The deployment of high-frequency carriers in 5G networks and beyond introduces significant challenges, particularly due to increased path loss and physical limitations of user equipment (UE). In particular, these challenges lead to uplink/downlink coverage imbalances, critically affecting applications that rely on robust uplink capacity such as autonomous driving, telemedicine, and live streaming. While downlink-uplink decoupling (DUDe) and supplementary uplink (SUL) have emerged as promising solutions for uplink enhancement, their flexible cell association mechanisms disrupt conventional handover strategies by introducing multidimensional, decoupled decision-making. To address this, we propose a heterogeneous network architecture that integrates DUDe into SUL, supporting non-co-located deployment of supplementary uplink carriers and enabling dual decoupling at both the base station and carrier levels. Building on this architecture, we introduce Counterfactual Multi-agent fuzzy neural network (COMA-FNN), a multi-agent reinforcement learning (MARL) algorithm based on centralized training with decentralized execution (CTDE). COMA-FNN incorporates fuzzy neural networks to model the complex, nonlinear relationships among multiple communication attributes, improving the precision and adaptability of handover decisions. The algorithm also employs a centralized critic with counterfactual baselines to effectively resolve credit assignment issues among competing agents. To accommodate varying service requirements, COMA-FNN incorporates service-specific reward functions aligned with four 3GPP-standardized service types. These rewards are weighted using the Analytic Hierarchy Process (AHP), enabling the algorithm to support different QoS policies. Simulation results demonstrate that COMA-FNN significantly improves handover efficiency, reduces latency, and enhances throughput, making it a robust solution for intelligent mobility management in decoupled uplink/downlink architectures.

This paper investigates latency-constrained resource synergization for mission-oriented non-terrestrial networks (NTNs) in post-disaster emergency scenarios. When terrestrial infrastructures are damaged, unmanned aerial vehicles (UAVs) equipped with edge information hubs (EIHs) are deployed to provide temporary coverage and synergize communication and computing resources for rapid situation awareness. We formulate a joint resource configuration and location optimization problem to minimize overall resource costs while guaranteeing stringent latency requirements. Through analytical derivations, we obtain closed-form optimal solutions that reveal the fundamental tradeoff between communication and computing resources, and develop a successive convex approximation method for EIH location optimization. Simulation results demonstrate that the proposed scheme achieves approximately 20% cost reduction compared with benchmark approaches, validating its optimality and effectiveness for mission-critical emergency response applications in the sixth-generation (6G) era.

Uncrewed Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) systems provide flexible and resilient computing capabilities for mobile users by leveraging UAVs as edge servers (ESs). However, in practical deployments, user devices typically exhibit spatially non-uniform distributions, which impose significant challenges for achieving optimal UAV placement. Conventional fixed or pre-determined deployment strategies cannot dynamically adapt to heterogeneous user distributions. To overcome these challenges, this article investigates an air–ground cooperative MEC architecture comprising multiple UAVs and terrestrial ESs. A multi-objective optimization problem incorporating UAV trajectory optimization is formulated to jointly minimize task offloading latency and total system energy consumption. As the problem is an NP-hard mixed-integer nonlinear programming model, a Joint Alternating Optimization framework for Task Offloading, Resource Allocation, and UAV Trajectory control (JAOTRU) is proposed. The JAOTRU framework adopts an iterative block coordinate descent structure to decouple the highly coupled optimization variables into two subproblems, which are efficiently solved via a differential evolution algorithm and a successive convex approximation technique, respectively. The simulation results show that the proposed JAOTRU method substantially surpasses several benchmark methods regarding total offloading latency and system energy efficiency, validating its effectiveness and robustness for MEC systems assisted by UAVs.

Satellites provide seamless coverage and are vital for establishing emergency communications during natural disasters. However, its effectiveness is limited by the allocated spectrum and high deployment costs. To address these challenges, we propose a solution based on the fluid antenna system (FAS), which enables dynamic signal adjustment for enhanced performance. Building on this concept, compact ultra massive antenna array (CUMA) is introduced, which activates multiple ports simultaneously, allowing the in-phase components of signals to be constructively combined. This approach mitigates interference while significantly reducing costs, as each fluid antenna requires only a single RF chain yet achieves substantial improvement in the received signal-to-interference-plus-noise ratio (SINR). In this paper, we consider a satellite CUMA network in which all ground users are assigned to the same satellite for uplink transmission, and the satellite leverages CUMA to mitigate inter-user interference. We derive closed-form expressions for the received CUMA signal power, interference power, and their distributions. Based on these expressions, we present the outage probability in a single integrated form, along with an approximated closed-form expression. The ergodic rate is hence provided. Our findings reveal the conditions under which CUMA outperforms maximum ratio combining in satellite communications scenario under various configurations. Notably, our analysis demonstrates that with a sufficient compact fluid antenna setting, the received CUMA signal becomes deterministic rather than a random variable, indicating that the system performance depends solely on the interference distribution. Moreover, for the compact fluid antenna configuration, increasing the number of ports results in a linear improvement in the beamforming gain. Finally, numerical results are provided to compare orthogonal multiple access CUMA (O-CUMA) and non-orthogonal multiple access CUMA (N-CUMA) in satellite communications, showing that with broad bandwidth,N-CUMA outperforms O-CUMA.

The existing unmanned aerial vehicle (UAV) enabled integrated sensing and communications (ISAC) systems primarily focus on the sensing capabilities of the UAV itself, overlooking the fact that the existing ground access points (APs) can receive the reflected signals for passive sensing, which can enhance the overall sensing performance. To address this issue, this paper introduces a UAV empowered air-ground ISAC system, where a UAV serves multiple communication users and performs detection for a potential target simultaneously, with the help of several ground APs. Specifically, the UAV works in active mode by transmitting ISAC signals and extracting information from echoes reflected from the target. In contrast, the ground APs function as sensing receivers, receiving and processing the reflected sensing signals from the target. Considering the limited capacity of the wireless backhaul links, we propose a two-step joint detection method, which contains local detection and result fusion steps. By incorporating the knowledge about the distribution of the reflection coefficient, we propose a maximum a-posteriori ratio test (MAPRT) detector, which is a generalization of earlier approaches such as the generalized likelihood ratio test detectors. Subsequently, the asymptotic distribution of test statistics of the MAPRT detector is derived. Furthermore, to improve the target detection performance, we propose an optimization algorithm that jointly optimizes the placement and transmit beamformer of the UAV, aiming at minimizing the probability of fusion error while guaranteeing the quality of service requirements of the users. Finally, numerical results demonstrate the effectiveness of the proposed algorithm.

