promote the performance achieved by NCJT, positioning it as an alternative between the existing two joint transmission strategies.

This paper investigates a reconfigurable intelligent surface (RIS)-assisted wireless powered mobile edge computing (MEC) system, where an access point (AP) integrated with an MEC server first transmits energy signals to charge multiple energy-constrained devices in the downlink (DL), and then the devices utilize the harvested energy to perform MEC offloading in the uplink (UL) assisted by an RIS. Specifically, three multiple access protocols for MEC offloading, namely pure non-orthogonal multiple access (NOMA), pure orthogonal multiple access, and hybrid NOMA, are proposed to exploit dynamic RIS beamforming and energy recycling among devices to enhance the efficiencies of energy harvesting and MEC offloading. For each of the three protocols, a joint resource allocation framework of the AP/devices transmit power, the RIS phase shifts, and the DL/UL time allocation is formulated to minimize the energy consumption at the AP. Because of those highly coupled optimization variables, the formulated optimization problems are first shown to be non-convex, and then the intrinsic structure of the problems is exploited to devise computationally-efficient algorithms and solve them iteratively. Numverical results are provided to demonstrate the performance improvement of our proposed designs compared to various benchmark schemes, and reveal the practical significance of the considered multiple access protocols with dynamic RIS beamforming and energy recycling for spectral and energy efficient MEC offloading.

Unmanned aerial vehicle (UAV) assisted relaying has become the focus of the next generation network owing to its superiority of low cost and swift deployment. In addition, the orthogonal frequency division multiplexing (OFDM) based simultaneous wireless information and power transfer (SWIPT) is known to have significant advantages in system complexity compared to the traditional time-switching (TS) and power-splitting (PS) techniques. Since the UAV is known to have limited size, weight and power, we investigate the OFDM-based SWIPT for a multi-source-destination UAV relaying system in this paper. With the purpose of maximizing the average transmission rate (ATR) under the constraint of the harvested energy, we jointly optimize user scheduling, resource allocation and UAV trajectory. To tackle this issue, we first divide it into three subproblems of user scheduling, power and subcarrier allocation, and UAV trajectory optimization. Subsequently, the user scheduling subproblem is optimally solved. The power and subcarrier subproblem is approximately solved by addressing its dual problem and the UAV trajectory optimization subproblem is addressed by successive convex approximation technique. Then, we put forward an iterative algorithm based on block coordinate descent method by solving the three subproblems alternately. Numerical results demonstrate that our proposed algorithm is preferable than the two benchmark schemes.

This paper proposes a secrecy enhancement scheme for the space shift keying (SSK) assisted multiple-input single-output (MISO) visible light communications (VLC) system in a complex indoor environment, where the line-of-sight (LoS) link of the transmitter and legitimate user can be blocked or exist. By leveraging a properly arranged mirror array as an optical intelligent reflecting surface (ORIS), a legitimate user can access confidential information, while an eavesdropping user cannot intercept the confidential message. To achieve this goal, an optical artificial noise (OAN) assisted secrecy enhancement strategy is introduced. In this strategy, the transmitter transmits both the desired signal and the OAN signal simultaneously while adhering to power and amplitude constraints. The average mutual information (AMI) and achievable secrecy rate (ASR) are employed to analyze the secrecy performance of the ORIS aided SSK VLC system. Furthermore, to adapt to different environments, four system configuration scenarios are presented, and the corresponding secrecy performance is analyzed. To clarify the theoretical results of the OAN assisted indoor MISO SSK VLC system with an ORIS, extensive simulation results are performed.

Non-orthogonal multiple access (NOMA) with successive detection receivers, e.g., successive interference cancellation (SIC), is a potential technology for satellite multi-user communication due to its lower complexity. However, within a spot beam, multi-user interference (MUI) is complicated by channel-induced time-frequency offset, while the path loss differences that the receiver relies on for MUI suppression almost disappear. To overcome the above obstacle, this paper exploits the delay-Doppler (D-D) domain circular shifting property under time-frequency offsets and proposes a D-D domain spectral shaping multiple access (SSMA) technique. By analyzing the influence of D-D domain spectrum on channel capacity, we identify that an enlarged inter-user power gap can be derived at the receiver by constructing a non-uniform D-D domain spectrum. Inspired by this, a D-D domain multi-branch structure-based shaping framework is proposed to flexibly construct the user-consistent power envelope. Meanwhile, two additional signal designs are introduced to ensure that the D-D domain information density and constellation fit to the constructed power envelope. First, by adjusting the transmission rate of the signal on each branch, we optimize the information density with a non-uniform pattern. Second, by introducing a branch-wise phase rotation and deploying an iterative variational approximation method, the shape of the composite constellation is reconstructed. In addition, we also design a branch-bundling-based successive detection receiver using an alternating direction method of multipliers. This receiver can flexibly combine detectable branch signals while maintaining the complexity close to the traditional SIC receiver. Analysis and simulation results reveal that the proposed D-D domain SSMA has a higher achievable rate and can provide$1\sim 4.5$dB bit error rate performance gain compared to the typical D-D domain NOMA.

As the complexity of deep neural networks escalates, traditional federated learning (FL) frameworks increasingly struggle since the training overhead of the full model is costly for resource-limited clients. In addition, the class imbalance among local datasets and client heterogeneity may lead to significant deterioration in learning performance. To address these challenges, we first propose a novel wireless split federated learning (SFL) framework to enhance learning efficiency and performance in resource-constrained networks, which adaptively splits the global model between the clients and server to alleviate the computation burden for clients. Then, we theoretically analyze how the client sampling and wireless network parameters impact on the convergence bound. Based on the analysis, we identify the extent of class imbalance that significantly impacts learning performance. Inspired by this, we formulate an optimization problem to strike a balance between latency and performance by jointly optimizing the client selection, model splitting, and bandwidth allocation policies. To solve this problem, we introduce a latency and class imbalance-aware double greedy algorithm to obtain client scheduling policy. Additionally, bisection-enabled optimal bandwidth allocation and model splitting algorithms are developed to adaptively determine bandwidth allocation and model splitting policies, respectively. Extensive experimental results demonstrate that our approach significantly reduces latency and enhances learning performance.

Wireless federated learning (FL) faces significant challenges due to limited wireless resources and unreliable channels. To cope with these challenges, this work proposes a gradient compensation-based FL approach (FL-GC), in which the edge server estimates the local gradients of transmission failure and unselected clients by first-order Taylor approximation based on previously received local gradients. We then theoretically analyze the convergence bound, which reveals that selecting clients with large local gradient staleness helps reduce the estimation error and improve learning performance. Based on this, we jointly optimize the client selection and resource allocation strategies to enhance the FL performance under resource-limited wireless networks. Simulation results under a typical data heterogeneity scenario demonstrate the efficacy of our proposed scheme in mitigating the adverse effects of unreliable transmission and limited resources. It improves 7.34% model accuracy compared to the considered benchmarks and is able to save 42.5% training time to achieve the target accuracy.

Simultaneously transmitting and reflecting intelligent reflecting surfaces (STAR-IRS) can assist in achieving full-space signal coverage enhancement. Considering eavesdropping channels, a downlink system model with full coverage of STAR-IRS enabled unmanned aerial vehicle (UAV) secure communication is proposed. The aim is to attain the maximal value of the energy efficiency (EE) by exploring the joint resource allocation of the system. To solve this coupling problem, the lower and upper bounds of the sum-rate for legitimate and eavesdropping users are derived respectively, and Lagrange duality theory is employed to deal with the power control problem. Then the reflection/transmission amplitude splitting coefficient optimization of STAR-RIS using the Hybrid whale-bat (HWB) method in energy splitting (ES) mode are considered to fully exploit the performance gains brought by STAR-IRS deployment. Finally, the simulation verifies that the proposed joint design scheme can enormously promote the EE and safety performance of the system.

To reduce the synchronization latency in digital twin (DT)-enabled wireless edge networks, the DT migration provides an efficient roaming solution among edge servers by following users' trajectories. In this work, we formulate a joint DT migration, communication and computation resource management problem to minimize the data synchronization latency, where the time-varying network states and user mobility are considered. By decoupling edge servers under a deterministic migration strategy, we first derive the optimal communication and computation resource management policies at each server using convex optimization methods. For the DT migration problem between different servers, we transform it as a decentralized partially observable Markov decision process (Dec-POMDP). Then, we propose a novel agent-contribution-enabled multiagent reinforcement learning (AC-MARL) algorithm to enable distributed DT migration for users, in which the counterfactual baseline method is adopted to characterize the contribution of each agent and facilitate cooperation among agents. Simulation results show that the proposed DT migration scheme is able to reduce 30% data synchronization latency for users compared to the benchmark schemes.

Multi-access Edge Computing (MEC) is an emerging paradigm designed to provide storage, computing and communication capabilities in the proximity of end-user devices. This approach facilitates the deployment of real-time on mobile devices with limited capabilities. To realize the MEC goals, it is essential to effectively manage and offload computing tasks to both edge and cloud-based resources. However, the dynamic nature, uncertainty and mobility within edge computing environments pose significant challenges to resource management. Furthermore, the inherent software and hardware heterogeneity, coupled with the distributed nature of architecture, complicates the development of efficient task offloading strategies that can adeptly manage resources across both edge and cloud platforms. In this paper, we propose a cluster-based task edge orchestrator, where edge servers are grouped based on service demands, resource ability and other factors to improve the overall service. Our proposed method leverages the K-Medoids clustering algorithm to dynamically form clusters of suitable edge servers for offloading computing tasks with minimum response time. To validate our proposed solution, we have orchestrated a comprehensive series of tests using EdgeCloudSim. Results show that our approach outperforms its competitor in terms of average service time by around 8 %.

This work proposes an adaptive semi-asynchronous federated learning (FL) approach, namely ASAFL, to incorporate the strengths of synchronous and asynchronous FL while mitigating their inherent drawbacks. Specifically, the edge server dynamically adjusts the synchronous degree, i.e., the number of local gradients aggregated in each round, to strike a balance between learning latency and accuracy. Recognizing that data heterogeneity among clients may induce biased global model updating, we propose calibrating the global update by leveraging historical gradients received at the edge server from clients. Following that, we experimentally revealed that allocating more learning time to the later learning stages to increase the synchronous degree contributes to better learning performance. Inspired by this, we develop an adaptive synchronous degree control and resource allocation algorithm to enhance the learning performance of FL while adhering to the overall learning latency and wireless resources constraint. Numerical results demonstrate that the proposed approach is capable of attaining faster convergence speed and higher learning accuracy compared to the benchmark FL algorithms.

Federated Learning (FL) is a promising privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their private datasets. Hierarchical FL (HFL), as a device-edge-cloud aggregation hierarchy, can enjoy both the cloud server's access to more datasets and the edge servers' efficient communications with devices. However, the learning latency increases with the HFL network scale due to the increasing number of edge servers and devices with limited local computation capability and communication bandwidth. To address this issue, in this paper, we introduce model pruning for HFL in wireless networks to reduce the neural network scale. We present the convergence rate of an upper on the$l_{2}$-norm of gradients for HFL with model pruning, analyze the computation and communication latency of the proposed model pruning scheme, and formulate an optimization problem to maximize the convergence rate under a given latency threshold by jointly optimizing the pruning ratio and wireless resource allocation. By decoupling the optimization problem and using Karush-Kuhn-Tucker (KKT) conditions, closed-form solutions of pruning ratio and wireless resource allocation are derived. Simulation results show that our proposed HFL with model pruning achieves similar learning accuracy compared with the HFL without model pruning and reduces about 50% communication cost.

As the number of users connected to communication networks such as cellular networks and Internet of Things (IoT) networks increases, massive multiple-input multiple-output (MIMO) technique has been widely adopted to improve the spectral and energy efficiency. However, the multi-user frequency synchronization problem must be solved before channel estimation and data detection. Concurrent estimation of multiple carrier frequency offsets (CFO) at base station could be very challenging due to the coexisting and intertwined effects of multiple CFOs and uplink channels in the received signal. In this paper, we consider the frequency synchronization and channel estimation for multi-user uplink massive MIMO systems. To solve the complex multi-CFO estimation problem, we first derive the efficient joint multi-user frequency synchronization algorithm based on the maximum likelihood (ML) criterion, whose high computational complexity is reduced by the proposed Gauss-Newton method. Furthermore, we develop a multi-stage iteration update filtering (MIUF) based multi-user CFO and channel estimation method. The least squares (LS) algorithm is adopted to estimate the channels, based on which the filtering matrix is carefully designed to perform multi-user interference (MUI) suppression. Moreover, considering the effect of CFO error on the channel estimation, an iterative procedure is designed to improve MUI suppression and estimation accuracy. We also analyze the CFO estimation performance and obtain the theoretical expression of mean squared error (MSE). Finally, the effect of CFO error on channel estimation is derived. Numerical results are provided to corroborate the effectiveness of the proposed methods and their superiority over the existing ones.

An integrated sensing and communication (ISAC) system is investigated, where the base station (BS) provides both uplink and downlink Internet of Things (IoT) services as well as target sensing services. Furthermore, nonorthogonal transmission (NO-T) is introduced for improving the spectrum efficiency. The deleterious effects of hardware impairments, channel estimation errors, and imperfect successive interference cancellation are taken into account. Both the exact and asymptotic outage probabilities (OPs) of the IoT devices as well as the Probability of successful Detection (PoD) are derived for characterizing the communication and sensing (C&S) performances. As a further development, in the presence of the sensing requirements, a communication-centric power allocation (PA) problem is formulated for maximizing the sum rate of the IoT devices. Given the nonconvexity of the problem, an alternating optimization algorithm is developed for finding a near-optimal PA. The simulation results confirm the accuracy of the analysis and demonstrate that: 1) the above nonideal factors degrade the C&S performances; 2) the NO-T ISAC system considered outperforms pure ISAC in terms of both its OP and PoD; and 3) compared to other baseline PA schemes, the proposed algorithm maximizes the sum rates while meeting the sensing requirements.

Fingerprint-based indoor localization holds great potential for the Internet of Things. Despite numerous studies focusing on its algorithmic and practical aspects, a notable gap exists in theoretical performance analysis in this domain. This paper aims to bridge this gap by deriving several lower bounds and approximations of mean square error (MSE) for fingerprint-based localization. These analyses offer different complexity and accuracy trade-offs. We derive the equivalent Fisher information matrix and its decomposed form based on a wireless propagation model, thus obtaining the Cramér-Rao bound (CRB). By approximating the Fisher information provided by constraint knowledge, we develop a constraint-aware CRB. To more accurately characterize nonlinear transformation and constraint information, we introduce the Ziv-Zakai bound (ZZB) and modify it for adapt deterministic parameters. The Gauss–Legendre quadrature method and the trust-region reflective algorithm are employed to make the calculation of ZZB tractable. We introduce a tighter extrapolated ZZB by fitting the quadrature function outside the well-defined domain based on the Q-function. For the constrained maximum likelihood estimator, an approximate MSE expression, which can characterize map constraints, is also developed. The simulation and experimental results validate the effectiveness of the proposed bounds and approximate MSE.

Cooperative caching has emerged as an efficient way to alleviate backhaul traffic and enhance user experience by proactively prefetching popular videos at the network edge. However, it is challenging to achieve the optimal design of video caching, sharing, and delivery within storage-limited edge networks due to the growing diversity of videos, unpredictable video requirements, and dynamic user preferences. To address this challenge, this work explores cost-efficient cooperative video caching via video compression techniques while considering unknown video popularity. Firstly, we formulate the joint video caching, sharing, and delivery problem to capture a balance between user delay and system operative cost under unknown time-varying video popularity. To solve this problem, we develop a two-layer decentralized reinforcement learning algorithm, which effectively reduces the action space and tackles the coupling among video caching, sharing, and delivery decisions compared to the conventional algorithms. Specifically, the outer layer produces the optimal decisions for video caching and communication resource allocation by employing a multi-agent deep deterministic policy gradient algorithm. Meanwhile, the optimal video sharing and computation resource allocation are determined in each agent’s inner layer using the alternating optimization algorithm. Numerical results show that the proposed algorithm outperforms benchmarks in terms of the cache hit rate, delay of users and system operative cost, and effectively strikes a trade-off between system operative cost and users’ delay.

Driven by the ever-increasing requirements of ultrahigh spectral efficiency, ultralow latency, and massive connectivity, the forefront of wireless research calls for the design of advanced next-generation multiple access schemes to facilitate the provisioning of these stringent demands. This inspires the embrace of nonorthogonal multiple access (NOMA) in future wireless communication networks. Nevertheless, the support of massive access via NOMA leads to additional security threats due to the open nature of the air interface, the broadcast characteristic of radio propagation, and the intertwined relationship among paired NOMA users. To address this specific challenge, the superimposed transmission of NOMA can be explored as new opportunities for security-aware design; for example, multiuser interference inherent in NOMA can be constructively engineered to benefit communication secrecy and privacy. The purpose of this tutorial is to provide a comprehensive overview of the state-of-the-art physical layer security techniques that guarantee wireless security and privacy for NOMA networks, along with the opportunities, technical challenges, and future research trends.

The data heterogeneity across clients and the limited communication resources, e.g., bandwidth and energy, are two of the main bottlenecks for wireless federated learning (FL). To tackle these challenges, we first devise a novel FL framework with partial model aggregation (PMA). This approach aggregates the lower layers of neural networks, responsible for feature extraction, at the parameter server while keeping the upper layers, responsible for complex pattern recognition, at clients for personalization. The proposed PMA-FL is able to address the data heterogeneity and reduce the transmitted information in wireless channels. Then, we derive a convergence bound of the framework under a non-convex loss function setting to reveal the role of unbalanced data size in the learning performance. On this basis, we maximize the scheduled data size to minimize the global loss function through jointly optimize the client selection, bandwidth allocation, computation and communication time division policies with the assistance of Lyapunov optimization. Our analysis reveals that the optimal time division is achieved when the communication and computation parts of PMA-FL have the same power. We also develop a bisection method to solve the optimal bandwidth allocation policy and use the set expansion algorithm to address the client scheduling policy. Compared with the benchmark schemes, the proposed PMA-FL improves 3.13% and 11.8% absolute accuracy on two typical datasets with heterogeneous data distribution settings, i.e., MINIST and CIFAR-10, respectively. In addition, the proposed joint dynamic client selection and resource management approach achieve slightly higher accuracy than the considered benchmarks, but they provide a satisfactory energy and time reduction: 29% energy or 20% time reduction on the MNIST; and 25% energy or 12.5% time reduction on the CIFAR-10.

In this paper, we design a resource block (RB) oriented power pool (PP) for semi-grant-free non-orthogonal multiple access (SGF-NOMA) in the presence of residual errors resulting from imperfect successive interference cancellation (SIC). In the proposed method, the BS allocates one orthogonal RB to each grant-based (GB) user, and determines the acceptable received power from grant-free (GF) users and calculates a threshold against this RB for broadcasting. Each GF user as an agent, tries to find the optimal transmit power and RB without affecting the quality-of-service (QoS) and ongoing transmission of the GB user. To this end, we formulate the transmit power and RB allocation problem as a stochastic Markov game to design the desired PPs and maximize the long-term system throughput. The problem is then solved using multi-agent (MA) deep reinforcement learning algorithms, such as double deep Q networks (DDQN) and Dueling DDQN due to their enhanced capabilities in value estimation and policy learning, with the latter performing optimally in environments characterized by extensive states and action spaces. The agents (GF users) undertake actions, specifically adjusting power levels and selecting RBs, in pursuit of maximizing cumulative rewards (throughput). Simulation results indicate computational scalability and minimal signaling overhead of the proposed algorithm with notable gains in system throughput compared to existing SGF-NOMA systems. We examine the effect of SIC error levels on sum rate and user transmit power, revealing a decrease in sum rate and an increase in user transmit power as QoS requirements and error variance escalate. We demonstrate that PPs can benefit new (untrained) users joining the network and outperform conventional SGF-NOMA without PPs in spectral efficiency.

Satellite-ground integrated heterogeneous networks can relieve network congestion, release network resources and provide ubiquitous intelligence services for terrestrial users. Furthermore, digital twin technology can enable nearly-instant data mapping from the physical world to digital systems. The integration between satellite-ground integrated heterogeneous networks and digital twin alleviates the gap between data analyses and physical unities. However, the current challenges, such as the pricing policy, the stochastic task arrivals, the time-varying satellite locations, mutual channel interference, and resource scheduling mechanisms between the users and cloud servers, severely affect the improvement of quality of service. Hence, we establish a blockchain-aided Stackelberg game model for maximizing the pricing profits and network throughput in terms of minimizing privacy overhead, which is able to perform computation offloading, decrease channel interference, and improve privacy protection. Due to the long-term task queue in Stackelberg model, we propose a Lyapunov stability theory-based model-agnostic meta-learning aided multi-agent deep federated reinforcement learning framework to transfer the long-term task queue into the single time slot, and then optimize the central processing unit frequency, channel selection, task-offloading decision, block size, and cloud server price, which facilitate the integration of communication, computation, and block resources. Subsequently, several performance analyses show that the proposed learning framework can strengthen the privacy protection, approach the optimal time average function, and fulfill the long-term average queue size via lower computational complexity. Finally, our simulation results indicate that the proposed learning framework is superior to the existing baseline methods in terms of network throughput, channel interference, cloud server profits, and privacy overhead.

Matrix-variate optimization plays a central role in advanced wireless system designs. In this paper, we aim to explore optimal solutions of matrix variables under two special structure constraints using complex matrix derivatives, including diagonal structure constraints and constant modulus constraints, both of which are closely related to the state-of-the-art wireless applications. Specifically, for diagonal structure constraints mostly considered in the uplink multi-user single-input multiple-output (MU-SIMO) system and the amplitude-adjustable intelligent reflecting surface (IRS)-aided multiple-input multiple-output (MIMO) system, the capacity maximization problem, the mean-squared error (MSE) minimization problem and their variants are rigorously investigated. By leveraging complex matrix derivatives, the optimal solutions of these problems are directly obtained in closed forms. Nevertheless, for constant modulus constraints with the intrinsic nature of element-wise decomposability, which are often seen in the hybrid analog-digital MIMO system and the fully-passive IRS-aided MIMO system, we firstly explore inherent structures of the element-wise phase derivatives associated with different optimization problems. Then, we propose a novel alternating optimization (AO) algorithm with the aid of several arbitrary feasible solutions, which avoids the complicated matrix inversion and matrix factorization involved in conventional element-wise iterative algorithms. Numerical simulations reveal that the proposed algorithm can dramatically reduce the computational complexity without loss of system performance.

Reconfigurable intelligent surface (RIS) has the capability to significantly enhance physical layer security by reconfiguring the propagation in wireless communications. However, due to the cascaded channel brought by the RIS and the hostile nature of potential eavesdroppers, acquiring perfect channel state information (CSI) of the eavesdroppers is challenging. Worse still, if the eavesdroppers are equipped with multiple antennas and there exists spatial correlation at the RIS due to closely spaced RIS elements, the random channel matrices are complicatedly coupled with the phase shift and other wireless resources in the outage probabilistic constraint, making their optimizations intractable. To date, there has been no systematic and feasible approach to address such a challenge. To fill this gap, this paper for the first time reveals an analytical transformation for handling the intractable outage probabilistic constraint. It is theoretically established that when the maximum tolerable outage probability is smaller than a threshold around 0.4, which generally holds in practice, the proposed transformation is exact and suffers no performance loss. As an illustrative example of the developed constraint transformation, the secure energy efficiency maximization is selected as the objecitve function and the resultant resource optimization is handled by the alternating maximization framework. Numerical results are presented to show the rapid convergence behavior of the proposed algorithm and unveil that the proposed probabilistic constraint transformation has superiority over the Bernstein-Type Inequality approximation. Compared with several baseline schemes (e.g., random phase-shift, fixed phase-shift, RIS ignoring CSI uncertainty, and secure transmission without RIS), the proposed scheme significantly boosts the performance, underscoring the significance of appropriately managing the probabilistic constraint outage and optimizing RIS phase shifts for secure transmission against multi-antenna eavesdroppers.

With the evolutional development of maritime networks, the explosive growth of maritime data has put forward elevated demands for the computing capabilities of maritime devices (MDs). Unmanned aerial vehicle (UAV) is able to alleviate the computing pressure of MDs by forwarding the computing tasks to the edge server on the coast. However, UAV relaying introduces a significant security challenge due to the vulnerability of line-of-sight (LoS) communication channels, which can be exploited for eavesdropping on computing tasks. In this paper, an efficient secure communication scheme is proposed for UAV-relay-assisted maritime mobile edge computing (MEC) with a flying eavesdropper. The secure computing capacity of MDs is maximized by jointly optimizing the transmit power, time slot allocation factor, computation optimization and UAV trajectory. Due to multi-variable coupling, the formulated optimization problem (OP) is non-convex. We first transform OP by introducing auxiliary variables. Then, the transformed OP is decomposed and solved in an iterative manner by applying block coordinate descent (BCD) and successive convex approximation (SCA). Numerical results show that the secure computing capability of the UAV-relay-assisted maritime MEC system of proposed secure communication scheme can be effectively improved compared with benchmarks.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted uplink non-orthogonal multiple access (NOMA) framework for finite blocklength (FBL) transmission is proposed. Considering the different communication requirements of Internet of Things devices (IoTDs), a novel design to achieve high-rate and low-error is proposed. Two operating protocols for STAR-RIS are considered, namely energy splitting (ES) and mode switching (MS). 1) For STAR-RIS with ES, an alternating optimization (AO) algorithm is proposed to handle the highly-coupled mixed integer programming problem. More particularly, a low-complexity received-signal-strength-based device pairing scheme is proposed. Based on the given device pair, the closed-form solutions for the power allocation problem are obtained. The transmitting and reflecting coefficient optimization problem is solved by exploiting the successive convex approximation and semidefinite relaxation methods. 2) For STAR-RIS with MS, a double-layer penalty-based (DLPB) algorithm is proposed to tackle the newly introduced binary amplitude constraints. Numerical results reveal that: i) the proposed AO and DLPB algorithms can converge within a few iteration times; ii) the FBL transmission performance can be improved by employing the proposed STAR-RIS framework compared with conventional transmitting/reflecting-only RISs; iii) NOMA is capable of enhancing FBL rate while guaranteeing the reliability constraints compared with orthogonal multiple access.

Recently, there has been a growing interest in learning-based semantic communication because it can prioritize the preservation of meaningful semantic information over the accuracy of the transmitted symbols, resulting in improved communication efficiency. However, existing learning-based approaches still face limitations in defining semantic level loss and often struggle to find a good trade-off between preserving semantic information and preserving intricate details. In addition, the existing semantic communication approaches cannot effectively train semantic encoders and decoders without the support of downstream models. To address these limitations, this paper proposes a contrastive learning (CL)-based semantic communication system. First, inspired by practical observations, we introduce the concept of semantic contrastive loss and propose a semantic contrastive coding (SemCC) approach that treats data corruption during transmission as a form of data augmentation within the CL framework. Moreover, we propose a semantic re-encoding (SemRE) operation, which uses a duplicate of the semantic encoder deployed at the receiver to guide the entire training process when the downstream model is inaccessible. Further, we design the training procedure for SemCC and SemRE approaches, respectively, to balance the semantic information and intricate details. Finally, simulations are performed to demonstrate the superiority of the proposed approaches over competing approaches. In particular, our approaches achieve a significant accuracy improvement of up to 53% on the CIFAR-10 dataset with a bandwidth compression ratio of 1/24, and also obtain comparable image reconstruction quality as the bandwidth compression ratio is improved.

Non-orthogonal multiple access (NOMA) is capable of serving different numbers of users in the same time-frequency resource element, and this feature can be leveraged to carry additional information. In the orthogonal frequency division multiplexing (OFDM) system, a novel enhanced NOMA scheme called NOMA with informative envelope (NOMA-IE) is proposed to explore extra flexibility from the envelope of NOMA signals. In this scheme, data bits are conveyed by the quantified signal envelope in addition to classic signal constellations. The subcarrier activation patterns of different users are jointly decided by the envelope former at the transmitter of NOMA-IE. At the receiver, successive interference cancellation (SIC) is employed, and the envelope detection coefficient is introduced to eliminate the error floor. Theoretical expressions of spectral efficiency, energy efficiency, and detection complexity are provided first. Then, considering the binary phase shift keying modulation, the block error rate and bit error rate are derived based on the two-subcarrier element. The analytical results reveal that the SIC error and the index error are the main factors degrading the error performance. The numerical results demonstrate the superiority of the NOMA-IE over the OFDM and OFDM-NOMA in terms of the error rate performance when all the schemes have the same spectral efficiency and energy efficiency.

Signal identification, a vital task of intelligent communication radios, finds its applications in various military and civil communication systems. Previous works on identification for space-time block codes (STBC) of multiple-input multiple-output (MIMO) system employing orthogonal frequency division multiplexing (OFDM) are limited to additive white Gaussian noise. In this paper, we develop a novel automatic identification algorithm to exploit the generalized cross-correntropy function of the received signals to classify STBC-OFDM signals in the presence of Gaussian noise and impulsive interference. This algorithm first introduces the generalized cross-correntropy function to fully utilize the space-time redundancy of STBC-OFDM signals. The strongly-distinguishable discriminating matrix is then constructed by using the generalized cross-correntropy for multiple receive antennas. Finally, a decision tree identification algorithm is employed to identify the STBC-OFDM signals which is extended by the binary hypothesis test. The proposed algorithm avoids the traditionally required pre-processing tasks, such as channel coefficient estimation, noise and interference statistics prediction and modulation type recognition. Numerical results are presented to show that the proposed scheme provides good identification performance by exploiting the generalized cross-correntropy function of STBC-OFDM signals under impulsive interference circumstances.

In this article, we propose a novel federated learning (FL) framework for wireless Internet of Medical Things (IoMT) based healthcare systems, where multiple mobile clients and one edge server (ES) collaboratively train a shared model on long-tail data through wireless channels. However, the presence of long-tailed data in this system may introduce a biased global model which fails to handle the tail classes. Additionally, the occurrence of severe fading in wireless channels may prevent mobile clients from successfully uploading local models to the ES, thereby excluding them from participating in the model aggregation. These situations adversely affect the performance of FL. To overcome these challenges, we propose a novel scoring aided FL framework that uses a scoring-based sampling strategy to select mobile clients with more tailed data and better transmission conditions to upload their local models. Specifically, we leverage the logits to explore the data distribution among local clients and propose a logits based scoring client selection method to alleviate the impact of long-tailed data. Moreover, we address the impact of severe fading by incorporating the channel state information (CSI) and data rate of clients into the logits based scoring and proposing a novel logits and model upload rate based client selection method. Experimental results demonstrate the effectiveness of our proposed framework. In particular, compared to the conventional FedAvg, the proposed framework can achieve accuracy gains ranging from 4.44% to 28.36% on the CIFAR-10-LT dataset with an imbalance factor (IF) of 50.

The upcoming sixth generation (6G) driven Internetof- Things (IoT) will face the great challenges of extremely low power demand, high transmission reliability and massive connectivities. To meet these requirements, we propose a novel hybrid ambient backscatter communication (ABCom) or relay communication (RCom) assisted non-orthogonal multiple access (NOMA) network, which simultaneously enables traditional relay networks and ABCom-assisted IoT networks. Specifically, we investigate the reliability and the finite signal-to-noise ratio (SNR) diversity-multiplexing trade-off (f-DMT) of the proposed system to characterize the outage performance of the proposed system in the non-asymptotic SNR region. We derive the outage probability (OP) and the finite SNR diversity gain when two sources aim to communicate through either ABCom or RCom. On the basis that the results of Monte Carlo simulation and analysis are in perfect agreement, we discover that in the high SNR regime, the OP for ABCom tends to be a constant, leading to a zero diversity gain and an error floor, while the OP for RCom is monotone decreasing with respect to the SNR. Also, compared with the imperfect successive interference cancellation (ipSIC) mode, the reliability of the system under the ideal condition is significantly improved; Moreover, in the lower multiplexing gain regime, for both ABCom and RCom, the higher finite SNR diversity gain results in better system reliability, which provides good opportunities for ABCom to adapt f-DMT and improve relevant performance metrics by adapting the reflection parameter.

With the increasing number of mobile devices, mobile crowdsensing (MCS) has garnered significant attention in research. However, computing infrastructures such as edge/cloud nodes, which are necessary for processing sensor data, are not always readily available. To address this issue, we propose a cooperative computing framework that enables the offloading of sensor data to nearby mobile devices with unused computational resources (known as helpers) for processing. Our approach considers a scenario with multiple sources and multiple helpers, where computational tasks can be partially offloaded to several helpers. We jointly optimize task offloading strategy, communication resources, and computational resources to minimize the weighted sum energy consumption of mobile devices. We model the optimization problem as a mixed- integer nonlinear programming (MINLP), with the source-helper assignment solved using a distributed algorithm based on matching theory, and the joint task partition and resource allocation problem solved using an alternating optimization (AO) method. Simulation results demonstrate the efficacy of our cooperative computing framework and scheduling scheme, which offer significant advantages over local computing in terms of reducing the weighted sum energy consumption and improving the task completion ratio.

Unmanned Aerial Vehicle (UAV) has emerged as a promising technology to provide wireless signals from air to the ground users in specific scenarios such as earthquakes, tsunamis and other disasters. The performance of the UAV is degraded when the signals are blocked by obstacles in dense urban scenarios. To address this issue and enhance the signal quality available to the ground users, Reconfigurable Intelligent Surface (RIS) has emerged as a new technological paradigm. It offers an intelligent configuration for the signal propagation environment by redirecting the signals to the users. In this article, we solve a non-convex optimization problem of RIS-assisted UAV network by jointly optimizing the RIS phase shift and 3D trajectory of UAV to maximize the energy efficiency of a rotatory-wing UAV. The considered optimization problem is solved using Deep Reinforcement Learning (DRL) based techniques in an on-line fashion to reduce the computational complexity. We leverage Twin-delayed Deep Deterministic Policy Gradient (TD3) to solve the problem by considering the UAV trajectory as a set of continuous actions. For comparison, we also use the Soft Actor-Critic (SAC), Deep Deterministic Policy Gradient (DDPG) and Double Deep Q-Network (DDQN) for continuous and discrete optimization of the UAV trajectory, respectively. Extensive simulations show that the TD3 outperforms all the considered DRL techniques with the highest energy efficiency and throughput, and the lowest propulsion energy.

Federated learning (FL) is an efficient and privacy-preserving distributed learning paradigm that enables massive edge devices to train machine learning models collaboratively. Although various communication schemes have been proposed to expedite the FL process in resource-limited wireless networks, the unreliable nature of wireless channels was less explored. In this work, we propose a novel FL framework, namely FL with gradient recycling (FL-GR), which recycles the historical gradients of unscheduled and transmission-failure devices to improve the learning performance of FL. To reduce the hardware requirements for implementing FL-GR in the practical network, we develop a memory-friendly FL-GR that is equivalent to FL-GR but requires low memory of the edge server. We then theoretically analyze how the wireless network parameters affect the convergence bound of FL-GR, revealing that minimizing the average square of local gradients’ staleness (AS-GS) helps improve the learning performance. Based on this, we formulate a joint device scheduling, resource allocation and power control optimization problem to minimize the AS-GS for global loss minimization. To solve the problem, we first derive the optimal power control policy for devices and transform the AS-GS minimization problem into a bipartite graph matching problem. Through detailed analysis, we further transform the bipartite matching problem into an equivalent linear program which is convenient to solve. Extensive simulation results on three real-world datasets (i.e., MNIST, CIFAR-10, and CIFAR-100) verified the efficacy of the proposed methods. Compared to the FL algorithms without gradient recycling, FL-GR is able to achieve higher accuracy and fast convergence speed. In addition, the proposed device scheduling and resource allocation algorithm also outperforms the benchmarks in accuracy and convergence speed.

