Deep neural networks (DNNs) have been widely applied in mobile intelligent applications. However, their high computational complexity poses significant challenges for resource-constrained mobile devices. To address this issue, this paper proposes a multi-uncrewed aerial vehicle (UAV)-assisted mobile edge computing architecture tailored for DNN inference tasks. By hierarchically partitioning the DNN model and distributing different sub-tasks between local devices and aerial servers for collaborative processing, the system effectively reduces the computational burden on user terminals. Taking into account the factors such as unbalanced network load and limited UAV energy, a task migration mechanism is introduced to support resource coordination and load balancing among multiple UAVs. The aim is to minimize total weighted energy consumption through joint optimization of user-UAV association, DNN partitioning, UAV trajectory, task migration, and computing resource allocation. Due to the dynamic and complex nature of the resulting optimization problem, we model it as a Markov decision process, and a soft actor-critic with prioritized experience replay (SAC-PER) is proposed to solve it. Furthermore, we integrate convex optimization techniques into SAC-PER as a subroutine to allocate computing resources to enhance the learning efficiency. Simulation results show that the proposed method achieves faster convergence and reduces the total weighted energy consumption by up to 14.6% compared with baseline methods.

The advance of vehicle-to-everything (V2X) networks has led to many emerging data-intensive applications at the network edge. To meet the soaring data rate requirements of these applications, numerous coding schemes has been developed. However, the high heterogeneity of edge users bring challenges to these methods, including adaptation to performance requirements, coping with unknown or varying channels, as well as inefficient multicasting. In this paper, we address those problems by developing aratelessdeep joint source-channel coding scheme featuring fine-grained control over rate and informativeness at the user. Towards this end, we first design a novel class of variational information bottleneck (VIB) by employing the multinomial-Gaussian (MG) distribution, to achieve rateless transmission over an erasure channel. We derived important results on the statistical properties of this latent distribution to facilitate efficient training of MG-VIB. Then, we apply this framework to multicasting, proposing MG-VIB-M to enhance adaptability and scalability. Simulations show that our proposed method is more flexible regarding rate-relevance tradeoffs, has greater robustness against channel imperfections, and reduces bandwidth requirements for task-oriented multicasting.

Unmanned aerial vehicles (UAVs) are poised for explosive growth in the low-altitude economy, causing spectrum congestion and posing a challenge to airspace regulation. Although integrated sensing and communication (ISAC) enables simultaneous communication and sensing, alleviating the spectrum shortage, the capability of one single base station (BS) is generally limited. Therefore, a multi-BS cooperative ISAC system is developed to perceive the status of UAVs at the cell edge. Multiple BSs share the same time-frequency resources and adopt a time-division scheme to avoid mutual interference between communication and sensing functionalities. Specifically, the frame structure of the communication system is modified to accommodate the sensing functionality. A robust interference nulling based beam pattern is first proposed to prevent the line-of-sight (LoS) interference between BSs from overrunning the dynamic range of the analog-to-digital converter (ADC). Moreover, we designed a space-time block codec-based orthogonal frequency division multiplexing (OFDM) to separate echo signals originating from different BSs, which transforms the inter-BS reflected interference into bistatic sensing signals. Furthermore, a data-level fusion method based on the signal-to-interference-plus-noise ratio (SINR) of the range profile is applied to improve the positioning accuracy. The numerical results reveal that the proposed beam pattern greatly avoids LoS interference. The echo signals originating from neighboring BSs can assist in target detection and angle of arrival (AoA) estimation. Compared to soft fusion and single-BS schemes, the proposed fusion method enhances positioning precision by an order of magnitude, and is practically feasible even in the presence of clock synchronization errors.

Approximate message passing (AMP) iterative detection is recognized as a reliable and practical approach for multiple-input multiple-output (MIMO) systems. However, existing AMP detection algorithms face a critical challenge: high computational complexity due to redundant iterations, making them impractical for the coming 6G networks with increased data throughput demands. This paper addresses this challenge by investigating the mutual information (MI) update flow in AMP iterative MIMO detection and introducing a precise MI computation mechanism based on the error function, referred to as the EFA mechanism. Leveraging the EFA mechanism, we propose a convergence-based termination (CT) scheme to accurately track the convergent iteration number and eliminate redundant iterations in AMP iterative detection. Numerical results demonstrate that the MI flow calculated using the EFA mechanism is consistent with the convergence behavior of AMP iterative MIMO detection across different iterations and signal-to-noise ratios (SNRs). Specifically, the EFA mechanism can precisely identify the convergent iteration number and corresponding SNR. Additionally, the CT scheme achieves up to a 80% reduction in complexity compared to original AMP detection, while maintaining the expected BER performance.

Channel estimation in a fading environment can be regarded as a typical statistical estimation problem. Its optimal performance relies on the prior distribution of the channel coefficients, which are environment-specific. However, the conventional channel estimators, such as the least square (LS) and linear minimum mean square error (LMMSE) estimators, do not fully exploit the prior channel distribution law. To address this limitation, we propose to use the conditional diffusion model (DM) to achieve environment-aware channel estimation, referred to as EACE-DM. In this framework, the environment information is incorporated as the condition to guide the EACE-DM in learning the hidden features of channels from various environments. The trained EACE-DM is functionally decomposed into two components, i.e., the environment identification module and the channel estimation module. The environment identification module first uses the DM’s forward process to diffuse LS channel estimation into a noisy sample. Then it executes the DM’s reverse denoising process conditioned on candidate environments to recover the LS estimation from the noisy channel. The environment is then identified via maximum a posteriori (MAP) estimation by comparing these recovered estimations with the ground-truth LS estimation. Finally, the identified environment is utilized to guide the channel estimation module, denoising the LS estimation. Numerical simulations demonstrate that the proposed EACE-DM significantly decreases normalized mean square errors (NMSEs) of channel estimation across diverse environments while incurring a moderate increase in computational complexity compared to conventional estimators and existing DM-based approaches.