The fluid antenna system (FAS) enables position reconfigurability. A potential drawback of real-time FAS, however, is that it requires complete channel state information (CSI) for each FAS port at every communication time slot, an approach referred to as ideal-FAS. Recognizing the difficulties of achieving ideal-FAS, we propose a FAS scheme based on incomplete CSI, referred to as semi-blind FAS. This paper first introduces the spatial-temporal framework of FAS, upon which the proposed semi-blind FAS is developed. The proposed semi-blind FAS is lightweight and computationally efficient, scalable to an arbitrary number of ports and time slots, and operates without pre-training or deep learning structures. The scheme effectively exploits incomplete historical CSI to estimate the conditional distribution across all FAS ports at the desired time slot, thereby identifying the statistical optimal port for signal reception. Generally, the key idea of semi-blind FAS is to select the optimal port through conditional distribution analysis, from a statistical perspective, with optimality defined according to the scenario of interest. Inspired by information-theoretic entropy, we further develop the residual entropy power ratio to characterize how physical parameters influence the performance gap between semi-blind FAS and ideal-FAS. Our analysis reveals that estimation performance depends not only on the number of sampled ports and time slots, but also on the specific indices of ports with given CSI at each time slot, i.e., the port sampling strategy. This critical factor has been largely overlooked in existing port estimation studies. Numerical results demonstrate that the proposed semi-blind FAS achieves performance comparable to, and in some cases indistinguishable from, that of ideal-FAS, while requiring significantly fewer port CSI measurements and lower port switching speeds.

An auto-polarization fluid antenna (APFA) is developed for indoor wireless channel sounding and employed to reveal a novel “fluid polarization effect” (FPE) in wireless communications. Unlike conventional fluid antennas (FAs) that are steering their beams/nulls with the aid of external mechanical/electronic actuators, the APFA only relies on the natural swinging of human arms to yield a self-driven polarization switching ability. Compared with the conventional fixed circularly polarized antennas, the wrist-worn, self-driven APFA in indoor wireless channel sounding systems effectively reduces multipath clusters (MPCs), attains smaller path loss exponent (PLE), and consequently yields the FPE. Compared to the fixed circularly polarized case with PLE= 1.62, the measured PLE is reduced by 14% to 1.38, and the system packet error rate (PER) is improved by 76%. It realizes robust anti-multipath fading performance owing to the much-improved FPE. The fluid effect in polarization domain is anticipated to remarkably enhance the anti-multipath fading performance of wearable wireless communication systems. It opens a new horizon to develop self-driven, cost-effective fluid antenna systems (FASs) for universal applications.

Investigations on surface waves (SW) launchers wireless coupled to a long, single conductor in arbitrary azimuth angles rotation are carried out to inspire a novel SW wireless channel model in Industrial Internet-of-Things (IIoT) environment. A path loss (PL) model with two degrees in freedom, i.e., the coupled azimuth angle α and the transceiver separationd, is modeled at first. It is revealed that the angular factor governs the excitation degree of the SW propagation mode. Then, a dual-factor root mean square (RMS) delay extension model is developed. Next, the fast-fading distribution is modeled as a nonlinear combination of harmonic functions of the coupled azimuth angle α. It is validated that the PL of the SW propagation mode is reduced by 16~28dB, with the channel impulse response (CIR) principal path level increased by 18dB and the first multi-path suppressed by 7.5dB compared to the free-space propagation mode. Finally, calculations on channel capacity (CC) are performed to demonstrate a CC enhancement of 7~17Gbps. The advanced separation-angle joint channel model is expected to provide useful guidelines in future SW communication nodes deployments in IIoT scenarios.

Cooperation between satellite and terrestrial wireless networks promises great potential in meeting fast-growing demands for ubiquitous communications coverage. To tackle spectrum scarcity, spectrum sharing is studied for a hybrid satellite-terrestrial network where satellite links share the same group of time-slotted subcarriers with terrestrial links opportunistically. In particular, with coarse network-wide time synchronization, a time-scale-adaptable spectrum sharing framework is proposed based on a satellite-terrestrial cooperation time scale that can be flexibly adjusted according to practical requirements. For generality, it is assumed that both full and partial frequency reuse could be adopted among the base stations (BSs) and satellite selection is supported when multiple satellites are available. Relying on only statistical channel state information (CSI), joint link scheduling and power control are explored to maximize the average sum rate of the network while ensuring quality of service (QoS) for users. To solve the complicated mixed integer programming (MIP) problem, a low-complexity spectrum sharing scheme is presented based on link-feature-sketching-aided hierarchical link clustering and Monte-Carlo-and-successive-approximation-aided transmit power optimization. Simulation results demonstrate that by link feature sketching, diversity of the links brought by the spatial distribution of the users could be well utilized. The proposed scheme promises a significant performance gain even under strict inter-link interference constraints.

Satellite clustering has the potential to enhance inter-satellite cooperation, resist satellite malfunction, and enable more agile space-air-ground integrated applications. This paper investigates a promising model for clustered low Earth orbit (LEO) systems, in which one typical unmanned aerial vehicle (UAV) can assist one satellite cluster to serve one random terrestrial user. Particularly, intra-cluster satellites can communicate user, while inter-cluster satellites are regarded as interference. Two types of satellites and users are randomly deployed at three visible spherical spaces by adopting three independent spherical Poisson point processes. In the modeling, an adaptive selecting mechanism is proposed to pick the strongest received signal between direct and cooperative transmissions. To facilitate a simpler analysis, we firstly transform the three spaces into the three planes through modifying their respective density. Next, assuming that the shadowed-Rician fading is employed in the satellite channel, two Gamma random variables are utilized to approximately express the aggregated power of interference and noise received by the UAV and user, respectively. Subsequently, the exact conditional user association and approximate Laplace transform of the accumulated signal power are derived to further investigate the conditional coverage probability. Finally, simulation results illustrate that: 1) Moderate satellite cluster sizes combined with a UAV altitude of about 200m are beneficial for achieving higher coverage probability; and 2) The adaptive selection mechanism generally achieves comparable or better performance than traditional transmissions by leveraging spatial diversity.

The existing unmanned aerial vehicle (UAV) enabled integrated sensing and communications (ISAC) systems primarily focus on the UAV’s own sensing capabilities, overlooking the potential of existing ground access points (APs) in performing passive sensing via the reflected signals—thus limiting the overall performance. To address this issue, this paper introduces a UAV empowered air-ground ISAC system, where a UAV cooperates with several ground APs in detecting a potential target, with the UAV and APs working in active and passive modes, respectively. Different from the conventional generalized likelihood ratio test detectors, we exploit the reflection coefficient’s distribution to design a maximum a-posteriori ratio test (MAPRT) detector and derive its asymptotic test statistic distribution. To break through the capacity limitations of the wireless backhaul links, we introduce a two-step joint detection method involving local detection and result fusion. Further, we propose an optimization algorithm to jointly optimize the UAV’s placement and transmit beamforming, aiming at enhancing the target detection performance. Numerical results demonstrate the effectiveness of the proposed algorithm.

Unmanned aerial vehicles (UAVs) are increasingly considered as a key technology for the evolution of 6 G network, addressing limitations related to the fixed deployment of conventional base stations (BSs). In this paper, we investigate the dynamic optimization of an Area Spectral Efficiency (ASE) for UAV-enabled BSs over a realistic 3D terrain model. The goal is to maximize ASE by Line-of-Sight (LoS) queries approach to estimate the coverage area between UAV-enabled BS with ambient active user equipment (UEs), which are distributed within the 3D terrain model. Then proposed route selection strategy is able to successfully seek a minimum path and balance path efficiency. The results reveal that the proposed algorithm is able to successfully seek a minimum path and optimize the LoS coverage simultaneously, achieving similar performance in terms of ASE compared to the PSO method, highlighting its potential for enhancing future wireless communication networks in dynamic and challenging scenarios.