Existing device scheduling works in wireless federated learning (FL) mainly focused on selecting the devices with maximum gradient norm or loss function and require all devices to perform local training in each round. This may produce extra training costs and schedule devices with similar data statistics, thus degrading learning performance. To mitigate these problems, we first theoretically characterize the convergence behaviour of the considered FL system, finding that the learning performance is degraded by the difference between the aggregated gradient of scheduled devices and the full participation gradient. Inspired by this, we propose to find a subset of representative devices and the corresponding pre-device stepsizes to approximate the full participation aggregated gradient. Considering the limited wireless bandwidth, we formulate a problem to capture the trade-off between representativity and latency by optimizing device scheduling and bandwidth allocation policies. Our analysis reveals optimal bandwidth allocation is achieved when all scheduled devices have the same latency. Then, by proving the non-monotone submodularity of the problem, we develop a double greedy algorithm to solve the device scheduling policy. To avoid the local training of unscheduled devices, we utilize the historical gradient information of devices to estimate the current gradient for device scheduling design. Compared to existing scheduling algorithms, the proposed representativity-aware device scheduling algorithm improves 6.7% and 4.02% accuracies on two typical datasets under heterogeneous local data distributions, i.e., MNIST and CIFAR-10, respectively. In addition, the proposed latency- and representativity-aware scheduling algorithm saves over 16% and 12% training time for MNIST and CIFAR-10 datasets than the scheduling algorithms based on either latency and representativity individually.

Most existing wireless federated learning (FL) studies focused on homogeneous model settings where devices train identical local models. In this setting, the devices with poor communication and computation capabilities may delay the global model update and degrade the performance of FL. Moreover, in the homogenous model settings, the scale of the global model is restricted by the device with the lowest capability. To tackle these challenges, this work proposes an adaptive model pruning-based FL (AMP-FL) framework, where the edge server dynamically generates sub-models by pruning the global model for devices’ local training to adapt their heterogeneous computation capabilities and time-varying channel conditions. Since the involvement of diverse structures of devices’ sub-models in the global model updating may negatively affect the training convergence, we propose compensating for the gradients of pruned model regions by devices’ historical gradients. We then introduce an age of information (AoI) metric to characterize the staleness of local gradients and theoretically analyze the convergence behaviour of AMP-FL. The convergence bound suggests scheduling devices with large AoI of gradients and pruning the model regions with small AoI for devices to improve the learning performance. Inspired by this, we define a new objective function, i.e., the average AoI of local gradients, to transform the inexplicit global loss minimization problem into a tractable one for device scheduling, model pruning, and resource block (RB) allocation design. Through detailed analysis, we derive the optimal model pruning strategy and transform the RB allocation problem into equivalent linear programming that can be effectively solved. Experimental results demonstrate the effectiveness and superiority of the proposed approaches. The proposed AMP-FL is capable of achieving 1.9x and 1.6x speed up for FL on MNIST and CIFAR-10 datasets in comparison with the FL schemes with homogeneous model settings.

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) can provide flexible computing service for terminal-devices (TDs). However, malicious active aerial eavesdroppers can perform air-to-ground eavesdropping and air-to-air attacking, which makes TDs’ tasks offloading computation more vulnerable, posing significantly secure threats to UAV-enabled MEC. To overcome this challenge, we aim to design collaborative communication and computation schemes for the secure UAV-enabled MEC system, where an active aerial eavesdropper is capable of wiretapping the tasks information offloaded from TDs and transmitting attack signals to the legitimate network. The total weighted energy consumption of the system is minimized via optimizing time allocation, transmit power, local and offloading computation bits, as well as UAV trajectory. First, considering the given number of computational tasks of TDs, a block coordinate descent (BCD)-based scheme is proposed to decompose the original multi-variables-coupling and close-form-lacking problem into several tractable subproblems that can be addressed by iterations. Next, considering that there are dynamic and random tasks arriving to TDs’ original tasks, a deep reinforcement learning (DRL)-based scheme is proposed to maintain the stability of tasks, where the solution of computation, communication and trajectory optimization is intelligently obtained by adopting double-deep Q-learning (DDQN). Simulation results demonstrate that the proposed schemes outperform the respective benchmarks for secure UAV-enabled MEC against active aerial eavesdropping.

Federated Learning (FL) is a promising privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their private datasets. Hierarchical FL (HFL), as a device-edge-cloud aggregation hierarchy, can enjoy both the cloud server’s access to more datasets and the edge servers’ efficient communications with devices. However, the learning latency increases with the HFL network scale due to the increasing number of edge servers and devices with limited local computation capability and communication bandwidth. To address this issue, in this paper, we introduce model pruning for HFL in wireless networks to reduce the neural network scale. We present the convergence analysis of an upper on the l2-norm of gradients for HFL with model pruning, analyze the computation and communication latency of the proposed model pruning scheme, and formulate an optimization problem to maximize the convergence rate under a given latency threshold by jointly optimizing the pruning ratio and wireless resource allocation. By decoupling the optimization problem and using Karush–Kuhn–Tucker (KKT) conditions, closed-form solutions of pruning ratio and wireless resource allocation are derived. Simulation results show that our proposed HFL with model pruning achieves similar learning accuracy compared with the HFL without model pruning and reduces about 50% communication cost.

Downlink reconfigurable intelligent surface (RIS)-assisted multi-input-multi-output (MIMO) systems are considered with far-field, near-field, and hybrid-far-near-field channels. According to the angular or distance information contained in the received signals, 1) a distance-based codebook is designed for near-field MIMO channels, based on which a hierarchical beam training scheme is proposed to reduce the training overhead; 2) a combined angular-distance codebook is designed for hybrid-far-near-field MIMO channels, based on which a two-stage beam training scheme is proposed to achieve alignment in the angular and distance domains separately. For maximizing the achievable rate while reducing the complexity, an alternating optimization algorithm is proposed to carry out the joint optimization iteratively. Specifically, the RIS coefficient matrix is optimized through the beam training process, the optimal combining matrix is obtained from the closed-form solution for the mean square error (MSE) minimization problem, and the active beamforming matrix is optimized by exploiting the relationship between the achievable rate and MSE. Numerical results reveal that: 1) the proposed beam training schemes achieve near-optimal performance with a significantly decreased training overhead; 2) compared to the angular-only far-field channel model, taking the additional distance information into consideration will effectively improve the achievable rate when carrying out beam design for near-field communications.

Intelligent reflecting surface (IRS) is a promising technology that provides high throughput in future communication systems and is compatible with various communication techniques, such as non-orthogonal multiple-access (NOMA). This paper studies the downlink transmission of IRS-assisted NOMA communication, considering the practical case of imperfect channel state information (CSI). Aiming to maximize the system sum rate, a robust IRS-aided NOMA design is proposed to jointly find the optimal beamforming vector for the access point and the passive reflection matrix for the IRS. This robust design is realised using the penalty dual decomposition (PDD) scheme, and it is shown that the results have a close performance to their upper bound obtained from the corresponding perfect CSI scenario. The presented method is compatible with both continuous and discrete phase shift elements of the IRS. Our findings show that the proposed algorithms, for both continuous and discrete IRS, have low computational complexity compared to other schemes in the literature. Furthermore, we conduct a performance comparison between the IRS-aided NOMA and the IRS-aided orthogonal multiple access (OMA). This comparison shows that robust beamforming techniques are crucial for the system to reap the advantages of IRS-aided NOMA communication in the presence of CSI uncertainty.

Non-orthogonal multiple access (NOMA) has drawn much attention due to its capability in massive connections. It enables various joint designs with advanced technologies, e.g., cooperative transmission and aerial base station (ABS)-assisted networks. In this paper, we consider an efficient NOMA enabled cross-tier joint transmission design. This design jointly applies zero-forcing beamforming and NOMA to realize joint transmission and regular transmission for the users in different tiers. We evaluate the performance of the design under a large-scale clustered ABS-assisted network scenario. Theoretical expressions for the outage probability and area outage spectral efficiency are derived. Numerical results show that, although splitting power may lead to slight degradation for the macro-cell users, the system can still expect a decreased overall outage probability because joint transmission improves the received signal quality for the ABS-tier users. The macro-cell users may not always suffer outage performance degradation since they can keep receiving signals instead of staying idle until the cross-tier joint transmission is suspended as in the orthogonal scheme. Besides, with properly selected NOMA power allocation coefficient, a more reliable communication link can be guaranteed for the macro-cell user, if compared with the conventional orthogonal scheme.

In this paper, a stochastic geometry based analytical framework is proposed for secure simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) transmissions, where legitimate users (LUs) and eavesdroppers are randomly distributed. Both the time-switching protocol (TS) and energy splitting (ES) protocol are considered for the STAR-RIS. To characterize system performance, the channel statistics are first provided, and the Gamma approximation is adopted for general cascaded κ-μ fading. Afterward, the closed-form expressions for both the secrecy outage probability (SOP) and average secrecy capacity (ASC) are derived. To obtain further insights, the asymptotic performance for the secrecy diversity order and the secrecy slope are deduced. The theoretical results show that 1) the secrecy diversity orders of the strong LU and the weak LU depend on the path loss exponent and the distribution of the received signal-to-noise ratio, respectively; 2) the secrecy slope of the ES protocol achieves the value of one, higher than the slope of the TS protocol which is the mode operation parameter of TS. The numerical results demonstrate that: 1) there is an optimal STAR-RIS mode operation parameter to maximize the secrecy performance; 2) the STAR-RIS-NOMA significantly outperforms the STAR-RIS-orthogonal multiple access.

For wireless federated learning (FL), this work proposes an adaptive model pruning-based FL (AMP-FL) frame-work, where the edge server dynamically generates sub-models by pruning the global model to adapt devices' heterogeneous computation capabilities and time-varying wireless channel conditions. To mitigate the negative effect of different structures of sub-models on learning convergence, this work designs a new compensating strategy for the pruned regions of sub-models via historical gradients. Since the freshness of gradients dominates the convergence speed, this work also defines an age of information (AoI) metric to characterize the staleness of the regions of the local gradients. Based on the compensating strategy, we formulate a joint device scheduling, model pruning, and resource block allocation optimization problem to minimize the average AoI for local gradients. To solve this problem, we theoretically derive an optimal model pruning scheme. After that, we transform the original problem into equivalent linear programming that can be solved with polynomial time complexity. Simulation results on the CIFAR-IO dataset show that the proposed AMP-FL outperforms the benchmark schemes with faster convergence speed and over 7% learning accuracy improvement.

Reconfigurable intelligent surface (RIS) has the potential to significantly enhance the physical layer security by reconfiguring the wireless propagation environment. However, due to the hostile nature of potential eavesdroppers and the cascaded channel brought by the RIS, acquiring perfect channel state information (CSI) of the eavesdroppers is challenging. Worse still, if the eavesdroppers are equipped with multiple antennas, the design of the optimal phase-shift, power allocation, and secure transmission data rate are intractable due to the couplings of random channel matrices in the outage probability. To overcome these challenges, this paper for the first time reveals an analytical transformation for handling the outage probabilistic constraint in the secure energy efficiency maximization problem due to multi-antenna eavesdropper. The resultant problem is readily handled under the alternating maximization framework. Simulation results unveil that the proposed probabilistic constraint transformation and the associated optimization algorithm provide superior secure energy efficiency over the baseline schemes of random phase-shift, fixed phase-shift, RIS ignoring CSI uncertainty, and secure transmission without RIS.

With the rapid development of intelligent vehicles in recent years, in-vehicle heterogeneous networks (HetNets) incorporating base stations (BSs) and vehicle access points (VAPs) have been widely deployed to support the ever-emerging diverse vehicular applications. However, most of the existing works focused on the HetNets covering multiple vehicles and considered the needs of all in-vehicle users as a holistic entity to maximize the overall performance of networks, which inevitably deviates from the local in-vehicle HetNet quality that most users are concerned about. Additionally, improving the energy efficiency (EE) of mobile devices in vehicles to extend their battery life is another significant issue, which has not been well addressed. To address the above issues, an intelligent in-vehicle power control and spectrum allocation mechanism is proposed in this work to maximize the EE of devices in the cabin while satisfying their dynamic traffic demands. Since this optimization problem has a non-convex mixed integer programming form, which is difficult to solve with traditional optimization methods, we further transform the optimization problem into a Markov Decision Process (MDP) and utilize a Proximal Policy Optimization (PPO) algorithm combined with the vehicle's location and historical channel state information (CSI) to achieve optimization objectives. Simulation results validate that the proposed algorithm can satisfy the dynamic traffic requirements of devices with high EE. Further comparison with baselines highlights the robustness of the proposed scheme under different device quantities and ratios.

An analytical framework for physical layer security in simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) transmissions is proposed, where legitimate users and eavesdroppers are randomly deployed. To characterize system performance, the channel statistics are first provided, and the Gamma approximation is adopted for general cascaded$\kappa-\mu$fading. Afterwards, the energy splitting (ES) protocol is considered and closed-form expressions of average secrecy capacity are derived. To obtain further insights, the asymptotic secrecy slope is deduced. The theoretical results show that the secrecy slope of the ES protocol is one. The numerical results demonstrate that: 1) there is an optimal resource allocation ratio of STAR-RIS to maximize the system performance; 2) the STAR-RIS-aided NOMA significantly outperforms the STAR-RIS-aided orthogonal multiple access.

In the orthogonal frequency division multiplexing with index modulation (OFDM-IM) framework, we propose an enhanced non-orthogonal multiple access (NOMA) scheme called NOMA with informative envelope (NOMA-IE) to explore the flexibility of signal envelope. In this scheme, data bits are conveyed by the quantified signal envelope in addition to classic signal constellations. Subcarrier activation patterns of different users are jointly decided by the envelope former at the transmitter. At the receiver, successive interference cancellation (SIC) is employed, and we introduce the envelope detection coefficient to eliminate the detection error floor. Considering binary phase shift keying, we derive the asymptotic bit error rate (BER). Analytical results reveal that the imperfect SIC is the main factor degrading the error performance. Numerical results demonstrate the superiority of the NOMA-IE over the OFDM and OFDM-NOMA.

The emerging field of federated learning (FL) provides great potential for edge intelligence while protecting data privacy. However, as the system grows in scale or becomes more heterogeneous, new challenges, such as the spectrum shortage and stragglers issues, arise. These issues can potentially be addressed by over-the-air computation (AirComp) and asynchronous FL, respectively, however, their combination is difficult due to their conflicting requirements. In this paper, we propose a novel asynchronous FL with AirComp in a time-triggered manner (async-AirFed). The conventional async aggregation requests the historical data to be used for model updates, which can cause the accumulation of channel noise and interference when AirComp is applied. To address this issue, we propose a simple but effective truncation method which retains a limited length of historical data. Convergence analysis presents that our proposed async-AirFed converges on non-convex optimality function with sub-linear rate. Simulation results show that our proposed scheme achieves more than 34% faster convergence than the benchmarks, by achieving an accuracy of 85%, which also improves the time utilization efficiency and reduces the impact of staleness and the channel.

The conventional model aggregation-based federated learning (FL) approaches require all local models to have the same architecture and fail to support practical scenarios with heterogeneous local models. Moreover, the frequent model exchange is costly for resource-limited wireless networks since modern deep neural networks usually have over-million parameters. To tackle these challenges, we first propose a novel knowledge-aided FL (KFL) framework, which aggregates light high-level data features, namely knowledge, in the per-round learning process. The KFL allows devices to design their machine learning models independently and reduces the communication overhead in the training process. We then experimentally show that different temporal device scheduling patterns lead to considerably different learning performance. With this insight, we formulate a stochastic optimization problem for joint device scheduling and bandwidth allocation under limited devices' energy budgets and develop an efficient online algorithm to achieve an energy-learning trade-off in the learning process. Experimental results on the CIFAR-10 dataset show that the proposed KFL can reduce over 87% communication overhead while achieving better learning performance than the baselines. In addition, the proposed device scheduling algorithm converges faster than benchmark scheduling schemes.

This work aims to address two of the main challenges for federated learning (FL), i.e., the limited communication resources and the data heterogeneity across devices. To this end, we first devise a novel FL framework with partial model aggregation (PMA), which only aggregates the lower layers of neural networks responsible for feature extraction while the upper layers corresponding to complex pattern recognition remain at devices for personalization. This design is able to address the data heterogeneity and reduce the transmitted information in wireless channels. Then, we maximize the scheduled data sample volume by joint optimizing the device scheduling, bandwidth allocation, computation and communication time division. Specifically, our analysis reveals that the optimal time division is achieved when the communication and computation parts of PMA-FL have the same power. We also develop a bisection method to solve the optimal bandwidth allocation policy and use the set expansion algorithm to address the optimal device scheduling. Experimental results on the CIFAR-10 dataset show that the proposed PMA-FL improves 11.6% accuracy compared with the state-of-art benchmarks, and the proposed joint dynamic device scheduling and resource optimization approach achieves slightly higher accuracy than the considered benchmarks but reduced 25% energy or 12.5% time budgets.

The satellite-ground (SG) integrated networks are regarded as a promising network structure, which can provide ubiquitous intelligence and pervasive services for multiple ground users. Moreover, digital twin (DT) can drive real-time data mapping and wireless access from usual physical utilities to digital units. Therefore, the fusion of SG and DT can decrease the gap between real-time data analysis and physical system states, which can help boost SG-DT edge intelligence paradigms. Nevertheless, the unexpected task arrivals, time-varying channel gains, and distrust among ground devices cause the network service performance degradation. Hence, in this paper, we propose a privacy-assisted blockchain computation offloading model to shine upon original tasks to the corresponding aerial platforms, and then orchestrate the task scheduling, resource allocation, and privacy protection. Additionally, we envision a Lyapunov stability theory-based multi-agent federated reinforcement learning (LST-MAFRL) algorithm to further resolve the CPU cycle frequency, the size of each blockchain, the number of DTs, and related harvested solar energy to minimize the execution energy consumption and privacy time overhead. Finally, extensive simulation results indicate that the proposed LST-MAFRL algorithm framework outperforms some state-of-the-art benchmarks for the sake of execution energy efficiency, processed bit quantities, and privacy time overhead.

This paper investigates the covert millimeter wave (mmWave) communication in the finite block-length regime, where spatially random wardens attempt to determine the presence of transmission. First, we derive a novel expression of covertness constraint by using the tools of stochastic geometry, based on which the expression of average effective covert throughput (AECT) is also presented. Then, considering the constraint of maximal available block-length, the optimization problem for maximizing the AECT is formulated, and the optimal transmit power and block-length are analytically determined. Our results show the superiority of our optimization in terms of AECT in contrast to the fixed block-length case, and the improvement is more significant when the density of wardens becomes large. Furthermore, the performance of covert mmWave communication can indeed be improved via increasing the number of antennas even there exist random distributed wardens.

In this paper, we propose a secure transmission scheme for aerial intelligent reflecting surface (IRS) assisted wireless networks. A multi-antenna access point (AP) serves multiple legitimate users in the presence of multiple eavesdroppers, whose precise positions are unknown. An IRS is carried by the unmanned aerial vehicle (UAV) to help establish virtual line-of-sight links between the AP and ground users, as well as ensuring the secure transmission. The hovering position of UAV, the transmit beamforming of AP and the phase shifts of IRS are jointly optimized to maximize the worst-case sum secrecy rate, subject to the minimum rate requirement of legitimate users. The non-convex optimization problem is decomposed into three subproblems, each of which is transformed into a convex one by utilizing successive convex approximation. An alternating optimization algorithm is applied to tackle the subproblems iteratively. Simulation results validate the effectiveness of the proposed scheme and the security enhancement by the joint optimization.

How to tackle the unreliability in wireless channels is critical for federated learning (FL). To solve this problem, we propose a novel FL framework, namely FL with gradient recycling (FL-GR), which recycles the historical gradients of unscheduled and transmission-failure devices to improve the learning performance of FL. Based on the proposed FL-GR, we theoretically analyze how the wireless network parameters affect the convergence bound of FL-GR, revealing that scheduling devices with large staleness and increasing their transmit power in each round helps improve learning performance. Simulation results on MNIST and CIFAR-10 show that FL-GR is able to achieve higher accuracy and fast convergence speed than conventional FL algorithms without gradient recycling.

This study investigates the downlink transmission of a multiple-input single-output (MISO) communication system with an intelligent reflecting surface (IRS) under channel uncertainty. The problem of maximizing the sum-rate is formulated by incorporating the channel estimation error footprints, and it is solved through joint optimization of the active precoding at the access point (AP) and passive beamforming at the IRS using the penalty dual decomposition (PDD) algorithm. The PDD algorithm is iterative, has guaranteed convergence, and yields closed-form solutions in each iteration, resulting in low computational complexity. Two cases of continuous and discrete phase shifts at the IRS are considered. Simulation results indicate the effectiveness of the proposed method in handling channel uncertainty for both continuous and discrete phase shifts.

We investigate a reconfigurable intelligent surface (RIS)-aided wireless powered mobile edge computing (MEC) network, where a new RIS-aided hybrid non-orthogonal multiple access (NOMA) offloading scheme is proposed to improve the efficiency of both MEC offloading and energy harvesting. A joint optimization framework for the transmit power, time allocation, and RIS phase shifts is developed to minimize the overall energy consumption of the network, subject to the energy causality and MEC offloading constraints. Despite of the optimization problem's non-convexity with highly coupled optimization variables, we devise a computationally-efficient algorithm to solve it iteratively. Simulation results are also provided to facilitate the performance evaluation of the RIS-aided wireless powered MEC, quantify the value of the RIS for energy efficient MEC offloading, and validate the performance gains of the proposed hybrid NOMA offloading over various baseline schemes.

Recently, semantic communication has been widely applied in wireless image transmission systems as it can prioritize the preservation of meaningful semantic information in images over the accuracy of transmitted symbols, leading to improved communication efficiency. However, existing semantic communication approaches still face limitations in achieving considerable inference performance in downstream AI tasks like image recognition, or balancing the inference performance with the quality of the reconstructed image at the receiver. Therefore, this paper proposes a contrastive learning (CL)-based semantic communication approach to overcome these limitations. Specifically, we regard the image corruption during transmission as a form of data augmentation in CL and leverage CL to reduce the semantic distance between the original and the corrupted reconstruction while maintaining the semantic distance among irrelevant images for better discrimination in downstream tasks. Moreover, we design a two-stage training procedure and the corresponding loss functions for jointly optimizing the semantic encoder and decoder to achieve a good trade-off between the performance of image recognition in the downstream task and reconstructed quality. Simulations are finally conducted to demonstrate the superiority of the proposed method over the competitive approaches. In particular, the proposed method can achieve up to 56% accuracy gain on the CIFAR10 dataset when the bandwidth compression ratio is 1/48.

As for the time-division communications system, the pilot spoofing attack (PSA) technique is maliciously utilized by active eavesdroppers during the uplink training phase, for contaminating the legitimate channel estimation and thus altering the beamforming design towards the eavesdroppers. Nonorthogonal multiple access (NOMA) has been recognized as the key technology for the envisioned Internet of Things (IoT) networks. In order to prevent the aforementioned information leakage in NOMA-IoT systems, we develop a novel two-way training scheme to detect PSA and a robust secure beamforming design for providing secure transmission, by utilizing the emerging technique of reconfigurable intelligent surface (RIS), which is turned off during the uplink training phase and turned on during the downlink training phase, respectively. Considering that the perfect channel state information related to the eavesdropping channel is typically difficult to obtain, a secrecy outage probability-constrained robust secure beamforming design is proposed to maximize the achievable sum secrecy rate of the legitimate users, by alternatively optimizing the active beamforming and RIS passive beamforming, while satisfying the requirements of the NOMA transmission. Elaborate simulation results reveal that the proposed detection method attains a super PSA detection performance and the proposed robust secure beamforming design is capable of efficiently enhancing the achievable sum secrecy rate, compared with various benchmark schemes.

To mitigate the overlapping of the radar and communication frequency bands caused by large-scale devices access, we propose a novel integrated sensing and communication (ISAC) system, where a micro base station (MiBS) simultaneously carries out both target sensing and cooperative communication. Concretely, the MiBS, acting as the sensing equipment, can also serve as a full-duplex decode-and-forward relay to assist end-to-end communication. Moreover, nonorthogonal downlink transmission (NO-DLT) is adopted between the macro base station and the Internet of Things devices, so that the spectrum utilization can be further improved. To facilitate the performance evaluation, both the exact and asymptotic outage probabilities, the ergodic rates associated communication, and the probability of successful sensing detection are characterized. Subsequently, a pair of problems of maximizing the receive signal-to-interference-plus-noise ratio of the sensing signal and maximizing the sum rate of communication are formulated that are solved by the classic Lagrangian method while exploiting the associated function monotonicity. Our simulation results demonstrate that: 1) The proposed ISAC NO-DLT system improves both the communication and sensing performance under the same power consumption as noncooperative NO-DLT and 2) the proposed power allocation (PA) schemes are superior to the random PA scheme.

To cater for the data-hungry Internet of Things (IoT) applications, uplink centric broadband communication (UCBC) has been identified as a new service class in the vision of 5.5G, where the unlicensed spectrum has been regarded as a promising solution to boost the uplink capacity. The new radio unlicensed (NR-U) network adopts category-4 (Cat4) listen before talk (LBT) access scheme to exploit the unlicensed spectrum and fairly coexist with the incumbent wireless fidelity (WiFi) network. However, the existing Cat4 LBT access scheme adopts single fixed energy detection (ED) threshold and backoff speed, which cannot adapt to the sophisticated interference and achieve the expected uplink system throughput. To tackle this issue, in this article, we develop a novel Cat4 LBT access scheme with adaptive backoff procedure for UCBC, which includes instantaneous interference level quantification, instantaneous interference level sharing, and backoff speed determination. The results have shown that our proposed adaptive Cat4 LBT scheme achieves over 70% uplink system throughput performance gain where cell throughput of NR-U network rises by over 100%, and cell throughput of WiFi network increases by 25%.

In recent years, more and more attention has been paid to the unmanned aerial vehicle (UAV) cooperative task assignment. In order to complete the task with the lowest cost, some researchers use multi-objective optimization to solve the assignment problem. But few of them consider the complex dynamic scenarios. In this article, the time-varying resource supply and demands are provided by established digital twins (DTs) of UAVs and targets, thereby enabling accurate decision guidance for dynamic task assignment. It takes the scheduling cost, path cost, risk cost and total task time cost as the optimization objectives. To solve this model, an improved dynamic multi-objective adaptive weighted particle swarm Optimization algorithm (DMOAWPSO) is proposed. In the initialization stage, a heuristic method is used to increase the effectiveness of the solution. Besides, the adaptive mutation and subgroup methods are adopted to improve the diversity of the solution. Then, effective environment change detection and response strategies are designed to adapt to dynamic scenarios. Finally, the evaluation metrics are calculated in different instances. Compared with the popular and classic dynamic multi-objective algorithms, the simulation results verify that the proposed algorithm is effective and can cope with the environment changes better in solving the task assignment problem.

The sixth-generation (6G) communication research is currently in the early stage, where ultra-reliable low-latency communication (URLLC) is still an important service as in the fifth-generation (5G). Since 6G networks are expected to provide even higher levels of massive connectivity, high spectrum efficiency, high reliability, and low latency than 5G communication, it would confront much more severe spectrum scarcity problems, which make the new radio in unlicensed spectrum (NR-U) technology attractive. However, how to achieve URLLC requirements in NR-U networks is extremely challenging due to interference and collisions among multiple radio access technologies (e.g., WiFi). Therefore, it is urgent to design efficient spectrum-sharing algorithms to support URLLC in emerging 6G networks. In this paper, we develop novel centralized deep reinforcement learning (CDRL) and federated DRL (FDRL) frameworks, respectively, to optimize the downlink URLLC transmission in NR-U and WiFi coexistence systems through dynamically adjusting energy detection (ED) thresholds. Our results show that both CDRL and FDRL approaches have improved the reliability of the NR-U system significantly, but the CDRL framework has sacrificed the reliability of the WiFi system. To guarantee the reliability of the WiFi system while improving the NR-U system, we take fairness into account by redesigning the reward of CDRL.

With the development of next-generation wireless networks, the Internet of Things (IoT) is evolving towards the intelligent IoT (iIoT), where intelligent applications usually have stringent delay and jitter requirements. In order to provide low-latency services to heterogeneous users in the emerging iIoT, multi-tier computing was proposed by effectively combining edge computing and fog computing. More specifically, multi-tier computing systems compensate for cloud computing through task offloading and dispersing computing tasks to multi-tier nodes along the continuum from the cloud to things. In this paper, we investigate key techniques and directions for wireless communications and resource allocation approaches to enable task offloading in multi-tier computing systems. A multi-tier computing model, with its main functionality and optimization methods, is presented in detail. We hope that this paper will serve as a valuable reference and guide to the theoretical, algorithmic, and systematic opportunities of multi-tier computing towards next-generation wireless networks.

Multi-tier computing can enhance the task computation by multi-tier computing nodes. In this paper, we propose a cell-free massive multiple-input multiple-output (MIMO) aided computing system by deploying multi-tier computing nodes to improve the computation performance. At first, we investigate the computational latency and the total energy consumption for task computation, regarded as total cost. Then, we formulate a total cost minimization problem to design the bandwidth allocation and task allocation, while considering realistic heterogenous delay requirements of the computational tasks. Due to the binary task allocation variable, the formulated optimization problem is non-convex. Therefore, we solve the bandwidth allocation and task allocation problem by decoupling the original optimization problem into bandwidth allocation and task allocation subproblems. As the bandwidth allocation problem is a convex optimization problem, we first determine the bandwidth allocation for given task allocation strategy, followed by conceiving the traditional convex optimization strategy to obtain the bandwidth allocation solution. Based on the asymptotic property of received signal-to-interference-plus-noise ratio (SINR) under the cell-free massive MIMO setting and bandwidth allocation solution, we formulate a dual problem to solve the task allocation subproblem by relaxing the binary constraint with Lagrange partial relaxation for heterogenous task delay requirements. At last, simulation results are provided to demonstrate that our proposed task offloading scheme performs better than the benchmark schemes, where the minimum-cost optimal offloading strategy for heterogeneous delay requirements of the computational tasks may be controlled by the asymptotic property of the received SINR in our proposed cell-free massive MIMO-aided multi-tier computing systems.

Multi-tier computing effectively enables flexible computation and communication resource sharing by offloading computation-intensive tasks to nearby servers along the cloud-to-thing continuum. In essence, multi-tier computing networks can distribute computing, storage, and communication functions anywhere between the cloud and the endpoint to take full advantage of the resources available along this continuum, thus extending the traditional cloud computing architecture to the edge of the network. With multi-tier computing, some application component processing, such as delay-sensitive components, can take place at the edge of the network, while other components, such as time-tolerant and computation-intensive components, can be performed in the cloud. To best meet user requirements, centralized cloud computing with extensive resources, secure environments, and powerful algorithms is still needed, but also must be complemented by distributed fog and edge computing with shared resources, accessible environments, and simple algorithms for real-time decision-making. Given heterogeneous computing resources and collaborative service architectures, future multi-tier computing networks will be capable of supporting a full range of computing and networking services for different environments and applications. This Special Issue aims to provide a forum for the latest advances in multi-tier computing for next-generation wireless network research, innovations, and applications. Multi-tier computing enables low-latency processing by allowing data to be processed at the network edge close to end devices. It also facilitates the distribution of fog/edge nodes to collect data from end devices. Therefore, multi-tier computing effectively complements the cloud computing architecture.

Multi-tier computing effectively enables flexible computation and communication resource sharing by offloading computation-intensive tasks to nearby servers along the cloud-to-thing continuum. In essence, multi-tier computing networks can distribute computing, storage, and communication functions anywhere between the cloud and the endpoint to take full advantage of the resources available along this continuum, thus extending the traditional cloud computing architecture to the edge of the network. With multi-tier computing, some application component processing, such as delay-sensitive components, can take place at the edge of the network, while other components, such as time-tolerant and computation-intensive components, can be performed in the cloud. To best meet user requirements, centralized cloud computing with extensive resources, secure environments, and powerful algorithms is still needed, but also must be complemented by distributed fog and edge computing with shared resources, accessible environments, and simple algorithms for real-time decision-making. Given heterogeneous computing resources and collaborative service architectures, future multi-tier computing networks will be capable of supporting a full range of computing and networking services for different environments and applications. Multi-tier computing enables low-latency processing by allowing data to be processed at the network edge close to end devices. It also facilitates the distribution of fog/edge nodes to collect data from end devices. Therefore, multi-tier computing effectively complements the cloud computing architecture.

The conventional model aggregation-based federated learning (FL) approach requires all local models to have the same architecture, which fails to support practical scenarios with heterogeneous local models. Moreover, the frequent model exchange is costly for resource-limited wireless networks since modern deep neural networks usually have over a million parameters. To tackle these challenges, we first propose a novel knowledge-aided FL (KFL) framework, which aggregates light high-level data features, namely knowledge, in the per-round learning process. This framework allows devices to design their machine-learning models independently and reduces the communication overhead in the training process. We then theoretically analyze the convergence bound of the proposed framework under a non-convex loss function setting, revealing that scheduling more data volume in each round helps to improve the learning performance. In addition, large data volume should be scheduled in early rounds if the total scheduled data volume during the entire learning course is fixed. Inspired by this, we define a new objective function, i.e., the weighted scheduled data sample volume, to transform the inexplicit global loss minimization problem into a tractable one for device scheduling, bandwidth allocation, and power control. To deal with unknown time-varying wireless channels, we transform the considered problem into a deterministic problem for each round with the assistance of the Lyapunov optimization framework. Then, we derive the optimal bandwidth allocation and power control solution by convex optimization techniques. We also develop an efficient online device scheduling algorithm to achieve an energy-learning trade-off in the learning process. Experimental results on two typical datasets (i.e., MNIST and CIFAR-10) under highly heterogeneous local data distributions show that the proposed KFL is capable of reducing over 99% communication overhead while achieving better learning performance than the conventional model aggregation-based algorithms. In addition, the proposed device scheduling algorithm converges faster than the benchmark scheduling schemes.

In this paper, we study a relay-assisted federated edge learning (FEEL) network under latency and bandwidth constraints. In this network,$N$users collaboratively train a global model assisted by$M$intermediate relays and one edge server. We firstly propose partial aggregation and spectrum resource multiplexing at the relays in order to improve the communication of the relay-assisted FEEL system. Furthermore, we derive analytical and asymptotic expressions of the system outage probability and convergence rate. For the purpose of improving the system performance, we further optimize the relay-assisted FEEL network by maximizing the number of users who participate in each round of federated learning, through allocation of the wireless bandwidth among users and relays. Specifically, two bandwidth allocation (BA) schemes have been proposed, assuming either instantaneous or statistical channel state information (CSI). Simulations show the advantages of the proposed BA schemes over other benchmarks, regarding the accuracy and convergence rate of the considered relay-assisted FEEL network.