Pinching antennas (PAs), a new type of reconfigurable and flexible antenna structures, have recently attracted significant research interest due to their ability to create line-of-sight links and mitigate large-scale path loss. Owing to their potential benefits, integrating PAs into wireless powered mobile edge computing (MEC) systems is regarded as a viable solution to improve both the efficiency of the energy transfer and task offloading. Unlike prior studies that assume ideal continuous PA placement along waveguides, this paper investigates a practical discrete PA-assisted wireless powered MEC framework, where devices first harvest energy from PA-emitted radio-frequency signals and then adopt a partial offloading mode, allocating part of the harvested energy to local computing and the remainder to uplink offloading. The uplink phase considers both the time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA), each examined under three levels of PA activation flexibility. For each configuration, we formulate a joint optimization problem to maximize the total computational bits and conduct a theoretical performance comparison between the TDMA and NOMA schemes. To address the resulting mixed-integer nonlinear problems, we develop a two-layer algorithm that combines closed-form solutions based on Karush–Kuhn–Tucker (KKT) conditions with a cross-entropy-based learning method. Numerical results validate the superiority of the proposed design in terms of the harvested energy and computation performance, revealing that TDMA and NOMA achieve comparable performance under coarser PA activation levels, whereas finer activation granularity enables TDMA to achieve superior computation performance over NOMA.

The combination of Integrated Sensing and Communication (ISAC) and Mobile Edge Computing (MEC) enables devices to simultaneously sense the environment and offload data to the base stations (BS) for intelligent processing, thereby reducing local computational burdens. However, transmitting raw sensing data from ISAC devices to the BS often incurs substantial fronthaul overhead and latency. This paper investigates a three-tier collaborative inference framework enabled by Integrated Sensing, Communication, and Computing (ISCC), where cloud servers, MEC servers, and ISAC devices cooperatively execute different segments of a pre-trained deep neural network (DNN) for intelligent sensing. By offloading intermediate DNN features, the proposed framework can significantly reduce fronthaul transmission load. Furthermore, multiple-input multiple-output (MIMO) technology is employed to enhance both sensing quality and offloading efficiency. To minimize the overall sensing task inference latency across all ISAC devices, we jointly optimize the DNN partitioning strategy, ISAC beamforming, and computational resource allocation at the MEC servers and ISAC devices, subject to sensing beampattern constraints. We also propose an efficient two-layer optimization algorithm. In the inner layer, we derive closed-form solutions for computational resource allocation using the Karush-Kuhn-Tucker conditions. Moreover, we design the ISAC beamforming vectors via an iterative method based on the majorization–minimization and weighted minimum mean square error techniques. In the outer layer, we develop a cross-entropy-based probabilistic learning algorithm to determine an optimal DNN partitioning strategy. Simulation results demonstrate that the proposed framework substantially outperforms existing two-tier schemes in inference latency.

In this paper, we investigate the uplink joint signal detection for low-altitude aerial cell-free (CF) networks, where each flying access point (AP) locally processes the received signals and then forwards these information to a central processing unit (CPU) for the final detection. However, unlike terrestrial CF networks that typically adopt optic fiber fronthaul links, wireless fronthaul in aerial CF networks connecting flying APs with the CPU will significantly affect the communication performance, due to the practically limited fronthaul capacity. Therefore, we adopt a realistic channel model for wireless fronthaul links, which experience Rician fading and are shared among the flying APs through a combination of frequency division multiple access (FDMA) and space division multiple access (SDMA) protocol. Taking into account the imperfect and capacity-limited wireless fronthaul, we propose a joint uplink signal detection framework, where the local processing matrix at APs and the central detector at the CPU are designed based on the long-term statistical channel state information (CSI) by leveraging the operator-valued free probability theory. This approach significantly reduces the need for frequent, high-capacity signaling exchanges between APs and the CPU. Numerical results demonstrate the accuracy and effectiveness of the proposed joint signal detection framework.

Affine Frequency Division Multiplexing (AFDM) has attracted considerable attention for its robustness to Doppler effects. However, its high receiver-side computational complexity remains a major barrier to practical deployment. To address this, we propose a novel symbol-level precoding (SLP)-based AFDM transmission framework, which shifts the signal processing burden in downlink communications from user side to the base station (BS), enabling direct symbol detection without requiring channel estimation or equalization at the receiver. Specifically, in the uplink phase, we propose a Sparse Bayesian Learning (SBL) based channel estimation algorithm by exploiting the inherent sparsity of affine frequency (AF) domain channels. In particular, the sparse prior is modeled via a hierarchical Laplace distribution, and parameters are iteratively updated using the Expectation-Maximization (EM) algorithm. We also derive the Bayesian Cramér-Rao Bound (BCRB) to characterize the theoretical performance limit. In the downlink phase, the BS employs the SLP technology to design the transmitted waveform based on the estimated uplink channel state information (CSI) and channel reciprocity. The resulting optimization problem is formulated as a second-order cone programming (SOCP) problem, and its dual problem is investigated by Lagrangian function and Karush–Kuhn–Tucker conditions. Simulation results demonstrate that the proposed SBL estimator outperforms traditional orthogonal matching pursuit (OMP) in accuracy and robustness to off-grid effects, while the SLP-based waveform design scheme achieves performance comparable to conventional AFDM receivers while significantly reducing the computational complexity at receiver, validating the practicality of our approach.

To meet the growing demand for communication capacity and address the scarcity of wireless spectrum resources, we propose a novel orthogonal time frequency space (OTFS) channel estimation scheme enhanced by visual environment semantics. This approach establishes a theoretical foundation for semantic-assisted channel estimation by modeling the potential relationship between the wireless communication channel and its surrounding environment. Leveraging a computer vision-based adaptive environment semantic sensing framework, the system extracts and processes environment features to infer channel characteristics. To tackle the challenge of capturing small-scale fading solely through visual environment semantics, we design two new pilot structures and the corresponding channel estimation methods. These are tailored to maximize the utility of information derived from the environment while minimizing pilot overhead. The simulation results demonstrate that the proposed scheme outperforms the conventional channel estimation method in terms of spectral efficiency and robustness in high-mobility and low-SNR scenarios with fewer pilot symbols.