Integrated sensing and communication (ISAC) can boost the spectrum efficiency and facilitate the diverse emerging applications via sharing the same spectrum and hardware between communication and sensing. However, it may suffer from high complexity. In this paper, we develop a low-complexity deep unfolding learning aided transceiver design for ISAC. Particularly, the weighted sum of multi-user interference power and the reciprocal of sensing signal-to-interference-plus-noise ratio is minimized subject to the constraints of constant modulus signal and waveform similarity by transceiver design. An alternating direction method of multipliers (ADMM)-based iterative algorithm is first developed to solve this non-convex optimization problem. To reduce the complexity, we propose a deep unfolding neural network (NN), which can unfold the underlying ADMMbased iterative algorithm to a lightweight NN with some learnable parameters and circumvent the bisection method using the projected gradient descent. Simulation results demonstrate the effectiveness of our proposed deep unfolding NN.

Managing energy usage flexibility has been identified as an effective way to coordinate Internet of Things (IoT) technologies and transition to a low-carbon future. However, the various models of these flexibilities may not be fully understood, and some may even be characterized by closed-box neural networks, such as those used for electric vehicles (EVs) charging. In this article, we propose a novel framework called augmented shadow-price deep reinforcement learning (ASP-DRL) for the online, distributed management of IoT under multiple sources of uncertainties, including renewable energy sources (RESs), wholesale electricity prices, and EV behavior patterns. To address these challenges, the proposed framework combines scheduling mechanisms for neural networks and optimization models to maximize total social welfare. Within the ASP-DRL framework, the policy network adaptively learns about system uncertainties and delivers actions to different distributed entities, either to form augmented objective functions for optimization models or to guide neural networks. We also present a distribution correction algorithm that enhances the vanilla soft Actor-Critic method with attention-based maximal corrective feedback, resulting in faster convergence and better performance. Our numerical studies demonstrate the superiority of the proposed ASP-DRL framework compared to conventional deep reinforcement learning (DRL) and optimization-based approaches.

We explore the synergistic advantages of integrating intelligent reflecting surface (IRS) with rate splitting multiple access (RSMA) in a downlink cell-free massive multiple-input multiple-output (MIMO) system to meet the stringent requirements of ultra-reliable and low-latency communications. Taking into account the estimation errors, statistical channel knowledge, finite blocklength, and spatial correlation among IRS elements, a tight closed-form expression for the achievable rate is derived, which serves as a tool for evaluating the achievable rate across various system configurations. To enhance the weighted sum-rate (WSR) while adhering to the latency and reliability constraints, we formulate a joint WSR maximization problem with respect to both IRS phase shifts and power control coefficients. Given the non-convex nature of this problem, we develop an alternating optimization strategy that decouples the original problem into two distinct sub-problems. Specifically, the IRS phase shift design is reformulated as a min-max normalized mean squared error problem, enabling an efficient closed-form solution, whereas the power control optimization is addressed using a geometric programming approach. Numerical results validate the synergistic gain of integrating IRS with RSMA in terms of achievable rate and demonstrate that the proposed optimization scheme significantly enhances the WSR while fulfilling the latency and reliability requirements.

In addition to enhancing wireless communication coverage quality, reconfigurable intelligent surface (RIS) technique can also assist in positioning. In this work, we consider RIS-assisted superimposed pilot and data transmission without the assumption availability of prior channel state information and position information of mobile user equipments (UEs). To tackle this challenge, we design a frame structure of transmission protocol composed of several location coherence intervals, each with pure-pilot and data-pilot transmission durations. The former is used to estimate UE locations, while the latter is time-slotted, duration of which does not exceed the channel coherence time, where the data and pilot signals are transmitted simultaneously. We conduct the Fisher Information matrix (FIM) analysis and derive Cram´er-Rao bound (CRB) for the position estimation error. The inverse fast Fourier transform (IFFT) is adopted to obtain the estimation results of UE positions, which are then exploited for channel estimation. Furthermore, we derive the closed-form lower bound of the ergodic achievable rate of superimposed pilot (SP) transmission, which is used to optimize the phase profile of the RIS to maximize the achievable sum rate using the genetic algorithm. Finally, numerical results validate the accuracy of the UE position estimation using the IFFT algorithm and the superiority of the proposed SP scheme by comparison with the regular pilot scheme.

This paper investigates a multiuser millimeter-wave (mmWave) uplink system in which each user is equipped with a multi-antenna fluid antenna system (FAS) while the base station (BS) has multiple fixed-position antennas. Our primary objective is to maximize the system capacity by optimizing the transmit covariance matrices and the antenna position vectors of the users jointly. To gain insights, we start by deriving upper bounds and approximations for the capacity. Then we delve into the capacity maximization problem. Beginning with the simple scenario of a single user equipped with a single-antenna FAS, we demonstrate that a closed-form optimal solution exists when there are only two propagation paths between the user and the BS. In the case where multiple propagation paths are present, a near-optimal solution can also be obtained through a one-dimensional search method. Expanding our focus to multiuser cases, in which users are equipped with either single- or multi-antenna FAS, we show that the original capacity maximization problems can be reformulated into distinct rank-one programmings. Then, we propose alternating optimization algorithms to deal with the transformed problems. Simulation results indicate that FAS can improve the capacity of the multiple access channel (MAC) greatly, and the proposed algorithms outperform all the benchmarks.

This paper investigates secure transmission in the uplink of a cell-free massive multiple-input multiple-output (MIMO) system employing three distinct multiple access strategies: rate-splitting multiple access (RSMA), non-orthogonal multiple access (NOMA), and space-division multiple access (SDMA). RSMA, functioning as a unifying paradigm, merges the merits of both NOMA and SDMA, and holds substantial promise for enhancing system secrecy. We derive closed-form expressions for secrecy spectral efficiency (SE) under Rician fading channels and imperfect channel knowledge assumptions. The secrecy SE is subsequently evaluated across a range of system configurations, encompassing varying access point (AP) and user numbers, AP and eavesdropper antenna dimensions, line-of-sight probabilities, successive interference cancellation conditions, and multiple access protocols. Harnessing these expressions, we establish an optimization framework for the users’ power control coefficients and APs’ receiving weights to maximize the sum secrecy SE while ensuring quality-of-service secrecy requirements for users. Additionally, an alternative optimization algorithm is proposed to ascertain a high-quality solution. Comprehensive simulations substantiate our theoretical propositions and evaluate the efficacy of the proposed sum secrecy SE maximization algorithm.