Grant-free non-orthogonal multiple access (GF-NOMA) is a potential technique to support massive Ultra-Reliable and Low-Latency Communication (mURLLC) service. However, the dynamic resource configuration in GF-NOMA systems is challenging due to random traffics and collisions, that are unknown at the base station (BS). Meanwhile, joint consideration of the latency and reliability requirements makes the resource configuration of GF-NOMA for mURLLC more complex. To address this problem, we develop a novel learning framework for signature-based GF-NOMA in mURLLC service taking into account the multiple access signature collision, the UE detection, as well as the data decoding procedures for the K-repetition GF and the Proactive GF schemes. The goal of our learning framework is to maximize the long-term average number of successfully served users (UEs) under the latency constraint. We first perform a real-time repetition value configuration based on a double deep Q-Network (DDQN) and then propose a Cooperative Multi-Agent learning technique based DQN (CMA-DQN) to optimize the configuration of both the repetition values and the contention-transmission unit (CTU) numbers. Our results show the superior performance of CMA-DQN over the conventional load estimation-based uplink resource configuration approach (LE-URC) in heavy traffic and demonstrate its capability in dynamically configuring in long term for mURLLC service. In addition, with our learning optimization, the Proactive scheme always outperforms the K-repetition scheme in terms of the number of successfully served UEs, especially under the high backlog traffic scenario.

Reconfigurable intelligent surface (RIS) enables the configuration of the propagation environment. Channel estimation is an essential task in realizing the RIS-aided communication system. A RIS-aided multi-user multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) communication system involves cascaded channels with high dimensions and sophisticated statistics. Thus, implementing the optimal minimum mean square error (MMSE) with the integration computation is infeasible in practice. To accurately estimate channels with high accuracy in a RIS-aided multi-user MIMO-OFDM system, we model the channel state information (CSI) estimation as an image super-resolution (SR) problem to recover and denoise the channel matrix. Particularly, a convolutional neural network based on a super-resolution convolutional neural network (SRCNN) and denoising convolutional neural network (DnCNN), named SRDnNet, is then proposed. By taking estimated channels at pilot positions as a low-resolution image, the enhanced SRCNN can fully exploit the features of inputs to learn a suitable interpolation method and generate the coarse estimation of the channel matrix. The denoising model DnCNN with an element-wise subtraction structure can exploit features of the additive noise and recover channel coefficients from the coarse channel matrix. The simulation results demonstrate the effectiveness and excellent performance of the proposed SRDnNet.

Combining intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) offers a new degree of freedom to improve the coverage performance. However, it is more challenging to secure the air-ground transmission, due to the line-of-sight (LoS) links established by UAV. Since IRS is a promising solution for wireless environment reconfiguration, in this paper, we propose an aerial IRS-assisted secure transmission design in wireless networks. In particular, an access point (AP) equipped with a uniform planar array serves several single-antenna legitimate users in the presence of multiple single-antenna eavesdroppers, whose precise positions are unknown. An IRS is mounted on the UAV to help establish desired virtual LoS links between the AP and legitimate users, while ensuring their security. We aim to maximize the worst-case sum secrecy rate by jointly optimizing the hovering position of UAV, the transmit beamforming of AP and the phase shifts of IRS, subject to the requirement of minimum rate for legitimate users. To tackle this non-convex problem, we first decompose it into three subproblems, which are transformed into convex ones via successive convex approximation. An alternating algorithm is then proposed to solve them iteratively. Simulation results show the effectiveness of the proposed scheme and the security improvement by the joint optimization.

Semantic communication has been regarded as a promising technology to serve upcoming intelligent applications. However, few studies have addressed the problem of resource allocation in semantic communication networks. Most resource allocation mechanisms act fairly to all original data, ignoring the meaning behind the transmitted bits. In this paper, a dynamic resource allocation scheme for the task-oriented semantic communication network (TOSCN) based on deep reinforcement learning (DRL) is proposed, which allows data with richer semantic information to preferentially occupy limited communication resources. This paper aims to design a deep deterministic policy gradient (DDPG) agent at the micro base station to maximize the long-term transmission efficiency of tasks. Firstly, the relationship between semantic information and task performance is investigated. Subsequently, a novel wireless resource allocation model for TOSCN is proposed by taking the image classification task as an example. Then, a joint optimization problem of the semantic compression ratio, transmit power, and bandwidth of each user is formulated. The agent is trained in an interactive learning environment to obtain a decent trade-off between the amount of data delivered to the receiver and the accuracy of intelligent tasks. Simulation results demonstrate that the proposed scheme achieves significant advantages in relieving communication pressure and improving task performance in resource-constrained wireless networks.

Nowadays, the multi-access interference problem in the ISAC systems can not be ignored. The study on interference management in ISAC has been envisioned as one of key technologies to support ubiquitous sensing functions. Different from the current work, a communications-sensing-intelligence converged network architecture is proposed to coordinate interference in this paper. Each base station equips with the individual deep neural networks to allocate power and beamforming. On this basis, the interference management is transformed into a functional optimization with stochastic constraints. An unsupervised learning algorithm is proposed to allocate power for interference management. Furthermore, a transfer learning method is presented to obtain the interference management in terms of transmit beamforming. Finally, the distributed management is obtained from the local channel state information in the multi-cell scenario. Simulation results verify the effectiveness of the proposed unsupervised learning interference management method in the ISAC systems.

Signal detectors are quite important to attain the diversity of doubly-dispersive wireless channels. Detectors based on message-passing (MP) of factor graphs have been regarded as the way to achieve the near-optimal performance for OTFS. In this paper, by deriving the pattern of the multipath vectorized channel matrix of the orthogonal time frequency space (OTFS) system, it is shown that short girth (i.e. girth-4) may exist in the Tanner graphs, which will degrade the performance of MP detectors, especially with high modulation orders. By introducing interleavers at the transmitter and receiver, the vectorized channel matrix turns out to be a sparse upper block Heisenberg matrix, whose structure is beneficial for the computation of matrix QR decomposition (QRD). Successive interference canceling (SIC) detectors based on QRD and sorted QRD are constructed to eliminate the cross-symbol interference and improve the reliability of the symbol-level channel. Simulation results show that for 4QAM, the QRD-based SIC detectors can achieve about 4dB gain at 10−2 over the non-SIC detectors, while the sorted QRD-based SIC detectors can bring an additional 2dB at 10−3, which is only 1dB gap from the MP. For 16QAM, the sorted SIC detectors show superior BER performance than the MP method, and for 64QAM, the MP detector reaches the error floor while SIC detectors show their excellent performance in all configurations.

With the development of internet of things, latency-sensitive applications such as telemedicine are constantly emerging. Unfortunately, due to the limited computation capacity of wireless user devices, the real-time demands can not be met. Multi-access edge computing (MEC), which enables the deployment of edge access points (E-APs) to support computation-intensive applications, has become an effective way to meet the real-time demands. However, the number of WUDs that E-APs can serve are limited. To increase system capacity, the unmanned aerial vehicle (UAV) assisted computation offloading architecture in the terahertz (THz) band is proposed. In this paper, the problem of UAV placement optimization, resource allocation, and computation offloading is investigated considering the quality of service and resource constraints. The joint optimization problem is non-convex and hard to be solved in time by using traditional algorithms, such as successive convex approximation. Therefore, deep reinforcement learning (DRL) based approach is a promising way to solve the formulated non-convex problem of minimizing latency. Double deep Q-learning (DDQN) and deep deterministic policy gradient (DDPG) algorithms are provided to search for near-optimal solutions in highly dynamic environments. The effectiveness of the proposed algorithms is proved by simulation results in different scenarios.

Semi-grant-free non-orthogonal multiple access (SGF-NOMA) is a potential paradigm to support massive connec-tivity for the short packets Internet of things (IoT) applications while satisfying the undistracted transmission requirements of primary IoT users. However, resource allocation in SGF-NOMA is more challenging due to the sporadic traffic of grant-free (GF) users and the need to satisfy the quality of service (QoS) requirements of grant-based (GB) users. The GF users access and choose resources at random, resulting in frequent power collisions and decoding failures at the base station (BS). This paper develops a general learning framework that enables GF users to learn from historical information to avoid power collisions. We utilize a hybrid multi-agent deep reinforcement learning (hMA-DRL) framework to maximize the connectivity and enhance the number of successful decoded users at the BS. The numerical results show that the proposed scheme achieves a solution near to the optimal one and increases the successful decoded users by 42.38% as compared to the benchmark scheme. The considered algorithm performs well with an increasing number of users as compared to the competitive and cooperative MA-DRL algorithms.

Combining non-orthogonal multiple access (NO-MA) and unmanned aerial vehicles (UAVs) can achieve better performance for wireless networks. In this paper, we propose an effective scheme for NOMA-UAV network with multiple clusters. Due to the limited resource, the user clustering and optimal routing are first developed by the K-means algorithm and genetic algorithm, respectively. Then, the sum throughput is maximized by jointly optimizing the transmission power, hovering locations and transmission duration of UAV. To solve this non-convex problem with coupled variables, we decompose it into three subproblems. Among them, the non-convex sub-problems can be transformed into convex ones by successive convex approximation. Then, we propose an iterative algorithm to solve these three subproblems alternately. Finally, simulation results are presented to show the effectiveness of the proposed scheme.

Unmanned aerial vehicle-aided (UAV-aided) mobile edge computing (MEC) network can greatly reduce the data growth pressure of Internet of Things (IoT) and expand the wireless communication coverage. However, there is a risk of eavesdropping on the offloading information of terminal users (TUs) because of UAV light-of-sight (LoS) transmission. In this paper, we propose a Dinkelbach-guided deep reinforcement learning (DRL) scheme for secure communication in the UAV-aided MEC network. Specifically, the security calculating efficiency of the network is maximized by optimizing offloading decision and resource allocation under the condition of the data queue stability and minimum calculating requirement. The problem is intractable due to the fractional structure and binary constraint. Firstly, we deal with the fractional structure by taking advantage of Dinkelbach optimization. Then, offloading decision is generated based on DRL and the resource is allocated by successive convex approximation (SCA). Simulation results show that the proposed Dinkelbach-guided DRL scheme efficiently improves the security calculating efficiency of the network.

In this paper, we investigate secure wireless commu-nication in an intelligent reflecting surface (IRS)-assisted system where the IRS is used to secure the communication of one legitimate receiver in presence of an eavesdropper. We assume that the IRS is standalone, i.e. the passive beamforming of the IRS is carried out completely on its own. Thus, we design an IRS with several passive elements and only two RF chains that can obtain a partial channel state information (CSI) among each node and the IRS. The partial CSI is then mapped into full CSI by using the correlation information between the channels of different IRS elements. We develop a deep reinforcement learning (DRL)-based framework using the deep deterministic policy gradient (DDPG) algorithm to obtain the IRS beamforming vector resulting in maximizing the secrecy rate. Numerical results demonstrate the ability of this technique to secure the wireless communication system.

Channel Estimation is one of the essential tasks to realize a reconfigurable intelligent surface (RIS)-aided orthogonal frequency division multiplexing (OFDM) communication system. Compared with conventional systems, the RIS introduces a cascaded channel with high dimension and sophisticated statistics. In this case, it is infeasible to derive the optimal minimum mean square error (MMSE) estimator. Additionally, the analytical channel estimators, e.g., the least square (LS) estimator and the linear minimum mean square error (LMMSE) estimator are computational costly and imprecise for practical RIS-aided systems. To address these challenge problems and accurately estimate the channel in an RIS-aided OFDM system, we model the channel estimation as a super-resolution (SR) and image restoration (IR) problem to recover the channel matrix from estimated channel at pilot positions. A convolutional neural network based on super-resolution convolutional neural network (SRCNN) and denoising convolutional neural network (DnCNN), named SRDnNet, is then proposed. The simulation results show that the performance of the proposed SRDnNet outperforms the state-of-the-art deep learning-based estimation methods and the LMMSE estimator.

In this paper, we propose a novel distributed resource allocation mechanism for semi-grant-free non-orthogonal multiple access (SGF-NOMA) transmission to maximize the network throughput, where multi-agent deep reinforcement learning with prioritized experience replay (PER) is employed. We design a centralized training framework and decentralized decision making to increase the flexibility of the proposed scheme. More specifically, each grant-free user as an "agent" learns the dynamics of the environment and makes its decisions independently in a decentralized manner. No heavy information exchange is needed to find the optimal transmit power and sub-channel that maximize the throughput. Numerical results show that the proposed algorithm with PER enhances the learning efficiency compared to the algorithm with conventional replay buffer and outperforms the existing scheme with a 12% throughput increase.

AI-empowered Cognitive Radio (i.e., AI-enabled radios) is a paradigm shift to achieve the highest level of Self-Awareness in future wireless communications. This work proposes a joint automatic modulation conversion and classification (AMCC) framework, which allows an AI-enabled wireless node to predict signals' dynamics of different modulation schemes and explain how it can be transported (converted) with minimal effort and forwarded with higher spectral efficiency. To achieve this goal, we propose a Generalized Filtering framework integrated by Transport Planning to learn the way of converting low-order modulations to high-order modulations, which has also been validated by performing the automatic modulation classification. Simulation results demonstrate the effective performance of our novel framework on converting and classifying multiple modulation formats.

Realizing efficient, delay-bounded, and reliable communications for a massive number of user equipments (UEs) in massive Ultra-Reliable and Low-Latency Communications (mURLLC) is extremely challenging as it needs to simultaneously take into account the latency, reliability, and massive access requirements. To support these requirements, the third generation partnership project (3GPP) has introduced grant-free non-orthogonal multiple access (GF-NOMA) with multiple configured-grants (MCGs), where UE can choose any of these grants as soon as the data arrives. In this paper, we develop a novel learning framework for MCG-GF-NOMA systems. We first design the MCG-GF-NOMA model by characterizing each CG. We then formulate the MCG-GF-NOMA resources configuration problem taking into account three constraints. Finally, we propose a Cooperative Multi-Agent based Double Deep Q-Network (CMA-DDQN) algorithm to allocate the channel resources among MCGs to maximize the number of successful transmissions under the latency constraint. Our results show that the MCG-GF-NOMA framework can simultaneously improve the low latency and high reliability performances for mURLLC.

With the growing demand for latency-critical and computation-intensive Internet of Things (IoT) services, the IoT-oriented network architecture, mobile-edge computing (MEC), has emerged as a promising technique to reinforce the computation capability of the resource-constrained IoT devices. To exploit the cloud-like functions at the network edge, service caching has been implemented to reuse the computation task input/output data, thus effectively reducing the delay incurred by data retransmissions and repeated execution of the same task. In a multiuser cache-assisted MEC system, users’ preferences for different types of services, possibly dependent on their locations, play an important role in the joint design of communication, computation, and service caching. In this article, we consider multiple representative locations, where users at the same location share the same preference profile for a given set of services. Specifically, by exploiting the location-aware users’ preference profiles, we propose joint optimization of the binary cache placement, the edge computation resource, and the bandwidth (BW) allocation to minimize the expected sum-energy consumption, subject to the BW and the computation limitations as well as the service latency constraints. To effectively solve the mixed-integer nonconvex problem, we propose a deep learning (DL)-based offline cache placement scheme using a novel stochastic quantization-based discrete-action generation method. The proposed hybrid learning framework advocates both benefits from the model-free DL approach and the model-based optimization. The simulations verify that the proposed DL-based scheme saves roughly 33% and 6.69% of energy consumption compared with the greedy caching and the popular caching, respectively, while achieving up to 99.01% of the optimal performance.

Random access schemes in satellite Internet-of-Things (IoT) networks are being considered a key technology of new-type machine-to-machine (M2M) communications. However, the complicated situations and long-distance transmission can make the current random access schemes not suitable for the satellite IoT networks. The random access problem in the satellite IoT networks is studied in this article. A novel random access scheme for machine-type-communication devices (MTCDs) is proposed, to maximize the efficiency of random access for contention-based and contention-free random access. Under the set of random access opportunities (RAOs) and limited delay, the random access control model is designed via maximizing efficiency of random access. The model-free deep reinforcement learning (DRL) algorithm is proposed to tackle the problem based on the random access model. Subsequently, the deep Dyna-$Q$learning algorithm is introduced to deal with the proposed random access control model. In this proposed scheme, the random access model-free DRL algorithm is developed using simulated experience. The proposed algorithms’ performances are discussed, and simulation results show the desirable performance of the proposed DRL methods on different system parameters.

Massive connections and diverse Quality of Service (QoS) requirements pose a major challenge for machine-type communication (MTC) networks. In this article, to satisfy the various QoS requirements of a massive number of MTC devices (MTCDs), the devices are divided into multiple clusters based on the QoS characteristics. The cluster access control and intracluster resource allocation problems are studied to satisfy the double delay requirements in the access and data transmission phases in a cross-layer approach. Specifically, we formulate an access control problem to maximize the access efficiency with constraints on access and transmission delays. An efficient algorithm is proposed to adaptively adjust the access time intervals and backoff factors of the clusters for different numbers of active MTCDs and transmission rates. Given the access parameters, nonorthogonal multiple access is adopted in resource allocation to maximize the system utility function while guaranteeing the delay requirements for each accessed MTCD. An efficient sequential convex programming iterative algorithm is proposed to solve the NP-hard nonconvex problem with two typical utility objectives: 1) total throughput and 2) consumed power. Simulation results show that the proposed scheme can achieve better performance in terms of access efficiency, delay, throughput, and consumed power than other schemes. The impacts of various parameters, including delay and traffic rate, on the performance, are disclosed.

Massive Ultra-Reliable and Low-Latency Communications (mURLLC), which integrates URLLC with massive access, is emerging as a new and important service class in the next generation (6G) for time-sensitive traffics and has recently received tremendous research attention. However, realizing efficient, delay-bounded, and reliable communications for a massive number of user equipments (UEs) in mURLLC, is extremely challenging as it needs to simultaneously take into account the latency, reliability, and massive access requirements. To support these requirements, the third generation partnership project (3GPP) has introduced enhanced grant-free (GF) transmission in the uplink (UL), with multiple active configured-grants (CGs) for URLLC UEs. With multiple CGs (MCG) for UL, UE can choose any of these grants as soon as the data arrives. In addition, non-orthogonal multiple access (NOMA) has been proposed to synergize with GF transmission to mitigate the serious transmission delay and network congestion problems. In this paper, we develop a novel learning framework for MCG-GF-NOMA systems with bursty traffic. We first design the MCG-GF-NOMA model by characterizing each CG using the parameters: the number of contention-transmission units (CTUs), the starting slot of each CG within a subframe, and the number of repetitions of each CG. Based on the model, the latency and reliability performances are characterized. We then formulate the MCG-GF-NOMA resources configuration problem taking into account three constraints. Finally, we propose a Cooperative Multi-Agent based Double Deep Q-Network (CMA-DDQN) algorithm to balance the allocations of the channel resources among MCGs so as to maximize the number of successful transmissions under the latency constraint. Our results show that the MCG-GF-NOMA framework can simultaneously improve the low latency and high reliability performances in massive URLLC.

With the rapid development of electronic and information technologies, the Internet of Everything (IoE) has become one of the trendiest topics in both academia and industry. Therein, many types of space/air/ground platforms need to be connected to networks for breaking down the isolation of information islands and providing various services. Space/air/ground platforms, such as satellites, unmanned aerial vehicles (UAVs), airships, balloons, terrestrial vehicles, and high-speed trains (HSTs) have emerged for accomplishing various complex tasks. Wireless communication is one of the most important technologies to support the real-time delivery of control commands and mission-related data. On the other hand, the space-air-ground integrated network has become a promising paradigm for the six-generation (6G) mobile communication network, where the aerospace and terrestrial vehicles may need to connect to existing mobile cellular networks or act as base stations (BSs) or relays to assist terrestrial wireless communications. To meet the ever-increasing demands of high capacity, wide coverage, low latency, and strong robustness for communications, it is promising to adopt large-scale antenna arrays at the transceivers to obtain considerable array gains and improve the channel quality. Antenna array-enabled beamforming technologies can facilitate spectrum reuse, interference mitigation, coverage enhancement, and physical-layer security. Antenna arrays can also be used to promote the sensing capability of space/air/ground networks, where the sensing information may be carefully processed to assist communications. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive, and tricky challenges in antenna array design, physical layer, multiple access control layer, and network layer. As a result, numerous new research issues require to be addressed, which cover a wide range of disciplines including communication theory, network theory, antenna theory, signal processing, protocol design, resource allocation, optimization, hardware implementation, and experimentation.

Antenna arrays have a long history of more than 100 years and have evolved closely with the development of electronic and information technologies, playing an indispensable role in wireless communications and radar. With the rapid development of electronic and information technologies, the demand for all-time, all-domain, and full-space network services has exploded, and new communication requirements have been put forward on various space/air/ground platforms. To meet the ever increasing requirements of the future sixth generation (6G) wireless communications, such as high capacity, wide coverage, low latency, and strong robustness, it is promising to employ different types of antenna arrays (e.g., phased arrays, digital arrays, and reconfigurable intelligent surfaces, etc.) with various beamforming technologies (e.g., analog beamforming, digital beamforming, hybrid beamforming, and passive beamforming, etc.) in space/air/ground communication networks, bringing in advantages such as considerable antenna gains, multiplexing gains, and diversity gains. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive and tricky challenges, which has aroused extensive research attention. This paper aims to overview the field of antenna array enabled space/air/ground communications and networking. The technical potentials and challenges of antenna array enabled space/air/ground communications and networking are presented first. Subsequently, the antenna array structures and designs are discussed. We then discuss various emerging technologies facilitated by antenna arrays to meet the new communication requirements of space/air/ground communication systems. Enabled by these emerging technologies, the distinct characteristics, challenges, and solutions for space communications, airborne communications, and ground communications are reviewed. Finally, we present promising directions for future research in antenna array enabled space/air/ground communications and networking.

In multi-access edge computing (MEC), most existing task software caching works focus on statically caching data at the network edge, which may hardly preserve high reusability due to the time-varying user requests in practice. To this end, this work considers dynamic task software caching at the MEC server to assist users’ task execution. Specifically, we formulate a joint task software caching update (TSCU) and computation offloading (COMO) problem to minimize users’ energy consumption while guaranteeing delay constraints, where the limited cache size and computation capability of the MEC server, as well as the time-varying task demand of users are investigated. This problem is proved to be non-deterministic polynomial-time hard, so we transform it into two sub-problems according to their temporal correlations, i.e., the real-time COMO problem and the Markov decision process-based TSCU problem. We first model the COMO problem as a multi-user game and propose a decentralized algorithm to address its Nash equilibrium solution. We then propose a double deep Q-network (DDQN)-based method to solve the TSCU policy. To reduce the computation complexity and convergence time, we provide a new design for the deep neural network (DNN) in DDQN, named state coding and action aggregation (SCAA). In SCAA-DNN, we introduce a dropout mechanism in the input layer to code users’ activity states. Additionally, at the output layer, we devise a two-layer architecture to dynamically aggregate caching actions, which is able to solve the huge state-action space problem. Simulation results show that the proposed solution outperforms existing schemes, saving over 12% energy, and converges with fewer training episodes.

This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by searching the shortest path on the decision tree. Unfortunately, the existing optimal search algorithms often involve prohibitively high complexities, which indicates that they are infeasible in large-scale MIMO systems. To address this issue, we propose a general heuristic search algorithm, namely, hyper-accelerated tree search (HATS) algorithm. The proposed algorithm employs a deep neural network (DNN) to estimate the optimal heuristic, and then use the estimated heuristic to speed up the underlying memory-bounded search algorithm. This idea is inspired by the fact that the underlying heuristic search algorithm reaches the optimal efficiency with the optimal heuristic function. Simulation results show that the proposed algorithm reaches almost the optimal bit error rate (BER) performance in large-scale systems, while the memory size can be bounded. In the meanwhile, it visits nearly the fewest tree nodes. This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and thereby it is applicable for large-scale systems. Besides, the code for this paper is available athttps://github.com/skypitcher/hats.

Combining non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAVs) could achieve better performance for wireless networks. However, effective resource allocation for quality of service (QoS) provision among all users still remains as a great challenge for multi-cluster NOMA-UAV networks. In this paper, we propose a NOMA-UAV scheme, where a UAV is deployed as the mobile base station to serve ground users. To meet the QoS requirements of all users with limited resource, the user clustering and optimal routing are first developed by the K-means algorithm and genetic algorithm, respectively. Then, the sum throughput is maximized by jointly optimizing the transmission power, hovering locations and transmission duration of UAV. To solve this non-convex problem with coupled variables, we decompose it into three subproblems. Among them, the power and location optimizations are also non-convex, which can be transformed into convex ones by successive convex approximation. The duration optimization is a linear programming which can be solved directly. Then, we propose an iterative algorithm to solve these three subproblems alternately. Finally, simulation results are presented to show the effectiveness of the proposed scheme.

In this paper, we investigate a secure mobile edge computing (MEC) network in the presence of multiple eavesdroppers, where multiple users can offload parts of their tasks to the computational access point (CAP). The multiple eavesdroppers may overhear the confidential task offloading, which leads to information leakage. In order to address this issue, we present the minimization problem of the secrecy outage probability (SOP), by jointly taking into account the constraints from the latency and energy consumption. With the aim to improve the system secrecy performance, we then introduce three user selection criteria to choose the best user among multiple ones. Specifically,criterion Imaximizes the locally computational capacity, whilecriterion IIandIIImaximize the secrecy capacity and data rate of main links, respectively. For these criteria, we further analyze the system secrecy performance by deriving analytical and asymptotic expressions for the SOP, from which we can conclude important insights for the system design. Finally, simulation and analytical results are provided to verify the proposed analysis. The results show that the three criteria can efficiently safeguard the MEC networks, compared to the traditional local computing and fully offloading, especially with a large value of user number.

This paper studies the unmanned aerial vehicle (UAV) based multiple intelligent reflecting surface (IRS) network, where the hovering UAV acts as a base station, and the IRS enhances signal transmission to across obstacle between users and UAV. To achieve the maximum sum rate of proposed communication scenario, a non-convex problem considering IRS association results, hovering altitude of UAV, and the phase shift design of multi-IRS is formulated. From the IRS association problem, we can find that the IRS association results are coupled to the decoding order of non-orthogonal multiple access (NOMA). To tackle this, a mathematical interference expansion scheme is developed to decouple it and transform it to convex by binary relaxation method. The non-convexity of hovering altitude optimization problem is solved by logarithm operation, approximation, and auxiliary matrices. For the phase shift optimization problem of multi-IRS, we propose a gradient approximation based initial scheme and develop a univariate optimization based approach on the basis to achieve the users sum rate improvement in multi-IRS. In the end, we compare the proposed scheme with baseline scheme to present the superiority of this work under various network settings. The internal reasons for the variation of simulation results are also analyzed.

Non-orthogonal multiple access (NOMA) allows multiple users to share link resource for higher spectrum efficiency. It can be applied to unmanned aerial vehicle (UAV) and mobile edge computing (MEC) networks to provide convenient offloading computing service for ground users (GUs) with large-scale access. However, due to the line-of-sight (LoS) of UAV transmission, the information can be easily eavesdropped in NOMA-based UAV-MEC networks. In this paper, we propose a secure communication scheme for the NOMA-based UAV-MEC system towards a flying eavesdropper. In the proposed scheme, the average security computation capacity of the system is maximized while guaranteeing a minimum security computation requirement for each GU. Due to the uncertainty of the eavesdropper’s position, the coupling of multi-variables and the non-convexity of the problem, we first study the worst security situation through mathematical derivation. Then, the problem is solved by utilizing successive convex approximation (SCA) and block coordinate descent (BCD) methods with respect to channel coefficient, transmit power, central processing unit (CPU) computation frequency, local computation and UAV trajectory. Simulation results show that the proposed scheme is superior to the benchmarks in terms of the system security computation performance.

Hovering inaccuracy of unmanned aerial vehicle (UAV) degrades the performance of UAV-aided radio frequency energy transfer (RFET). Such inaccuracy arises due to positioning error and rotational motion of UAV, which lead to localization mismatch (LM) and orientation mismatch (OM). In this paper, antenna array beam steering based UAV hovering inaccuracy mitigation strategy is presented. The antenna beam does not accurately point towards the field sensor node due to rotational motion of the UAV along with pitch, roll, and yaw, which leads to deviation in the elevation angle. An analytical framework is developed to model this deviation, and its variation is estimated using the data collected through an experimental setup. Closed-form expressions of received power at the field node are obtained for the four cases arising from LM and OM. An optimization problem to estimate the optimal system parameters (transmit power, UAV hovering altitude, and antenna steering parameter) is formulated. The problem is proven to be nonconvex. Therefore, an algorithm is proposed to solve this problem. Simulation results demonstrate that the proposed framework significantly mitigates the hovering inaccuracy; compared to reported state-of-the-art the same performance can be achieved with substantially less transmit power.

The simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is capable of providing full-space coverage of smart radio environments. This work investigates STAR-RIS aided downlink non-orthogonal multiple access (NOMA) multi-cell networks, where the energy of incident signals at STAR-RISs is split into two portions for transmitting and reflecting. We first propose a fitting method to model the distribution of composite small-scale fading power as the tractable Gamma distribution. Then, a unified analytical framework based on stochastic geometry is provided to capture the random locations of RIS-RISs, base stations (BSs), and user equipments (UEs). Based on this framework, we derive the coverage probability and ergodic rate of both the typical UE and the connected UE. In particular, we obtain closed-form expressions of the coverage probability in interference-limited scenarios. We also deduce theoretical expressions in conventional RIS aided networks for comparison. The analytical results show that optimal energy splitting coefficients of STAR-RISs exist to simultaneously maximize the system coverage and ergodic rate. The numerical results demonstrate that: 1) STAR-RISs are able to meet different demands of UEs located on different sides; 2) STAR-RISs with appropriate energy splitting coefficients outperform conventional RISs in the coverage and the rate performance.

With the rapid growth of data traffic in cellular networks, edge caching has become an emerging technology for traffic offloading. We investigate the caching placement and content delivery in cache-enabling cellular networks. To cope with the time-varying content popularity and user location in practical scenarios, we formulate a long-term joint dynamic optimization problem of caching placement and user association for minimizing the content delivery delay which considers both content transmission delay and content update delay. To solve this challenging problem, we decompose the optimization problem into two sub-problems, the user association sub-problem in a short time scale and the caching placement in a long time scale. Specifically, we propose a low complexity user association algorithm for a given caching placement in the short time scale. Then we develop a deep deterministic policy gradient based caching placement algorithm which involves the short time-scale user association decisions in the long time scale. Finally, we propose a joint user association and caching placement algorithm to obtain a sub-optimal solution for the proposed problem. We illustrate the convergence and performance of the proposed algorithm by simulation results. Simulation results show that compared with the benchmark algorithms, the proposed algorithm reduces the long-term content delivery delay in dynamic networks effectively.

With the exponential growth of data traffic in wireless networks, edge caching has been regarded as a promising solution to offload data traffic and alleviate backhaul congestion, where the contents can be cached by an unmanned aerial vehicle (UAV) and user terminal (UT) with local data storage. In this article, a cooperative caching architecture of UAV and UTs with scalable video coding (SVC) is proposed, which provides the high transmission rate content delivery and personalized video viewing qualities in hotspot areas. In the proposed cache-enabling UAV-D2D networks, we formulate a joint optimization problem of UT caching placement, UAV trajectory, and UAV caching placement to maximize the cache utility. To solve this challenging mixed integer nonlinear programming problem, the optimization problem is decomposed into three sub-problems. Specifically, we obtain UT caching placement by a many-to-many swap matching algorithm, then obtain the UAV trajectory and UAV caching placement by approximate convex optimization and dynamic programming, respectively. Finally, we propose a low complexity iterative algorithm for the formulated optimization problem to improve the system capacity, fully utilize the cache space resource, and provide diverse delivery qualities for video traffic. Simulation results reveal that: i) the proposed cooperative caching architecture of UAV and UTs obtains larger cache utility than the cache-enabling UAV networks with same data storage capacity and radio resource; ii) compared with the benchmark algorithms, the proposed algorithm improves cache utility and reduces backhaul offloading ratio effectively.

The incorporation of intelligent reflecting surface (IRS) into wireless communication systems can extend the coverage and enhance the data transmission rate. This paper studies the joint transceiver and IRS designs in IRS-assisted multi-input multi-output (MIMO) systems under both perfect channel state information (CSI) and imperfect CSI. Specifically, the transmit precoder, reflection coefficients at the IRS, and receive equalizer are jointly optimized to minimize the data detection mean square error (MSE), subject to the transmission power constraint and the modulus constraints for IRS reflection coefficients. The design problems, non-convex and challenging, are tackled under the framework of alternating optimization. For the design with perfect CSI, we successively optimize the IRS reflection coefficients given the precoder and present the closed-form optimal angle of one reflection coefficient given the others. For the robust design with imperfect CSI, we first average the detection MSE over channel uncertainties by using a generalized statistical CSI error model. Then, the averaged MSE is approximated by a more tractable upper bound. Subsequently, the robust design problem is elaborately transformed into a form similar to the problem with perfect CSI. Numerical results demonstrate the effectiveness of the proposed designs as compared to various benchmark schemes.

In this paper, we consider a platform of flying mobile edge computing (F-MEC), where unmanned aerial vehicles (UAVs) serve as equipment providing computation resource, and they enable task offloading from user equipment (UE). We aim to minimize energy consumption of all UEs via optimizing user association, resource allocation and the trajectory of UAVs. To this end, we first propose a Convex optimizAtion based Trajectory control algorithm (CAT), which solves the problem in an iterative way by using block coordinate descent (BCD) method. Then, to make the real-time decision while taking into account the dynamics of the environment (i.e., UAV may take off from different locations), we propose a deep Reinforcement leArning based trajectory control algorithm (RAT). In RAT, we apply the Prioritized Experience Replay (PER) to improve the convergence of the training procedure. Different from the convex optimization based algorithm which may be susceptible to the initial points and requires iterations, RAT can be adapted to any taking off points of the UAVs and can obtain the solution more rapidly than CAT once training process has been completed. Simulation results show that the proposed CAT and RAT achieve the considerable performance and both outperform traditional algorithms.

In this paper, we propose a deep state-action-reward-state-action (SARSA)$\lambda $learning approach for optimising the uplink resource allocation in non-orthogonal multiple access (NOMA) aided ultra-reliable low-latency communication (URLLC). To reduce the mean decoding error probability in time-varying network environments, this work designs a reliable learning algorithm for providing a long-term resource allocation, where the reward feedback is based on the instantaneous network performance. With the aid of the proposed algorithm, this paper addresses three main challenges of the reliable resource sharing in NOMA-URLLC networks: 1) user clustering; 2) Instantaneous feedback system; and 3) Optimal resource allocation. All of these designs interact with the considered communication environment. Lastly, we compare the performance of the proposed algorithm with conventional Q-learning and SARSA Q-learning algorithms. The simulation outcomes show that: 1) Compared with the traditional Q learning algorithms, the proposed solution is able to converge within 200 episodes for providing as low as$10^{-2}$long-term mean error; 2) NOMA assisted URLLC outperforms traditional OMA systems in terms of decoding error probabilities; and 3) The proposed feedback system is efficient for the long-term learning process.