In this paper, sensing-assisted secure communication in a multi-user multi-eavesdropper integrated sensing and communication (ISAC) system is investigated. Confidential communication signals and dedicated sensing signals are jointly transmitted by a base station (BS) to simultaneously serve users and sense aerial eavesdroppers (AEs). A sum rate maximization problem is formulated under AEs’ Signal-to-Interference-plus-Noise Ratio (SINR) and sensing Signal-to-Clutter-plus-Noise Ratio (SCNR) constraints. A fractional-programming-based alternating optimization algorithm is developed to solve this problem for fully digital arrays, where successive convex approximation (SCA) and semidefinite relaxation (SDR) are leveraged to handle non-convex constraints. Furthermore, the minimum number of dedicated sensing beams is analyzed via a worst-case rank bound, upon which the proposed beamforming design is further extended to the hybrid analog-digital (HAD) array architecture, where the unit-modulus constraint is addressed by manifold optimization. Simulation results demonstrate that only a small number of sensing beams are sufficient for both sensing and jamming AEs, and the proposed designs consistently outperform strong baselines while also revealing the communication–sensing trade-off.

The identification of channel scenarios in wireless systems plays a crucial role in channel modeling, radio fingerprint positioning, and transceiver design. Traditional methods to classify channel scenarios are based on typical statistical characteristics of channels, such as K-factor, path loss, delay spread, etc. However, statistic-based channel identification methods cannot accurately differentiate implicit features induced by dynamic scatterers, thus performing very poorly in identifying similar channel scenarios. In this paper, we propose a novel channel scenario identification method, formulating the identification task as a maximum a posteriori (MAP) estimation. Furthermore, the MAP estimation is reformulated by a maximum likelihood estimation (MLE), which is then approximated and solved by the conditional generative diffusion model. Specifically, we leverage a transformer network to capture hidden channel features in multiple latent noise spaces within the reverse process of the conditional generative diffusion model. These detailed features, which directly affect likelihood functions in MLE, enable highly accurate scenario identification. Experimental results show that the proposed method outperforms traditional methods, including convolutional neural networks (CNNs), back-propagation neural networks (BPNNs), and random forest-based classifiers, improving the identification accuracy by more than 10%.

In this paper, we investigate a novel wireless powered mobile edge computing (MEC) system assisted by pinching antennas (PAs), where devices first harvest energy from a base station and then offload computation-intensive tasks to an MEC server. As an emerging technology, PAs utilize long dielectric waveguides embedded with multiple localized dielectric particles, which can be spatially configured through a pinching mechanism to effectively reduce large-scale propagation loss. This capability facilitates both efficient downlink energy transfer and uplink task offloading. To fully exploit these advantages, we adopt a non-orthogonal multiple access (NOMA) framework and formulate a joint optimization problem to maximize the system’s computational capacity by jointly optimizing device transmit power, time allocation, PA positions in both uplink and downlink, and radiation control. To address the resulting non-convexity caused by variable coupling, we develop an alternating optimization algorithm that integrates particle swarm optimization (PSO) with successive convex approximation. Simulation results demonstrate that the proposed PA-assisted design substantially improves both energy harvesting efficiency and computational performance compared to conventional antenna systems.

The increasing demand for tasks and dynamically changing loads in the Low Earth Orbit (LEO) satellite networks creates significant challenges in terms of computing and routing. Currently, LEO satellites primarily offload tasks to ground stations or satellites within their line of sight, failing to fully utilize the computational resources of the entire network. In addition, existing routing algorithms fail to consider on-satellite loads and computational capacities, leading to bottlenecks in network routing as some satellites with limited processing capacity become overwhelmed. In this paper, the tasks generated by the source satellite can be offloaded to either satellites or ground stations while routing to the destination satellite. The offloading computation and routing decision problems are investigated to minimize the maximum delay. To solve this challenging problem, we first convert the optimization variables, encompassing both routing and computation offloading, into a form that depends solely on the latter, and model the problem as the Markov Decision Process (MDP). Subsequently, the problem is addressed using an algorithm based on Multi-Agent Proximal Policy Optimization (MAPPO), where multiple agents cooperatively determine routing and offloading computation strategies. Simulation results show that the proposed scheme achieves better delay performance.

Rateless codes, also known as fountain codes, are very suitable for communication in unknown and complex channel environments. However, the overhead and complexity of rateless codes will increase sharply when the receiver’s buffer is limited. In this paper, we first present a transmission process for Luby Transform (LT) code with intermediate feedback. Subsequently, we propose a buffer-limited degree distribution optimization method based on deep reinforcement learning (DRL). The proposed method is applicable both with and without intermediate feedback. Furthermore, we analyze and verify the relationship between buffer capacity and the optimal feedback point under the condition of single intermediate feedback. Simulation results show that under the condition of limited buffer, the proposed method outperforms conventional schemes in terms of intermediate recovery rate, bit error rate and overhead performance.

Digital semantic communication has attracted considerable attention attributed to its potential for integration with modern digital communication systems, which has demonstrated significant performance gains. However, despite its ability to save transmission bandwidth, digital semantic communication can degrade the performance of tasks at the receiver, particularly in low-bitrate scenarios. In this article, we propose a novel low-bitrate digital semantic communication method based on a generative model for speech transmission to achieve high-quality reconstructed speech at low-bitrate transmission. In particular, we first investigate a multiscale semantic codec based on residual vector quantization with a generative adversary network (RVQGAN) model for extracting semantic information and obtaining high speech reconstruction quality while transmitting at a low bitrate. We then, design a channel noise suppression (CNS) module based on U-Net to alleviate the channel effect at low signal-to-noise ratio (SNR) by restoring high-quality semantic features, which is capable of improving the performance of the proposed method under challenging channel conditions. Moreover, a Transformer-based code predictor is utilized to further improve the robustness of the proposed method by accounting for both the channel impact and reconstruction quality. Finally, a three-stage training strategy is also presented in this article to ensure the effective operation of the proposed multiscale semantic codec, CNS module, and code predictor module. Experimental results demonstrate that the proposed method operating at 3 kb/s can save at least 50% of bandwidth while achieving higher speech restoration quality than the baseline method.