Fluid antenna enables position reconfigurability that gives transceiver access to a high-resolution spatial signal and the ability to avoid interference through the ups and downs of fading channels. Previous studies investigated this fluid antenna multiple access (FAMA) approach in a single-cell setup only. In this paper, we consider a cell-free network architecture in which users are associated with the nearest base stations (BSs) and all users share the same physical channel. Each BS has multiple fixed antennas that employ maximum ratio transmission (MRT) to beam to its associated users while each user relies on its fluid antenna system (FAS) on one radio frequency (RF) chain to overcome the inter-user interference. Our aim is to analyze the outage probability performance of such cell-free FAMA network when both large-and small-scale fading effects are considered. To do so, we derive the distribution of the received magnitude for a typical user and then the interference distribution under both fast and slow port switching techniques. The outage probability is finally obtained in integral form in each case. Numerical results demonstrate that in an interference-limited situation, although fast port switching is typically understood as the superior method for FAMA, slow port switching emerges as a more effective solution when there is a large antenna array at the BS. Moreover, it is revealed that FAS at each user can serve to greatly reduce the burden of BS in terms of both antenna costs and CSI estimation overhead, thereby enhancing the scalability of cell-free networks.

In the near-field region of an extremely large-scale multiple-input multiple-output (XL MIMO) system, channel reconstruction is typically addressed through sparse parameter estimation based on compressed sensing (CS) algorithms after converting the received pilot signals into the transformed domain. However, the exhaustive search on the codebook in CS algorithms consumes significant computational resources and running time, particularly when a large number of antennas are equipped at the base station (BS). To overcome this challenge, we propose a novel scheme to replace the high-cost exhaustive search procedure. We visualize the sparse channel matrix in the transformed domain as a channel image and design the channel keypoint detection network (CKNet) to locate the user and scatterers in high speed. Subsequently, we use a small-scale newtonized orthogonal matching pursuit (NOMP) based refiner to further enhance the precision. Our method is applicable to both the Cartesian domain and the Polar domain. Additionally, to deal with scenarios with a flexible number of propagation paths, we further design FlexibleCKNet to predict both locations and confidence scores. Our experimental results validate that the CKNet and FlexibleCKNet-empowered channel reconstruction scheme can significantly reduce the computational complexity while maintaining high accuracy in both user and scatterer localization and channel reconstruction tasks.

The integration of non-orthogonal multiple access (NOMA) into low earth orbit (LEO) systems has the potential to facilitate the ubiquitous coverage with high spectrum efficiency. To characterize the fundamental limits of NOMA assisted LEO systems, this paper proposes a model for downlink NOMA-LEO system via modelling the locations of terrestrial users and LEO satellites as two homogeneous spherical Poisson point processes. In particular, a typical satellite uses NOMA to simultaneously serve the nearest and the farthest users within its visible range. The novelty of this paper is to first introduce an equivalent two-dimensional model that can significantly simplify the performance analysis of LEO systems. Then, considering that the satellite-terrestrial channel follows the Nakagami-mfading, the closed-form expressions of the user association and the visible probability are studied under the scenario where the number of users visible to a randomly selected satellite is greater than one. Subsequently, the derived results are utilized to analyze the approximate moments of the conditional success probability and the signal-to-interference-plus-noise ratio Meta distribution for both NOMA-LEO and orthogonal multiple access (OMA) LEO transmissions. Finally, the numerical results demonstrate that:1)Asymmetric target rates can achieve a performance gain of NOMA over OMA in terms of the link reliability and the coverage probability, while symmetric settings still have merit for NOMA if there is a low requirement for reliability; and2)Enhancements in the link reliability and the coverage probability are achievable through improvements in channel quality and reductions in orbital altitude and density. However, improving path loss develops the coverage probability but may not always yield an increase in the link reliability.

Integrated sensing and communication (ISAC) can enhance spectral efficiency and facilitate the diverse emerging applications via sharing the same spectrum and hardware between communication and sensing. However, effective operation of ISAC may suffer from high complexity. In this paper, we develop a low-complexity deep unfolding learning-aided transceiver design scheme for ISAC in a cluttered environment. In particular, we optimize the transmit waveform and receive filtering to minimize the weighted sum of multi-user interference power and the reciprocal of sensing signal-to-interference-plus-noise ratio (SINR), while adhering to the constraints of a constant modulus signal and waveform similarity. An alternating direction method of multipliers (ADMM)-based iterative algorithm is first developed to address this non-convex optimization problem with both equality and inequality constraints. To further reduce the computational complexity, we develop two deep unfolding neural networks (NNs), termed ADMM-DL-NET and ADMM-PGD-NET, to handle this problem, which can unfold the underlying ADMM-based iterative algorithm to a lightweight neural network with learnable parameters and eliminate the need for the bisection method by adopting the Uzawa’s method and projected gradient descent, respectively. Simulation results demonstrate that our proposed deep unfolding NNs can achieve comparable performance to the ADMM-based iterative algorithm with significantly reduced complexity, and outperform the unsupervised learning benchmarks in performance and number of learnable parameters.

This paper proposes a downlink non-orthogonal multiple access (NOMA) low earth orbit (LEO) satellite system by modeling the locations of terrestrial users and LEO satellites as two homogeneous spherical Poisson point processes, respectively. Firstly, considering that the satellite-terrestrial channel follows the Nakagami-m fading, the closed-form expressions of the user association and the visible probability are studied under the scenario where the number of user visible to the random selected satellite is greater than one. Subsequently, the above results are adopted to analyze the approximate results for the moments of the conditional success probability and the signal-to-interference-plus-noise ratio Meta distribution. The numerical results demonstrate that the asymmetric target rates can realize a performance gain of NOMA over orthogonal multiple access in terms of the link reliability and the coverage probability, while symmetric settings still have a merit for NOMA when there is a low requirement for the link reliability.

Terahertz (THz) transmission technologies show great promise in enabling ultra-broadband short-range in the next-generation communications. The incorporation of THz transmission with Aerial Base Stations (ABS) networks offers promising advantages, particularly in establishing favorable ultra-broadband line-of-sight links. In this paper, we provide a performance analysis for a finite THz ABS network serving a given region. We model the spatial distribution of the ABSs as a binomial point process within an overlapped finite circular area. To derive the coverage probability, we consider a probabilistic model that encompasses both line-of-sight and non-line-of-sight propagation scenarios, and a user association policy based on the strongest average received power. The association probabilities and Laplace transform of the interference are also derived. The derived coverage probability is validated through Monte Carlo simulation. We further investigate the impacts of the ABS’s height and the radius of the area on the coverage performance.

Millimeter wave (mmWave) has attracted considerable attention due to its wide bandwidth and high frequency. However, it is highly susceptible to blockages, resulting in significant degradation of the coverage and the sum rate. A promising approach is deploying distributed reconfigurable intelligent surfaces (RISs), which can establish extra communication links. In this paper, we investigate the impact of distributed RISs on the coverage probability and the sum rate in mmWave wireless communication systems. Specifically, we first introduce the system model, which includes the blockage, the RIS and the user distribution models, leveraging the Poisson point process. Then, we define the association criterion and derive the conditional coverage probabilities for the two cases of direct association and reflective association through RISs. Finally, we combine the two cases using Campbell's theorem and the total probability theorem to obtain the closed-form expressions for the ergodic coverage probability and the sum rate. Simulation results validate the effectiveness of the proposed analytical approach, demonstrating that the deployment of distributed RISs significantly improves the ergodic coverage probability by 45.4% and the sum rate by over 1.5 times.