Increasing the deployment density of small base stations (SBS) is a key method designed to satisfy high data traffic in 5th generation mobile network (5G). However, a large number of SBSs in such ultra-dense network (UDN) may cause ping-pong handovers (HOs), accompanied by increased delay and HO failure. In addition, because of the separation of control and data signaling in 5G, the HO procedure must be performed in both layers. In this paper, we introduce an SDN-based intelligent dynamic HO parameter optimization strategy to minimize both HO failures and ping-pong HOs together. The goal of the proposed strategy is to reduce the HO failure rate and redundant HO (i.e. ping-pong HO) while enabling user equipment (UE) to make full use of the benefits of dense deployment of BSs. Simulation results present that the method proposed in this paper effectively suppresses the ping-pong effect and keeps it at a low level in all of the investigated scenes. In addition, compared with the other algorithms, the HO failure rate is significantly reduced and the throughput of UE is greatly increased, especially in the case of high BS density. Therefore, the benefits of intensive BS deployment are retained.

The cache-enabling unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks for mixture of augmented reality (AR) and normal multimedia applications are investigated, which is assisted by UAV base stations. The user association, power allocation of NOMA, deployment of UAVs and caching placement of UAVs are jointly optimized to minimize the content delivery delay. A branch and bound (BaB) based algorithm is proposed to obtain the per-slot optimization. To cope with the dynamic content requests and mobility of users in practical scenarios, the original optimization problem is transformed to a Stackelberg game. Specifically, the game is decomposed into a leader level user association sub-problem and a number of power allocation, UAV deployment and caching placement follower level sub-problems. The long-term minimization was further solved by a deep reinforcement learning (DRL) based algorithm. Simulation result shows that the content delivery delay of the proposed BaB based algorithm is much lower than benchmark algorithms, as the optimal solution in each time slot is achieved. Meanwhile, the proposed DRL based algorithm achieves a relatively low long-term content delivery delay in the dynamic environment with lower computation complexity than BaB based algorithm.

As revolutionary technologies that can actively change the communication link signal, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have emerged as reliable, economical and convenient wireless communication solutions for a variety of practical scenarios. Therefore, this paper focuses on an IRS empowered UAV downlink communication network, where the dynamic UAV establishes a cascade link via IRS to provide signal enhancement services for multiple users. Considering constraints of transmit power, flight speed and area at the UAV and the reflecting constraints at the IRS, the block coordinate descent (BCD) method based on resource allocation, reflecting design and trajectory optimization is adopted to maximize the sum-rate of all users. The proposed problem is converted by using quadratic transformation and Lagrangian dual transformation. Then applying for the approximate linear method and Iterative Rank Minimization (IRM) to optimize the transmit power of UAV and phase shift of IRS respectively. Since additional reflection propagation paths by IRS, the complexity of the channel model makes the trajectory design difficult. To tackle this problem, this paper proposes a UAV trajectory optimization method based on enhanced reinforcement learning with the fixed initial location and destination. In the end, the convergence of the proposed scheme is effectively verified by simulations. Moreover, abundant simulation comparisons between the proposed scheme and other benchmark schemes demonstrate the validity and high performance gains of the proposed algorithm.

In this paper, we propose a deep state-action-reward-state-action (SARSA)$A$learning approach for optimising the uplink resource allocation in non-orthogonal multiple access (NOMA) aided ultra-reliable low-latency communication (URLLC). To reduce the mean decoding error probability in time-varying network environments, this work designs a reliable learning algorithm for providing a long-term resource allocation, where the reward feedback is based on the instantaneous network performance. With the aid of the proposed algorithm, this paper addresses three main challenges of the reliable resource sharing in NOMA-URLLC networks: 1) Dynamic user clustering; 2) Instantaneous feedback system; and 3) Optimal resource allocation. All of these designs interact with the considered communication environment. The simulation outcomes show that: 1) Compared with the traditional Q learning algorithm, the proposed solution converges faster and obtains better performance; 2) NOMA assisted URLLC outperforms traditional OMA systems in terms of decoding error probabilities; and 3) The dynamic feedback system is efficient for the long-term learning process.

Grant-free non-orthogonal multiple access (GF-NOMA) is a potential multiple access framework for internet-of-things (IoT) networks to enhance connectivity. However, the resource allocation problem in GF-NOMA is challenging and the effectiveness of such a solution is limited due to the absence of closed-loop power control. In this paper, we design a prototype of layer-based transmit power pool by utilizing multi-agent reinforcement learning to provide open-loop power control and offload the computing tasks at the base station (BS) side. IoT users in each layer decide their own transmit power level from this layer-based power pool, instead of transmitting on the allocated sub-channel with allocated transmit power level. The proposed algorithm does not require any information exchange between IoT users and does not rely on any assistance from the BS. Numerical results confirm that the double deep Q network based GF-NOMA algorithm achieves high accuracy and finds out an accurate transmit power level for each layer. Moreover, the proposed GF-NOMA system outperforms the traditional GF with orthogonal multiple access techniques in terms of throughput.

Queuing delay is of essential importance in the Internet-of-Things scenarios where the buffer sizes of devices are limited. The existing cross-layer research contributions aiming at minimizing the queuing delay usually rely on either transmit power control or dynamic spectrum allocation. Bearing in mind that the transmission throughput is dependent on the distance between the transmitter and the receiver, in this context we exploit the agility of the unmanned-aerial-vehicle (UAV)-mounted base stations (BSs) for proactively adjusting the aerial BS (ABS)’s placement in accordance with wireless teletraffic dynamics. Specifically, we formulate a minimum-delay ABS placement problem for UAV-enabled networks, subject to realistic constraints on the ABS’s battery life and velocity. Its solutions are technically realized under three different assumptions in regard to the wireless teletraffic dynamics. The backward induction technique is invoked for both the scenario where the full knowledge of the wireless teletraffic dynamics is available, and for the case where only their statistical knowledge is available. In contrast, a reinforcement learning aided approach is invoked for the case when neither the exact number of arriving packets nor that of their statistical knowledge is available. The numerical results demonstrate that our proposed algorithms are capable of improving the system’s performance compared to the benchmark schemes in terms of both the average delay and of the buffer overflow probability.

Cellular-based networks are expected to offer connectivity for massive Internet of Things (mIoT) systems. However, their Random Access CHannel (RACH) procedure suffers from unreliability, due to the collision from the simultaneous massive access. Despite that this collision problem has been treated in existing RACH schemes, these schemes usually organize IoT devices' transmission and re-transmission along with fixed parameters, thus can hardly adapt to time-varying traffic patterns. Without adaptation, the RACH procedure easily suffers from high access delay, high energy consumption, or even access unavailability. With the goal of improving the RACH procedure, this paper targets to optimize the RACH procedure in real-time by maximizing a long-term hybrid multi-objective function, which consists of the number of access success devices, the average energy consumption, and the average access delay. To do so, we first optimize the long-term objective in the number of access success devices by using Deep Reinforcement Learning (DRL) algorithms for different RACH schemes, including Access Class Barring (ACB), Back-Off (BO), and Distributed Queuing (DQ). The converging capability and efficiency of different DRL algorithms including Policy Gradient (PG), Actor-Critic (AC), Deep Q-Network (DQN), and Deep Deterministic Policy Gradient (DDPG) are compared. Inspired by the results from this comparison, a decoupled learning strategy is developed to jointly and dynamically adapt the access control factors of those three access schemes. This decoupled strategy integrates predicted traffic into the learning process to improve training efficiency, where a Recurrent Neural Network (RNN) model is first employed to predict the real-time traffic values of the network environment, and then multiple DRL agents are employed to cooperatively configure parameters of each RACH scheme. Our results demonstrate that the decoupled strategy remarkably accelerate the training speedy.

5G New Radio (NR) is expected to support new ultra-reliable low-latency communication (URLLC) service targeting at supporting the small packets transmissions with very stringent latency and reliability requirements. Current Long Term Evolution (LTE) system has been designed based on grant-based (GB) (i.e., dynamic grant) random access, which can hardly support the URLLC requirements. Grant-free (GF) (i.e., configured grant) access is proposed as a feasible and promising technology to meet such requirements, especially for uplink transmissions, which effectively saves the time of requesting/waiting for a grant. While some basic GF access features have been proposed and standardized in NR Release-15, there is still much space to improve. Being proposed as 3GPP study items, three GF access schemes with Hybrid Automatic Repeat reQuest (HARQ) retransmissions including Reactive, K-repetition, and Proactive, are analyzed in this article. Specifically, we present a spatio-temporal analytical framework for the contention-based GF access analysis. Based on this framework, we define the latent access failure probability to characterize URLLC reliability and latency performances. We propose a tractable approach to derive and analyze the latent access failure probability of the typical UE under three GF HARQ schemes. Our results show that under shorter latency constraints, the Proactive scheme provides the lowest latent access failure probability, whereas, under longer latency constraints, the K-repetition scheme achieves the lowest latent access failure probability, which depends on K. If K is overestimated, the Proactive scheme provides lower latent access failure probability than the K-repetition scheme.

This paper aims to devise a generalized maximum likelihood (ML) estimator to robustly detect signals with unknown noise statistics in multiple-input multiple-output (MIMO) systems. In practice, there is little or even no statistical knowledge on the system noise, which in many cases is non-Gaussian, impulsive and not analyzable. Existing detection methods have mainly focused on specific noise models, which are not robust enough with unknown noise statistics. To tackle this issue, we propose a novel ML detection framework to effectively recover the desired signal. Our framework is a fully probabilistic one that can efficiently approximate the unknown noise distribution through a normalizing flow. Importantly, this framework is driven by an unsupervised learning approach, where only the noise samples are required. To reduce the computational complexity, we further present a low-complexity version of the framework, by utilizing an initial estimation to reduce the search space. Simulation results show that our framework outperforms other existing algorithms in terms of bit error rate (BER) in non-analytical noise environments, while it can reach the ML performance bound in analytical noise environments.

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for a massive number of low-throughput low-cost devices in delay-tolerant applications with low power consumption. To provide reliable connections with extended coverage, a repetition transmission scheme is introduced to NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure. To avoid the difficulty in replacing the battery for IoT devices, the energy harvesting is considered as a promising solution to support energy sustainability in the NB-IoT network. In this work, we analyze RACH success probability in a self-powered NB-IoT network taking into account the repeated preamble transmissions and collisions, where each IoT device with data is active when its battery energy is sufficient to support the transmission. We model the temporal dynamics of the energy level as a birth-death process, derive the energy availability of each IoT device, and examine its dependence on the energy storage capacity and the repetition value. We show that in certain scenarios, the energy availability remains unchanged despite randomness in the energy harvesting. We also derive the exact expression for the RACH success probability of a randomly chosen IoT device under the derived energy availability, which is validated under different repetition values via simulations. We show that the repetition scheme can efficiently improve the RACH success probability in a light traffic scenario, but only slightly improves that performance with very inefficient channel resource utilization in a heavy traffic scenario.

In this paper, we study the average packet error probability (APEP) and effective throughput (ET) of the control link in unmanned-aerial-vehicle (UAV) communications, where the ground central station (GCS) sends control signals to the UAV that requires ultra-reliable and low-latency communications (URLLC). To ensure the low latency, short packets are adopted for the control signal. As a result, the Shannon capacity theorem cannot be adopted here due to its assumption of infinite channel blocklength. We consider both free space (FS) and 3-Dimensional (3D) channel models by assuming that the locations of the UAV are randomly distributed within a restricted space. We first characterize the statistical characteristics of the signal-to-noise ratio (SNR) for both FS and 3D models. Then, the closed-form analytical expressions of APEP and ET are derived by using Gaussian-Chebyshev quadrature. Also, the lower bounds are derived to obtain more insights. Finally, we obtain the optimal value of packet length with the objective of maximizing the ET by applying one-dimensional search. Our analytical results are verified by the Monte-Carlo simulations.

Non-orthogonal multiple access (NOMA) exploits the potential of the power domain to enhance the connectivity for the Internet of Things (IoT). Due to time-varying communication channels, dynamic user clustering is a promising method to increase the throughput of NOMA-IoT networks. This article develops an intelligent resource allocation scheme for uplink NOMA-IoT communications. To maximise the average performance of sum rates, this work designs an efficient optimization approach based on two reinforcement learning algorithms, namely deep reinforcement learning (DRL) and SARSA-learning. For light traffic, SARSA-learning is used to explore the safest resource allocation policy with low cost. For heavy traffic, DRL is used to handle traffic-introduced huge variables. With the aid of the considered approach, this work addresses two main problems of fair resource allocation in NOMA techniques: 1) allocating users dynamically and 2) balancing resource blocks and network traffic. We analytically demonstrate that the rate of convergence is inversely proportional to network sizes. Numerical results show that: 1) Compared with the optimal benchmark scheme, the proposed DRL and SARSA-learning algorithms have lower complexity with acceptable accuracy and 2) NOMA-enabled IoT networks outperform the conventional orthogonal multiple access based IoT networks in terms of system throughput.

Wireless powered mobile edge computing (WP-MEC) has been recognized as a promising technique to provide both enhanced computational capability and sustainable energy supply to massive low-power wireless devices. However, its energy consumption becomes substantial, when the transmission link used for wireless energy transfer (WET) and for computation offloading is hostile. To mitigate this hindrance, we propose to employ the emerging technique of intelligent reflecting surface (IRS) in WP-MEC systems, which is capable of providing an additional link both for WET and for computation offloading. Specifically, we consider a multi-user scenario where both the WET and the computation offloading are based on orthogonal frequency-division multiplexing (OFDM) systems. Built on this model, an innovative framework is developed to minimize the energy consumption of the IRS-aided WP-MEC network, by optimizing the power allocation of the WET signals, the local computing frequencies of wireless devices, both the sub-band-device association and the power allocation used for computation offloading, as well as the IRS reflection coefficients. The major challenges of this optimization lie in the strong coupling between the settings of WET and of computing as well as the unit-modules constraint on IRS reflection coefficients. To tackle these issues, the technique of alternating optimization is invoked for decoupling the WET and computing designs, while two sets of locally optimal IRS reflection coefficients are provided for WET and for computation offloading separately relying on the successive convex approximation method. The numerical results demonstrate that our proposed scheme is capable of monumentally outperforming the conventional WP-MEC network without IRSs. Quantitatively, about 80% energy consumption reduction is attained over the conventional MEC system in a single cell, where 3 wireless devices are served via 16 sub-bands, with the aid of an IRS comprising of 50 elements.

Covert communication can hide the information transmission process from the warden to prevent adversarial eavesdropping. However, it becomes challenging when the location of warden is uncertain. In this paper, we propose a covert communication scheme against a warden with uncertain locations, which maximizes the connectivity throughput between a multi-antenna transmitter and a full-duplex jamming receiver with the limit of covert outage probability (the probability of the transmission found by the warden). First, we analyze the monotonicity of the covert outage probability to obtain the optimal location for the warden. Then, under this worst situation, we optimize the transmission rate, the transmit power and the jamming power of covert communication to maximize the connection throughput. This problem is solved in two stages. First, we derive the transmit-to-jamming power ratio limit from the maximum allowed covert outage probability. With this constraint, the connection probability is maximized over the transmit-to-jamming power ratio for a fixed transmission rate. Since the connection probability and the transmission rate are coupled, the bisection method is applied to maximize the connectivity throughput via optimizing the transmission rate iteratively. Simulation results are presented to evaluate the effectiveness of the proposed scheme.

Grant-free non-orthogonal multiple access (GF-NOMA) is a potential multiple access framework for short-packet internet-of-things (IoT) networks to enhance connectivity. However, the resource allocation problem in GF-NOMA is challenging due to the absence of closed-loop power control. We design a prototype of transmit power pool (PP) to provide open-loop power control. IoT users acquire their transmit power in advance from this prototype PP solely according to their communication distances. Firstly, a multi-agent deep Q-network (DQN) aided GF-NOMA algorithm is proposed to determine the optimal transmit power levels for the prototype PP. More specifically, each IoT user acts as an agent and learns a policy by interacting with the wireless environment that guides them to select optimal actions. Secondly, to prevent the Q-learning model overestimation problem, double DQN (DDQN) based GF-NOMA algorithm is proposed. Numerical results confirm that the DDQN based algorithm finds out the optimal transmit power levels that form the PP. Comparing with the conventional online learning approach, the proposed algorithm with the prototype PP converges faster under changing environments due to limiting the action space based on previous learning. The considered GF-NOMA system outperforms the networks with fixed transmission power, namely all the users have the same transmit power and the traditional GF with orthogonal multiple access techniques, in terms of throughput.

In this paper, we investigate the design of robust and secure transmission in intelligent reflecting surface (IRS) aided wireless communication systems. In particular, a multi-antenna access point (AP) communicates with a single-antenna legitimate receiver in the presence of multiple single-antenna eavesdroppers, where the artificial noise (AN) is transmitted to enhance the security performance. Besides, we assume that the cascaded AP-IRS-user channels are imperfect due to the channel estimation error. To minimize the transmit power, the beamforming vector at the transmitter, the AN covariance matrix, and the IRS phase shifts are jointly optimized subject to the outage rate probability constraints under the statistical cascaded channel state information (CSI) error model. To handle the resulting non-convex optimization problem, we first approximate the outage rate probability constraints by using the Bernstein-type inequality. Then, we develop a suboptimal algorithm based on alternating optimization, the penalty-based and semidefinite relaxation methods. Simulation results reveal that the proposed scheme significantly reduces the transmit power compared to other benchmark schemes.

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks. To provide reliable connections with extended coverage, a repetition transmission scheme and up to three Coverage Enhancement (CE) groups are introduced into NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure, where each CE group is configured with different repetition values and transmission resources. To characterize the RACH performance of the NB-IoT network with three CE groups, this paper develops a novel traffic-aware spatio-temporal model to analyze the RACH success probability, where both the preamble transmission outage and the collision events of each CE group jointly determine the traffic evolution and the RACH success probability. Based on this analytical model, we derive the analytical expression for the RACH success probability of a randomly chosen IoT device in each CE group over multiple time slots with different RACH schemes, including baseline, back-off (BO), access class barring (ACB), and hybrid ACB and BO schemes (ACB&BO). Our results have shown that the RACH success probabilities of the devices in three CE groups outperform that of a single CE group network but not for all the groups, which is affected by the choice of the categorizing parameters.This mathematical model and analytical framework can be applied to evaluate the performance of multiple group users of other networks with spatial separations.

In this paper, we investigate a mobile edge computing (MEC) network with two computational access points (CAPs), where the source is equipped with multiple antennas and it has some computational tasks to be accomplished by the CAPs through Nakagami-m distributed wireless links. Since the MEC network involves both communication and computation, we first define the outage probability by taking into account the joint impact of latency and energy consumption. From this new definition, we then employ receiver antenna selection (RAS) or maximal ratio combining (MRC) at the receiver, and apply selection combining (SC) or switch-and-stay combining (SSC) protocol to choose a CAP to accomplish the computational task from the source. For both protocols along with the RAS and MRC, we further analyze the network performance by deriving new and easy-to-use analytical expressions for the outage probability over Nakagami-m fading channels, and study the impact of the network parameters on the outage performance. Furthermore, we provide the asymptotic outage probability in the low regime of noise power, from which we obtain some important insights on the system design. Finally, simulations and numerical results are demonstrated to verify the effectiveness of the proposed approach. It is shown that the number of transmit antenna and Nakagami parameter can help reduce the latency and energy consumption effectively, and the SSC protocol can achieve the same performance as the SC protocol with proper switching thresholds of latency and energy consumption.

In this paper, we investigate the robust transmission design for the intelligent reflecting surface (IRS) aided secure wireless communication systems, where a multi-antenna access point (AP) communicates with a single-antenna legitimate receiver in the presence of multiple single-antenna eavesdroppers via IRS. The estimation error of the imperfect cascaded AP-IRS-user channels is considered in the robust beamforming. Specifically, a transmit power minimization problem is formulated subject to the outage rate probability of information leakage to Eves under the statistical cascaded CSI error model, and the beamformer at the transmitter, the covariance matrix of artificial noise (AN), and the IRS phase shifts are jointly optimized. To handle the resulting non-convex optimization problem, we first approximate the rate outage probability constraints by using the Bernstein-type inequality. Then, we develop a suboptimal algorithm based on the alternating optimization, penalty-based and semidefinite relaxation methods. Simulation results reveal that the proposed scheme significantly reduces the transmit power while ensures the system security.

In this paper, we study the resource allocation for a secure mission-critical IoT communication system with URLLC, where the security capacity formula under finite blocklength is adopted. In specific, we jointly optimize the power and channel bandwidth unit allocation to minimize the system power consumption subject to each device's security capacity requirement and total available channel bandwidth. We express the power for each device as a function of channel bandwidth unit, and equivalently transform the original problem into a channel bandwidth unit allocation problem. By relaxing the discrete variables into continuous ones, a sufficient condition when the transformed problem is a convex problem is provided. Efficient method is proposed to solve the problem. Simulation results confirm the performance advantage of our proposed algorithm over the benchmark method.

Intelligent reflecting surface (IRS) has been proved to be an efficient technology to improve the spectrum and energy efficiency in cognitive radio (CR) networks. Unfortunately, due to the fact that the primary users (PUs) and the secondary users (SUs) are non-cooperative, it is challenging to obtain the perfect PUs-related channel sate information (CSI). In this paper, we investigate the robust beamforming design based on the statistical CSI error model for PU-related cascaded channels in IRS-aided CR systems. We jointly optimize the transmit precoding (TPC) matrix and phase shifts to minimize the SU's total transmit power, meanwhile subject to the quality of service (QoS) of SUs, the interference imposed on the PU and unit-modulus of the reflective beamforming. The non-convex optimization problems are transformed into two second-order cone programming (SOCP) subproblems and efficient algorithms are proposed for solving these subproblems. Simulation results verify the efficiency of the proposed algorithms and reveal the impacts of CSI uncertainties on ST's transmit power and feasibility rate of the optimization problem.

Intelligent reflection surface (IRS) has recently been recognized as a promising technique to enhance the performance of wireless systems due to its ability of reconfiguring the signal propagation environment. However, the perfect channel state information (CSI) is challenging to obtain at the base station (BS) due to the lack of radio frequency (RF) chains at the IRS. Since most of the existing channel estimation methods were developed to acquire the cascaded BS-IRS-user channels, this paper is the first work to study the robust beamforming based on the imperfect cascaded BS-IRS-user channels at the transmitter (CBIUT). Specifically, the transmit power minimization problems are formulated subject to the rate outage probability constraints under the statistical CSI error model, respectively. After approximating the rate outage probability constraints by using the Bernstein-type inequality, the reformulated problems can be efficiently solved. Numerical results show that the negative impact of the CBIUT error on the system performance is greater than that of the direct CSI error.

Unmanned aerial vehicles (UAVs) are developing rapidly owing to flexible deployment and access services as air base stations. However, the energy efficiency of the UAVs cells using non-orthogonal multiple access (NOMA) with imperfect channel state information (CSI) hasnt been well studied yet. Therefore, we maximize energy efficiency in the downlink NOMA UAV network considering imperfect CSI between the UAV and users. Resource allocation schemes including user scheduling as well as power allocation are designed for system energy efficiency optimization. Because of the non-convexity of optimization function with an probability constraint for imperfect CSI, the original problem is converted into a nonprobability problem and then decoupled into two convex subproblems by successive convex approximation method. First, a user scheduling method is applied in the two-side matching of users and subchannels by the difference of convex programming. Then based on user scheduling, the energy efficiency in UAV cells is optimized through a suboptimal power allocation algorithm. The simulation results prove that our proposed algorithm is more effective compared with existing resource allocation schemes.

Cellular-based networks are expected to offer connectivity for massive Internet of Things (mIoT) systems, however, their Random Access CHannel (RACH) procedure suffers from unreliability, due to the collision during the simultaneous massive. Despite that this collision problem has been treated in existing RACH schemes by organizing IoT devices' transmission and retransmission via the central control at the Base Station (BS), these existing RACH schemes are usually fixed over time, thus can hardly adapt to time-varying traffic patterns. In order to optimize the long-term objective in the number of success devices, this paper aims to design Deep Reinforcement Learning (DRL)-based optimizers with Deep Q-Network (DQN) and Deep Deterministic Policy Gradients (DDPG) for optimizing RACH schemes, including Access Class Barring (ACB), Back-Off (BO), and Distributed Queuing (DQ). Specifically, we apply DQN to handle discrete action selection for the BO as well as the DQ schemes, and DDPG to handle continuous action selection for the ACB scheme. Both agents are integrated with Gated recurrent unit Gated Recurrent Unit (GRU) network to approximate their value function/policy, which can improve the optimization performance by capturing temporal traffic correlations. Numerical results showcase that our proposed DRL-based optimizers considerably outperform conventional heuristic solutions in terms of the number of success access devices.

In this article, we investigate the content distribution in the hotspots area, whose traffic is offloaded by the combination of the unmanned aerial vehicle (UAV) communication and edge caching. In cache-enabling UAV-assisted cellular networks, the network deployment and caching placement are vital for quality of experience (QoE) of users with content distribution applications. We formulate a joint optimization problem of UAV deployment and caching placement for maximizing QoE of users, which is evaluated by mean opinion score (MOS). To solve this challenging problem, we decompose the optimization problem into two sub-problems. Specifically, we propose a swap matching based UAV deployment algorithm, then obtain the near-optimal caching placement by greedy algorithm. Finally, we propose a low complexity iteration algorithm for the joint UAV deployment and caching placement optimization, which achieves good computational complexity-optimality tradeoff. Simulation results reveal that the proposed algorithm obtains the near-optimal solution of exhaustive search, converges within several iterations and achieves better performance compared with the benchmark algorithms.

Computation off-loading in mobile edge computing (MEC) systems constitutes an efficient paradigm of supporting resource-intensive applications on mobile devices. However, the benefit of MEC cannot be fully exploited, when the communications link used for off-loading computational tasks is hostile. Fortunately, the propagation-induced impairments may be mitigated by intelligent reflecting surfaces (IRS), which are capable of enhancing both the spectral- and energy-efficiency. Specifically, an IRS comprises an IRS controller and a large number of passive reflecting elements, each of which may impose a phase shift on the incident signal, thus collaboratively improving the propagation environment. In this paper, the beneficial role of IRSs is investigated in MEC systems, where single-antenna devices may opt for off-loading a fraction of their computational tasks to the edge computing node via a multi-antenna access point with the aid of an IRS. Pertinent latency-minimization problems are formulated for both single-device and multi-device scenarios, subject to practical constraints imposed on both the edge computing capability and the IRS phase shift design. To solve this problem, the block coordinate descent (BCD) technique is invoked to decouple the original problem into two subproblems, and then the computing and communications settings are alternatively optimized using low-complexity iterative algorithms. It is demonstrated that our IRS-aided MEC system is capable of significantly outperforming the conventional MEC system operating without IRSs. Quantitatively, about 20 % computational latency reduction is achieved over the conventional MEC system in a single cell of a 300 m radius and 5 active devices, relying on a 5-antenna access point.

An intelligent reflecting surface (IRS) is invoked for enhancing the energy harvesting performance of a simultaneous wireless information and power transfer (SWIPT) aided system. Specifically, an IRS-assisted SWIPT system is considered, where a multi-antenna aided base station (BS) communicates with several multi-antenna assisted information receivers (IRs), while guaranteeing the energy harvesting requirement of the energy receivers (ERs). To maximize the weighted sum rate (WSR) of IRs, the transmit precoding (TPC) matrices of the BS and passive phase shift matrix of the IRS should be jointly optimized. To tackle this challenging optimization problem, we first adopt the classic block coordinate descent (BCD) algorithm for decoupling the original optimization problem into several subproblems and alternately optimize the TPC matrices and the phase shift matrix. For each subproblem, we provide a low-complexity iterative algorithm, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) point of each subproblem. The BCD algorithm is rigorously proved to converge to the KKT point of the original problem. We also conceive a feasibility checking method to study its feasibility. Our extensive simulation results confirm that employing IRSs in SWIPT beneficially enhances the system performance and the proposed BCD algorithm converges rapidly, which is appealing for practical applications.

The Fourth Industrial Revolution (Industrial 4.0) is coming, and this revolution will fundamentally enhance the way factories manufacture products. The conventional wired lines connecting central controller to robots or actuators will be replaced by wireless communication networks due to its low cost of maintenance and high deployment flexibility. However, some critical industrial applications require ultra-high reliability and low latency communication (URLLC). In this paper, we advocate the adoption of massive multiple-input multiple output (MIMO) to support the wireless transmission for industrial applications as it can provide deterministic communications similar as wired lines thanks to its channel hardening effects. To reduce the latency, the channel blocklength for packet transmission is finite, which incurs transmission rate degradation and decoding error probability. Thus, conventional resource allocation for massive MIMO transmission based on Shannon capacity assuming the infinite channel blocklength is no longer optimal. We first derive the closed-form expression of lower bound (LB) of achievable uplink data rate for massive MIMO system with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Then, we propose novel low complexity algorithms to solve the achievable data rate maximization problems by jointly optimizing the pilot and payload transmission power for both MRC and ZF. Simulation results confirm the rapid convergence speed and performance advantage over the existing benchmark algorithms.

The design of communication systems capable of processing and exchanging information through molecules and chemical processes is a rapidly growing interdisciplinary field, which holds the promise to revolutionize how we realize computing and communication devices. While molecular communication (MC) theory has had major developments in recent years, more practical aspects in designing components capable of MC functionalities remain less explored. This paper designs chemical reactions-based microfluidic devices to realize binary concentration shift keying (BCSK) modulation and demodulation functionalities. Considering existing MC literature on information transmission via molecular pulse modulation, we propose a microfluidic MC transmitter design, which is capable of generating continuously predefined pulse-shaped molecular concentrations upon rectangular triggering signals to achieve the modulation function. We further design a microfluidic MC receiver capable of demodulating a received signal to a rectangular output signal using a thresholding reaction and an amplifying reaction. Our chemical reactions-based microfluidic molecular communication system is reproducible and its parameters can be optimized. More importantly, it overcomes the slow-speed, unreliability, and non-scalability of biological processes in cells. To reveal design insights, we also derive the theoretical signal responses for our designed microfluidic transmitter and receiver, which further facilitate the transmitter design optimization. Our theoretical results are validated via simulations performed through the COMSOL Multiphysics finite element solver. We demonstrate the predefined nature of the generated pulse and the demodulated rectangular signal together with their dependence on design parameters.

This article considers an artificial noise (AN)-aided secure MIMO wireless communication system. To enhance the system security performance, the advanced intelligent reflecting surface (IRS) is invoked, and the base station (BS), legitimate information receiver (IR) and eavesdropper (Eve) are equipped with multiple antennas. With the aim for maximizing the secrecy rate (SR), the transmit precoding (TPC) matrix at the BS, covariance matrix of AN and phase shifts at the IRS are jointly optimized subject to constrains of transmit power limit and unit modulus of IRS phase shifts. Then, the secrecy rate maximization (SRM) problem is formulated, which is a non-convex problem with multiple coupled variables. To tackle it, we propose to utilize the block coordinate descent (BCD) algorithm to alternately update the variables while keeping SR non-decreasing. Specifically, the optimal TPC matrix and AN covariance matrix are derived by Lagrangian multiplier method, and the optimal phase shifts are obtained by Majorization-Minimization (MM) algorithm. Since all variables can be calculated in closed form, the proposed algorithm is very efficient. We also extend the SRM problem to the more general multiple-IRs scenario and propose a BCD algorithm to solve it. Simulation results validate the effectiveness of system security enhancement via an IRS.

Device-to-Device (D2D) communication has become a promising candidate in future cellular networks to improve spectrum efficiency and energy efficiency, while reducing the latency. As the capacity of D2D user equipments (DUEs) increases, it makes DUEs caching possible, and it can offload traffic from macro base stations, perform computation-intensive and latency-critical tasks. In this paper, in-band communication is considered, and the Poisson cluster process is utilized to model and analyze the clustered D2D networks under cluster-centric caching strategy. Firstly, we use the Thomas cluster process to model cellular user equipments (CUEs) and DUEs, and give a deployment scheme of clustered D2D caching networks. Secondly, the aggregated interference of the typical D2D receiver is analyzed in the clustered D2D networks. Then the Laplace transform of the aggregated interference is analyzed, and the expressions of coverage probability, average achievable rate and cache hit probability of the typical D2D receiver are deduced. The simulation results show that we can adjust the path loss exponent, densities of DUEs and CUEs, transmitting power of CUEs, mean of simultaneously active transmitters in each cluster and Zipf exponent to improve the performance of clustered D2D caching networks.

Ultra-reliable low latency communication (URLLC) is one of three primary use cases in the fifth-generation (5G) networks, and its research is still in its infancy due to its stringent and conflicting requirements in terms of extremely high reliability and low latency. To reduce latency, the channel blocklength for packet transmission is finite, which incurs transmission rate degradation and higher decoding error probability. In this case, conventional resource allocation based on Shannon capacity achieved with infinite blocklength codes is not optimal. Security is another critical issue in mission-critical internet of things (IoT) communications, and physical-layer security is a promising technique that can ensure the confidentiality for wireless communications as no additional channel uses are needed for the key exchange as in the conventional upper-layer cryptography method. This paper is the first work to study the resource allocation for a secure mission-critical IoT communication system with URLLC. Specifically, we adopt the security capacity formula under finite blocklength and consider two optimization problems: weighted throughput maximization problem and total transmit power minimization problem. Each optimization problem is non-convex and challenging to solve, and we develop efficient methods to solve each optimization problem. Simulation results confirm the fast convergence speed of our proposed algorithm and demonstrate the performance advantages over the existing benchmark algorithms.

In order to satisfy the requirement of high throughput in most UAV applications, the potential of integrating millimeter wave (mmWave) communications with UAV networks is explored in this paper. A tractable three-dimensional (3D) spatial model is proposed for evaluating the average downlink performance of UAV networks at mmWave bands, where the locations of UAVs and users are randomly distributed with the aid of a Poisson cluster process. Moreover, an actual 3D antenna model with the uniform planar array is deployed at all UAVs to examine the impact of both azimuth and elevation angles. Based on this framework and two typical user selection schemes, closed-form approximation equations of the evaluated coverage probability and area spectral efficiency (ASE) are derived. In a noise-limited scenario, an exact expression is provided, which theoretically demonstrates that a large scale of antenna elements is able to enhance the coverage performance. Regarding the altitude of UAVs, there exists at least one optimal height for maximizing the coverage probability. Numerical results verify the proposed insight that non-line-of-sight transmission caused by obstacles have negligible effects on the proposed system. Another interesting result is that the ASE can be maximized by optimizing both the targeted data rate and the density of UAVs.

Fuel-powered UAVs have long endurance of flight, heavy payload and adaptation to extreme environment. The mechanical operation and communication power are supported independently by fuel and batteries. In the paper, we study the energy efficiency of the communication system with a fuel-powered UAV relay. We consider a three-node communication network, consisting of a mobile relay, a source node, and a destination node. The UAV relay is able to change its 3-D trajectory to maintain high probability of LoS channels, receiving information from the fixed source node and transmitting it to the fixed destination node. The power allocation scheme and UAV's trajectory are designed to maximize the system energy efficiency, considering the constraints of speeds, UAV's altitudes, communication and mechanical energy consumption, the required data rates of the destination node and information-causality. We solve the power allocation sub-problem by splitting the domain of variables and transforming it into a convex optimization problem. And then a suboptimal scheme is provided to design the trajectory based on successive convex approximation method. Numerical results show the convergence of the proposed schemes and the performance of the proposed algorithms. The influences of time slots, constraints of fuel, communication power and required data rates are discussed.