Backscatter communication and harvest-then-transmit (HTT) communication are regarded as promising technologies for enabling green Internet of Things (IoT). The current works on the joint use of backscatter communication and HTT are limited in single cell scenarios with the fixed backscatter-then-HTT transmission structure. In this work, we propose a transmission scheme with flexible mode selection for the hybrid backscatter-HTT multi-cell system to achieve much improved communication performance, and then study the joint design for such a system. Specifically, by utilizing multi-antenna technology and enabling the flexible mode selecting between backscatter and HTT, a novel transmission scheme is developed. With the aim to maximize the sum rate of the considered system, we formulate a joint optimization problem for the base station transmission beamforming (TB), the transmission mode (TM), and the transmit power (TP) of the hybrid backscatter-HTT devices. To address the formulated non-convex problem, we propose a block coordinate descent-based algorithm, namely J3TO, to jointly optimize TB, TM, and TP, by decoupling the original problem into three sub-problems. Therein, the weighted minimum mean square error approach, matching theory, and the fractional programming technique are leveraged to deal with the sub-problems efficiently. Simulation results show that the proposed algorithm flexibly integrates the merits of backscatter and HTT technologies, achieving superior performance across various scenarios, compared with the benchmark schemes, e.g., backscatter-only SDMA, HTT-only SDMA, and backscatter-HTT TDMA.

The development of integrated sensing and communication (ISAC) technique makes it possible to exploit echoes of communication signals to localize aerial eavesdropper (AE) and enhance the secrecy performance. In this paper, we investigate the sensing-assisted secure precoding design in rate-splitting multiple access (RSMA) systems. In particular, we aim at maximizing the minimum achievable rate among all users while satisfying the Cramér-Rao bound (CRB) constraint for AE’s 2-dimensional angle estimation and protecting both common stream and private streams from being intercepted. We first consider the ideal case where perfect CSI is available and propose an iterative optimization algorithm, where successive convex approximation technique, fractional programming, and the Schur complement condition are leveraged to handle the non-convex constraints and objective function. This scenario is further extended to a more general case with channel estimation errors, for which we propose a robust precoding design algorithm to ensure worst-case performance. Simulation results validate the effectiveness of leveraging the sensing capability to enhance secrecy performance and show that the RSMA scheme is able to achieve higher user rates and lower eavesdropping rates compared to spatial division multiple access (SDMA)-based sensing-assisted secure communication system. Furthermore, we demonstrate the trade-off between achievable minimum user rate and sensing accuracy.

Recently, reconfigurable intelligent surfaces (RISs) are gaining increasing attention in communication systems due to their ability to adapt themself according to the wireless environment. Therefore, the communication systems with massive RIS deployments are able to extend high-quality coverage ubiquitously. This is especially beneficial for Internet of Things (IoT) systems as the IoT devices can be located in hard-to-reach area. On the other hand, current methods to analyze and optimize the performance of communication systems with multiple RISs deployment are ad hoc, which depends on the specific system configurations. There lacks a unified theoretical framework that provides general analytical treatment for both passive and active RIS-assisting systems, possibly having multiple concatenated reflections via RISs, which is typical in IoT communication scenarios. Thus, we investigate general uplink RIS-assisted multi-user multiple-input multiple-output (MU-MIMO) communication systems under general Rician fading channels, where the number of cascaded RIS panels are arbitrary and the RIS elements can be active or passive. By utilizing the linearization trick and the operator-valued free probability theory, a unified analytic expression of the ergodic sum rate for the considered MU-MIMO systems is derived. Then, we further propose a low-complexity optimization approach to design the RISs’ phase shifts, thus enhancing the ergodic sum rate. Numerical results verify the accuracy of the analytical results for RIS-assisted systems. In addition, the convergence and effectiveness of the proposed optimization algorithm are demonstrated.

When applying integrated sensing and communications (ISAC) in future mobile networks, many sensing tasks have low latency requirements, preferably being implemented at terminals. However, terminals often have limited computing capabilities and energy supply. In this paper, we investigate the effectiveness of leveraging the advanced computing capabilities of mobile edge computing (MEC) servers and the cloud server to address the sensing tasks of ISAC terminals. Specifically, we propose a novel three-tier integrated sensing, communication, and computing (ISCC) framework composed of one cloud server, multiple MEC servers, and multiple terminals, where the terminals can optionally offload sensing data to the MEC server or the cloud server. The offload message is sent via the ISAC waveform, whose echo is used for sensing. We jointly optimize the computation offloading and beamforming strategies to minimize the average execution latency while satisfying sensing requirements. In particular, we propose a low-complexity distributed algorithm to solve the problem. Firstly, we use the alternating direction method of multipliers (ADMM) and derive the closed-form solution for offloading decision variables. Subsequently, we convert the beamforming optimization sub-problem into a weighted minimum mean-square error (WMMSE) problem and propose a fractional programming based algorithm. Numerical results demonstrate that the proposed ISCC framework and distributed algorithm significantly reduce the execution latency and the energy consumption of sensing tasks at a lower computational complexity compared to existing schemes.