Federated learning (FL) framework enables user devices to collaboratively train a global model based on their local data sets without privacy leak. However, the training performance of FL is degraded when the data distributions of different devices are incongruent. Fueled by this issue, we consider a clustered FL (CFL) method where the devices are divided into several clusters according to their data distributions and are trained simultaneously. Convergence analysis is conducted, which shows that the clustered model performance depends on cosine similarity, device number per cluster, and device participation probability. Besides, to quantify the training performance, the utility of clustered model training is defined based on the analysis results. Then, aiming at optimizing the system utility, a joint problem of resource allocation and device clustering is formulated, which is solved by decoupling it into two subproblems. First, given the results of device clustering, a low-complexity iterative algorithm based on the convex optimization theory is proposed to make the bandwidth allocation and the transmit power control. Then, according to the individual stability, a coalition formation algorithm is proposed for the device clustering. Finally, the real-data experiments on the classification tasks (e.g., MNIST, CIFAR-10, and CIFAR-100) validate the results of convergence analysis and advantages of the proposed algorithm in terms of the test accuracy.

Ultra-reliable and low-latency communication (URLLC) is recognized as the most challenging use case for the next generation of wireless networks. Existing research on URLLC is based on the regular pilot (RP) scheme, which is tough to ensure a high transmission rate with stringent latency and reliability requirements due to the impact of finite blocklength, especially in massive connectivity scenarios. In this paper, we propose to use generalized superimposed pilot (GSP) scheme for URLLC transmission in massive multi-input multi-output (mMIMO) systems. Distinguishing from the conventional superimposed pilot (SP) scheme, the GSP scheme eliminates mutual interference between the pilot and data, where the data length is optimized, and the data symbols are precoded to spread over the whole transmission block. With the GSP scheme, we first formulate a weighted sum rate maximization problem by jointly optimizing the data length, pilot power, and data power and then derive closed-form results, including suboptimal data length and achievable rate lower bounds with maximum-ratio combining (MRC) and zero-forcing (ZF) detectors, respectively. Based on the closed-form results, we provide the corresponding iterative algorithms for the MRC and ZF cases where the problems are transformed into geometry program format by using log-function and successive convex approximation methods. Finally, the performance of the RP, SP, and GSP schemes are compared through simulation results, which reflect the superiority and robustness of the GSP scheme in URLLC scenarios.

Given the frequently changing and potentially unreliable environment, the seamless handover authentication is essential to achieve zero-trust Internet of Vehicles (IoV) network with dramatically enhanced communication and transportation safety. The traditional centralized handover authentication schemes may suffer from the excessive latency and situation agnostic limitation, leading to potential interruption of critical services for fast moving vehicles. To overcome the above challenges, this paper proposes a novel decentralized edge collaboration-based handover authentication scheme with the assistance of blockchain for providing continuous protections in zero-trust IoV. A distributed learning process is designed by involving multiple authentication cooperators (ACs) to collect device/location-related features of vehicles at network edge and then to verify their identities. During the movement of vehicles, the access point (AP) could select new ACs by transferring the security information from existing ACs to the new members for seamless handover authentication. A situation-aware AC selection and update algorithm is proposed for maximizing handover authentication accuracy. Moreover, a hierarchical blockchain-assisted security information transfer and reputation management mechanism is designed for reliable collaboration and efficient management in zero-trust IoV. Compared with the existing schemes, our results characterize the outperformance of the proposed scheme in authentication accuracy and time cost of handover.

The millimeter wave (mmWave) has received considerable interest due to its expansive bandwidth and high frequency. However, a noteworthy challenge arises from its vulnerability to blockages, leading to reduced coverage and achievable rate. To address these limitations, a potential solution is to deploy distributed reconfigurable intelligent surfaces (RISs), which comprise many low-cost and passively reflected elements, and can facilitate the establishment of extra communication links. In this paper, we leverage stochastic geometry to investigate the ergodic coverage probability and the achievable rate in both distributed RISs-assisted single-cell and multi-cell mmWave wireless communication systems. Specifically, we first establish the system model considering the stochastically distributed blockages, RISs and users by the Poisson point process. Then we give the association criterion and derive the association probabilities, the distance distributions, and the conditional coverage probabilities, for two cases of associations between base stations and users without or with RISs. Finally, we use Campbell’s theorem and the total probability theorem to obtain the closed-form expressions of the ergodic coverage probability and the achievable rate. Simulation results verify the effectiveness of our analysis method, and demonstrate that by deploying distributed RISs, the ergodic coverage probability is significantly improved by approximately 50%, and the achievable rate is increased by more than 1.5 times.

The application of distributed reconfigurable intelligent surface (RIS) to massive access multiple-input single-output (MISO) is significant to extend the communication coverage. In this paper, a novel framework is proposed in distributed RIS-aided massive access MISO systems with supporting non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) transmissions simultaneously, where a joint active and passive beamforming scheme is designed to fully eliminate the inter-cluster interference and improve the channel gains of prioritized users, respectively. Based on the proposed framework, firstly, we derive two exact channel statistics to characterize the equivalent channel gains of prioritized and non-prioritized users, respectively. Then, by taking into account the influence of imperfect channel state information (CSI) and successive interference cancellation (SIC), the approximate expressions of outage probability and ergodic rate for all users of one cluster under MISO-NOMA and MISO-OMA transmissions are analyzed to obtain their corresponding system throughput. Moreover, by utilizing the above results, we also determine the diversity order and high slope of these users to attain more viewpoints. Finally, simulation results prove our analyses and reveal that: 1) enhancing the estimated accuracy of CSI and the ability of SIC process can remarkably enhance the system performance; 2) the performance of priority users will be significantly improved with the increase of the number of reflecting elements and Rician factor; 3) heterogeneous quality of service requirements and deployment behaviors of users are beneficial for NOMA, while homogenous settings are competitive for OMA.

In the realm of industrial Internet of Things, the imperative for ultra-reliable and low-latency communication (URLLC) is underscored by the demand for up to 99.999% reliability and sub-microsecond latency. In this paper, we delve into a downlink cell-free massive multiple-input multiple-output (MIMO) system designed to facilitate URLLC, operating over spatially correlated Rician fading channels with inherent phase shifts. Utilizing short-packet transmission and accounting for imperfect channel state information, we derive stringent closed-form expressions for the lower-bound achievable rates, considering both phase-aware and phase-unaware minimum mean squared error estimations. Employing these expressions, we execute an in-depth performance analysis across diverse system configurations, including the availability of phase shifts and the counts of access points (APs), connected devices, antennas per AP, and pilot sequences. Additionally, we propose a path-following power control algorithm that employs geometric programming to enhance the downlink sum-rate. This algorithm is meticulously designed to meet the stringent latency and reliability requirements of URLLC for all connected devices. The theoretical underpinnings and the efficacy of the proposed power control algorithm are substantiated through extensive simulations.