Unmanned aerial vehicles (UAVs) are capable of serving as aerial base stations (BSs) for providing both cost-effective and on-demand wireless communications. This article investigates dynamic resource allocation of multiple UAVs enabled communication networks with the goal of maximizing long-term rewards. More particularly, each UAV communicates with a ground user by automatically selecting its communicating user, power level and subchannel without any information exchange among UAVs. To model the dynamics and uncertainty in environments, we formulate the long-term resource allocation problem as a stochastic game for maximizing the expected rewards, where each UAV becomes a learning agent and each resource allocation solution corresponds to an action taken by the UAVs. Afterwards, we develop a multi-agent reinforcement learning (MARL) framework that each agent discovers its best strategy according to its local observations using learning. More specifically, we propose an agent-independent method, for which all agents conduct a decision algorithm independently but share a common structure based on Q-learning. Finally, simulation results reveal that: 1) appropriate parameters for exploitation and exploration are capable of enhancing the performance of the proposed MARL based resource allocation algorithm; 2) the proposed MARL algorithm provides acceptable performance compared to the case with complete information exchanges among UAVs. By doing so, it strikes a good tradeoff between performance gains and information exchange overheads.

This paper investigates the impact of residual transceiver hardware impairments (RTHIs) on cooperative nonorthogonal multiple access (NOMA) networks, where generic α - μ fading channel is considered. To be practical, imperfect channel state information (CSI) and imperfect successive interference cancellation (SIC) are taken into account. More particularly, two representative NOMA scenarios are proposed, namely non-cooperative NOMA and cooperative NOMA. For the non-cooperative NOMA, the base station (BS) directly performs NOMA with all users. For the cooperative NOMA, the BS communicates with NOMA users with the aid of an amplify-and-forward (AF) relay, and the direct links between BS and users are existent. To characterize the performance of the proposed networks, new closed-form and asymptotic expressions for the outage probability (OP), ergodic capacity (EC) and energy efficiency (EE) are derived, respectively. Specifically, we also design the relay location optimization algorithms from the perspectives of minimize the asymptotic OP. For non-cooperative NOMA, it is proved that the OP at high signal-to-noise ratios (SNRs) is a function of threshold, distortion noises, estimation errors and fading parameters, which results in 0 diversity order. In addition, high SNR slopes and high SNR power offsets achieved by users are studied. It is shown that there are rate ceilings for the EC at high SNRs due to estimation error and distortion noise, which cause 0 high SNR slopes and ∞ high SNR power offsets. For cooperative NOMA, similar results can be obtained, and it also demonstrates that the outage performance of cooperative NOMA scenario exceeds the non-cooperative NOMA scenario in the high SNR regime.

Intelligent reflecting surfaces (IRSs) constitute a disruptive wireless communication technique capable of creating a controllable propagation environment. In this paper, we propose to invoke an IRS at the cell boundary of multiple cells to assist the downlink transmission to cell-edge users, whilst mitigating the inter-cell interference, which is a crucial issue in multicell communication systems. We aim for maximizing the weighted sum rate (WSR) of all users through jointly optimizing the active precoding matrices at the base stations (BSs) and the phase shifts at the IRS subject to each BS's power constraint and unit modulus constraint. Both the BSs and the users are equipped with multiple antennas, which enhances the spectral efficiency by exploiting the spatial multiplexing gain. Due to the non-convexity of the problem, we first reformulate it into an equivalent one, which is solved by using the block coordinate descent (BCD) algorithm, where the precoding matrices and phase shifts are alternately optimized. The optimal precoding matrices can be obtained in closed form, when fixing the phase shifts. A pair of efficient algorithms are proposed for solving the phase shift optimization problem, namely the Majorization-Minimization (MM) Algorithm and the Complex Circle Manifold (CCM) Method. Both algorithms are guaranteed to converge to at least locally optimal solutions. We also extend the proposed algorithms to the more general multiple-IRS and network MIMO scenarios. Finally, our simulation results confirm the advantages of introducing IRSs in enhancing the cell-edge user performance.

Ultra-reliable and low-latency communication (URLLC) is one of three pillar applications defined in the fifth generation new radio (5G NR), and its research is still in its infancy due to the difficulties in guaranteeing extremely high reliability (say 10-9packet loss probability) and low latency (say 1 ms) simultaneously. In URLLC, short packet transmission is adopted to reduce latency, such that conventional Shannon's capacity formula is no longer applicable, and the achievable data rate in finite blocklength becomes a complex expression with respect to the decoding error probability and the blocklength. To provide URLLC service in a factory automation scenario, we consider that the central controller transmits different packets to a robot and an actuator, where the actuator is located far from the controller, and the robot can move between the controller and the actuator. In this scenario, we consider four fundamental downlink transmission schemes, including orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), relay-assisted, and cooperative NOMA (C-NOMA) schemes. For all these transmission schemes, we aim for jointly optimizing the blocklength and power allocation to minimize the decoding error probability of the actuator subject to the reliability requirement of the robot, the total energy constraints, as well as the latency constraints. We further develop low-complexity algorithms to address the optimization problems for each transmission scheme. For the general case with more than two devices, we also develop a low-complexity efficient algorithm for the OMA scheme. Our results show that the relay-assisted transmission significantly outperforms the OMA scheme, while the NOMA scheme performs well when the blocklength is very limited. We further show that the relay-assisted transmission has superior performance over the C-NOMA scheme due to larger feasible region of the former scheme.

This paper investigates the impact of physical layer secrecy on the performance of a unified non-orthogonal multiple access (NOMA) framework, where both external and internal eavesdropping scenarios are examined. The spatial locations of legitimate users (LUs) and eavesdroppers are modeled by invoking stochastic geometry. To characterize the security performance, new exact and asymptotic expressions of secrecy outage probability (SOP) are derived for both code-domain NOMA (CD-NOMA) and power-domain NOMA (PD-NOMA), in which imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) are taken into account. For the external eavesdropping scenario, the secrecy diversity orders by a pair of LUs (the n-th user and m-th user) for CD/PD-NOMA are obtained. Analytical results make known that the diversity orders of the n-th user with ipSIC/pSIC for CD-NOMA and PD-NOMA are equal to zero/K and zero/one, respectively. The diversity orders of the m-th user are equal to K/one for CD/PD-NOMA. For the internal eavesdropping scenario, we examine the analysis of secrecy diversity order and observe that the m-th user to wiretap the n-th user with ipSIC/pSIC for CD-NOMA and PDNOMA provide the diversity orders of zero/K and zero/one, respectively, which is consistent with external eavesdropping scenario. Numerical results are present to confirm the accuracy of the analytical results developed and show that: i) The secrecy outage behavior of the n-th user is superior to that of the m-th user; ii) By increasing the number of subcarriers, CD-NOMA is capable of achieving a larger secrecy diversity gain compared to PD-NOMA.PD-NOMA.

In this article, we investigate the content distribution in the hotspot area, whose traffic is offloaded by the combination of the unmanned aerial vehicle (UAV) communication and edge caching. In cache-enabling UAV-assisted cellular networks, the network deployment and resource allocation are vital for quality of experience (QoE) of users with content distribution applications. We formulate a joint optimization problem of UAV deployment, caching placement and user association for maximizing QoE of users, which is evaluated by mean opinion score (MOS). To solve this challenging problem, we decompose the optimization problem into three sub-problems. Specifically, we propose a swap matching based UAV deployment algorithm, then obtain the near-optimal caching placement and user association by greedy algorithm and Lagrange dual, respectively. Finally, we propose a low complexity iterative algorithm for the joint UAV deployment, caching placement and user association optimization problem, which achieves good computational complexity-optimality tradeoff. Simulation results reveal that: i) the MOS of the proposed algorithm approaches that of the exhaustive search method and converges within several iterations; and ii) compared with the benchmark algorithms, the proposed algorithm achieves better performance in terms of MOS, content access delay and backhaul traffic offloading.

In this paper, we consider an unmanned aerial vehicle (UAV) enabled wireless network with a set of ground devices that are randomly distributed in an area and each having a certain amount of data for transmission. The UAV flies over this region from a starting point to a destination. During its flight, the UAV wants to communicate to the ground devices for maximizing the cumulative collected data by optimizing the trajectory of the UAV subject to its flight time constraint. Due to uncertainty in the locations of the ground devices and the communication dynamics, an accurate system model is difficult to acquire and maintain. With the help of stochastic modelling, we present a reinforcement learning based automated trajectory optimization algorithm. By dividing the considered region into small grids with finite state space and action space, we apply the Q-learning based automated trajectory optimization approach for maximizing the cumulative collected data during its flight time. Simulation results demonstrate that the reinforcement learning approach can find an optimal strategy under the flight time constraint.

Ultra dense heterogeneous network (UDHN) has become one of the main frameworks of 5G. Traditional user association methods are difficult to satisfy this new scenario for load balancing. On the other hand, the concept of green communication requires the network to increase energy efficiency. Therefore, it is necessary to study power allocation and user association in UDHN. This paper focuses on load balancing and energy efficiency of UDHN. The joint user association and power allocation is modelled as an appropriate optimization problem. Then we introduce reinforcement learning and propose a multiagent Q-learning based algorithm for solving the optimization problem. According to analysis of simulation result, the convergence of the proposed scheme is verified and the proposed approach is effective on achieving load balancing and enhancing energy efficiency in UDHN.

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks. To provide reliable connections with extended coverage, a repetition transmission scheme and up to three Coverage Enhancement (CE) groups are introduced into NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure, where each CE group is configured with different repetition values. Rather than our previous work only modeled RACH success probability in NB-IoT networks with a single CE group, this paper develops a novel model to analyze the RACH success probabilities in NB-IoT networks with three CE groups, which allow flexible RACH configuration for each CE group. Based on this analytical model, we derive the expression for the RACH success probability of a randomly chosen IoT device in each CE group. The analytical results can also be extended to analyze multiple group users of other networks with spatial separations.

Due to owning huge free bandwidth, millimeter-wave (mmWave) becomes a promising technique to provide ultra-low latencies and ultra-fast data rates for vehicular networks. In this paper, a practical spatial framework for mmWave-enabled vehicle-to- everything networks is proposed by utilizing stochastic geometry approach. More particularly, base stations and vehicles are modeled by a Poisson point process (PPP) and multiple type II Matern hard-core processes (MHCPs), respectively. To characterize the blockage process caused by vehicles, a novel expression is deduced to distinguish line-of-sight (LOS) and non- LOS transmission. This expression demonstrates that LOS links are independent of horizontal communication distances. Furthermore, several closed-form probability density functions of desired communication distances are derived for analyzing the generated path loss. Considering two practical user association schemes, tractable expressions for coverage probabilities are figured out. The result shows that mmWave outperforms sub-6 GHz and MHCP-based model has higher accuracy than the traditional framework with multi-PPPs.

Millimeter wave (mmWave) communication technology has become an attractive solution to meet exponential growth demand for mobile data services. In this paper, we propose a deep neural networks (DNN) based algorithm for user association and power optimization problem in mmWave heterogeneous network on the basis of gradient iterative algorithm. We jointly design the user association and power optimization to maximize energy efficiency (EE) utilizing Lagrange dual decomposition and then approximate it by DNN models. In addition, an asynchronous distributed DNN based scheme is proposed, which divides the large network model into small distributed networks for distributed data processing on each small base station side to reduce computational time. Simulation results show that the proposed scheme can achieve a high EE with low computation time.

NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies the amount of radio resources allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, the problem is to determine, in an online fashion at each Transmission Time Interval (TTI), the configurations that maximizes the long-term average number of IoT devices that are able to both access and deliver data. Given the complexity of optimal algorithms, a Cooperative Multi-Agent Deep Neural Network based Q-learning (CMA-DQN) approach is developed, whereby each DQN agent independently control a configuration variable for each group. The DQN agents are cooperatively trained in the same environment based on feedback regarding transmission outcomes. CMA-DQN is seen to considerably outperform conventional heuristic approaches based on load estimation.

This paper presents a novel channel estimation scheme for massive multiple input multiple output (MIMO) millimeter wave (mmWave) communication system with massive uniform linear array (ULA) at base station (BS) and hybrid architecture. Through practical channel modeling, each channel path is composed of angle information and channel gain information that can be estimated separately. We first propose a general iterative index detection-based channel estimation algorithm (IDCEA) that can obtain both direction of arrival (DOA) and channel gain of each channel path. We then design an enhanced hybrid precoding scheme from the angle domain viewpoint to reduce the inter-beam interferences. Simulation results show that the proposed channel estimation can be better than traditional methods. Finally, numerical examples are provided to corroborate the proposed studies.

To provide reliable connections with extended coverage in NarrowBand-Internet of Things (NB-IoT), a repetition transmission scheme is introduced during both Random Access CHannel (RACH) procedure and data transmission procedure. To avoid the difficulty in replacing the battery for IoT devices, energy harvesting from natural resources is considered to be a promising solution to support energy sustainability of NB-IoT network. In this work, we analyze RACH in the self-powered NB-IoT network taking into account the repeated preamble transmission and collision using stochastic geometry. We model the temporal dynamics of the energy level as a birth-death process, and we derive the energy availability of each IoT device and examine its dependence on the energy storage capacity, the cutoff value, and the repetition value. We also derive the exact expression for the RACH success probability of NB-IoT network under time correlated interference and the energy availability, which is validated under different repetition values via practical packet evolution simulations.

Ultra-reliable and low-latency communication (URLLC) is one of three pillar applications that should be supported by the fifth generation (5G) communications. The research on this topic is still in its infancy due to the difficulties in guaranteeing extremely high reliability (say 10-9) and low latency (say 1 ms) simultaneously. The achievable data rate under the short packet transmission is a complicated function of the transmission power, the blocklength and the decoding error probability. In this paper, we consider resource allocation problem in a factory automation scenario, where the central controller aims for transniitting different packets to two devices (e.g., a robot and an actuator). Two transmission schemes are considered: orthogonal multiple access (OMA) and relay-assisted transmission. We aim to jointly optimize the blocklength and power allocation to minimize the error probability of the actuator subject to reliability requirement of the robot as well as the latency constraints. We develop low-complexity algorithms to address the optimization problems for each transmission scheme. Simulation results demonstrate that the relay-assisted transmission significantly outperforms the OMA scheme.

This paper investigates the impact of physical layer secrecy on the performance of a unified non-orthogonal multiple access (NOMA) framework. The spatial locations of legitimate users (LUs) and eavesdroppers are governed by invoking stochastic geometry. To characterize the secrecy performance of the unified framework, new exact and asymptotic expressions of secrecy outage probabilities are derived, in which both imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) schemes are taken into account. The secrecy diversity orders achieved by a pair of LUs (the n-th user and m-th user) for CD/PD-NOMA are obtained. Analytical results make known that the secrecy diversity orders of LUs with ipSIC/pSIC for CD/PD-NOMA are equal to zero/K and zero/one, respectively. Finally, numerical results are present to confirm the accuracy of the developed analytical results.

Non-orthogonal multiple access (NOMA) is a prospective technology for radio resource constrained future mobile networks. However, NOMA users far from base station (BS) tend to be more susceptible to eavesdropping because they are allocated more transmit power. In this paper, we aim to jointly optimize the precoding vectors at BS to ensure the legitimate security in a downlink multiple-input single-output (MISO) NOMA network. In the proposed scheme, we can maximize the sum secrecy rate by joint precoding optimization. Owing to its non-convexity, the problem is converted into a convex one, which is solved by a second-order cone programming based iterative algorithm. Simulation results are presented to demonstrate that the proposed schemes can improve the security performance for MISO NOMA systems effectively.

We introduce a full duplex non-orthogonal multiple access (FD-NOMA)-based decentralized vehicle to everything (V2X) system model and focus on its capacity performance analysis. In order to solve the computation complicated problems of the involved exponential integral functions and infinite factorial expressions, we give approximate closed-form expressions with controllable arbitrary small errors. We find the accuracy of our approximate expressions is controlled by the division of $\frac{\pi}{2}$ in the urban and crowded (UC) scenario, and the truncation point $T$ in the suburban and remote (SR) scenario. Numerical results manifest 1) Increasing the number of V2X device, NOMA power and Rician factor value yields better capacity performance. 2) Effect of FD-NOMA is determined by the FD self-interference and the channel noise. 3) FD-NOMA has better latency performance compared to other schemes.

NarrowBand Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies the amount of radio resource allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, there exists an important challenge in “how to determine the configuration that maximizes the long-term average number of served IoT devices at each transmission time interval (TTI) in an online fashion.” Given the complexity of searching for optimal configuration, we first develop real-time configuration selection based on the tabular Q-learning (tabular-Q), the linear approximation-based Q-learning (LA-Q), and the deep neural network-based Q-learning (DQN) in the single-parameter single-group scenario. Our results show that the proposed reinforcement learning-based approaches considerably outperform the conventional heuristic approaches based on load estimation (LE-URC) in terms of the number of served IoT devices. This result also indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve almost the same performance with much less training time. We further advance LA-Q and DQN via actions aggregation (AA-LA-Q and AA-DQN) and via cooperative multi-agent learning (CMA-DQN) for the multi-parameter multi-group scenario, thereby solve the problem that Q-learning agents do not converge in high-dimensional configurations. In this scenario, the superiority of the proposed Q-learning approaches over the conventional LE-URC approach significantly improves with the increase of configuration dimensions, and the CMA-DQN approach outperforms the other approaches in both throughput and training efficiency.

Due to the low traffic congestion, high fuel efficiency, and comfortable travel experience, vehicular platoon systems (VPSs) become one of the most promising applications in millimeter wave (mmWave) vehicular networks. In this paper, an effective spatial framework for mmWave vehicle-to-everything (V2X) networks with VPSs is proposed by utilizing stochastic geometry approaches. Base stations (BSs) are modeled by a Poisson point process and vehicles are distributed according to multiple type II Matérn hard-core processes. To characterize the blockage process caused by vehicles, a closed-form expression is deduced to distinguish line-of-sight (LOS) and non-LOS transmission. This expression demonstrates that LOS links are independent of horizontal communication distances. Several closed-form probability density functions of the communication distance between a reference platoon and its serving transmitter (other platoons or BSs) are derived for analyzing the generated path loss. After designing three practical user association techniques, tractable expressions for coverage probabilities are figured out. Our work theoretically shows that the maximum density of VPSs exists and large antenna scales benefit the networks' coverage performance. The numerical results illustrate that platoons outperform individual vehicles in terms of road spectral efficiency and the considered system is LOS interference-limited.

In recent years, communicating information using molecules via diffusion has attracted significant interest in bio-medical applications. To date, most of the studies have concentrated on point-to-point molecular communication (MC), whereas in a realistic environment, multiple MC transmitters are likely to transmit molecular messages simultaneously sharing the same propagation medium, resulting in significant performance variation of the MC system. In this type of large-scale MC system, the collective signal strength at the desired receiver can be impaired by the interference caused by other MC transmitters, which may degrade the system reliability and efficiency. This paper presents the first tractable analytical framework for the collective signal strength at a partially absorbing receiver due to the desired transmitter under the impact of a swarm of interfering transmitters in a 3D large-scale MC system using stochastic geometry. To combat the multi-user interference and the intersymbol interference (ISI) in the multi-user environment, we propose Reed-Solomon (RS) error correction coding, due to its high effectiveness in combating burst and random errors, as well as the two types of information molecule modulating scheme, where the transmitted bits are encoded using two types of information molecules at consecutive bit intervals. We derive analytical expressions for the bit error probability (BEP) of the large-scale MC system with the proposed two schemes to show their effectiveness. The results obtained using Monte Carlo simulations, match exactly with the analytical results, justifying the accuracy of the derivations. Results reveal that both schemes improve the BEP by a factor of 3-4 compared with that of a conventional MC system without using any ISI mitigation techniques. Due to the implementation simplicity, the two-type molecule encoding scheme is better than the RS error correction coding scheme, as the RS error correction coding scheme involves additional encoding and decoding process at both the transmitter and receiver nodes. Furthermore, the proposed analytical framework can be generalized to the analysis of other types of receiver designs and performance characterization in multi-user large-scale MC systems. Also, the two types of information molecule modulating scheme can be extended to M-type of information molecule modulating scheme without loss of generality.

In recent years, unmanned aerial vehicle (UAV) communication technology has played an important role in both military and civilian applications. However, with the rapid development of military equipment, the execution efficiency of single UAVs is often limited, for which complex combat missions cannot be completed well. Therefore, UAV swarm has become an important research trend in the field of UAVs. In this paper, we consider the problem of channel estimation and self-positioning for the UAV swarm, where multiple small UAVs are displaced by arbitrarily unknown displacements due to the dynamic moving. To explore the physical characteristics of UAV swarm, the parameters of the channel are decomposed into the direction of arrival (DOA) information, the relative position information, and the channel gain information. Utilizing the rank reduction (RARE) estimator, DOAs of the different target users can be estimated efficiently, regardless of the position of the UAVs. After obtaining the DOA information, we estimate the channel gain information using small amount of training resources, which significantly reduces the training overhead and the feedback cost. Moreover, the unknown displacements among UAVs can be self-recovered from the mixed integer nonlinear programming (MINLP). To reduce the computational complexity, we develop both the sphere decoding (SD) and the least square (LS) based methods. The deterministic Cramér-Rao bound (CRB) of the self-positioning estimation is derived in closed-form. Finally, numerical examples are provided to corroborate the proposed studies.

In this paper, we investigate the downlink transmission for cache-enabled fog radio access networks aiming at maximizing the delivery rate under the constraints of fronthaul capacity, maximum transmit power, and size of files. To reduce the delivery latency and the burden on fronthaul links and make full use of the local cache and baseband signal processing capabilities of enhanced remote radio heads (eRRHs), a two-level transmission scheme including cache-level and network-level transmission is proposed. In cache-level transmission, only requested files cached at the local cache are transmitted to the corresponding users. The duration of cache-level transmission is the delay caused by the transfer between the baseband unit (BBU) and eRRHs as well as the signal processing at the BBU. The remaining requested files are jointly transmitted to the corresponding users at network-level transmission. For cache-level transmission, a centralized optimization algorithm is first presented and then a decentralized optimization algorithm is provided to avoid the exchange of signaling among eRRHs. Meanwhile, another centralized optimization algorithm is presented to tackle the optimization problem for network-level transmission. All presented algorithms are proved to converge to the Karush-Kuhn-Tucker solutions of the problems. Numerical results are provided to validate the effectiveness of the proposed transmission scheme as well as evaluating the system performance.

Motivated by the numerous healthcare applications of molecular communication inside blood vessels of the human body, this paper considers multiple relay/cooperative nanomachine (CN)-assisted molecular communication between a source nanomachine (SN) and a destination nanomachine (DN) where each nanomachine is mobile in a diffusion-advection flow channel. Using the first hitting time model, the impact of the intermediate CNs on the performance of the aforementioned system with fully absorbing receivers is comprehensively analyzed taking into account the presence of various degrading factors, such as inter-symbol interference, multi-source interference, and counting errors. For this purpose, the optimal decision rules are derived for symbol detection at each of the CNs and the DN. Furthermore, closed-form expressions are derived for the probabilities of detection and false alarm at each CN and DN, along with the overall end-to-end probability of error and channel achievable rate for communication between the SN and DN. Simulation results are presented to corroborate the theoretical results derived and also to yield insights into the system performance under various mobility conditions.

In this paper, we investigate the security of a cache-aided multi-relay communication network in the presence of multiple eavesdroppers, where each relay can pre-store a part of the requested files in order to assist secure data transmission from source to destination. If the relays have cached the requested file, then they can directly send it to the destination; otherwise, traditional dual-hop data transmission is used. For both cases, relay selection is performed to assist the secure data transmission. We analyze the network secrecy performance in both scenarios ofnon-colludingandcolludingeavesdroppers, and obtain a closed-form expression for the average secrecy outage probability (SOP), as well as an asymptotic expression for the high main-to-eavesdropper ratio (MER). Through minimizing the network SOP, we further optimize the cache placement by proposing a stochastic sampling based cache learning (SacLe) strategy, which can be implemented in parallel and thus reduces the implementation latency substantially. Numerical and simulation results are finally presented to verify the proposed analysis, and show that the caching strategy has a significant impact on the network secrecy performance through affecting the caching diversity gain and signal cooperation gain at the relays. The proposed SacLe strategy is shown to be able to achieve the optimal performance obtained by the brute force (BF) algorithm.

With the proliferation of computation-extensive and latency-critical applications in the 5G and beyond networks, mobile-edge computing (MEC) or fog computing, which provides cloud-like computation and/or storage capabilities at the network edge, is envisioned to reduce computation latency as well as to conserve energy for wireless devices (WDs). This paper studies a novel device-to-device (D2D)-enabled multi-helper MEC system, in which a local user solicits its nearby WDs serving as helpers for cooperative computation. We assume a time division multiple access (TDMA) transmission protocol, under which the local user offloads the tasks to multiple helpers and downloads the results from them over orthogonal pre-scheduled time slots. Under this setup, we minimize the computation latency by optimizing the local user's task assignment jointly with the time and rate for task offloading and results downloading, as well as the computation frequency for task execution, subject to individual energy and computation capacity constraints at the local user and the helpers. However, the formulated problem is a mixed-integer non-linear program (MINLP) that is difficult to solve. To tackle this challenge, we propose an efficient algorithm by first relaxing the original problem into a convex one, and then constructing a suboptimal task assignment solution based on the obtained optimal one. Furthermore, we consider a benchmark scheme that endows the WDs with their maximum computation capacities. To further reduce the implementation complexity, we also develop a heuristic scheme based on the greedy task assignment. Finally, the numerical results validate the effectiveness of our proposed algorithm, as compared against the heuristic scheme and other benchmark ones without either joint optimization of radio and computation resources or task assignment design.

In this paper, a security-aware energy-efficient resource allocation is modeled as a fractional programming problem for heterogeneous multi-homing networks. The security-aware resource allocation is formulated as a secrecy energy efficiency maximization problem subject to the average packet delay, the average packet dropping probability, and the total available power consumption. In order to guarantee the packet-level quality of service (QoS), first, the average packet delay and the average packet dropping probability requirement for each mobile terminal at the link layer are transformed into a minimum secrecy rate constraint at the physical layer. Then, the non-convex secrecy energy efficiency maximization problem is approximated by a convex problem through epigraph representation. A security-aware energy-efficient resource allocation algorithm is then proposed leveraging dual-decomposition method and bi-section search method. Finally, a heuristic security-aware resource allocation algorithm is proposed to serve as a benchmark. Simulation results demonstrate that the proposed security-aware energy-efficient resource allocation algorithm not only improves the secrecy energy efficiency and throughput, but also guarantees the packet-level QoS.

For unmanned aerial vehicle (UAV) aided millimeter wave (mmWave) networks, we propose a unified three-dimensional (3D) spatial framework in this paper to model a general case that uncovered users send messages to base stations via UAVs. More specifically, the locations of transceivers in downlink and uplink are modeled through the Poisson point processes and Poisson cluster processes (PCPs), respectively. For PCPs, Matern cluster and Thomas cluster processes, are analyzed. Furthermore, both 3D blockage processes and 3D antenna patterns are introduced for appraising the effect of altitudes. Based on this unified framework, several closed-form expressions for the coverage probability in the uplink and downlink, are derived. By investigating the entire communication process, which includes the two aforementioned phases and the cooperative transmission between them, tractable expressions of system coverage probabilities are derived. Next, three practical applications in UAV networks are provided as case studies of the proposed framework. The results reveal that the impact of thermal noise and non-line-of-sight mmWave transmissions is negligible. In the considered networks, mmWave outperforms sub-6 GHz in terms of the data rate, due to the sharp direction beamforming and large transmit bandwidth. Additionally, there exists an optimal altitude of UAVs, which maximizes the system coverage probability.

We introduce clustered millimeter-wave (mmWave) networks with invoking non-orthogonal multiple access (NOMA) techniques, where the NOMA users are modeled as Poisson cluster processes and each cluster contains a base station (BS) located at the center. To provide realistic directional beamforming, an actual antenna array pattern is deployed at all BSs. We propose three distance-dependent user selection strategies to appraise the path loss impact on the performance of our considered networks. With the aid of such strategies, we derive tractable analytical expressions for the coverage probability and system throughput. Specifically, closed-form expressions are deduced under a sparse network assumption to improve the calculation efficiency. It theoretically demonstrates that the large antenna scale benefits the near user, while such influence for the far user is fluctuant due to the randomness of the beamforming. Moreover, the numerical results illustrate that: 1) the proposed system outperforms traditional orthogonal multiple access techniques and the commonly considered NOMA-mmWave scenarios with the random beamforming; 2) the coverage probability has a negative correlation with the variance of intra-cluster receivers; 3) 73 GHz is the best carrier frequency for the near user, and 28 GHz is the best choice for the far user; and 4) an optimal number of the antenna elements exists for maximizing the system throughput.

In order to meet the requirements of massively connected devices, different quality of services (QoS), various transmit rates, and ultra-reliable and low latency communications (URLLC) in vehicle-to-everything (V2X) communications, we introduce a full duplex non-orthogonal multiple access (FD-NOMA)-based decentralized V2X system model. We, then, classify the V2X communications into two scenarios and give their exact capacity expressions. To solve the computation complicated problems of the involved exponential integral functions, we give the approximate closed-form expressions with arbitrary small errors. Numerical results indicate the validness of our derivations. Our analysis has that the accuracy of our approximate expressions is controlled by the division of π/2 in the urban and crowded scenarios, and the truncation point T in the suburban and remote scenarios. Numerical results manifest that: 1) increasing the number of V2X device, NOMA power, and Rician factor value yields a better capacity performance; 2) effect of FD-NOMA is determined by the FD self-interference and the channel noise; and 3) FD-NOMA has a better latency performance compared with other schemes.

The ultra-dense cloud radio access network (UD-CRAN), in which remote radio heads are densely deployed in the network, is considered. To reduce the channel estimation overhead, we focus on the design of robust transmit beamforming for user-centric frequency division duplex UD-CRANs, where only limited channel state information (CSI) is available. Specifically, we conceive a complete procedure for acquiring the CSI that includes two key steps: channel estimation and channel quantization. The phase ambiguity (PA) is also quantized for coherent cooperative transmission. Based on the imperfect CSI, we aim to optimize the beamforming vectors in order to minimize the total transmit power subject to the users' rate requirements and fronthaul capacity constraints. We derive the closed-form expression of the achievable data rate by exploiting the statistical properties of multiple uncertain terms. Then, we propose a low-complexity iterative algorithm for solving this problem based on the successive convex approximation technique. In each iteration, the Lagrange dual-decomposition method is employed for obtaining the optimal beamforming vector. Furthermore, a pair of low-complexity user selection algorithms is provided to guarantee the feasibility of the problem. The simulation results confirm the accuracy of our robust algorithm in terms of meeting the rate requirements. Finally, our simulation results verify that using a single bit for quantizing the PA achieves good performance.

The weighted sum-rate maximization problem of ultra-dense cloud radio access networks is considered. The user-centric clustering is adopted for reducing the complexity. To reduce the training overhead, one only needs to estimate the intra-cluster channel-state information (CSI), while only the large-scale channel gains are available outside the cluster. We first derive the rate lower bound (LB) relying on Jensen's inequality. For the special case of non-overlapping clusters, the accurate data rate expression is derived in the closed form. The simulation results show the tightness of the LB for both the overlapped and non-overlapped cases. Then, we consider an alternative problem where the actual data rate is replaced by its LB, which constitutes a non-convex optimization problem. First, the globally optimal solution is obtained by applying the high-complexity outer polyblock approximation (OPA) algorithm. Then, we invoke the reduced-complexity modified weighted minimum mean square error (WMMSE) algorithm for mitigating the deleterious effects of the realistic imperfect CSI. For the subproblem solved by each WMMSE iteration, the beamforming vectors are derived in the closed form relying on the Lagrangian dual decomposition method. Finally, our simulation results show that the modified WMMSE algorithm's performance is comparable to that of the high-complexity OPA algorithm, which outperforms other benchmark algorithms.

In this paper, user association and power allocation are investigated in a non-orthogonal multiple access (NOMA)-based multi-cell network. In order to perform successive interference cancellation (SIC) techniques for removing the intra-base station (BS) interference, the optimal decoding order is derived for all users associated with the same BS. In an effort to improve the system, a sum rate maximization problem is formulated by jointly designing user association and power allocation. Two game theory based algorithms are proposed to obtain the stable user structure by dividing users into different BSs' clusters, where the sub-optimal and global optimal solutions can be achieved. The properties of the proposed algorithms, including complexity, convergence, stability and optimality, are analyzed. Based on the quality-of-service (QoS) constraint, the closed-from solutions for power allocation are derived, and thus the expressions for the sum rate of all users in each cluster is obtained. Moreover, the case that the QoS threshold cannot be achieved by all users in each cluster is considered. Simulation results demonstrate that: i) the proposed user association algorithms and the closed-form solutions for power allocation can significantly enhance the sum rate and outage probability; and ii) the proposed NOMA-based system is capable of achieving promising gains over the conventional orthogonal multiple access (OMA)-based framework in the multi-cell scenario.

The use of aerial base stations (ABSs) is gaining attention due to its potentials to provide a flexible wireless coverage in adverse scenarios. Investigations on key performance metrics are desirable to ensure the feasibility of these ABS-assisted networks, especially when they are jointly designed with advanced transmission technologies, e.g., cooperative transmissions and non-orthogonal multiple access (NOMA). In this paper, we consider an ABS-assisted cooperative system with NOMA enabled to boost connectivity ability. It is assumed that multiple ABSs hover around a macro base station to relay downlink signals to user equipments, while interfering nodes are randomly distributed on the ground. We assume a more realistic channel model featured with a distance-related probabilistic line-of-sight and non-line-of-sight propagation, as well as non-identical small-scale fading. We derive the outage probability, and study the impacts of various parameters on the system performance. Numerical results unveil that: 1) The reliability of backhauls plays an important role in the system and determines the outage performance floor. 2) Joint transmissions from the sky can bring in a significant performance enhancement for the system. 3) The NOMA based transmission outperforms the traditional orthogonal multiple access with an improved outage performance, provided a properly selected NOMA power allocation coefficient.

In this paper, we consider the problem of self- positioning for the unmanned aerial vehicle (UAV) swarm, where multiple small UAVs are arranged by unknown displacement due to the dynamic moving. These multiple small UAVs also formulate a virtual massive antenna array that can estimate the direction of arrivals (DOAs) of target users efficiently, regardless of the relative position of the UAVs. After obtaining the DOA information, the unknown displacements among UAVs can also be self-recovered, automagically realizing the important functionality of self-positioning for UAV swarm. The self-positioning problem falls into the category of the mixed integer nonlinear programming (MINLP). To reduce the computational complexity, we develop a novel self-positioning algorithm based on least square (LS) method. Moreover, the deterministic Cramer-Rao bound (CRB) of the self-positioning estimation is derived in closed- form. Finally, numerical examples are provided to corroborate the proposed studies.