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 propose a multi-unmanned aerial vehicle (UAV)-assisted integrated sensing, communication, and computation network. Specifically, the treble-functional UAVs are capable of offering communication and edge computing services to mobile users (MUs) in proximity, alongside their target sensing capabilities by using multi-input multi-output arrays. For the purpose of enhance the computation efficiency, we consider task compression, where each MU can partially compress their offloaded data prior to transmission to trim its size. The objective is to minimize the weighted energy consumption by jointly optimizing the transmit beamforming, the UAVs’ trajectories, the compression and offloading partition, the computation resource allocation, while fulfilling the causal-effect correlation between communication and computation as well as adhering to the constraints on sensing quality. To tackle it, we first reformulate the original problem as a multi-agent Markov decision process (MDP), which involves heterogeneous agents to decompose the large state spaces and action spaces of MDP. Then, we propose a multi-agent proximal policy optimization algorithm with attention mechanism to handle the decision-making problem. Simulation results validate the significant effectiveness of the proposed method in reducing energy consumption. Moreover, it demonstrates superior performance compared to the baselines in relation to resource utilization and convergence speed.

Designing short Luby transformation (LT) codes with low overhead and good error performance is crucial and challenging for the deployment of vehicle-to-everything networks, which require high reliability, high spectral efficiency, and low latency. In this paper, we investigate the design of globally optimal transmission strategies that consider interactions between feedback for short LT codes using reinforcement learning (RL), where traditional asymptotic analysis based on random graph theory is known to be inaccurate in this context. First, in order to reduce the decoding overhead of short LT codes, we derive the gradient expression for optimizing the degree distribution of LT codes, and propose a RL-based distribution optimization (RL-DO) algorithm for designing short LT codes. Then, to improve the reliability and overhead of LT codes under limited feedback, we model the feedback optimization problem as a Markov decision process, and propose the RL-based joint feedback and distribution optimization (RL-JFDO) algorithm, which aims to design globally-optimal feedback schemes. Simulations show that our methods have lower decoding overhead, error rate, and decoding complexity compared to existing feedback fountain codes.

Autonomous aerial vehicle (AAV) can be threatening to the information security of wireless communications. By launching the pilot spoofing attack (PSA), a AAV, operating as the active aerial-eavesdropper (A-Eve), is able to intercept the confidential messages sent over the air. On one hand, it is difficult to distinguish the channel state information (CSI) of the ground users (GUs) and the CSI of A-Eve in the contaminated pilots. On the other hand, due to the high-mobility of A-Eve, the CSI of A-Eve is rapidly changing, making the design of secure transmissions challenging. To address these issues, we first propose a location-based minimum mean square error (MMSE) channel estimation algorithm to separate the CSI of GUs and the CSI of A-Eve, where the location of A-Eve is obtained by designing a cooperative localization neural network (CLNet), leveraging its angular-domain channel fingerprint (CF) of A-Eve. Furthermore, we propose an artificial noise (AN) injected MMSE precoding scheme to maximize the worst-case secrecy rate of the multi-user communications, where the power allocation between signal and AN is optimized via a long short-term memory (LSTM)-based secure predictive beamforming neural network (SPBNet). Numerical results verify the secrecy performance gain of the proposed scheme achieved by utilizing the localization ability via the CLNet and the channel tracking ability via the SPBNet, compared to the canonical nullspace AN injection scheme without prior knowledge of A-Eve’s location.

Among the key enabling 6G techniques, multiple-input multiple-output (MIMO) and non-orthogonal multiple-access (NOMA) play an important role in enhancing the spectral efficiency of the wireless communication systems. To further extend the coverage and the capacity, the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has recently emerged out as a cost-effective technology. To exploit the benefit of STAR-RIS in the MIMO-NOMA systems, in this paper, we investigate the analysis and optimization of the downlink dual-user MIMO-NOMA systems assisted by multiple STAR-RISs under the generalized singular value decomposition (GSVD) precoding scheme, in which the channel is assumed to be Rician faded with the Weichselberger’s correlation structure. To analyze the asymptotic information rate of the users, we apply the operator-valued free probability theory to obtain the Cauchy transform of the generalized singular values (GSVs) of the MIMO-NOMA channel matrices, which can be used to obtain the information rate by Riemann integral. Then, considering the special case when the channels between the BS and the STAR-RISs are deterministic, we obtain the closed-form expression for the asymptotic information rates of the users. Furthermore, a projected gradient ascent method (PGAM) is proposed with the derived closed-form expression to design the STAR-RISs thereby maximizing the sum rate based on the statistical channel state information. The numerical results show the accuracy of the asymptotic expression compared to the Monte Carlo simulations and the superiority of the proposed PGAM algorithm.

Orthogonal Frequency Division Multiplexing (OFDM) underpins modern wireless communication due to its resilience against multi-path fading and computationally efficient implementations. However, on one hand, in scenarios with time-frequency doubly selective fading channels, OFDM systems face significant challenges, as channel variation induces inter-carrier interference (ICI) that disrupts subcarrier orthogonality. On the other hand, the limited length of the cyclic prefix (CP), often constrained to a fraction of the symbol duration, may be insufficient to fully mitigate inter-symbol interference (ISI) in scenarios with large time spreads. Extending CP length would reduce spectral efficiency and increase latency, making it incompatible with the demands of high-efficiency, low-latency systems. In this paper, we propose an autoencoder based OFDM architecture integrating adaptive pulse shaping with an equalization neural network (PS-EQNet) that jointly addresses ISI caused by insufficient CP and ICI due to channel dynamics through learnable time-frequency filters. Additionally, we introduce a detection network tailored for simplified channels, which reduces computational complexity compared to existing schemes while maintaining robust performance. Simulation results confirm that the proposed PS-EQNet OFDM system significantly relaxes CP requirements, enhances spectral efficiency (SE), and achieves reliable bit error rate (BER) performance in time-frequency selective channels, establishing a flexible trade-off among SE, BER, and computational complexity.