In this paper, we investigate the secure transmission in the downlink of a cell-free massive multiple-input multiple-output (mMIMO) system that relies on rate-splitting multiple access (RSMA). We specifically evaluate the impact of hardware impairments (HWIs) originating from non-ideal access points (APs), user equipments (UEs), and Eavesdroppers (Eves) on the system’s secrecy performance. The investigation encompasses scenarios with both colluding and non-colluding Eves orchestrating pilot spoofing attacks against a designated UE, subsequently intercepting transmissions from both common and private streams. By taking into account a spatially correlated Ricean fading channel model and imperfect channel state information, we derive closed-form expressions for both legitimate and secrecy rates. The secrecy performance is scrutinized across different system configurations, including varying HWI levels, power splitting ratios, AP/Eve transmission powers, spatial correlations, line-of-sight components, and the presence of colluding versus non-colluding Eves. To enhance the secrecy rate for the compromised UE, we propose a secure power control strategy for adjusting the downlink transmission powers of the common and private streams. A sequential convex approximation-based algorithm is introduced to iteratively address this non-convex problem. Through comprehensive simulations, we validate our theoretical propositions and extract pivotal insights for system design.

This paper investigates the physical layer security (PLS) issue in reconfigurable intelligent surface (RIS) aided millimeter-wave rotary-wing unmanned aerial vehicle (UAV) communications under the presence of multiple eavesdroppers and imperfect channel state information (CSI). The goal is to maximize the worst-case secrecy energy efficiency (SEE) of UAV via a joint optimization of flight trajectory, UAV active beamforming and RIS passive beamforming. By interacting with the dynamically changing UAV environment, real-time decision making per time slot is possible via deep reinforcement learning (DRL). To decouple the continuous optimization variables, we introduce a twin- twin-delayed deep deterministic policy gradient (TTD3) to maximize the expected cumulative reward, which is linked to SEE enhancement. Simulation results confirm that the proposed method achieves greater secrecy energy savings than the traditional twin-deep deterministic policy gradient DRL (TDDRL)-based method.

Intelligent reflecting surface (IRS) is a promising technology for the 6G networks and attracts much attention. However, existing research seldom considers its energy demand. In this paper, we study an IRS assisted multiple-input single-output downlink broadcasting system where there exist one access point (AP), multiple users and a wireless powered IRS. We focus on the broadcasting data transmission which includes two phases. In the first phase, the AP transmits broadcasting data to users and energy signals for IRS energy harvesting (EH). In the second phase, the AP broadcasts messages with the assistance of the IRS. We aim at maximizing the transmission throughput by designing the phase duration scheduling, the transmit beamforming at the AP in each phase, the energy signal covariance matrix and the IRS reflect beamforming with discrete phase shifts. We first propose a semidefinite relaxation (SDR) based transmission design by also employing one-dimensional line search and further proposing a randomization process. Then, a low complexity transmission design has been further developed. Simulation results demonstrate that the SDR based design can almost achieve the optimal and the low complexity design can perform close to the SDR based design with much lower complexity.

Federated learning (FL) framework enables user devices collaboratively train a global model based on their local datasets without privacy leak. However, the training performance of FL is degraded when the data distributions of different devices are incongruent. Fueled by this issue, we consider a clustered FL (CFL) method where the devices are divided into several clusters according to their data distributions and are trained simultaneously. Convergence analysis is conducted, which shows that the clustered model performance depends on cosine similarity, device number per cluster, and device participation probability. Then, aiming at optimizing the model training performance, a joint problem of resource allocation and device clustering is formulated, which is solved by decoupling it into two sub-problems. Specifically, a coalition formation algorithm is proposed for the device clustering sub-problem, and the sub-problem of bandwidth allocation and transmit power control is solved directly due to its convexity. Finally, simulation experiments are conducted on the MNIST dataset to validate the performance of the proposed algorithm in terms of test accuracy.

The unmanned aerial vehicle (UAV) technique provides a potential solution to scalable wireless edge networks. This paper uses two UAVs, with accelerated motions and fixed altitudes, to realize a wireless edge network, where one UAV forwards downlink signals to user terminals (UTs) distributed over an area while the other one collects uplink data. The conditional average achievable rates, as well as their lower bounds, of both the uplink and downlink transmission are derived considering the active probability of UTs and the service queues of two UAVs. In addition, a problem aiming to maximize the energy efficiency of the whole system is formulated, which takes into account communication related energy and propulsion energy consumption. Then, we develop a novel multi-agent Q-learning (MA-QL) algorithm to maximize the energy efficiency, through optimizing the trajectory and transmit power of the UAVs. Finally, simulation results are conducted to verify our analysis and examine the impact of different parameters on the downlink and uplink achievable rates, UAV energy consumption, and system energy efficiency. It is demonstrated that the proposed algorithm achieves much higher energy efficiency than other benchmark schemes.

The cell-free massive multiple-input multiple-output (CFmMIMO) aims to provide uniform quality of service (QoS) for all users, and can be used in small-area scenarios such as indoor industrial networks. This paper studies the CFmMIMO system for indoor industrial scenarios. Firstly, an access point (AP) grouping based hierarchical network topology is proposed. Based on this, we propose an effective AP selection method. To reduce pilot contamination, a pilot assignment scheme based on inspection robot (IR) location is proposed. Considering the high reliability requirement of industrial data transmission, the power control and backhaul combining are jointly optimized to maximize the minimum signal to interference plus noise ratio (SINR). The scalability of the proposed CFmMIMO system is analyzed, and a scalable power control method is proposed. The simulations demonstrate the effectiveness of the AP selection method, pilot assignment scheme, and the joint optimization algorithm for power control and backhaul combining. Moreover, the impact of network scale and network load on system performance is evaluated and analyzed in the simulations.

As the preferred frequency band for future high frequency communication, the terahertz (THz) band has at-tracted wide attention. In this paper, an energy efficient resource optimization problem in THz band is studied. The massive Multiple-Input Multiple-Output (MIMO) technology and intelligent reflecting surface (IRS) are adopted to improve the capacity and energy efficiency (EE) of proposed network. An IRS assisted THz-MIMO downlink wireless network system is established. The original EE problem is decomposed into phase-shift matrix optimization and power allocation. On this basis, a distributed EE optimization algorithm is designed, which transforms the original nonlinear problem into a convex optimization problem. The simulation results reveal that the proposed distributed optimization method converges rapidly and abtains the maximum EE. This also proves that it is feasible and effective to apply both the IRS and the massive MIMO technology into THz communication network.