Mobile-edge computation offloading (MECO) has been recognized as a promising solution to alleviate the burden of resource-limited Internet of Thing (IoT) devices by offloading computation tasks to the edge of cellular networks (also known as {\em cloudlet}). Specifically, latency-critical applications such as virtual reality (VR) and augmented reality (AR) have inherent collaborative properties since part of the input/output data are shared by different users in proximity. In this paper, we consider a multi-user fog computing system, in which multiple single-antenna mobile users running applications featuring shared data can choose between (partially) offloading their individual tasks to a nearby single-antenna cloudlet for remote execution and performing pure local computation. The mobile users' energy minimization is formulated as a convex problem, subject to the total computing latency constraint, the total energy constraints for individual data downloading, and the computing frequency constraints for local computing, for which classical Lagrangian duality can be applied to find the optimal solution. Based upon the semi-closed form solution, the shared data proves to be transmitted by only one of the mobile users instead of multiple ones. Besides, compared to those baseline algorithms without considering the shared data property or the mobile users' local computing capabilities, the proposed joint computation offloading and communications resource allocation provides significant energy saving.

We investigate the outage performance of cooperative non-orthogonal multiple access (NOMA) networks with transceiver hardware impairments, where the estimated channel state information and α - μ fading channels are considered. Closed-form expressions for the outage probability of cooperative NOMA networks are derived for two representative scenarios. The first scenario is the base station (BS) directly communicates with NOMA users. The second scenario is BS communicates with NOMA users via the AF relaying. The diversity orders are presented for the two scenarios. It reveals that there is an error floor for the outage probability due to the distortion noise and estimation error.

To evaluate the system-level performance of clustered unmanned aerial vehicle (UAV) networks, we apply stochastic geometry in order to establish a tractable analytical framework. The users' locations are modeled as Poisson cluster processes (PCPs) and the UAVs are assumed to hover above cluster centers at a fixed altitude. In order to enhance the generality of the analysis, two typical patterns of PCPs, namely Thomas cluster process and Matern cluster process, are analyzed. Based on this spatial framework, a unified expression for the coverage probability is derived, for both millimeter wave (mmWave) and sub-6 GHz scenarios. Theoretical and numerical results demonstrate that mmWave outperforms sub-6 GHz, and an optimal altitude of UAVs exists, which maximizes the coverage probability. This result indicates that in most cases UAVs perform better than traditional terrestrial base stations, due to their mobility.

To estimate the molecular communication (MC) parameters (e.g., diffusion coefficient, reaction rate, and absorption rate) via observations at the transmitter and the receiver, we present an analytical framework for a diffusive MC system with a partially absorbing receiver and a general first-order chemical reaction during propagation, in both spherically asymmetric and spherically symmetric scenarios. The time-varying spatial distributions and the expected numbers of messenger molecules and their first-order reaction products inside the transmitter, as well as at the surface of the partially absorbing receiver, are derived in both scenarios, which can be simplified in the special cases of a fully absorbing receiver. Importantly, our analytical expressions are verified by particle-based simulations, which showcase the effect of the reaction rate on the transmitter and the receiver observations. The analytical results of channel impulse responses at the absorbing receiver as well as that inside the transmitter are first treated and solved for spherically asymmetric scenario in this work.

The combination of resource allocation and fog computing based radio access network (Fog-RAN) have great potential for future wireless networks. However, the cross-tier interference in the spectrum-sharing deployment of Fog BSs could affect the network performance seriously and most of the solutions focus on the spectral efficiency optimization. In this paper, the user association, caching strategy, and power allocation are investigated in Fog-RAN with consideration of energy efficiency and cross-tier interference mitigation. The user association, caching, and power allocation are formulated as a non-convex optimization problem and then transformed into a convex problem, which is solved by Alternating Direction Method of Multipliers (ADMM). Then ADMM-based resource allocation algorithms are proposed to improve the energy efficiency of Fog-RAN. Simulation results demonstrate the proposed algorithms's convergence and effectiveness by comparing with existing method.

The Random Access (RA) procedure is used to request channel resources for the uplink data transmission in the cellular-based massive Internet of Things (mIoT). To ease the RA failure and the network congestion, power ramping (PR) technique is used to step up the preamble transmit power after each unsuccessful RA attempt. In this paper, we develop a traffic aware spatio-temporal model to analyze the PR scheme in the mIoT network, where the Signal-to-Interference-and-Noise Ratio (SINR) outage and collision events jointly determine the traffic evolution and the RA success probability. Compared with existing literature only modelled collision from single cell perspective, we model both the SINR outage and the collision from the network perspective. Based on this analytical model, we derive the exact expression for the RA success probability to show the effectiveness of the PR scheme. Our results show that the geometry PR scheme with smooth increased transmission power is effective in heavy traffic scenario in terms of increasing the RA success probability.

There is an increase in usage of smaller cells or femtocells to improve performance and coverage of next-generation heterogeneous wireless networks (HetNets). However, the interference caused by femtocells to neighboring cells is a limiting performance factor in dense HetNets. This interference is being managed via distributed resource allocation methods. However, as the density of the network increases so does the complexity of such resource allocation methods. Yet, unplanned deployment of femtocells requires an adaptable and self-organizing algorithm to make HetNets viable. As such, we propose to use a machine learning approach based on Q-learning to solve the resource allocation problem in such complex networks. By defining each base station as an agent, a cellular network is modeled as a multi-agent network. Subsequently, cooperative Q-learning can be applied as an efficient approach to manage the resources of a multi-agent network. Furthermore, the proposed approach considers the quality of service (QoS) for each user and fairness in the network. In comparison with prior work, the proposed approach can bring more than a four-fold increase in the number of supported femtocells while using cooperative Q-learning to reduce resource allocation overhead.

In this paper, the flexible user association is inves- tigated in non- orthogonal multiple access (NOMA)-based multi- ple base stations (BSs) networks. More particularly, users are partitioned into multiple orthogonal clusters to allocate into different resource blocks (RBs) for avoiding serious co-channel interferences. In an effort to maximize the weighted sum rate of the system, a user association optimization problem is formulated. Two algorithms based on coalitional game are proposed for obtaining suboptimal and global optimal solutions, respectively. The properties of the proposed algorithms, including complexity, convergence, stability and optimality are analyzed. Simulation re- sults demonstrate that: i) the proposed algorithms can significantly enhance the weighted sum rate of the considered systems; ii) the proposed NOMA-based system is capable of achieving promising gains over conventional orthogonal multiple access (OMA)-based framework, and iii) the considered distance based weight factor can greatly improve the fairness of users.

This paper studies a multi-user cooperative mobile- edge computing (MEC) system, in which a local mobile user can offload intensive computation tasks to multiple nearby edge devices serving as helpers for remote execution. We focus on the scenario where the local user has a number of independent tasks that can be executed in parallel but cannot be further partitioned. We consider a time division multiple access (TDMA) communication protocol, in which the local user can offload computation tasks to the helpers and download results from them over pre- scheduled time slots. Under this setup, we minimize the local user's computation latency by optimizing the task assignment jointly with the time and power allocations, subject to individual energy constraints at the local user and the helpers. However, the joint task assignment and wireless resource allocation problem is a mixed-integer non-linear program (MINLP) that is hard to solve optimally. To tackle this challenge, we first relax it into a convex problem, and then propose an efficient suboptimal solution based on the optimal solution to the relaxed convex problem. Finally, numerical results show that our proposed joint design significantly reduces the local user's computation latency, as compared against other benchmark schemes that design the task assignment separately from the offloading/downloading resource allocations and local execution.

In this paper, we introduce a clustered millimeter wave network with non-orthogonal multiple access (NOMA), where the base station (BS) is located at the center of each cluster and all users follow a Poisson Cluster Process. To provide a realistic directional beamforming, an actual antenna pattern is deployed at all BSs. We provide a nearest-random scheme, in which near user is the closest node to the corresponding BS and far user is selected at random, to appraise the coverage performance and universal throughput of our system. Novel closed- form expressions are derived under a loose network assumption. Moreover, we present several Monte Carlo simulations and numerical results, which show that: 1) NOMA outperforms orthogonal multiple access regarding the system rate; 2) the coverage probability is proportional to the number of possible NOMA users and a negative relationship with the variance of intra-cluster receivers; and 3) an optimal number of the antenna elements is existed for maximizing the system throughput.

In this paper, we consider a novel cache-enabled heterogeneous network (HetNet), where macro base stations (BSs) with traditional sub-6 GHz are overlaid by dense millimeter wave (mmWave) pico BSs. These two-tier BSs, which are modeled as two independent homogeneous Poisson Point Processes, cache multimedia contents following the popularity rank. High-capacity backhauls are utilized between macro BSs and the core server. A maximum received power strategy is introduced for deducing novel algorithms of the success probability and area spectral efficiency (ASE). Moreover, Monte Carlo simulations are presented to verify the analytical conclusions and numerical results demonstrate that: 1) the proposed HetNet is an interference limited system and it outperforms the traditional HetNets; 2) there exists an optimal pre-decided rate threshold that contributes to the maximum ASE; and 3) 73 GHz is the best mmWave carrier frequency regarding ASE due to the large antenna scale.

This paper investigates a two-way relay non- orthogonal multiple access (TWR-NOMA) system, where two groups of NOMA users exchange messages with the aid of one half-duplex (HD) decode-and-forward (DF) relay. Since the signal-plus-interference-to-noise ratios (SINRs) of NOMA signals mainly depend on effective successive interference cancellation (SIC) schemes, imperfect SIC (ipSIC) and perfect SIC (pSIC) are taken into consideration. To characterize the performance of TWR-NOMA systems, we derive closed-form expressions for both exact and asymptotic outage probabilities of NOMA users' signals with ipSIC/pSIC. Based on the results derived, the diversity order and throughput of the system are examined. Numerical simulations demonstrate that: 1) TWR-NOMA is superior to TWR-OMA in terms of outage probability in low SNR regimes; and 2) Due to the impact of interference signal (IS) at the relay, error floors and throughput ceilings exist in outage probabilities and ergodic rates for TWR-NOMA, respectively.

In this paper, we investigate the joint subcarrier (SC) assignment and power allocation problem for non-orthogonal multiple access (NOMA) amplify-and- forward two-way relay wireless networks. We aim to maximize the achievable secrecy energy efficiency by jointly designing the SC assignment, user pair scheduling and power allocation. Assuming the perfect knowledge of the channel state information (CSI) at the relay station, we propose a low-complexity subcarrier assignment scheme (SCAS-1), which is equivalent to many-to-many matching games, and then SCAS-2 is formulated as a secrecy energy efficiency maximization problem. The secure power allocation problem is modeled as a convex geometric programming (GP) problem, and then solved by interior point methods. Simulation results demonstrate that the effectiveness of the proposed SSPA algorithms.

Recently, non-orthogonal multiple access (NOMA) transmission has aroused an upsurge of interest due to its obvious superiority in spectral efficiency and user connectivity for the next generation cellular networks. However, as NOMA is intrinsically in favour of the users with strong channels who are capable of carrying out successive decoding, judicious design is required for ensuring user fairness. In this paper, we consider a two-user downlink NOMA with delay-tolerant transmission over fading channels in both scenarios of full and partial channel state information at the transmitter (CSIT). The average sum-rate is maximized subject to both an average and a peak power constraint as well as a minimum individual rate constraint. The dynamic resource allocation policy is optimally obtained using Lagrangian dual decomposition in the full CSIT case, while the power allocation in the partial CSIT case is also developed based on analytical results. Finally, the effectiveness of the proposed algorithms for NOMA over orthogonal multiple access (OMA) are verified in simulations by means of trade-offs for the average sum-rate and/or individual rate versus the minimum average rate requirement.

The weighted sum rate maximization problem of ultra-dense cloud radio access networks (C-RANs) is considered, where realistic fronthaul capacity constraints are incorporated. To reduce the training overhead, pilot reuse is adopted and the transmit beamforming is designed to be robust to the channel estimation errors. In contrast to the conventional C-RAN where the remote radio heads (RRHs) coherently transmit their data symbols to the user, we consider their non-coherent transmission, where no strict phase synchronization is required. By exploiting the classic successive interference cancellation technique, we first derive the closed-form expressions of the individual data rates from each serving RRH to the user and the overall data rate for each user that is not related to their decoding order. Then, we adopt the reweighted l1-norm technique to approximate the l0-norm in the fronthaul capacity constraints as the weighted power constraints. A low-complexity algorithm based on a novel sequential convex approximation (SCA) algorithm is developed to solve the resultant optimization problem with convergence guarantee. A beneficial initialization method is proposed to find the initial points of the SCA algorithm. Our simulation results show that in the high fronthaul capacity regime, the coherent transmission is superior to the non-coherent one in terms of its weighted sum rate. However, significant performance gains can be achieved by the non-coherent transmission over the coherent one in the low fronthaul capacity regime, which is the case in ultradense C-RANs, where mmWave fronthaul links with stringent capacity requirements are employed.

Massive Internet of Things (mIoT) has provided an auspicious opportunity to build powerful and ubiquitous connections that face a plethora of new challenges, where cellular networks are potential solutions due to their high scalability, reliability, and efficiency. The random access channel (RACH) procedure is the first step of connection establishment between IoT devices and base stations in the cellular-based mIoT network, where modeling the interactions between static properties of the physical layer network and dynamic properties of queue evolving in each IoT device are challenging. To tackle this, we provide a novel traffic-aware spatio-temporal model to analyze RACH in cellular-based mIoT networks, where the physical layer network is modeled and analyzed based on stochastic geometry in the spatial domain, and the queue evolution is analyzed based on probability theory in the time domain. For performance evaluation, we derive the exact expressions for the preamble transmission success probabilities of a randomly chosen IoT device with different RACH schemes in each time slot, which offer insights into the effectiveness of each RACH scheme. Our derived analytical results are verified by the realistic simulations capturing the evolution of packets in each IoT device. This mathematical model and the analytical framework can be applied to evaluate the performance of other types of RACH schemes in the cellular-based networks by simply integrating its preamble transmission principle.

We thoroughly investigate the downlink beamforming problem of a two-tier network in a reversed time-division duplex system, where the interference leakage from a tier-2 base station (BS) toward nearby uplink tier-1 BSs is controlled through pricing. We show that soft interference control through the pricing mechanism does not undermine the ability to regulate interference leakage while giving flexibility to sharing the spectrum. Then, we analyze and demonstrate how the interference leakage is related to the variations of both the interference prices and the power budget. Moreover, we derive a closed-form expression for the interference leakage in an asymptotic case, where both the charging BSs and the charged BS are equipped with a large number of antennas, which provides further insights into the lowest possible interference leakage that can be achieved by the pricing mechanism.

In this paper, an uplink power and bandwidth allocation problem for multiple services with multi-homing technology is formulated for cognitive heterogeneous networks. The joint power and bandwidth allocation with multiple services is subject to constraints in system available bandwidth, proportional fairness transmission rate for non-real-time secondary mobile terminals (MTs), minimum required transmission rate for real-time secondary MTs, interference power for primary base station, and total power consumption for each secondary MT. The joint power and bandwidth allocation problem with multiple services based on risk-return model is formulated as a bargaining game framework, first. Then, an optimal power and bandwidth allocation algorithm utilizing a dual decomposition method is proposed to obtain Nash bargaining solution. Finally, a heuristic algorithm is proposed to reduce computational complexity. Simulation results demonstrate the optimal and heuristic algorithms not only improve the spectrum efficiency, but also guarantee the fairness for secondary MTs with non-real-time service.

In this paper, we consider a novel cache-enabled heterogeneous network (HetNet), where macro base stations (BSs) with traditional sub-6 GHz are overlaid by dense millimeter wave (mmWave) pico BSs. These two-tier BSs, which are modeled as two independent homogeneous Poisson point processes, cache multimedia contents following the popularity rank. High-capacity backhauls are utilized between macro BSs and the core server. In contrast to the simplified flat-top antenna pattern analyzed in previous articles, we employ an actual antenna model with the uniform linear array at all mmWave BSs. To evaluate the performance of our system, we introduce two distinctive user association strategies: 1) maximum received power (Max-RP) scheme; and 2) maximum rate (Max-Rate) scheme. With the aid of these two schemes, we deduce new theoretical equations for success probabilities and area spectral efficiencies. Considering a special case with practical path loss laws, several closed-form expressions for coverage probabilities are derived to gain several insights. Monte Carlo simulations are presented to verify the analytical conclusions. We show that: 1) the proposed HetNet is an interference-limited system and it outperforms the traditional HetNets in terms of the success probability; 2) there exists an optimal pre-decided rate threshold that contributes to the maximum ASE; and 3) Max-Rate achieves higher success probability and ASE than Max-RP but it needs the extra information of the interference effect.

A two-way relay non-orthogonal multiple access (TWR-NOMA) system is investigated, where two groups of NOMA users exchange messages with the aid of one half-duplex decode-and-forward relay. Since the signal-plus-interference-to-noise ratios of NOMA signals mainly depend on effective successive interference cancellation (SIC) schemes, imperfect SIC (ipSIC), and perfect SIC (pSIC) are taken into account. In order to characterize the performance of TWR-NOMA systems, we first derive closed-form expressions for both exact and asymptotic outage probabilities of NOMA users' signals with ipSIC/pSIC. Based on the derived results, the diversity order and throughput of the system are examined. Then, we study the ergodic rates of users' signals by providing the asymptotic analysis in high signal-to-noise ratio (SNR) regimes. Finally, numerical simulations are provided to verify the analytical results and show that: 1) TWR-NOMA is superior to TWR-OMA in terms of outage probability in low SNR regimes; 2) due to the impact of interference signal at the relay, error floors and throughput ceilings exist in outage probabilities, and ergodic rates for TWR-NOMA, respectively; and 3) in delay-limited transmission mode, TWR-NOMA with ipSIC and pSIC have almost the same energy efficiency. However, in delay-tolerant transmission mode, TWR-NOMA with pSIC is capable of achieving larger energy efficiency compared with TWR-NOMA with ipSIC.

In this paper, a novel cooperative non-orthogonal multiple access (NOMA) system is proposed, where one near user is employed as decode-and-forward relaying switching between full-duplex (FD) and half-duplex (HD) mode to help a far user. Two representative cooperative relaying scenarios are investigated insightfully. The first scenario is that no direct link exists between the base station (BS) and far user. The second scenario is that the direct link exists between the BS and far user. To characterize the performance of potential gains brought by the FD NOMA in two considered scenarios, three performance metrics outage probability, ergodic rate, and energy efficiency are discussed. More particularly, we derive new closed-form expressions for both exact and asymptotic outage probabilities as well as delay-limited throughput for two NOMA users. Based on the derived results, the diversity orders achieved by users are obtained. We confirm that the use of direct link overcomes zero diversity order of far NOMA user inherent to FD relaying. In addition, we derive new closed-form expressions for asymptotic ergodic rates. Based on these, the high signal-to-noise ratio (SNR) slopes of two users for FD NOMA are obtained. Simulation results demonstrate that: 1) the FD NOMA is superior to the HD NOMA in terms of outage probability and ergodic sum rate in the low SNR region; and 2) in delay-limited transmission mode, the FD NOMA has higher energy efficiency than the HD NOMA in the low SNR region; However, in delay-tolerant transmission mode, the system energy efficiency of the HD NOMA exceeds the FD NOMA in the high SNR region.

This paper investigates the impact of relay selection (RS) on the performance of cooperative non-orthogonal multiple access (NOMA), where relays are capable of working in either full-duplex (FD) or half-duplex (HD) mode. A number of relays (i.e., K relays) are uniformly distributed within the disc. A pair of RS schemes are considered insightfully: 1) single-stage RS (SRS) scheme; and 2) two-stage RS (TRS) scheme. In order to characterize the performance of these two RS schemes, new closed-form expressions for both exact and asymptotic outage probabilities are derived. Based on analytical results, the diversity orders achieved by the pair of RS schemes for FD/HD cooperative NOMA are obtained. Our analytical results reveal that: 1) the FD-based RS schemes obtain a zero diversity order, which is due to the influence of loop interference at the relay; and 2) the HD-based RS schemes are capable of achieving a diversity order of K , which is equal to the number of relays. Finally, simulation results demonstrate that: 1) the FD-based RS schemes have better outage performance than HD-based RS schemes in the low signal-to-noise ratio (SNR) region; 2) as the number of relays increases, the pair of RS schemes considered are capable of achieving the lower outage probability; and 3) the outage behaviors of FD/HD-based NOMA SRS/TRS schemes are superior to that of random RS and orthogonal multiple access based RS schemes.

In this paper, a downlink security-aware resource allocation problem with delay constraint via spectrum sensing is modeled as a mixed integer non-linear problem for non-orthogonal multiple access-based cognitive radio network. The security-aware resource allocation is subject to constraints in required delay for each secondary user, maximum number of accessed secondary users at each subchannel, total interference power threshold introduced to primary users, and total power consumption at secondary BS. The security-aware resource allocation is based on channel state information at the physical layer and queue state information at the link layer. According to the queue buffer occupancy, a probability upper bound of exceeding the maximum packet delay based on M/D/1 queuing model is analyzed in terms of a required minimum secrecy transmission rate. Then, a secondary user scheduling problem and a power allocation problem are solved, separately. Finally, the secondary user scheduling problem is solved via greedy algorithm, and a power allocation algorithm is proposed by successive convex approximation method. The simulation results demonstrate that the performance of proposed algorithms can be improved significantly.

This paper focuses on edge caching in dense heterogeneous cellular networks, in which small base stations (SBSs) with limited cache size store the popular contents, and massive multiple-input multiple-output (MIMO)-aided macro base stations provide wireless self-backhaul when SBSs require the non-cached contents. Our aim is to address the effects of cell load and hit probability on the successful content delivery (SCD) and present the minimum required base station density for avoiding the access overload in an arbitrary small cell and backhaul overload in an arbitrary macrocell. The achievable rate of massive MIMO backhaul without any downlink channel estimation is derived to calculate the backhaul time, and the latency is also evaluated in such networks. The analytical results confirm that hit probability needs to be appropriately selected in order to achieve SCD. The interplay between cache size and SCD is explicitly quantified. It is theoretically demonstrated that when non-cached contents are requested, the average delay of the non-cached content delivery could be comparable to the cached content delivery with the help of massive MIMO-aided self-backhaul, if the average access rate of cached content delivery is lower than that of self-backhauled content delivery. Simulation results are presented to validate our analysis.

Soft iterative detection/decoding algorithms are fundamentally necessary for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) adopted in the Third Generation Long Term Evolution (LTE)-Advanced in order to increase the capacity and achieve high data rates. However, their high performance critically requires log likelihood ratio computations with prohibitive complexity. This challenge will be addressed in this paper. We first use the assumption of Gaussian transmit symbols to show the equivalence among several existing algorithms. We next develop a non-Gaussian approximation for high-order constellations, which paves the way for interference cancellation-based detectors. Based on both Gaussian and non-Gaussian approximations, we thus develop several capacity-achieving iterative MIMO-OFDM demodulation and decoding algorithms. To this end, we adopt K-best algorithms to take advantage of both the types of approximations and the list decoder. Unlike existing algorithms, our proposed K-best algorithms make use of the a priori probabilities to generate the list. Simulations of standard-compliant LTE systems demonstrate that the proposed algorithms outperform the existing ones.

This paper investigates the application of non-orthogonal multiple access (NOMA) in millimeter wave (mm-Wave) communications by exploiting beamforming, user scheduling, and power allocation. Random beamforming is invoked for reducing the feedback overhead of the considered system. A non-convex optimization problem for maximizing the sum rate is formulated, which is proved to be NP-hard. The branch and bound approach is invoked to obtain the ∈-optimal power allocation policy, which is proved to converge to a global optimal solution. To elaborate further, a low-complexity suboptimal approach is developed for striking a good computational complexity-optimality tradeoff, where the matching theory and successive convex approximation techniques are invoked for tackling the user scheduling and power allocation problems, respectively. Simulation results reveal that: 1) the proposed low complexity solution achieves a near-optimal performance and 2) the proposed mm-Wave NOMA system is capable of outperforming conventional mm-Wave orthogonal multiple access systems in terms of sum rate and the number of served users.

Quality of experience (QoE) is an important indicator in the fifth generation (5G) wireless communication systems. For characterizing user-base station (BS) association, subchannel assignment, and power allocation, we investigate the resource allocation problem in multi-cell multicarrier non-orthogonal multiple access (MC-NOMA) networks. An optimization problem is formulated with the objective of maximizing the sum mean opinion scores (MOSs) of users in the networks. To solve the challenging mixed integer programming problem, we first decompose it into two subproblems, which are characterized by combinational variables and continuous variables, respectively. For the combinational subproblem, a 3-D matching problem is proposed for modeling the relation among users, BSs, and subchannels. Then, a two-step approach is proposed to attain a suboptimal solution. For the continuous power allocation subproblem, the branch and bound approach is invoked to obtain the optimal solution. Furthermore, a low complexity suboptimal approach based on successive convex approximation techniques is developed for striking a good computational complexity-optimality tradeoff. Simulation results reveal that: 1) the proposed NOMA networks is capable of outperforming conventional orthogonal multiple access networks in terms of QoE and 2) the proposed algorithms for sum-MOS maximization can achieve significant fairness improvement against the sum-rate maximization scheme.

This paper proposes a novel direction-of-arrival (DOA)-aided channel estimation for a hybrid millimeter-wave (mm-wave) massive multiple-input multiple-output system with a uniform planar array at the base station. To explore the physical characteristics of the antenna array in mm-wave systems, the parameters of each channel path are decomposed into the DOA information and the channel gain information. We first estimate the initial DOAs of each uplink path through the 2-D discrete Fourier transform and enhance the estimation accuracy via the angle rotation technique. We then estimate the channel gain information using a small amount of training resources, which significantly reduces the training overhead and the feedback cost. More importantly, to examine the estimation performance, we derive the theoretical bounds of the mean squared errors (MSEs) and the Cramér-Rao lower bounds (CRLBs) of the joint DOA and channel gain estimation. The simulation results show that the performances of the proposed methods are close to the theoretical MSEs' analysis. Furthermore, the theoretical MSEs are also close to the corresponding CRLBs.

The cellular-based infrastructure is regarded as one of the potential solutions for massive Internet of Things (mIoT), where the random access (RA) procedure is used for requesting channel resources in the uplink data transmission. Due to the nature of the mIoT network with the sporadic uplink transmissions of a large amount of IoT devices, massive concurrent channel resource requests lead to a high probability of RA failure. To relieve the congestion during the RA in mIoT networks, we model RA procedure and analyze as well as evaluate the performance improvement due to different RA schemes, including power ramping (PR), back-off (BO), access class barring (ACB), hybrid ACB and back-off schemes, and hybrid power ramping and back-off (PR&BO). To do so, we develop a traffic-aware spatio-temporal model for the contention-based RA analysis in the mIoT network, where the signal-to-noise-plus-interference ratio (SINR) outage and collision events jointly determine the traffic evolution and the RA success probability. Compared to existing literature that only models collision from the single-cell perspective, we model both SINR outage and the collision from the network perspective. Based on this analytical model, we derive the analytical expression for the RA success probabilities to show the effectiveness of different RA schemes. We also derive the average queue lengths and the average waiting delays of each RA scheme to evaluate the packets accumulation status and packets serving efficiency. Our results show that our proposed PR&BO scheme outperforms other schemes in heavy traffic scenarios in terms of the RA success probability, the average queue length, and the average waiting delay.

This paper considers the unavailability of complete channel state information (CSI) in ultra-dense cloud radio access networks. The user-centric cluster is adopted to reduce the computational complexity, while the incomplete CSI is considered to reduce the heavy channel training overhead, where only large-scale inter-cluster CSI is available. Channel estimation for intra-cluster CSI is also considered, where we formulate a joint pilot allocation and user equipment (UE) selection problem to maximize the number of admitted UEs with fixed number of pilots. A novel pilot allocation algorithm is proposed by considering the multi-UE pilot interference. Then, we consider robust beam-vector optimization problem subject to UEs' data rate requirements and fronthaul capacity constraints, where the channel estimation error and incomplete inter-cluster CSI are considered. The exact data rate is difficult to obtain in closed form, and instead we conservatively replace it with its lower-bound. The resulting problem is non-convex, combinatorial, and even infeasible. A practical algorithm, based on UE selection, successive convex approximation and semi-definite relaxation approach, is proposed to solve this problem with guaranteed convergence. We strictly prove that the semidefinite relaxation is tight with probability 1. Finally, extensive simulation results are presented to show the fast convergence of our proposed algorithm and demonstrate its superiority over the existing algorithms.

Recently, non-orthogonal multiple access (NOMA) has attracted considerable interest as one of the 5G-enabling techniques. However, the users with better channel conditions in downlink communications intrinsically benefit more from NOMA than the users with worse channel conditions thanks to successive decoding, judicious designs are required to guarantee user fairness. In this paper, a two-user downlink NOMA system over fading channels is considered. For delay-tolerant transmission, the average sum rate is maximized subject to both average and peak-power constraints as well as a minimum average user rate constraint. The optimal resource allocation is obtained using the Lagrangian dual decomposition under full channel state information at the transmitter (CSIT), while an effective power allocation policy under partial CSIT is also developed based on analytical results. In parallel, for delay-limited transmission, the sum of delay-limited throughput (DLT) is maximized subject to a maximum allowable user outage constraint under full CSIT, and the analysis for the sum of DLT is also performed under partial CSIT. Furthermore, an optimal orthogonal multiple access (OMA) scheme is also studied as a benchmark to prove the superiority of NOMA over OMA under full CSIT. Finally, the theoretical analysis is verified by simulations via different tradeoffs for the average sum rate (sum-DLT) versus the minimum (maximum) average user rate (outage) requirement.

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) is a promising technique for next generation wireless communications. Using NOMA, more than one user can access the same frequency-time resource simultaneously and multi-user signals can be separated successfully using SIC. In this paper, resource allocation algorithms for subchannel assignment and power allocation for a downlink NOMA network are investigated. Different from the existing works, here, energy efficient dynamic power allocation in NOMA networks is investigated. This problem is explored using the Lyapunov optimization method by considering the constraints on minimum user quality of service and the maximum transmit power limit. Based on the framework of Lyapunov optimization, the problem of energy efficient optimization can be broken down into three subproblems, two of which are linear and the rest can be solved by introducing a Lagrangian function. The mathematical analysis and simulation results confirm that the proposed scheme can achieve a significant utility performance gain and the energy efficiency and delay tradeoff is derived as [O(1/V), O(V)] with V as a control parameter under maintaining the queue stability.

In this paper, motivated by recent developments in physical layer (PHY) security, we study the achievable secrecy rate of a multi-power level cognitive radio network (CRN) based on stochastic geometry distribution. We consider a realistic transmission scheme with multiple levels of transmission power. Additionally, having multi-level power strategy can help improve security as eavesdroppers will find it more difficult to recognize the exact power level of the legitimate users. We derive the achievable secrecy rate in an additive white Gaussian noise channel for a cognitive radio model. Furthermore, the cumulative distribution function of the achievable secrecy rate between the primary transmitter and receiver is studied. Finally, we investigate the outage probability of secrecy capacity of the legitimate user from a secure communication graph point of view.

Massive multiple input multiple output (MIMO) and full duplex (FD) communication are being considered as potential candidates for the spectrum efficient 5G wireless networks. In this paper, we develop a tractable model for downlink (DL) and uplink (UL) transmission in K-tier heterogeneous cellular networks (HCNs) with massive MIMO macrocells and full duplex (FD) small cells for spectrum efficiency. In the considered HCNs, the performance of the mobile user (MU) is limited by several sources of interference, specifically due to FD nature of small cell base stations (SBSs). A stochastic geometry based model of the proposed HCNs is provided which allows to derive the DL and UL rate coverage probabilities of such a system. Monte Carlo simulations confirm the accuracy of the analytical results, while numerical results reveal that equipping large number of MIMO antennas at macro base stations (MBSs) enhances the DL rate coverage probability of a random MU in HCNs. The results show that to achieve the maximum joint DL and UL performance gain in HCNs with FD small cells, both SBSs' density and SBSs' transmit power should be optimized. Moreover, the UL performance can be improved by decreasing the SBSs receivers sensitivity and increasing the UL power control factor.

Considering simultaneous wireless information and power transfer (SWIPT), we study a multiple-input- single-output (MISO) secrecy channel which consists of a multi-antenna trans- mitter and a cooperative jammer (CJ), multiple multi-antenna energy receivers (ERs), i.e., potential eavesdroppers, and multiple single-antenna co-located receivers (CRs). Both transmitter and CJ send the intend signal with artificial noise (AN) and jamming signal to interfere with the ERs. All receivers (CRs and ERs) adopt a power splitter to decode information and harvest power simultaneously. We exploit AN and CJ to facilitate efficient wireless energy transfer and secure transmission. Our aim is to maximize the minimum harvested energy among all ERs and CRs subject to the total power constraints at the transmitter and CJ while guaranteeing the minimum secrecy rate for each CR above its requirement. By incorporating norm-bounded channel uncertainty model, we propose a joint design of robust secure transmission. The original problem is solved by a two- step approach. In the first step, the proposed problem is reformulated as a sequence of semidefinite programs (SDPs). In the second step, the proposed problem can be handled by one-dimensional search to attain the optimal solution. Simulation results indicate that the performance of the proposed scheme outperforms that of separated AN-aided or CJ-aided scheme.

This paper investigates the application of nonorthogonal multiple access (NOMA) in millimeter wave (mmWave) communication with beamforming, user selection and power allocation. To overcome the burden of feedback, random beamforming to mmWave NOMA systems is considered. We then formulate an optimization problem to maximize the sum rate of the proposed mmWave NOMA systems, which is nonconvex. To solve the challenging problem, we invoke the branch and bound (BB) technique to develop an optimal power allocation algorithm. Then a low complexity suboptimal algorithm based on matching theory is proposed to realize user selection. Simulation results are provided for validating the effectiveness of the proposed algorithms and to show that the sum rate performance of the mmWave NOMA systems can be substantially improved by the proposed algorithms compared to the conventional mmWave orthogonal multiple access (OMA) systems.

The design of communication systems capable of processing and exchanging information through molecules and chemical processes is a rapidly growing interdisciplinary field, which holds the promise to revolutionize how we realize computing and communication devices. While molecular communication (MC) theory has had major developments in recent years, more practical aspects in the design and prototyping of components capable of MC functionalities remain less explored. In this paper, motivated by a bulk of MC literature on information transmission via molecular pulse modulation, the design of a pulse generator is proposed as an MC component able to output a predefined pulse-shaped molecular concentration upon a triggering input. The chemical processes at the basis of this pulse generator are inspired by how cells generate pulse-shaped molecular signals in biology. At the same time, the slow-speed, unreliability, and non-scalability of these processes in cells are overcome with a microfluidic-based implementation based on standard reproducible components with well-defined design parameters. Mathematical models are presented to demonstrate the analytical tractability of each component, and are validated against a numerical finite element simulation. Finally, the complete pulse generator design is implemented and simulated in a standard engineering software framework, where the predefined nature of the output pulse shape is demonstrated together with its dependence on practical design parameters.