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 consider cell-free communication systems with several access points (APs) serving terrestrial users (UEs) simultaneously. To enhance the uplink multi-user multiple-input multiple-output communications, we adopt a hybrid-CSI-based two-layer distributed multi-user detection scheme comprising the local minimum mean-squared error (MMSE) detection at APs and the one-shot weighted combining at the central processing unit (CPU). Furthermore, to improve the propagation environment, we introduce multiple reconfigurable intelligent surfaces (RISs) to assist the transmissions from UEs to APs. Aiming to maximize the weighted sum rate, we formulate the weighted sum-MMSE (WMMSE) problem, where the UEs’ beamforming matrices, the CPU’s weighted combining matrix, and the RISs’ phase-shifting matrices are alternately optimized. Considering the limited fronthaul capacity constraint in cell-free networks, we resort to the operator-valued free probability theory to derive the asymptotic alternating optimization (AO) algorithm to solve the WMMSE problem, which only depends on long-term channel statistics and thus reduces the interaction overhead. Numerical results demonstrate that the asymptotic AO algorithm can achieve a high communication rate as well as reduce the interaction overhead.

In this paper, we propose a novel symbiotic sensing and communication (SSAC) framework, comprising a base station (BS) and a passive sensing node. In particular, the BS transmits communication waveform to serve vehicle users (VUEs), while the sensing node is employed to execute sensing tasks based on the echoes in a bistatic manner, thereby avoiding the issue of self-interference. Besides the weak target of interest, the sensing node tracks VUEs and shares sensing results with BS to facilitate sensing-assisted beamforming. By considering both fully digital arrays and hybrid analog-digital (HAD) arrays, we investigate the beamforming design in the SSAC system. We first derive the Cramér-Rao lower bound (CRLB) of the two-dimensional angles of arrival estimation as the sensing metric. Next, we formulate an achievable sum rate maximization problem under the CRLB constraint, where the channel state information is reconstructed based on the sensing results. Then, we propose two penalty dual decomposition (PDD)-based alternating algorithms for fully digital and HAD arrays, respectively. Simulation results demonstrate that the proposed algorithms can achieve an outstanding data rate with effective localization capability for both VUEs and the weak target. In particular, the HAD beamforming design exhibits remarkable performance gain compared to conventional schemes, especially with fewer radio frequency chains.

The performance of the sixth-generation (6G) vehicle-to-vehicle (V2V) communication systems will be significantly improved, but they are also confronted with many technical challenges like massive terminal access and low transmission delay. A fundamental and difficult problem is how to establish an intelligent 6G V2V channel model with high accuracy, low complexity, and generality. In this paper, we propose a dynamic V2V channel model in complicated urban scenarios driven by effective environment information, channel data, and physical statistics. To begin with, the bimodal features representing the environment information are extracted from vector maps by a set of fully automatic algorithms. Heuristic graph datasets are constructed using features coupled with locations and ground-truth large-scale parameters (LSPs), i.e., the channel data reflecting realistic statistical properties. Then, we design a novel network based on attention-assisted graph convolution and pooling layers, which enables us to perform prediction for path loss, delay spread, and angular spreads. Compared with convolutional neural networks-based methods, the proposed LSPs prediction model can reduce both the number of trainable parameters and the FLOPs by two orders of magnitude with higher accuracy. Moreover, the predicted LSPs are next fed into multi-link V2V simulations based on physical statistics. Dynamic channel impulse response generation is implemented based on a spatially consistent geometrical modeling methodology. Eventually, we validate our model by comparing key channel characteristics with those of the ground-truth values, and better agreements are shown compared with existing methods.

To meet the capacity requirements of future communications systems, multiple-input multiple-output (MIMO) and non-orthogonal multiple-access (NOMA) are promising techniques to improve the link and system capacities. In addition, the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can be well integrated with the communication systems to effectively extend the wireless coverage in a cost-efficient manner. Therefore, in this paper, we investigate the performance of the downlink dual-user MIMO-NOMA systems assisted by STAR-RIS under the generalized singular value decomposition (GSVD) precoding scheme. Specifically, we first apply the operator-valued free probability theory to obtain the Cauchy transform of the generalized singular values (GSVs) of the MIMO-NOMA channel matrices under Rician fading with the Weichselberger’s correlation structure, in which a linearization trick for the matrix-valued rational functions is applied to simplify the derivations. Then, based on the Cauchy transform of GSVs, we obtain the closed-form expression for the asymptotic information rates of the users under the considered system. Furthermore, we propose a projected gradient ascent method (PGAM) to enhance the sum rate of the system by designing the phase shifts of the STAR-RIS based on the derived closed-form expressions. The numerical results show the accuracy of the asymptotic expression compared to the Monte Carlo simulations and the effectiveness of the proposed PGAM algorithm.

Deep learning-based semantic communication is an emerging communication method that achieves cooperative transmission between source and channel. The primary objectives of semantic communication are to enhance the efficiency of information transmission and ensure the accurate restoration of semantic content. Recent studies have shown that semantic communication performs well in enhancing transmission rates, especially in low signal-to-noise ratio environments. However, existing speech semantic communication methods neglect to account for speech perception at the receiver and the complexity of the method, which limits the practical implementation of semantic communication methods. In this paper, we propose a perceptually-motivated, low-complexity speech semantic communication method. Specifically, we employ an end-to-end communication approach to transmit the source speech and obtain the reconstructed speech at the receiver. To ensure the accurate extraction of semantic information, we present a low-complexity fully convolutional semantic encoder, which increases the accuracy of semantic information extraction and improves transmission efficiency. Considering the sensitivity of human perception, a multi-resolution joint loss function has been implemented to enhance the model’s performance and guarantee that the reconstructed speech aligns with the human ear’s auditory perception. Experimental results show that the proposed method performs better on objective and subjective metrics than existing speech transmission methods. Compared with existing neural semantic transmission methods, we improve the transmission efficiency, and the number of symbols needed for transmission is decreased by 60% without compromising the quality of speech. Furthermore, the proposed semantic communication method has a lower complexity and consumes less time to transmit.