This paper considers a multi-antenna multicast system with programmable metasurface (PMS) based transmitter. Taking into account of the finite-resolution phase shifts of PMSs, a novel beam training approach is proposed, which achieves comparable performance as the exhaustive beam searching method but with much lower time overhead. Then, a closed-form expression for the achievable individual rate is presented, which is valid for arbitrary system configurations. Besides, assuming a large number of reflecting elements, a simple approximated expression for the multicast rate is derived. A closed-form solution is obtained for the optimal power allocation scheme, and it is shown that equal power allocation is optimal when the number of reflecting elements is sufficiently large. The analytical findings indicate that, increasing the number of radio frequency (RF) chains or reflecting elements can significantly improve the multicast rate, and as the phase shift number becomes larger, the multicast rate improves first and gradually converges to a limit. Moreover, increasing the number of users would significantly degrade the multicast rate, but this rate loss can be compensated by implementing a large number of reflecting elements.

This paper considers a multi-antenna multicast system with programmable metasurface (PMS) based transmitter. Taking into account of the finite-resolution phase shifts of PMSs, a novel beam training approach is proposed, which achieves comparable performance as the exhaustive beam searching method but with much lower time overhead. Then, a closed-form expression for the achievable multicast rate is presented, which is valid for arbitrary system configurations. In addition, for certain asymptotic scenario, simple approximated expressions for the multicase rate are derived. Closed-form solutions are obtained for the optimal power allocation scheme, and it is shown that equal power allocation is optimal when the pilot power or the number of reflecting elements is sufficiently large. However, it is desirable to allocate more power to weaker users when there are a large number of RF chains. The analytical findings indicate that, with large pilot power, the multicast rate is determined by the weakest user. Also, increasing the number of radio frequency (RF) chains or reflecting elements can significantly improve the multicast rate, and as the phase shift number becomes larger, the multicast rate improves first and gradually converges to a limit. Moreover, increasing the number of users would significantly degrade the multicast rate, but this rate loss can be compensated by implementing a large number of reflecting elements.

Reliable and efficient networks are the trend for next-generation wireless communications. Recent improved hardware technologies - known as Large Intelligent Surfaces (LISs) - have decreased the energy consumption of wireless networks, while theoretically being capable of offering an unprecedented boost to the data rates and energy efficiency (EE). In this paper, we use stochastic geometry to provide performance analysis of a realistic two-step user association based millimeter wave (mmWave) networks consisting of multiple users, transmitters and one-hop reflection from a LIS. All the base stations (BSs), users and LISs are equipped with multiple uniform linear antenna arrays. The results confirm that LIS-assisted networks significantly enhance capacity and achieve higher optimal EE as compared to traditional systems when the density of BSs is not large. Moreover, there is a trade-off between the densities of LIS and BS when there is a total density constraint. It is shown that the LISs are excellent supplements for traditional cellular networks, which enormously enhance the average rate and area spectral efficiency (ASE) of mmWave networks. However, when the BS density is higher than the LIS density, the reflected interference and phase-shift energy consumption will limit the performance of LIS-assisted networks, so it is not necessary to employ the LIS devices.

Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-single-output (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for real-time implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems.

5G millimeter-wave (mmWave) cloud radio access networks (CRANs) provide new opportunities for accurate multilateration: large bandwidth, large antenna arrays, and increased densities of base stations allow for unparalleled delay and angular resolution. However, combining localization into communications and designing joint position and velocity estimation algorithms are challenging problems. This paper considers the joint estimation in three-dimensional (3-D) lens antenna array based mmWave CRAN architecture. We embed multilateration into communications and explain its benefits for the initial access and beam training stages. We propose a closed-form solution for the joint estimation problem by forming the pseudo-linear matrix representation and designing the weighted least squares estimator with hybrid measurements. The proposed method is proven asymptotically unbiased and confirmed by simulations to achieve the Cramer- Rao lower bound and attain the desired sub-decimeter level accuracy.

Led by industrialization of smart cities, numerous interconnected mobile devices, and novel applications have emerged in the urban environment, providing great opportunities to realize industrial automation. In this context, autonomous driving is an attractive issue, which leverages large amounts of sensory information for smart navigation while posing intensive computation demands on resource constrained vehicles. Mobile edge computing (MEC) is a potential solution to alleviate the heavy burden on the devices. However, varying states of multiple edge servers as well as a variety of vehicular offloading modes make efficient task offloading a challenge. To cope with this challenge, we adopt a deep Q-learning approach for designing optimal offloading schemes, jointly considering selection of target server and determination of data transmission mode. Furthermore, we propose an efficient redundant offloading algorithm to improve task offloading reliability in the case of vehicular data transmission failure. We evaluate the proposed schemes based on real traffic data. Results indicate that our offloading schemes have great advantages in optimizing system utilities and improving offloading reliability.

In this paper, we analyze the achievable rate for ambient backscatter communications under three different channels: the binary input and binary output (BIBO) channel, the binary input and signal output (BISO) channel, and the binary input and energy output (BIEO) channel. Instead of assuming Gaussian input distribution, the proposed study matches the practical ambient backscatter scenarios, where the input of the tag can only be binary. We derive the closed-form capacity expression as well as the capacity-achieving input distribution for the BIBO channel. To show the influence of the signal-to-noise ratio (SNR) on the capacity, a closed-form tight ceiling is also derived when SNR turns relatively large. For BISO and BIEO channel, we obtain the closed-form mutual information, while the semi-closed-form capacity value can be obtained via one dimensional searching. Simulations are provided to corroborate the theoretical studies. Interestingly, the simulations show that: (i) the detection threshold maximizing the capacity of BIBO channel is the same as the one from the maximum likelihood signal detection; (ii) the maximal of the mutual information of all channels is achieved almost by a uniform input distribution; and (iii) the mutual information of the BIEO channel is larger than that of the BIBO channel, but is smaller than that of the BISO channel.

Blockchain has created a revolution in digital networking by using distributed storage, cryptographic algorithms, and smart contracts. Many areas are benefiting from this technology, including data integrity and security, as well as authentication and authorization. Internet of Things (IoTs) networks often suffers from such security issues, which is slowing down wide-scale adoption. In this paper, we describe the employing of blockchain technology to construct a decentralized platform for storing and trading information in the air-to-ground IoT heterogeneous network. To allow both air and ground sensors to participate in the decentralized network, we design a mutual-benefit consensus process to create uneven equilibrium distributions of resources among the participants. We use a Cournot model to optimize the active density factor set in the heterogeneous air network and then employ a Nash equilibrium to balance the number of ground sensors, which is influenced by the achievable average downlink rate between the air sensors and the ground supporters. Finally, we provide numerical results to demonstrate the beneficial properties of the proposed consensus process for air-to-ground networks and show the maximum active sensor's density utilization of air networks to achieve a high quality of service.

Communications aided by low-altitude unmanned aerial vehicles (UAVs) have emerged as an effective solution to provide large coverage and dynamic capacity for both military and civilian applications, especially in unexpected scenarios. However, because of their broad coverage, UAV communications are prone to passive eavesdropping attacks. This paper analyzes the secrecy performance of UAVs networks at the millimeter wave band and takes into account unique features of air-to-ground channels and practical constraints of UAV deployment. To be specific, it explores the 3-D antenna gain in the air-to-ground links and uses the Matérn hardcore point process to guarantee the safety distance between the randomly deployed UAV base stations. In addition, we propose the transmit jamming strategy to improve the secrecy performance in which part of UAVs send jamming signals to confound the eavesdroppers. Simulation results verify our analysis and demonstrate the impact of different system parameters on the achievable secrecy rate. It is also revealed that optimizing the density of jamming UAVs will significantly improve security of UAV-enabled networks.