This paper proposes a novel direction of arrival (DOA) estimation for hybrid millimeter wave (mmWave) massive MIMO systems with the uniform planar array (UPA) at base station (BS). To explore the physical characteristics of antenna array in mmWave systems, the parameters of each channel path are decomposed into the DOA information and the channel gain information. We first estimate the initial DOAs of each uplink path through the two dimension discrete Fourier transform (2D-DFT) efficiently, and then the estimation accuracy can be further enhanced via the angle rotation technique. To examine the estimation performance, we derive the theoretical bounds of the mean squared error (MSE) performance of the DOA estimation in high signal-to-noise ratio (SNR) region. Simulation results are provided to corroborate the proposed studies, and show that the proposed DOA estimation method is close to the theoretical MSE performance.

Massive Internet of Things (mIoT) has provided an auspicious opportunity to build powerful and ubiquitous connections that faces a plethora of new challenges, where cellular networks are potential solutions due to their high scalability, reliability, and efficiency. The contention-based random access procedure (RACH) is the first step of connection establishment between IoT devices and Base Stations (BSs) in the cellular-based mIoT network, where modelling the interactions between static properties of physical layer network and dynamic properties of queue evolving in each IoT device are challenging. To tackle this, we provide a novel traffic-aware spatio- temporal model to analyze RACH in cellular-based mIoT networks, where the physical layer network are modelled and analyzed based on stochastic geometry, and the queue evolution are analyzed based on probability theory. For performance evaluation, we derive the exact expressions for the preamble transmission success probabilities of a randomly chosen IoT device with baseline scheme in each time slot. Our derived analytical results are verified by the realistic simulations capturing the evolution of packets in each IoT device.

This paper deals with the unavailability of full CSI in ultra-dense user-centric TDD C-RAN. To reduce the channel training overhead, we consider the incomplete CSI case, where only large-scale inter-cluster CSI is available. Channel estimation for intra-cluster CSI is also considered, where we formulate a joint pilot allocation and user equipment (UE) selection problem to maximize the number of admitted UEs with fixed number of pilots. A novel pilot allocation algorithm is proposed by considering the multi-UE pilot interference. Then, we consider robust beam-vector optimization problem subject to UEs' data rate requirements and fronthaul capacity constraints, where the channel estimation error and incomplete inter-cluster CSI are considered. Simulation results demonstrate its superiority over the existing algorithms.

This paper investigates the impact of relay selection (RS) on the performance of cooperative non-orthogonal multiple access (NOMA), where relays are capable of working in either full-duplex (FD) or half-duplex (HD) mode. The locations of relays considered in the networks are modeled using stochastic geometry. The single-stage relay selection (SRS) scheme is proposed. In order to characterize the performance of SRS scheme, new closed-form expressions for both exact and asymptotic outage probabilities are derived. Based on the analytical results, the diversity orders achieved by the SRS scheme for FD/HD cooperative NOMA are obtained. It is confirmed that the FD-based SRS schemes obtain a zero diversity order, while the HD-based RS is capable of achieving a diversity order of K. Simulation results show that the outage performance of FD-based RS scheme outperforms HD-based RS scheme in the low signal-to-noise radio (SNR) region rather than in the high SNR region.

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) is a promising technique for next generation wireless communications. Using NOMA, more than one user can access the same frequency-time resource simultaneously and multi-user signals can be separated successfully using SIC. In this paper, resource allocation algorithms for subchannel assignment and power allocation for a downlink NOMA network are investigated. Different from the existing works, here, energy efficient dynamic power allocation in NOMA networks is investigated. This problem is explored using the Lyapunov optimization method by considering the constraints on minimum user quality of service (QoS), the maximum transmit power limit. Based on the framework of Lyapunov optimization, the problem of energy efficient optimization can be broken down into three subproblems. Two of which are linear and the rest can be solved by introducing Lagrangian function. The mathematical analysis and simulation results confirm that the proposed scheme can achieve a significant utility performance gain and the energy efficiency and delay tradeoff is derived as [O(1/V), O(V)] with V as a control parameter under maintaining the queue stability.

This paper proposes a practical channel estimation scheme for downlink 60GHz indoor systems with the massive uniform rectangular array (URA) at base station (BS). Through array signal processing theory, the parameter of each channel path can be decomposed into the angular information and the channel gain information that can be estimated separately. We first prove that the two dimensional Discrete Fourier transform (2D-DFT) with phase rotation operation can be applied to efficiently estimate the angular information. Then, the channel gain information could be easily obtained with small amount of training resources in a linear manner. Interestingly, since uplink and the downlink angular information is reciprocal, the proposed method applicable for both TDD and FDD systems. Simulation results are provided to corroborate the proposed studies.

We investigate the global throughput maximization of distributed spectrum reusing (DSR) in cognitive radio (CR) network, which reaches the two-dimensional spectrum multiplexing. Most previous works only consider the temporal-domain accessing, which greatly underutilize the spectrum resources. In this paper, we propose a new temporal-spatial spectrum reusing scheme by fully exploiting the location information of devices, where multiple users can access one channel simultaneously. In distributed applications, the global information will be unavailable, and therefore a non-cooperative game is formulated. It is proved as an exact potential game (EPG), which has at least one pure strategy Nash equilibrium (NE). Then, an improved decentralized reinforcement learning (RL) algorithm is developed to achieve the NE points. The network performance is evaluated by computer simulations.

Traditional cellular networks are moving towards heterogenous cellular networks (HetNets) to satisfy the stringent demand for data rates and capacity. To enable the new applications in 5G, such as haptic communications, we face new challenges of achieving high availability with low latency in HetNets. In this paper, we introduce coordinated multi-point (CoMP) and carrier aggregation (CA) techniques in HetNets to guarantee the availability of all UEs, where CoMP improves the single-path availability, and CA enhances availability via multi carrier gain combining. To characterize the availability, we first derive an exact closed-form expression for the availability of a random UE in a CoMP&CA-enabled HetNets. To achieve the maximum UE availability, we formulate a max-min optimization problem. To solve it, we then propose a joint two-step optimization algorithm (JTOA), and our results showcase the effective of our proposed JTOA, and the effective of CoMP in availability improvement in HetNets.

In this paper, we propose a cooperative approach to improve the security of both primary and secondary systems in cognitive radio multicast communications. During their access to the frequency spectrum licensed to the primary users, the secondary unlicensed users assist the primary system in fortifying security by sending a jamming noise to the eavesdroppers, while simultaneously protect themselves from eavesdropping. The main objective of this work is to maximize the secrecy rate of the secondary system, while adhering to all individual primary users' secrecy rate constraints. In the case of passive eavesdroppers and imperfect channel state information knowledge at the transceivers, the utility function of interest is nonconcave and involved constraints are nonconvex, and thus, the optimal solutions are troublesome. To address this problem, we propose an iterative algorithm to arrive at a local optimum of the considered problem. The proposed iterative algorithm is guaranteed to achieve a Karush-Kuhn-Tucker solution.

This paper investigates the performance of cooperative non-orthogonal multiple access (NOMA) systems, where one near user works as a decode-and-forward (DF) full-duplex (FD) or half-duplex (HD) relaying to help far user. Two cooperative relay scenarios are considered insightfully. 1) The first scenario is that no direct link exists between the base station (BS) and the far user; and 2) The second scenario is that direct link exists between the BS and the far user. To characterize the performance of FD NOMA in two considered scenarios, new closed-form expressions for both exact and asymptotic outage probability as well as delay-limited throughput are derived for each NOMA user. Based on the analytical results derived, the diversity orders achieved by users are obtained. It is confirmed that the use of the direct link overcomes the zero diversity order of far NOMA user inherent to FD relaying. Simulation results demonstrate that the outage performance of FD NOMA is superior to HD NOMA at low SNR region rather than at high SNR region.

In this paper, novel resource allocation design is investigated for NOMA-enhanced heterogeneous networks (Het-Nets), where small cell base stations (SBSs) are enabled to communicate with multiple small cell users (SCUs) via the NOMA protocol. The resource allocation problem with the aim of maximizing the sum rate of SCUs is formulated as a many-to-one matching game. Due to the existence of co-channel interference, this game is shown to belong to a class of matching games with peer effects. To solve this game, we propose a novel distributed algorithm where the SBSs and resource blocks (RBs) can interact to decide their desired allocation. The proposed algorithm is proved to converge to a two-sided exchange-stable matching with much lower complexity compared to the centralized method. Simulation results unveil that: 1) The proposed algorithm closely approaches the global optimal solution by around 92.5% within a limited number of iterations; and 2) The developed NOMA-enhanced HetNets scheme achieves a higher sum rate of SCUs compared to the traditional OMA-based HetNets scheme.

This paper considers multiple-input multiple-output (MIMO) full-duplex (FD) two-way secrecy systems. Specifically, both multi-antenna FD legitimate nodes exchange their own confidential message in the presence of an eavesdropper. Taking into account the imperfect channel state information (CSI) of the eavesdropper, we formulate a robust sum secrecy rate maximization (RSSRM) problem subject to the outage probability constraint of the achievable sum secrecy rate and the transmit power constraint. Unlike other existing channel uncertainty models, e.g., norm- bounded and Gaussian-distribution, we exploit a moment-based random distributed CSI uncertainty model to recast our formulate RSSRM problem into convex optimization frameworks based on a Markov's inequality and robust conic reformulation, i.e., semidefinite programming (SDP). In addition, difference-of-concave (DC) approximation is employed to iteratively tackle the transmit covariance matrices of these legitimate nodes. Simulation results are provided to validate our proposed FD approaches.

This paper investigates joint maximum likelihood (ML) Doppler shift and channel estimation problem for high speed railway (HSR) wireless communication systems with applying massive uniform linear array (ULA). For characterizing the performance of the considered scenarios, we provide a tractable framework for analyzing the performance of on Doppler shift estimation. Furthermore, the analytical expressions of ML channel estimation are derived, under the joint consideration of Doppler shift. Both analysis match their corresponding Cramer-Rao Bounds (CRBs) well. Finally, simulation results are provided to corroborate our proposed studies.

This paper proposes a practical channel estimation for 60-GHz indoor systems with the massive uniform rectangular array at base station. Through antenna array theory, the parameters of each channel path can be decomposed into the angular information and the channel gain information. We first prove that the true direction of arrivals of each uplink path can be extracted via an efficient array signal processing method. Then, the channel gain information could be obtained linearly with small amount of training resources, which significantly reduces the training overhead and the feedback cost. More importantly, the proposed scheme unifies the uplink/downlink channel estimations for both the time duplex division and frequency duplex division systems, making itself particularly suitable for protocol design. Compared with the existing channel estimation algorithms, the newly proposed one does not require any knowledge of channel statistics and can be efficiently deployed by the 2-D fast Fourier transform. Meanwhile, the number of user terminals simultaneously served can be increased from a sophisticatedly designed angle division multiple access scheme. Simulation results are provided to corroborate the proposed studies.

In this paper, the potential benefits of applying non-orthogonal multiple access (NOMA) technique in K -tier hybrid heterogeneous networks (HetNets) is explored. A promising new transmission framework is proposed, in which NOMA is adopted in small cells and massive multiple-input multiple-output (MIMO) is employed in macro cells. For maximizing the biased average received power for mobile users, a NOMA and massive MIMO based user association scheme is developed. To evaluate the performance of the proposed framework, we first derive the analytical expressions for the coverage probability of NOMA enhanced small cells. We then examine the spectrum efficiency of the whole network by deriving exact analytical expressions for NOMA enhanced small cells and a tractable lower bound for massive MIMO enabled macro cells. Finally, we investigate the energy efficiency of the hybrid HetNets. Our results demonstrate that: 1) the coverage probability of NOMA enhanced small cells is affected to a large extent by the targeted transmit rates and power sharing coefficients of two NOMA users; 2) massive MIMO enabled macro cells are capable of significantly enhancing the spectrum efficiency by increasing the number of antennas; 3) the energy efficiency of the whole network can be greatly improved by densely deploying NOMA enhanced small cell base stations; and 4) the proposed NOMA enhanced HetNets transmission scheme has superior performance compared with the orthogonal multiple access-based HetNets.

Driven by the rapid escalation of the wireless capacity requirements imposed by advanced multimedia applications (e.g., ultrahigh-definition video, virtual reality, etc.), as well as the dramatically increasing demand for user access required for the Internet of Things (IoT), the fifth-generation (5G) networks face challenges in terms of supporting large-scale heterogeneous data traffic. Nonorthogonal multiple access (NOMA), which has been recently proposed for the third-generation partnership projects long-term evolution advanced (3GPP-LTE-A), constitutes a promising technology of addressing the aforementioned challenges in 5G networks by accommodating several users within the same orthogonal resource block. By doing so, significant bandwidth efficiency enhancement can be attained over conventional orthogonal multiple-access (OMA) techniques. This motivated numerous researchers to dedicate substantial research contributions to this field. In this context, we provide a comprehensive overview of the state of the art in power-domain multiplexing-aided NOMA, with a focus on the theoretical NOMA principles, multiple-antenna-aided NOMA design, on the interplay between NOMA and cooperative transmission, on the resource control of NOMA, on the coexistence of NOMA with other emerging potential 5G techniques and on the comparison with other NOMA variants. We highlight the main advantages of power-domain multiplexing NOMA compared to other existing NOMA techniques. We summarize the challenges of existing research contributions of NOMA and provide potential solutions. Finally, we offer some design guidelines for NOMA systems and identify promising research opportunities for the future.

Full duplex (FD) communication has emerged as an attractive solution for increasing the network throughput, by allowing downlink (DL) and uplink (UL) transmissions in the same spectrum. However, only employing FD base stations in heterogeneous cellular networks (HCNs) cause coverage reduction, due to the DL and UL interferences as well as the residual loop interference. We, therefore, propose HCNs with half duplex massive multiuser multiple-input multiple-output macrocell base stations (MBSs) to relax the coverage reduction, and FD small cell base stations (SBSs) to improve spectrum efficiency. A tractable framework of the proposed system is presented, which allows to derive exact and asymptotic expressions for the DL and the UL rate coverage probabilities, and the DL and the UL area spectral efficiencies (ASEs). Monte Carlo simulations confirm the accuracy of the analytical results, and it is revealed that the equipping massive number of antennas at MBSs enhances the DL rate coverage probability, whereas increasing FD SBSs increases the DL and the UL ASEs. The results also demonstrate that by tuning the UL fractional power control, a desirable performance in both UL and DL can be achieved.

Traditional cellular networks are moving toward heterogeneous cellular networks (HetNets) to satisfy the stringent demand for data rates and capacity. To enable the new applications in 5G, such as haptic communications, we face new challenges of achieving high availability with low latency in HetNets. In this paper, we introduce coordinated multi-point (CoMP) and carrier aggregation (CA) techniques in HetNets to guarantee the availability of all user equipment (UE), where the CoMP improves the single-path availability, and the CA enhances availability via multi-carrier gain combining. To characterize the availability, we first derive an exact closed-form expression for the availability of a random UE in a CoMP and CA-enabled HetNets. To achieve the maximum UE availability, we formulate a max-min optimization problem. To solve it, we then propose a two-step optimization algorithm (TSOA) and a joint (JTOA). The TSOA is based on heuristic algorithm for the optimal subcarrier assignment and UE association, and based on the Lagrangian dual method for the power allocation. The JTOA is based on genetic algorithm to achieve the interaction between the first step and the second step. Our results showcase the effective of our proposed JTOA, and the effective of the CoMP in availability improvement in HetNets.

In this paper, we consider cognitive radio networks in which energy constrained secondary users (SUs) can harvest energy from the randomly deployed power beacons. A new frame structure is proposed for the considered networks. In the considered network, a wireless power transfer model is proposed, and the closed-form expressions for the power outage probability are derived. In addition, in order to reduce the energy consumption at SUs, sub-Nyquist sampling are performed at SUs. Subsequently, compressive sensing and matrix completion techniques are invoked to recover the original signals at the fusion center by utilizing the sparsity property of spectral signals. Throughput optimizations of the secondary networks are formulated into two linear constrained problems, which aim to maximize the throughput of a single SU and the whole cooperative network, respectively. Three methods are provided to obtain the maximal throughput of secondary networks by optimizing the time slots allocation and the transmit power. Simulation results show that the maximum throughput can be improved by implementing compressive spectrum sensing in the proposed frame structure design.

This paper investigates the performance of millimeter wave (mmWave) communications in clustered device-to-device (D2D) networks. The locations of D2D transceivers are modeled as a Poisson Cluster Process. In each cluster, devices are equipped with multiple antennas, and the active D2D transmitter (D2D-Tx) utilizes mmWave to serve one of the proximate D2D receivers. Specifically, we introduce three user association strategies: 1) uniformly distributed D2D-Tx model; 2) nearest D2D-Tx model; and 3) closest line-of-site (LOS) D2D-Tx model. To characterize the performance of the considered scenarios, we derive new analytical expressions for the coverage probability and area spectral efficiency (ASE). Additionally, in order to efficiently illustrating the general trends of our system, a closed-form lower bound for the special case interfered by intra-cluster LOS links is derived. We provide Monte Carlo simulations to corroborate the theoretical results and show that: 1) the coverage probability is mainly affected by the intra-cluster interference with LOS links; 2) there exists an optimum number of simultaneously active D2D-Txs in each cluster for maximizing ASE; and 3) the closest LOS model outperforms the other two scenarios but at the cost of extra system overhead.

The non-regenerative massive multi-input-multi-output (MIMO) non-orthogonal multiple access (NOMA) relay systems are introduced in this paper. The NOMA is invoked with a superposition coding technique at the transmitter and successive interference cancellation (SIC) technique at the receiver. In addition, a maximum mean square error-SIC receiver design is adopted. With the aid of deterministic equivalent and matrix analysis tools, a closed-form expression of the signal to interference plus noise ratio (SINR) is derived. To characterize the performance of the considered systems, closed-form expressions of the capacity and sum rate are further obtained based on the derived SINR expression. Insights from the derived analytical results demonstrate that the ratio between the transmitter antenna number and the relay number is a dominate factor of the system performance. Afterward, the correctness of the derived expressions are verified by the Monte Carlo simulations with numerical results. Simulation results also illustrate that: 1) the transmitter antenna, averaged power value, and user number display the positive correlations on the capacity and sum rate performances, whereas the relay number displays a negative correlation on the performance and 2) the combined massive-MIMO-NOMA scheme is capable of achieving higher capacity performance compared with the conventional MIMO-NOMA, relay-assisted NOMA, and massive-MIMO orthogonal multiple access (OMA) scheme.

With the recent advances in radio frequency (RF) energy harvesting (EH) technologies, wireless powered cooperative cognitive radio network (CCRN) has drawn an upsurge of interest for improving the spectrum utilization with incentive to motivate joint information and energy cooperation between the primary and secondary systems. Dedicated energy beamforming is aimed at remedying the low efficiency of wireless power transfer, which nevertheless arouses out-of-band EH phases and thus low cooperation efficiency. To address this issue, in this paper, we consider a novel CCRN aided by full-duplex (FD)-enabled energy access points (EAPs) that can cooperate to wireless charge the secondary transmitter while concurrently receiving primary transmitter's signal in the first transmission phase, and to perform decode-and-forward relaying in the second transmission phase. We investigate a weighted sum-rate maximization problem subject to transmitting power constraints as well as a total cost constraint using successive convex approximation techniques. A zero-forcing-based suboptimal scheme that requires only local channel state information for the EAPs to obtain their optimum receiving beamforming is also derived. Various tradeoffs between the weighted sum-rate and other system parameters are provided in numerical results to corroborate the effectiveness of the proposed solutions against the benchmark ones.

In this paper, a joint video scheduling, subchannel assignment, and power allocation problem in cognitive heterogeneous networks are modeled as a mixed integer non-linear programming (MINLP), which maximizes the minimum video transmission quality among different secondary mobile terminals (MTs) subject to the total available energy at each secondary, the total interference power at each primary base station, the total available capacity at each radio interface of each secondary MTs, and the video sequence encoding characteristic. In order to solve it, we decompose the original MINLP as joint subchannel and power allocation problem and video packet scheduling problem. Then, we model the joint subchannel and power allocation problem as a max-min fractional programming, and transform it as a convex optimization problem. Finally, we utilize dual decomposition method to design a joint subchannel and power allocation algorithm, and propose a video packet scheduling scheme based on auction theory to maximize the video quality for each secondary MT. Simulation results demonstrate that the proposed framework not only improves the video transmission quality significantly, but also guarantees the fairness among different secondary MTs.

Cognitive radio enabled small cell network is an emerging technology to address the exponential increase of mobile traffic demand in next generation mobile communications. Recently, many technological issues, such as resource allocation and interference mitigation pertaining to cognitive small cell network have been studied, but most studies focus on maximizing spectral efficiency. Different from the existing works, we investigate the power control and sensing time optimization problem in a cognitive small cell network, where the cross-tier interference mitigation, imperfect hybrid spectrum sensing, and energy efficiency are considered. The optimization of energy efficient sensing time and power allocation is formulated as a non-convex optimization problem. We solve the proposed problem in an asymptotically optimal manner. An iterative power control algorithm and a near optimal sensing time scheme are developed by considering imperfect hybrid spectrum sensing, cross-tier interference mitigation, minimum data rate requirement, and energy efficiency. Simulation results are presented to verify the effectiveness of the proposed algorithms for energy efficient resource allocation in the cognitive small cell network.

In this paper, a novel resource allocation design is investigated for non-orthogonal multiple access (NOMA) enhanced heterogeneous networks (HetNets), where small cell base stations (SBSs) are capable of communicating with multiple small cell users (SCUs) via the NOMA protocol. With the aim of maximizing the sum rate of SCUs while taking the fairness issue into consideration, a joint problem of spectrum allocation and power control is formulated. In particular, the spectrum allocation problem is modeled as a many-to-one matching game with peer effects. We propose a novel algorithm where the SBSs and resource blocks interact to decide their desired allocation. The proposed algorithm is proved to converge to a two-sided exchange-stable matching. Furthermore, we introduce the concept of `exploration' into the matching game for further improving the SCUs' sum rate. The power control of each SBS is formulated as a non-convex problem, where the sequential convex programming is adopted to iteratively update the power allocation result by solving the approximate convex problem. The obtained solution is proved to satisfy the Karush-Kuhn-Tucker conditions. We unveil that: 1) the proposed algorithm closely approaches the optimal solution within a limited number of iterations; 2) the `exploration' action is capable of further enhancing the performance of the matching algorithm; and 3) the developed NOMA-enhanced HetNets achieve a higher SCUs' sum rate compared with the conventional OMA-based HetNets.

Full-duplex (FD) small cells provide a promising solution for meeting the sought requirements of future wireless networks, specifically, the capacity, coverage and spectral efficiency. Motivated by the recent developments in self-interference (SI) cancellation techniques, the main objective of this paper is to further investigate the impact of using fully FD-capable small cells on conventional HetNets. We analyse a two-tier heterogeneous cellular networks (HetNets), wherein tier 1 consists of legacy half-duplex (HD) macro base stations (BSs) while tier 2 consists of FD small cells. Based on the stochastic geometry approach, we develop a theoretical model and derive closed-form expressions for the outage probability of downlink macrocell users, in addition to downlink and uplink users of small cells since they operate in FD mode. Analytical and simulation results are provided to verify the derived expressions and evaluate the variation of different parameters on the network performance.

Information delivery using chemical molecules is an integral part of biology at multiple distance scales and has attracted recent interest in bioengineering and communication. The collective signal strength at the receiver (i.e., the expected number of observed molecules inside the receiver), resulting from a large number of transmitters at random distances (e.g., due to mobility), can have a major impact on the reliability and efficiency of the molecular communication system. Modeling the collective signal from multiple diffusion sources can be computationally and analytically challenging. In this paper, we present the first tractable analytical model for the collective signal strength due to randomly-placed transmitters, whose positions are modelled as a homogeneous Poisson point process in three-dimensional (3D) space. By applying stochastic geometry, we derive analytical expressions for the expected number of observed molecules at a fully absorbing receiver and a passive receiver. Our results reveal that the collective signal strength at both types of receivers increases proportionally with increasing transmitter density. The proposed framework dramatically simplifies the analysis of large-scale molecular systems in both communication and biological applications.

The exponential growth in data traffic and dramatic capacity demand in fifth generation (5G) has inspired the move from traditional single-tier cellular networks towards heterogeneous cellular networks (HetNets). To face the coming trend in 5G, the high availability requirement in new applications, needs to be satisfied to achieve low latency service. In this work, we present a tractable multi-tier multi-band availability model to examine the high availability in carrier aggregation (CA)-enabled HetNets. We first derive a closed-form expression for the availability in CA- enabled HetNets based on the signal-to-interference- plus-noise model. By doing so, we formulate the joint subcarrier and power allocation problem, to maximize the availability under the power constraint. The optimization problem is non-convex problem, which is challenging to solve. To cope with it, the genetic algorithm (GA) is proposed to optimize availability through joint subcarrier and power allocation. The average availability in CA-enabled HetNets improves with decreasing the number of UEs, and increasing the power budget ratio interestingly.

In this paper, the application of non-orthogonal multiple access (NOMA) into K-tier heterogeneous networks (HetNets) is investigated. A new promising transmission framework is proposed, in which massive multiple-input multiple-output (MIMO) is employed in macro cells and NOMA is adopted in small cells. For maximizing the biased average received power at mobile users, a massive MIMO and NOMA based user association scheme is developed. In an effort to evaluate the performance of the proposed framework, analytical expressions for the spectrum efficiency of each tier are derived using stochastic geometry. Simulation results are presented to verify the accuracy of the proposed analytical derivations and confirm that NOMA is capable of enhancing the spectrum efficiency of the network compared to the orthogonal multiple access (OMA) based HetNets.

Cognitive radio (CR) has been advocated to improve the network spectrum efficiency for decades, and the cooperation between the primary and secondary systems has become a new paradigm to further improve the spectrum utilization. However, in practice, secondary transmitters (STs) are usually power constrained, which limits the application of cooperative cognitive radio networks (CCRN). In this paper, to tackle this, we consider a novel spectrum sharing CCRN powered by energy access points (EAPs) that can charge users wirelessly, in which a multi-antenna secondary user (SU) solely powered by its harvested energy seeks cooperation with a single-antenna primary user (PU) by serving as a deocde-and-forward (DF) relay. We investigate a payoff maximization problem from the SU's perspective, who gets paid by offering data relaying service for PU but has to pay for WEH, and obtain its optimal DF relay and WEH strategy. A greedy-based algorithm that can assign the ST to right EAPs is also proposed for the ease of implementation. The proposed scheme is shown to be effective by simulations with a negligible gap to the optimal solution.

In this paper, we present an analytical model for a diffusive molecular communication (MC) system with a reversible adsorption receiver in a fluid environment. The time-varying spatial distribution of the information molecules under the reversible adsorption and desorption reaction at the surface of a bio-receiver is analytically characterized. Based on the spatial distribution, we derive the number of newly-adsorbed information molecules expected in any time duration. Importantly, we present a simulation framework for the proposed model that accounts for the diffusion and reversible reaction. Simulation results show the accuracy of our derived expressions, and demonstrate the positive effect of the adsorption rate and the negative effect of the desorption rate on the net number of newly-adsorbed information molecules expected. Moreover, our analytical results simplify to the special case of an absorbing receiver.

We investigate a pricing based approach to control interference from a tier-2 base station (BS) to a tier-1 BS in reversed time division duplex (TDD) multi-antenna systems. The tier-2 BS is being charged for causing interference to the tier-1 BS. Also, the tier-2 BS has to satisfy the signal-to-interference-plus-noise ratio (SINR) targets of its downlink users under a maximum transmission power constraint. Analytical and simulation studies are carried out to understand the behavior of the tier-2 BS for different charges and for different power budgets. Observations from the analyses suggest that the tier-1 BS can perform interference control and/or profit maximization without knowing the downlink channels of the tier-2 BS.

It is expected that the use of cognitive radio for smart grid communication will be indispensable in near future. Recently, RPL for cognitive radio enabled Advanced Metering Infrastructure (AMI) networks is attractive. Our objective in this paper is to propose an enhance RPL to improve efficiency and reliability of cognitive radio enabled AMI networks. Our protocol is receiver-based in nature, which can achieve better reliability of the network along with protecting the primary users as well as meeting the utility requirements of secondary network. System level performance evaluation shows the effectiveness of proposed protocol as a viable solution for practical cognitive AMI networks.

In this paper, we consider a multi-level power allocation scheme for cognitive users in the cognitive radio networks. The proposed system uses a new frame structure to improve overall throughput of the cognitive radio systems. We propose a strategy in which, the licensed user can transmit with multiple levels of power which seems to be more realistic and practical. In this work, the proposed strategy also allows cognitive users to choose different power levels according to their receiving energy. The optimal achievable rate under the constraints of average transmit power at the cognitive users and average interference temperature to the licensed users is investigated. Simulation results indicate the enhancement in the throughput of the system compared to conventional strategy.

This study considers amplify‐and‐forward two‐way relay networks, where an energy constrained relay node harvests energy from the received radio‐frequency signal. Based on time switching receiver, they separate the energy harvesting (EH) phase and the information processing (IP) phase in time. In the EH phase, three practical wireless power transfer policies are proposed: (i) dual‐source (DS) power transfer, where both sources transfer power to the relay; (ii) single‐fixed‐source power transfer, where a fixed source transfers power to the relay; and (iii) single‐best‐source (SBS) power transfer, where a source with the strongest channel transfers power to the relay. In the IP phase, a new comparative framework of the proposed wireless power transfer policies is presented in two bi‐directional relaying protocols, known as multiple access broadcast (MABC) and time division broadcast (TDBC). To characterise the performance of the proposed policies, new analytical expressions are derived for the outage probability, the throughput, and the system energy efficiency. Numerical results corroborate the authors’ analysis and show: (i) the DS policy performs the best in terms of both outage probability and throughput among the proposed policies, (ii) the TDBC protocol achieves lower outage probability than the MABC protocol, and (iii) there exits an optimal value of EH time fraction to maximise the throughput.

Two direct sampling correlator‐type receivers for differential chaos shift keying (DCSK) communication systems under frequency non‐selective fading channels are proposed. These receivers operate based on the same hardware platform with different architectures. In the first scheme, namely sum‐delay‐sum (SDS) receiver, the sum of all samples in a chip period is correlated with its delayed version. The correlation value obtained in each bit period is then compared with a fixed threshold to decide the binary value of recovered bit at the output. On the other hand, the second scheme, namely delay‐sum‐sum (DSS) receiver, calculates the correlation value of all samples with its delayed version in a chip period. The sum of correlation values in each bit period is then compared with the threshold to recover the data. The conventional DCSK transmitter, frequency non‐selective Rayleigh fading channel, and two proposed receivers are mathematically modelled in discrete‐time domain. The authors evaluated the bit error rate performance of the receivers by means of both theoretical analysis and numerical simulation. The performance comparison shows that the two proposed receivers can perform well under the studied channel, where the performances get better when the number of paths increases and the DSS receiver outperforms the SDS one.

This study investigates cooperative spectrum sensing (CSS) in cognitive wireless radio networks (CWRNs). A practical system is considered where all channels experience Nakagami‐ m fading and suffers from background noise. The realisation of the CSS can follow two approaches where the final spectrum decision is based on either only the global decision at fusion centre (FC) or both decisions from the FC and secondary user (SU). By deriving closed‐form expressions and bounds of missed detection probability (MDP) and false alarm probability (FAP), the authors are able to not only demonstrate the impacts of the m ‐parameter on the sensing performance, but also evaluate and compare the effectiveness of the two CSS schemes with respect to various fading parameters and the number of SUs. It is interestingly noticed that a smaller number of SUs could be selected to achieve the lower bound of the MDP rather using all the available SUs while still maintaining a low FAP. As a second contribution, they propose a SU selection algorithm for the CSS to find the optimised number of SUs for lower complexity and reduced power consumption. Finally, numerical results are provided to demonstrate the findings.

In this paper, an energy-efficient resource allocation problem is modeled as a chance-constrained programming for multicast cognitive orthogonal frequency division multiplexing (OFDM) network. The resource allocation is subject to constraints in service quality requirements, total power, and probabilistic interference constraint. The statistic channel state information (CSI) between cognitive-based station (CBS) and primary user (PU) is adopted to compute the interference power at the receiver of PU, and we develop an energy-efficient chance-constrained subcarrier and power allocation algorithm. Support vector machine (SVM) is employed to compute the probabilistic interference constraint. Then, the chance-constrained resource allocation problem is transformed into a deterministic resource allocation problem, and Zoutendijk's method of feasible direction is utilized to solve it. Simulation results demonstrate that the proposed algorithm not only achieves a tradeoff between energy efficiency and satisfaction index, but also guarantees the probabilistic interference constraint very well.

Indoor localization based on existing Wi-Fi Received Signal Strength Indicator (RSSI) is attractive since it can reuse the existing Wi-Fi infrastructure. However, it suffers from dramatic performance degradation due to multipath signal attenuation and environmental changes. To improve the localization accuracy under the above-mentioned circumstances, an improved Spearman-distance-based K-Nearest-Neighbor (KNN) scheme is proposed. Simulation results demonstrate that our improved method outperforms the original KNN method under the indoor environment with severe multipath fading and temporal dynamics.

In this paper, a game theory-based cooperation scheme is investigated to enhance the physical layer security in both primary and secondary transmissions of a cognitive radio network (CRN). In CRNs, the primary network may decide to lease its own spectrum for a fraction of time to the secondary nodes in exchange of appropriate remuneration. We consider the secondary transmitter node as a trusted relay for primary transmission to forward primary messages in a decode-and-forward fashion and, at the same time, allows part of its available power to be used to transmit artificial noise (i.e., jamming signal) to enhance primary and secondary secrecy rates. In order to allocate power between message and jamming signals, we formulate and solve the optimization problem for maximizing the secrecy rates under malicious attempts from eavesdroppers. We then analyze the cooperation between the primary and secondary nodes from a game-theoretic perspective where we model their interaction as a Stackelberg game with a theoretically proved and computed Stackelberg equilibrium. We show that the spectrum leasing based on trading secondary access for cooperation by means of relay and jammer is a promising framework for enhancing security in CRNs.

This paper develops a tractable framework for exploiting the potential benefits of physical layer security in three-tier wireless sensor networks (WSNs) using stochastic geometry. In such networks, the sensing data from the remote sensors are collected by sinks with the help of access points, and the external eavesdroppers intercept the data transmissions. We focus on the secure transmission in two scenarios: 1) the active sensors transmit their sensing data to the access points and 2) the active access points forward the data to the sinks. We derive new compact expressions for the average secrecy rate in these two scenarios. We also derive a new compact expression for the overall average secrecy rate. Numerical results corroborate our analysis and show that multiple antennas at the access points can enhance the security of three-tier WSNs. Our results show that increasing the number of access points decreases the average secrecy rate between the access point and its associated sink. However, we find that increasing the number of access points first increases the overall average secrecy rate, with a critical value beyond which the overall average secrecy rate then decreases. When increasing the number of active sensors, both the average secrecy rate between the sensor and its associated access point, and the overall average secrecy rate decrease. In contrast, increasing the number of sinks improves both the average secrecy rate between the access point and its associated sink, and the overall average secrecy rate.