The deployment of mobile edge computing services in LEO satellite networks achieves seamless coverage of computing services. However, the time-varying wireless channel conditions between satellite–terrestrial channels and the random arrival characteristics of ground users’ (GUs) tasks bring new challenges for managing the LEO satellite’s communication and computing resources. Facing these challenges, a stochastic computation offloading problem of joint optimizing communication and computing resources allocation and computation offloading decisions is formulated for minimizing the long-term average total power cost of the GUs and the LEO satellite, with the constraint of long-term task queue stability. However, the computing resource allocation and the computation offloading decisions are coupled within different slots, thus making it challenging to address this problem. To this end, we first employ the Lyapunov optimization to decouple the long-term stochastic computation offloading problem into the deterministic subproblem in each slot. Then, an online algorithm combining deep reinforcement learning and conventional optimization algorithms is proposed to solve these subproblems. Simulation results show that the proposed algorithm can achieve the superior performance while ensuring the stability of all task queues in LEO satellite networks.

By exploiting the degree of freedom on the altitude, unmanned aerial vehicle (UAV) communication can provide ubiquitous communication for future wireless networks. In the case of concurrent transmission of multiple UAVs, the directional beamforming formed by multiple antennas is an effective way to reduce co-channel interference. However, factors, such as airflow disturbance or estimation error for UAV communications, can cause the occurrence of beam misalignment. In this article, we investigate the system performance of a multitier UAV communication network with the consideration of unstable beam alignment. In particular, we propose a tractable random model to capture the impacts of beam misalignment in the 3-D space. Based on this, by utilizing stochastic geometry, an analytical framework for obtaining the outage probability in the downlink of a multitier UAV communication network for the closest distance association scheme and the maximum average power association scheme is established. The accuracy of the analysis is verified by Monte Carlo simulations. The results indicate that in the presence of random beam misalignment, the optimal number of UAV antennas needs to be adjusted to be relatively larger when the density of UAVs increases or the altitude of UAVs becomes higher.

Integrated sensing and communications (ISACs) technique is being considered as a promising technique for future networks. Facing the diverse services required for different nodes, in this article, we investigate the ISAC systems with simultaneous public and confidential transmission, where an ISAC base station simultaneously provides integrated public and confidential services for different nodes and performs target tracking using the echo of communication signals. We study the optimization of public, confidential signals, and artificial noise (AN) in both the time-invariant and time-varying channels. Our primary goal is to minimize the differences between the actual and desired beampatterns, while meeting the constraints of the public message rate (PMR) and confidential message secrecy rate (CMSR). For time-invariant channels with typically negligible estimation error of the channel state information (CSI), we aim to synthesize the target beampattern while satisfying the PMR and CMSR constraints. To this end, we first propose a successive convex approximation-based algorithm to jointly design the transmit covariance matrices and the AN covariance matrix; we then propose a low-complexity two-stage algorithm that is more suitable for the practical implementation. The proposed algorithms are further extended to the time-varying channels where the estimated may contain large errors. Simulation results are provided and verify the effectiveness of the proposed algorithms.

Recent years have witnessed an increasing interest in leveraging reconfigurable intelligent surfaces (RISs) to enhance the capabilities of integrated sensing and communication (ISAC) systems. RISs are advantageous in improving detection and communication performance, especially in challenging environments characterized by nonLine of Sight (NLOS) conditions and dense urban settings. In this article, a hybrid RIS, comprising passive reflecting elements and active sensors, and multiple fully passive RISs are deployed to enhance an ISAC system, where the direct paths between the base station (BS) and users/targets are blocked. The signal sent from the BS and reflected by RISs is received by the communication user, and simultaneously scattered by the target toward the sensors of the hybrid RIS. A joint optimization of the transmit covariance matrix at the BS and phase-shifting matrices at RISs is formulated, which considers the tradeoff between the communication and sensing performance. The optimization is based on the derived closed-form communication achievable rate by leveraging the free probability theory and positioning error bound (PEB) via the Cramér-Rao lower bound (CRLB) analysis. The block coordinate descent (BCD) algorithm is utilized to tackle the nonconvex problem, where the transmit covariance matrix and phase-shifting matrices are optimized iteratively. Therein, the Riemannian gradient descent algorithm is exploited for optimizing the phase-shifting matrices. Numerical results verify the effectiveness of the proposed algorithm, and both communication and sensing performance gains increase with the number of RIS panels and RIS elements.

The temporary events are generally gathered around many users interested in the same content. An unmanned aerial vehicle (UAV) with caching and multicasting is attractive for such scenarios with high traffic demands, since the multicasting allows concurrently serving users, and the caching can alleviate the burden on backhaul links. Hence, in this work, we investigate a cellular network assisted by caching-and-multicasting-empowered UAVs, where UAVs multicast the files from their caching storage or base stations via wireless backhaul links. Particularly, a popularity-aware (PA) file selection and multicast scheme is proposed, where the files to be multicasted are determined by the instantaneous requested popularity, the maximum number of allowable fetched files and the performance on wireless backhaul links. By leveraging stochastic geometry, we obtain the approximated yet accurate result for the average number of successfully multicasted users. Our results confirm the effectiveness of the PA scheme, i.e., achieving a larger average number of successfully multicasted users compared to the random scheme for most cases. Moreover, the results suggest that a relatively smaller number of multicast channels or reducing the maximum number of allowable fetched files can benefit network performance in the case of the high signal-to-interference ratio threshold on the backhaul links.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is emerging as a promising technology by achieving full-space coverage and further improving system performance. However, most existing works adopted an independent phase-shift model, which is high-cost and may be difficult to achieve in realistic wideband systems. Consequently, a coupled phase-shift STAR-RIS enhanced downlink multi-user multiple-input single-output orthogonal frequency division multiplexing system is investigated for both unicast and broadcast communications in this paper. We aim to maximize the average sum-rate (ASR) for all subcarriers by jointly optimizing the precoding matrices and the reflecting and transmitting coefficients (RTCs). Specifically, a block coordinate descent algorithm is proposed to iteratively design each block of a multiblock problem reformulated by the original one. The precoding matrices are optimized by the Lagrangian multiplier method for low computational complexity. For the RTCs, an element-based alternating optimization algorithm is proposed to optimize the coupled phase-shift and amplitude coefficients. Simulation results validate the effectiveness of the proposed algorithm by comparing the ASR with that of other benchmarks. Moreover, its performance closely approaches the upper bound under various practical user proportion scenarios on both sides of the STAR-RIS.