Network virtualization (NV) is widely accepted as one enabling technology for future network, which enables multiple virtual networks (VNs) with different paradigms and protocols to coexist on the shared substrate network (SN). One key challenge in NV is VN embedding (VNE), which maps a VN onto the shared SN. Since VNE is NP-hard, existing efforts mainly focus on proposing heuristic algorithms that try to achieve feasible VNE in reasonable time, consequently the resulted embedding is not optimal. To tackle this difficulty, we propose a candidate assisted (CAN-A) optimal VNE algorithm with lower computational complexity. The key idea of the CAN-A algorithm lies in constructing the candidate substrate node subset and the candidate substrate path subset before embedding. This reduces the mapping execution time substantially without performance loss. In the following embedding, four types of node and link constraints are considered in the CAN-A algorithm, making it more applicable to realistic networks. Simulation results show that the execution time of CAN-A is hugely cut down compared with pure VNE-MIP algorithm. CAN-A also outperforms the typical heuristic algorithms in terms of other performance indices, such as the average VN request acceptance ratio and the average virtual link propagation delay.

This paper studies the performance of cache-enabled dense small cell networks consisting of multi-antenna sub-6 GHz and millimeter-wave (mm-wave) base stations. Different from the existing works which only consider a single antenna at each base station, the optimal content placement is unknown when the base stations have multiple antennas. We first derive the successful content delivery probability by accounting for the key channel features at sub-6 GHz and mm-wave frequencies. The maximization of the successful content delivery probability is a challenging problem. To tackle it, we first propose a constrained cross-entropy algorithm which achieves the near-optimal solution with moderate complexity. We then develop another simple yet effective heuristic probabilistic content placement scheme, termed two-stair algorithm, which strikes a balance between caching the most popular contents and achieving content diversity. Numerical results demonstrate the superior performance of the constrained cross-entropy method and that the two-stair algorithm yields significantly better performance than only caching the most popular contents. The comparisons between the sub-6 GHz and mm-wave systems reveal an interesting tradeoff between caching capacity and density for the mm-wave system to achieve similar performance as the sub-6 GHz system.

This paper studies the performance of cache-enabled dense small cell networks consisting of multi- antenna sub-6 GHz and millimeter-wave base stations. We first derive the successful content delivery probability by accounting for the key channel features at sub-6 GHz and mmWave frequencies. In general, the optimal content placement is unknown when the base stations have multiple antennas. Then we propose a simple yet effective probabilistic content placement scheme to maximize the successful content delivery probability, which could balance caching both the most popular contents and achieving content diversity. Numerical results demonstrate that our proposed content placement scheme yields significantly better performance than only caching the most popular contents. The comparisons between the sub-6 GHz and millimeter-wave systems reveal an interesting tradeoff between caching capacity and base station density for the millimeter-wave system to achieve similar performance as the sub-6 GHz system.

Wireless networks with directional antennas, like millimeter wave (mmWave) networks, have enhanced security. For a large-scale mmWave ad hoc network in which eavesdroppers are randomly located, however, eavesdroppers can still intercept the confidential messages, since they may reside in the signal beam. This paper explores the potential of physical layer security in mmWave ad hoc networks. Specifically, we characterize the impact of mmWave channel characteristics, random blockages, and antenna gains on the secrecy performance. For the special case of uniform linear array (ULA), a tractable approach is proposed to evaluate the average achievable secrecy rate. We also characterize the impact of artificial noise in such networks. Our results reveal that in the low transmit power regime, the use of low mmWave frequency achieves better secrecy performance, and when increasing transmit power, a transition from low mmWave frequency to high mmWave frequency is demanded for obtaining a higher secrecy rate. More antennas at the transmitting nodes are needed to decrease the antenna gain obtained by the eavesdroppers when using ULA. Eavesdroppers can intercept more information by using a wide beam pattern. Furthermore, the use of artificial noise may be ineffective for enhancing the secrecy rate.

Wireless networks with directional antennas, like millimeter wave (mmWave) networks, have enhanced security. For a large scale mmWave ad hoc network in which eavesdroppers are randomly located, however, eavesdroppers can still intercept the confidential messages, since they may reside in the signal beam. This paper explores the potential of physical layer security in the mmWave ad hoc networks. Specifically, we characterize the impact of mmWave channel characteristics and large antenna arrays on the secrecy performance. We also characterize the impact of artificial noise in this networks. Our results reveal that in the low transmit power regime, the use of low mmWave frequency achieves better secrecy performance, when increasing transmit power, a transition from low mmWave frequency to high mmWave frequency is demanded for obtaining more secrecy rate. Eavesdroppers can intercept more information by using wide beam pattern. Furthermore, the use of artificial noise may be unable to enhance the secrecy rate for the case of low node density.

This paper explores the potential of wireless power transfer (WPT) in massive multiple-input multiple-output (MIMO)-aided heterogeneous networks (HetNets), where massive MIMO is applied in the macrocells, and users aim to harvest as much energy as possible and reduce the uplink path loss for enhancing their information transfer. By addressing the impact of massive MIMO on the user association, we compare and analyze user association schemes: 1) downlink received signal power (DRSP)-based approach for maximizing the harvested energy and 2) uplink received signal power (URSP)-based approach for minimizing the uplink path loss. We adopt the linear maximal-ratio transmission beamforming for massive MIMO power transfer to recharge users. By deriving new statistical properties, we obtain the exact and asymptotic expressions for the average harvested energy. Then, we derive the average uplink achievable rate under the harvested energy constraint. Numerical results demonstrate that the use of massive MIMO antennas can improve both the users' harvested energy and uplink achievable rate in the HetNets; however, it has negligible effect on the ambient RF energy harvesting. Serving more users in the massive MIMO macrocells will deteriorate the uplink information transfer because of less harvested energy and more uplink interference. Moreover, although DRSP-based user association harvests more energy to provide larger uplink transmit power than the URSP-based one in the massive MIMO HetNets, URSP-based user association could achieve better performance in the uplink information transmission.

This paper proposes a new codebook structure called Kronecker-product based codebook (KPC), where each codeword is the Kronecker product of two oversampled DFT codewords in both the horizontal and vertical domains. The KPC is especially suitable for the three-dimensional (3D) multiuser multi-input multi-output (MU-MIMO) systems. Besides, channel state information feedback based on the best companion cluster scheme is investigated. Since all codewords have been grouped into several clusters, each user feeds back its best precoding matrix index, best interference cluster index and channel quality information, then the BS pairs and schedules users according to the received feedback. Different codewords clustering methods affect the performance of the limited feedback schemes. We proposes two kinds of codewords clustering methods based on 3D beam patterns, including both the symmetric and asymmetric one. Simulation shows that with properly clustered codewords, our proposed 3D MU-MIMO feedback scheme has a significant throughput gain against 2D MU-MIMO feedback scheme.