We consider secure resource allocations for orthogonal frequency division multiple access (OFDMA) two-way relay wireless sensor networks (WSNs). The joint problem of subcarrier (SC) assignment, SC pairing and power allocations, is formulated under scenarios of using and not using cooperative jamming (CJ) to maximize the secrecy sum rate subject to limited power budget at the relay station (RS) and orthogonal SC allocation policies. The optimization problems are shown to be mixed integer programming and nonconvex. For the scenario without CJ, we propose an asymptotically optimal algorithm based on the dual decomposition method and a suboptimal algorithm with lower complexity. For the scenario with CJ, the resulting optimization problem is nonconvex, and we propose a heuristic algorithm based on alternating optimization. Finally, the proposed schemes are evaluated by simulations and compared with the existing schemes.

In this paper, we develop a tractable model for joint downlink (DL) and uplink (UL) transmission of K -tier heterogeneous cellular networks (HCNs) with simultaneous wireless information and power transfer (SWIPT) for efficient spectrum and energy utilization. In the DL, the mobile users (MUs) with power splitting receiver architecture decode information and harvest energy based on SWIPT. While in the UL, the MUs use the harvested energy for information transmission. Since cell association greatly affects the energy harvesting in the DL and the performance of wireless powered HCNs in the UL, we compare the DL and UL performance of a random MU in HCNs with nearest base station (NBS) cell association to that with maximum received power (MRP) cell association. We first derive the DL average received power for the MU with the NBS and the MRP cell associations. To evaluate the system performance, we then derive the outage probability and the average ergodic rate in the DL and UL of a random MU in HCNs with the NBS and MRP cell associations. Our results show that increasing the small cell base station (BS) density, the BS transmit power, the time allocation factor, and the energy conversion efficiency, weakly affects the DL and UL performance of both the cell associations. However, the UL performance of both the cell associations can be improved by increasing the fraction of the DL received power used for energy harvesting.

This paper studies secrecy transmission with the aid of a group of wireless energy harvesting-enabled amplify-and-forward (AF) relays performing cooperative jamming (CJ) and relaying. The source node in the network does simultaneous wireless information and power transfer with each relay employing a power splitting receiver in the first phase; each relay further divides its harvested power for forwarding the received signal and generating artificial noise for jamming the eavesdroppers in the second transmission phase. In the centralized case with global channel state information (CSI), we provide the closed-form expressions for the optimal and/or suboptimal AF-relay beamforming vectors to maximize the achievable secrecy rate subject to individual power constraints of the relays, using the technique of semidefinite relaxation (SDR), which is proved to be tight. A fully distributed algorithm utilizing only local CSI at each relay is also proposed as a performance benchmark. Simulation results validate the effectiveness of the proposed multi-AF relaying with CJ over other suboptimal designs.

The emerging fifth-generation (5G) wireless communication system is expected to provide higher capacity, seamless connectivity and reduced energy consumption to support data intensive multimedia applications. Simultaneous wireless information and power transfer (SWIPT) in heterogeneous cellular networks (HCNs) is a promising approach to offer efficient spectrum and energy utilization in the 5G system. In this paper, we develop a tractable model for joint uplink (UL) and downlink (DL) transmission in a K-tier HCN with SWIPT. In this model, we use the power splitting (PS) protocol where the receiver splits the received signal power in two parts for harvesting the energy and decoding the information. The harvested energy in the DL is utilized for UL information transmission. We derive the exact analytical expressions for the average received power and the outage probability for both DL and UL for the system design. Monte carlo simulations confirm the accuracy of the derived results, and numerical analysis reveal that SWIPT is a reasonably efficient technique to power the cellular users. In particular, we observe that with the increase of the picocell density, both the DL and the UL outage probability in macrocell decreases significantly. Moreover, the DL and the UL outage probability in a tier is shown to decrease with the increase of BS transmit power of its own tier.

This paper develops a tractable framework for exploiting the potential benefits of physical layer security in three-tier wireless sensor networks. In such networks, the sensing data from the remote sensors are collected by sinks with the help of access points, and the external eavesdroppers intercept the data transmissions. We adopt the stochastic geometry approach to model the random locations and spatial densities of the sensors, access points, sinks, and eavesdroppers. We focus on the secure transmission in two scenarios: i) the active sensors transmit their sensing data to the access points, and ii) the active access points forward the data to the sinks. We derive new compact expressions for the overall average secrecy rate in such networks. Numerical results corroborate our analysis and show that multiple- antenna technique at the access points can enhance the security. Our results show that the overall average secrecy rate first increases with increasing the number of access points, and there exists a critical value beyond which the overall average secrecy rate decreases with increasing the number of access points. When adding the number of active sensors, the overall average secrecy rate decreases. In contrast, increasing the number of sinks improves the overall average secrecy rate.

In this paper, we consider a cognitive radio network in which energy constrained secondary users (SUs) can harvest energy from the randomly deployed power beacons. A new frame structure with four time slots, namely, energy harvesting, spectrum sensing, energy harvesting and data transmission is proposed. In the energy harvesting slot, a new wireless power transfer (WPT) scheme in a bounded power transfer model is proposed to enable power SUs wirelessly. Closed-form expression for the power outage probability of the proposed WPT scheme is derived. In the spectrum sensing slot, we propose to utilize the compressive sensing technique which enables sub-Nyquist sampling to further reduce the energy consumption at SUs. Throughput of the secondary network with the proposed frame structure is formulated into a nonlinear constraint problem. Three optimization methods are provided to obtain the maximal throughput of secondary network by optimizing the time slots allocation and the transmit power of SUs.

This paper investigates the game theory based cooperation method to optimize the PHY security in both primary and secondary transmissions of a cognitive radio network (CRN) that include a primary transmitter (PT), a primary receiver (PR), a secondary transmitter (ST), a secondary receiver (SR) and an eavesdropper (ED). In CRNs, the primary terminals may decide to lease its own given bandwidth for a fraction of time to the secondary nodes in exchange for appropriate remuneration. We consider the ST as a trusted relay for primary transmission in the presence of the ED. The ST forwards the source message in a decode-and-forward (DF) fashion and, at the same time, allows part of its available power to be used to transmit an artificial noise (i.e., jamming signal) to enhance secrecy rates and avoid the employment of a separate jammer. In order to allocate power between message and jamming signals, we formulate and solve optimization problem of maximizing the primary secrecy rate (PSR) and secondary secrecy rate (SSR). We then analyse the cooperation between the primary and secondary transmitters from a game-theoretic perspective, where we model their interaction as a Stackelberg game. Finally, we apply numerical examples to illustrate the impact of the Stackelberg game on the achievable PSR and SSR. It shows that spectrum leasing based on trading secondary access for cooperation by means of relay and jammer is a promising framework for enhancing secrecy rate in cognitive radio.

Cognitive radio enabled small cell network is an emerging technology to address the exponentially increasing mobile traffic demand of next generation mobile communications. Recently, many technological issues pertaining to cognitive small cell network have been studied, such as resource allocation, but most studies focus on spectral efficiency maximization. Different from the existing works, we investigate the power control and sensing time optimization problem in cognitive small cell, where imperfect hybrid spectrum sensing and energy efficiency are considered. The energy efficient sensing time and power allocation optimization is modeled as a non-convex optimization problem. We solve the problem in asymptotically optimal manner. An iterative power control algorithm and a near optimal sensing time scheme are developed with the consideration of imperfect hybrid spectrum sensing and energy efficiency. Simulation results are presented to verify the effectiveness of the proposed algorithms for energy efficient resource allocation in cognitive small cell network.

In this paper, a new detection framework, namely, deep sensing (DS), is proposed for 5G spectrum sharing, which is designed to proactively recover some informative states associated with realistic cognitive links (e.g., fading gains), except for detecting the occupancy of primary-band. Relying on a dynamic state-space approach, a unified mathematical model is formulated. The Bernoulli random finite set (BRFS) is exploited to theoretically characterize the complex DS procedures. A Bernoulli filter algorithm is suggested to recursively estimate unknown PU states accompanying related link information, which is further implemented by particle filtering. The proposed DS algorithm is applied to detect primary users over more challenging time-varying fading channels. Numerical simulations validate the new scheme. Spectrum sensing can be effectively implemented by estimating time-varying fading gains jointly.

Due to the enormous potential of improving the spectral utilization by using Cognitive Radio (CR), designing adaptive access system and addressing its physical layer security are the most important and challenging issues in CR networks. Since CR transceivers need to transmit over multiple non-contiguous frequency holes, multi-carrier based system is one of the best candidates for CR's physical layer design. In this paper, we propose a combined chaotic scrambling (CS) and chaotic shift keying (CSK) scheme in Orthogonal Frequency Division Multiplexing (OFDM) based CR to enhance its physical layer security. By employing chaos based third order Chebyshev map which allows optimum bit error rate (BER) performance of CSK modulation, the proposed combined scheme outperforms the traditional OFDM system in overlay scenario with Rayleigh fading channel. Importantly, with two layers of encryption based on chaotic scrambling and CSK modulation, large key size can be generated to resist any brute-force attack, leading to a significantly improved level of security.

Simultaneous wireless information and power transfer (SWIPT) has recently drawn much attention for its dual use of radio signals. A new type of relays, wireless energy harvesting (WEH)-enabled relays, are thus motivated to support cooperation. In this paper, we consider the use of power splitter (PS) at such relays for a distributed WEH-enabled amplify-and-forward (AF) relaying network, where a multi-antenna transmitter communicates with a single-antenna receiver with the aid of several single-antenna WEH-enabled AF relays, in the presence of a single-antenna eavesdropper. Assuming global channel state information (CSI) at the transmitter but local CSI from/to legitimate parties at the relays, the secrecy rate maximization problem is studied to optimize the beamforming vector at the transmitter as well as the PS ratios and the AF coefficients at the relays. We devise an efficient secure relay beamforming (SRB) algorithm to first obtain the PS ratios in a distributed manner and then iteratively adapt the transmit beam and the AF coefficients. The efficiency of the proposed algorithm is evaluated against other heuristic schemes by simulations.

In this paper, we propose cyclic prefix single carrier (CP-SC) full-duplex transmission in cooperative spectrum sharing to achieve multipath diversity gain and full-duplex spectral efficiency. Integrating full-duplex transmission into cooperative spectrum sharing systems results in two intrinsic problems: 1) the peak interference power constraint at the PUs are concurrently inflicted on the transmit power at the secondary source (SS) and the secondary relays (SRs); and 2) the residual loop interference occurs between the transmit and the receive antennas at the secondary relays. Thus, examining the effects of residual loop interference under peak interference power constraint at the primary users and maximum transmit power constraints at the SS and the SRs is a particularly challenging problem in frequency selective fading channels. To do so, we derive and quantitatively evaluate the exact and the asymptotic outage probability for several relay selection policies in frequency selective fading channels. Our results manifest that a zero diversity gain is obtained with full-duplex.

Spectrum sensing based dynamic spectrum sharing is one of the key innovative techniques in future 5G communications. When realistic mobile scenarios are concerned, the location of primary user (PU) is of great significance to reliable spectrum detections and cognitive network enhancements. Given the dynamic disappearance of its emission signals, the passive locations tracking of PU, nevertheless, remains dramatically different from existing positioning problems. In this investigation, a new joint estimation paradigm, namely deep sensing, is proposed for such challenging spectrum and location awareness applications. A major advantage of this new sensing scheme is that the mutual interruption between the two unknown quantities is fully considered and, therefore, the PU's emission state is identified by estimating its moving positions jointly. Taking both PU's unknown states and its evolving positions into account, a unified mathematical model is formulated relying on a dynamic state-space approach. To implement the new sensing framework, a random finite set (RFS) based Bernoulli filtering algorithm is then suggested to recursively estimate unknown PU states accompanying its time-varying locations. Meanwhile, the sequential importance sampling is used to approximate intractable posterior densities numerically. Furthermore, an adaptive horizon expanding mechanism is specially designed to avoid the mis-tracking aroused by the intermittent disappearance of PU. Experimental simulations demonstrate that, even with mobile PUs, spectrum sensing can be realized effectively by tracking its locations incessantly. The location information, as an extra gift, may be utilized by cognitive performance optimizations.

In this paper, spectrum sensing is investigated and a new detection framework, namely, deep sensing (DS), is proposed for more challenging scenarios of future dynamic spectrum sharing. In contrast to existing methods, the DS scheme is designed to proactively recover and exploit some other informative states associated with realistic cognitive links (e.g., fading gains), except detecting the occupancy of primary-band. A unified mathematical model, relying on the dynamic state-space approach, is formulated, in which the Bernoulli random finite set (RFS) is further exploited to theoretically characterize complex DS procedures. A Bernoulli filter algorithm is suggested to recursively estimate unknown PU states accompanying related link information, which is implemented by particle filtering based on numerical approximations. The proposed DS algorithm is applied to detect primary users under time-varying fading channel, which may increase the observation uncertainty and, therefore, deteriorate the sensing performance. With this new framework, the time-varying fading gain, modeled as a stochastic discrete-state Markov chain (DSMC), is estimated along with unknown PU states. Simulations demonstrate that, by exploiting the underlying dynamic fading property, the sensing performance will surpass other traditional schemes. The DS scheme may be conveniently generalized to other applications, which will promote sensing performance and provides a new paradigm for next-generation spectrum sharing.

Cluster identification of ultra-wideband (UWB) propagations is of great significance to the parameter extraction and measurement analysis of channel modeling. In this paper, we address this challenging problem within a promising biological processing framework. Both the two large-scale characteristics of each multipath component, i.e., the decaying amplitude and the time of arrivals, are organically combined and fully explored in the suggested cluster identification algorithm. Each resolvable trajectory component is first projected onto a 2-D amplitude-time plane and further modeled as a virtual ant-agent, which can move around in this 2-D workspace with a preference to the high local-environment similarity. By establishing a subtle population similarity and specifying an efficient position adaptation strategy, cluster identifications can be realized by the biological ant colony clustering procedure. Owing to the population-based intelligence and the involved positive-feedback collaboration during the agents evolution, the suggested algorithm can efficiently identify the involved multiple clusters in a completely automatic manner. Experiments on UWB channels validate the proposed method. The practical parameter configuration is analyzed, and a group of numerical performance metrics is derived. As demonstrated by numerical investigations, multiple clusters involved in UWB channel impulse responses can be accurately extracted.

For the emerging 5G millimeter-wave communications, the nonlinearity is inevitable due to RF power amplifiers of the enormous bandwidth operating in extremely high frequency, which, in collusion with frequency-selective propagations, may pose great challenges to signal detections. In contrast to classical schemes, which calibrate nonlinear distortions in transmitters, we suggest a nonlinear equalization algorithm, with which the multipath channel and unknown symbols contaminated by nonlinear distortions and multipath interferences are estimated in receiver-ends. Attributed to the nonlinearity and marginal integration, the involved posterior density is analytically intractable and, unfortunately, most existing linear equalization schemes may become invalid. To solve this problem, the Monte-Carlo sequential importance sampling based particle filtering is suggested, and the non-analytical distribution is approximated numerically by a group of random measures with the evolving probability-mass. By applying the Taylor's series expansion technique, a local-linearization observation model is further constructed to facilitate the practical design of a sequential detector. Thus, the unknown symbols are detected recursively as new observations arrive. Simulation results validate the proposed joint detection scheme. By excluding transmitting pre-distortion of high complexity, the presented algorithm is specially designed for the receiver-end, which provides a promising framework to nonlinear equalization and signal detection in millimeter-wave communications.

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This paper exploits an amplify-and-forward (AF) two-way relaying network (TWRN), where an energy constrained relay node harvests energy with wireless power transfer. Two bidirectional protocols, multiple access broadcast (MABC) protocol and time division broadcast (TDBC) protocol, are considered. Three wireless power transfer policies, namely, dual-source (DS) power transfer; single-fixed-source (SFS) power transfer; and single-best-source (SBS) power transfer are proposed and well-designed based on time switching receiver architecture. We derive analytical expressions to determine the throughput both for delay-limited transmission and delay-tolerant transmission. Numerical results corroborate our analysis and show that MABC protocol achieves a higher throughput than TDBC protocol. An important observation is that SBS policy offers a good tradeoff between throughput and power.

The emerging radio signal enabled simultaneous wireless information and power transfer (SWIPT), has drawn significant attention. To achieve secrecy transmission by cooperative jamming, especially in the upcoming 5G networks with self-sustainable mobile base stations (BSs) and yet not to add extra power consumption, we propose in this paper a new relay protocol, i.e., harvest-and-jam (HJ), in a relay wiretap channel with an additional set of spare helpers. Specifically, in the first transmission phase, a single-antenna transmitter (Tx) transfers signals carrying both information and energy to a multi-antenna amplify-and-forward (AF) relay and a group of multi-antenna helpers; in the second transmission phase, the AF relay processes the information and forwards it to the receiver while each of the helpers generates an artificial noise (AN), the power of which is constrained by its previously harvested energy, to interfere with the eavesdropper. By optimizing the transmit beamforming matrix for the AF relay and the covariance matrix for the AN, we maximize the secrecy rate for the receiver subject to transmit power constraints for the AF relay and all helpers. The formulated problem is shown to be non-convex, for which we propose an iterative algorithm based on alternating optimization. Finally, the performance of the proposed scheme is evaluated by simulations as compared to other heuristic schemes.

Recently, cognitive radio enabled femtocell is regarded as a promising technique in wireless communications, where the issues of resource allocation and interference management have been investigated intensively. However, spectrum sensing errors are neglected in most of the existing works. In this paper, we propose a resource allocation scheme for orthogonal frequency division multiple access (OFDMA) based cognitive femtocells. The target is to maximize the sum rate of all femtocell users (FUs) under QoS constraints and co-tier/cross-tier interference constraints under imperfect channel sensing. The subchannel and power allocation problem is first modeled as a mixed integer programming problem, and then transformed into a convex optimization problem by relaxing subchannel sharing and imposing co-tier interference constraints, which is finally solved using the dual decomposition method. Based on the obtained solution, an iterative subchannel and power allocation algorithm is proposed. The effectiveness in terms of instantaneous maximum achievable rate of the proposed algorithm as compared with perfect spectrum sensing schemes is verified by simulations.

For the emerging 60GHz millimeter-wave communications, the nonlinearity is usually inevitable due to RF power amplifiers operating in the ultra-high frequency and enormous bandwidth, which, in collusion with frequency-selective propagations, poses great challenges to signal detections. In contrast to classical schemes calibrating nonlinear distortions in transmitters, a blind detection algorithm is presented in this investigation, with which both the multipath response and symbols contaminated by nonlinear distortions and multipath interferences are estimated in receiver-ends. The Monte-Carlo sequential importance sampling based particle filtering is used, and the non-analytical distribution is approximated numerically by a group of random measures with evolving weights. By applying the Taylor's series expansion techniques, a local linearization model is further constructed to facilitate the practical design of a sequential detector. Simulation results validate the proposed blind detection scheme. By excluding the transmitting predistorter with complex computations and implementations, the presented algorithm provides a promising signal detection framework in 60GHz systems.

Increasing the cell edge user throughput in the Long Term Evolution (LTE) systems is a relatively new arising research area. The requirement for higher data rates especially at cell edges is becoming ever more pressing as the advantages could help satisfy better Quality of Service (QoS) on an enduser level and mean higher profitability on the operator side. Therefore developing algorithms and techniques to mitigate the inherently increased inter-cell interference (ICI) and reduced signal to interference and noise ratio (SINR) in the cell edge is a challenge with high potential benefits and rewards. To serve the above objective, we study the downlink (DL) packet scheduling and resource allocation of an LTE system under proportional fairness (PF) with an innovated utility function and a frequency reuse factor of one. We present our joint ICI-avoiding cooperative packet scheduling algorithm and show through simulations, cell edge throughput improvements of about 40% as an exchange for a 16% decrease in cooperating cells' throughput in comparison to the results obtained under no cooperation between the evolved Node Bs (eNB).

Most of the existing spectrum sensing schemes utilize only the statistical property of fading channels, which unfortunately fails to cope with the time-varying fading channel that has disastrous effects on sensing performance. As a consequence, such sensing schemes may not be applicable to distributed cognitive radio networks. In this paper, we develop a promising spectrum sensing algorithm for time-variant flat-fading (TVFF) channels. We first formulate a dynamic state-space model (DSM) to characterize the evolution behaviors of two hidden states, i.e., the primary user (PU) state and the fading gain, by utilizing a two-state Markov process and another finite-state Markov chain, respectively. The summed energy, which serves as the observation of DSM, is employed for the ease of implementation. Relying on a Bayesian statistical inference framework, the sequential importance sampling based particle filtering is then exploited to numerically and recursively estimate the involved posterior probability, and thus, the PU state and the fading gain are jointly estimated in time. The estimations of two states are soft-outputs, which are successively refined with a designed iterative approach. Simulation results demonstrate that the new scheme can significantly improve the sensing performance in TVFF channels, which, in turn, provides particular promise to realistic applications.

Distributed switch-and-stay combining (DSSC) has been envisioned as an effective transmission technique to achieve spatial diversity in a distributed fashion, with low implementation complexity. In this paper, we take a step further to incorporate DSSC into spectrum-limited environment, where the operating nodes have to share the frequency radio spectrum with licensed users. In particular, by deploying DSSC scheme in cognitive radio networks, we have shown that the low transmit power at unlicensed users, inflicted by the peak interference power constraint at licensed users, can be alleviated. We present closed-form expressions for outage probability and spectral efficiency, enabling us to evaluate and optimize the considered network performance. Numerical and simulation results show that when the switching threshold is below the outage threshold the full diversity order can be guaranteed at the secondary networks.

Cognitive relay technology has been envisioned as a promising transmission scheme to enhance the reliability and coverage of secondary networks. However, the performance of cognitive relay networks (CRNs) is limited by the lack of accurate channel state information (CSI). As such, this paper adequately addresses the impact of imperfect CSI on the performance of cognitive multihop networks by proposing a simple yet effective backoff control power method. In addition, novel exact and asymptotic expressions for outage probability and ergodic capacity over Rayleigh fading channels are also derived. These tractable analytical results reveal new insight into the design, e.g., the number of hops for secondary network, and optimization of cognitive multihop networks.

This paper tackles the important question of how to compensate the inherent spectrum efficiency loss in cognitive relay networks. Particularly, by considering two-way cognitive relaying, we seek to enhance the performance of the secondary network in terms of the reliability due to limited transmit power, and the spectral efficiency of the half-duplex dual-hop relay transmission. We derive new closed-form expressions for the outage probability of a cognitive relay network with two-way communications in the presence of multiple primary users. Our expressions accurately take into account the impact of the maximum allowable interference constraint at the primary users on the secondary network.

The traditional spectrum sensing schemes can only utilize the statistical probability of fading channels, which may fail to deal with the time-varying fading gain. Thus, the performance of such sensing techniques will degrade dramatically and may even become inapplicable to distributed cognitive radio networks. In this investigation, we develop a promising spectrum sensing algorithm for time-variant flat-fading (TVFF) channels. Firstly, a promising dynamic state-space model (DSM) is established to thoroughly characterize the evolution behaviors of both primary user (PU) state and fading channels, by utilizing a two-state Markov process and the finite-states Markov chain (FSMC), respectively. Relying on an optimal Bayesian inference framework, the sequential importance sampling based particle filtering is then suggested to recursively estimate PUs state and fading gain jointly. Experimental simulations demonstrated that the new scheme can significantly improve the sensing performance in TVFF channels, which provides particular promise to realistic applications.

In this paper, we investigate the cluster identification of ultra-wideband (UWB) multipath propagations from a promising biological processing perspective. In the presented biological cluster extraction method, both the amplitude decay and time of arrival of UWB channel impulse response (CIR) are fully taken into considerations. Each resolvable multipath component is projected onto a two dimensional amplitude-time workspace, and then modeled as a virtual ant-agent. Thus, these ant-agents can move around in this 2-D space with a preference to the high local environment similarity. By establishing a subtle population similarity and specifying an efficient position adaptation strategy, cluster identification can be elegantly realized by the biological ant colony clustering (ACC) procedure. As the experimental simulations shown, the suggested algorithm can accurately and efficiently identify the involved multiple clusters in a completely automatic manner, which is of great importance to UWB channel modeling and parameters extractions.

As a promising interference management technique, interference alignment (IA) has many applications, such as in cognitive radio (CR) networks. In CR networks, due to the coexistence of the secondary users (SUs) and the primary users (PUs), the signal-to-interference-plus-noise-ratio (SINR) at the PUs may decrease dramatically, leading to degraded performance of the PUs. In this paper, a novel IA algorithm based on frequency scheduling is proposed to guarantee the performance of PUs while sharing the spectrum with the SUs. In the algorithm, we divide SUs into multiple clusters, each of which forms an individual IA-CR network while guaranteeing the performance of the PUs. Thus a double-win game is established such that the PUs achieve performance gain with the aid of SUs while the SUs obtain more spectral opportunities. Simulation results are presented to verify the effectiveness of the proposed IA algorithm and its suitability for spectrum sharing in CR networks.

This paper investigates the coordinated beam-forming design for multi-cell MISO downlink beamforming system, aiming at maximizing the sum rate. In the proposed scheme, convex approximation approach is used to first recast the primal non-convex problem into an approximate problem of minimizing the sum of weighted inverse SINR. Then, an alternating optimization method is developed to address the approximate problem based on uplink-downlink duality. We show that our solution is globally optimal in the case of two-BS cooperation with a total power constraint, and is also effective in a general case. When extending to per-BS power constraints, an alternating optimization algorithm with provable convergence to stationary point is proposed following a similar procedure. Our simulation results show that the proposed scheme has a fast convergence and achieves a sum rate performance very close to the optimal performance obtained by exhaustive search.

Interference alignment (IA) is a promising technique that can effectively eliminate the interference in wireless networks. However, in traditional IA schemes, the signal to interference plus noise ratio (SINR) may significantly degrade, and the quality of service (QoS) may be unacceptable. In this paper, a novel IA scheme based on antenna switching (AS-IA) is proposed to improve the SINR of the received signal while guaranteeing the QoS in IA wireless networks. In the proposed scheme, some of the antennas are replaced by reconfigurable ones that can switch among preset modes, and the best channel coefficients are selected. Furthermore, to reduce the computational complexity, a sequential antenna switching IA (SAS-IA) scheme is proposed with only one antenna switching in each time slot, and the communication proceeds during the process of searching for the optimal solution. To further improve the performance of the SAS-IA scheme under imperfect channel state information (CSI), a filtering SAS-IA scheme is proposed through averaging the estimated CSI during the iterations of the distributed IA algorithm. Simulation results are presented to show the effectiveness and efficiency of the proposed schemes in improving the QoS of IA wireless networks.

In this paper, a new analytical expression of the max-min SINR duality between the multi-cell downlink and the virtual uplink subject to per-BS power constraints is firstly given. Based on that, a hierarchical iterative scheme is proposed to solve the virtual uplink optimization problem. The uplink solution is then converted to achieve the solution to the multi-cell downlink beamforming problem. Simulation results show that, in contrast to existing multi-cell beamforming schemes, the proposed scheme achieves better performance in terms of both the worst-user rate and the rate per energy.

This paper presents a green data transmission approach to enhance the energy efficiency of power line communications (PLC) by jointly utilizing signal detection and resource allocation techniques. Due to the awareness of the interference as enabled by the signal detection function, the proposed PLC system can adaptively adjust the transmission parameters. Furthermore, given a power budget, a performance optimization algorithm is proposed that maximizes the energy efficiency of PLC by optimally choosing the signal detection duration and the transmit power. Simulation results show that the proposed system can not only mitigate the effects of interference, but also considerably improve the energy efficiency of PLC when compared with the existing PLC systems.

In this paper, we consider the problem of secure resource allocation in orthogonal frequency division multiple access (OFDMA) two-way relay networks. Multiple sources exchange information with the assistance of an amplify-and-forward (AF) relay node in the presence of an eavesdropper. The joint subcarrier allocation, subcarrier pairing and power allocation problem aims to maximize the secrecy capacity for legitimate sources subject to limited power budget and orthogonal subcarrier allocation constraints. The optimization problem is modeled as a mixed integer programming problem, and then solved in an asymptotically optimal manner based on the dual method. Moreover, a suboptimal algorithm is proposed to reduce the complexity. Simulations are conducted to evaluate the effectiveness of the proposed near optimal and suboptimal algorithms.

In this paper, we consider the joint subchannel and power allocation problem in both the uplink and the downlink for two-tier networks comprising spectrum-sharing macrocells and femtocells. A joint subchannel and power allocation scheme for co-channel femtocells is proposed, aiming to maximize the capacity for delay-tolerant users subject to delay-sensitive users' quality of service and interference constraints imposed by macrocells. The joint subchannel and power allocation problem is modeled as an mixed integer programming problem, then transformed into a convex optimization problem by relaxing subchannel sharing, and finally solved by a dual decomposition approach. The effectiveness of the proposed approach is verified by simulations and compared with existing scheme.

Rapidly rising energy costs and increasingly rigid environmental standards have led to an emerging trend of addressing “energy efficiency” aspect of wireless communication technologies. Cognitive radio can play an important role in improving energy efficiency in wireless networks. In this paper, we propose an energy-efficient and time-saving one-bit cooperative spectrum sensing scheme, which has two stages. If the signal-to-noise ratio (SNR) is high or no primary user exists, only one stage of coarse spectrum sensing is needed, by which the sensing time and energy are saved. Otherwise, the second stage of fine spectrum sensing will be performed to increase the spectrum sensing accuracy. Furthermore, only one-bit decision is sent by each secondary user to minimize the overhead. Plenty of simulation is performed, and the results show that the sensing time and energy consumption are both reduced significantly in the proposed scheme.

Interference alignment (IA) is a promising technique that can eliminate the interference in multi-user communication networks effectively. However, it requires highly accurate and real-time channel state information (CSI) at both transmitters and receivers. In practical systems, it is difficult to obtain the perfect knowledge of a dynamic channel due to channel estimation errors, communication latency and capacity constraints. Particularly, transmitters in IA systems usually get imperfect CSI fed back from receivers with a delay, which will greatly affect the performance of IA. In this paper, the performance of IA with delayed CSI is studied, and the decrease of the total network capacity due to the delayed CSI is analyzed. To mitigate the influence of the delayed CSI, an IA scheme based on channel prediction is proposed using two easy-to-implement and practical channel predictors, minimum mean square estimate (MMSE) and weighted least squares error (WLSE) predictors. The CSI of the next time instant is predicted using the present and past CSI. Simulation results are presented to show the effectiveness of the channel prediction IA schemes with the delayed channel knowledge.

The distributed Alamouti space-time code in two-way fixed gain amplify-and-forward (AF) relay is proposed in this paper. In particular, closed-form expressions for approximated ergodic sum-rate and exact pairwise error probability (PWEP) are derived for Nakagami-m fading channels. To reveal further insights into array and diversity gains, an asymptotic PWEP is also obtained. Finally, numerical results are provided to corroborate the proposed theoretical analysis.

In this paper, we study the joint resource allocation problem in multi-carrier multi-relay aided dual hop single-user communication. We adopt orthogonal frequency division multiplexing (OFDM) as the transmission modulation and consider amplify-and-forward (AF) relaying scheme. The optimization is performed over power allocation at source node, beamforming at relay nodes and subcarrier pairing at two hops, such that the overall system throughput is maximized under limited power budget at the source and relay nodes. The optimization is a mixed integer programming problem which is solved through dual decomposition approach. To further reduce the complexity, we propose a suboptimal algorithm which sacrifices very little on the performance. Finally, simulation results are provided to corroborate the proposed studies.

The potential of improving the achievable throughput of spectrum sharing cognitive radio networks by enhancing them with spectrum sensing capabilities has recently lead to the introduction of a new spectrum access scheme called sensing-based spectrum sharing (SSS) or sensing-enhanced spectrum sharing (SESS). In this paper, we study the truncated channel inversion with fixed rate (TIFR) capacity and the outage capacity of a sensing-enhanced spectrum sharing (SESS) cognitive radio system for Rayleigh and Nakagami-m fading channels. We consider various constraints on the capacity, which include average transmit and interference power constraints, peak interference power constraints, and target detection probability constraints. Finally, we provide simulation results, which indicate that the sensing-enhanced spectrum sharing (SESS) cognitive radio systems can achieve higher TIFR and outage capacity compared to the (non-sensing) spectrum sharing cognitive radio systems.

Spectrum sharing has been considered an effective method of mitigating the spectrum scarcity in wireless communications by allowing the coexistence of both unlicensed and licensed devices in the same frequency bands, something which can be done by limiting the received interference power at the licensed user at an acceptable low level. In this paper, we study the outage and truncated channel inversion with fixed rate (TIFR) capacity of a sensing-enhanced spectrum sharing (SESS) cognitive radio system (CRS) under missed-detection interference power constraints for the protection of the primary users. In our analysis, we consider (i) average transmit power constraints, (ii) peak interference power constraints and (iii) average interference power constraints, and derive the power allocation strategy, as well as the TIFR and outage capacity for Nakagami-m fading channels. We provide numerical results which show that a SESS CRS can achieve improved outage and TIFR capacity compared to a conventional non-sensing spectrum sharing CRS.

Compressive spectrum sensing techniques present many advantages over traditional spectrum sensing approaches, e.g., low sampling rate, and reduced energy consumption. However, when the spectral sparsity level is unknown, there are two significant challenges. They are: 1) how to choose an appropriate number of measurements, and 2) when to terminate the greedy recovery algorithm. In this paper, a compressive autonomous sensing (CASe) framework is presented that gradually acquires the wideband signal using sub-Nyquist rate. Further, a sparsity-aware recovery algorithm is proposed to reconstruct the full spectrum while solving the problem of under-fitting or over-fitting. Simulation results show that the proposed system can not only reconstruct the spectrum using the appropriate number of measurements, but also considerably improve the recovery performance when compared with the existing approaches.

The performance of spectrum sensing using the received secondary frames is analyzed. Unlike the previous work that assumes perfect decoding of the secondary signal, the new analysis takes the decoding errors into account and therefore provides a more realistic comparison between the new model and the conventional model. Both the receiver operating characteristics for spectrum sensing and the achievable throughput for data transmission are derived. Numerical results show that the new model that considers the decoding error outperforms the conventional model when the number of transmitted secondary frames is below a certain threshold. An upper bound performance can also be obtained by ignoring the decoding error.

In this paper, the effect of keyhole on the performance of multiple-input multiple-output (MIMO) amplify-and-forward (AF) relay networks with orthogonal space-time block codes (OSTBCs) transmission is investigated. In particular, we analyze the asymptotic symbol error probability (SEP) performance of a downlink communication system where the amplifying processing at the relay can be implemented by either the linear or squaring approach. Our tractable asymptotic SEP expressions enable us to obtain both diversity and array gains. Our finding reveals that with condition n_S > min(n_R,n_D), the linear approach can provide the full achievable diversity gain of min(n_R,n_D) when only the second hop suffers from the keyhole effect, i.e., single keyhole effect (SKE), where n_S, n_R, and n_D are the number of antennas at source, relay, and destination, respectively. However, for the case that both the source-relay and relay-destination links experience the keyhole effect, i.e., double keyhole effect (DKE), the achievable diversity order is only one regardless of the number of antennas. In contrast, utilizing the squaring approach, the overall diversity gain can be achieved as min(n_R,n_D) for both SKE and DKE. An important observation corroborated by our studies is that for satisfying the tradeoff between performance and complexity, we should use the linear approach for SKE and the squaring approach for DKE.