In this paper, we investigate the uplink signal detection in cell-free massive MIMO systems with unmanned aerial vehicles (UAVs) serving as aerial access points (APs). The ground users are equipped with multiple antennas and the ground-to-air propagation channels are subject to correlated Rician fading. To overcome huge signaling overhead in the fully-centralized detection in cell-free systems, we propose a two-layer distributed uplink detection scheme, where the uplink signals are first detected in AP-UAVs by using the minimum mean-squared error (MMSE) detector based on local channel state information (CSI), and then collected and weighted combined at the CPU-UAV to obtain the refined detection. By using the operator-valued free probability theory, the asymptotic expressions of the combining weights are obtained, which only depend on the statistical CSI and show excellent accuracy compared to the exact but intractable expressions. Based on the proposed scheme, we further investigate the impacts of different deployment scenarios on the spectral efficiency (SE). Numerical results show that in urban and dense urban environments, it is more beneficial to deploy more AP-UAVs to increase SE. Nonetheless, in suburban environment, an optimal combination of the number of AP-UAVs and the number of antennas per AP-UAV exists to maximize SE.

In this paper, we investigate the realization of covert communication in a general radar-communication cooperation system, which includes integrated sensing and communications as a special example. We explore the possibility of utilizing the sensing ability of radar to track and jam the aerial adversary target attempting to detect the transmission. Based on the echoes from the target, the extended Kalman filtering technique is employed to predict its trajectory as well as the corresponding channels. Depending on the maneuvering altitude of adversary target, two channel state information (CSI) models are considered, with the aim of maximizing the covert transmission rate by jointly designing the radar waveform and communication transmit beamforming vector based on the constructed channels. For perfect CSI under the free-space propagation model, by decoupling the joint design, we propose an efficient algorithm to guarantee that the target cannot detect the transmission. For imperfect CSI due to the multi-path components, a robust joint transmission scheme is proposed based on the property of the Kullback-Leibler divergence. The convergence behaviour, tracking MSE, false alarm and missed detection probabilities, and covert transmission rate are evaluated. Simulation results show that the proposed algorithms achieve accurate tracking. For both channel models, the proposed sensing-assisted covert transmission design is able to guarantee the covertness, and significantly outperforms the conventional schemes.

Reconfigurable intelligent surface (RIS) has recently been widely investigated in wireless communication systems due to its low deployment cost and high-performance gain. In this work, we study the multiple-RIS-assisted uplink multiple-user multiple-input multiple-output (MU-MIMO) communication systems, where each user’s signal is sent to the base station via both the direct and the reflected links. To obtain informative insight into the considered system with the statistical channel information, we first derive the closed-form expression for the ergodic sum rate of the MU-MIMO systems by applying the operator-valued free probability theory. Then, the covariance matrices of the transmit signals and the phase shifts of the RIS elements are jointly optimized to maximize the derived asymptotic ergodic sum rate via alternating optimization (AO). Specifically, the AO procedure is composed of a water-filling algorithm and a gradient descent algorithm over the Riemannian manifold and the two algorithms iterate until convergence. The numerical results show the accuracy of the asymptotic expression compared to the Monte Carlo simulation and the superiority of the proposed AO algorithm compared to the benchmark. Furthermore, the rank deficiency of the MIMO channel can be significantly improved by the deployment of multiple RISs and the proposed AO algorithm.

The massive multi-input multi-output (mMIMO) systems are vulnerable to both pilot contamination and pilot spoofing attack (PSA), which jeopardize the uplink channel estimation and cause information leakage in the downlink transmissions. Motivated by the canonical large-scale fading precoding (LSFP) [1], a secure LSFP is proposed to eliminate the impact of the coexistence of pilot contamination and PSA. The proposed framework enables multi-cell coordinated transmissions leveraging the large-scale fading coefficients, thus is suitable to be implemented in mMIMO systems with limited fronthaul. Specifically, the presence of the eavesdropper is first identified by designing a distributed mixture-of-experts neural network (D-MoENN)-based PSA detector, which combines the detected results of distributed nodes to improve the detection accuracy. Subsequently, an optimal jamming base station (BS) is selected by designing a D-MoENN-based localizer, which estimates the locating cell of the eavesdropper and selects the nearest jamming BS to the eavesdropper. Numerical results show that the proposed D-MoENN-based PSA detector outperforms the existing detectors in the low SNR regime. Moreover, the average secrecy rate achieved by the secure LSFP with the jamming BS selected by the D-MoENN-based localizer is close to the upper bound achieved by the genie-aided selector that perfectly knows the location of the eavesdropper.

Vehicle-to-infrastructure (V2I) communications have been regarded as an emerging application in next-generation wireless networks. However, guaranteeing high-quality wireless communications in high-mobility scenarios remains a major challenge. In this paper, we investigate the deployment of reconfigurable intelligent surface (RIS) for improving the communication performance of V2I systems. In particular, integrated sensing and communication (ISAC) signals are exploited to facilitate sensing-assisted beamforming. Aiming at maximizing the achievable rate, two deep learning-based predictive beamforming mechanisms are proposed. First, a two-stage beamforming design is devised, where the channel state information (CSI) is estimated based on the echo signals and predicted by a dedicated neural network for time-varying channels. Then, the transmit beamforming vector at the base station (BS) and the reflect beamforming matrix at the RIS are jointly optimized. To further reduce the computational complexities, we develop an end-to-end beamforming design by employing the parameter sharing mechanism and weighted loss function. Simulation results demonstrate that the proposed algorithms can achieve an outstanding data rate that approaches the upper bound exploiting perfect CSI. In particular, the end-to-end design exhibits remarkable robustness against the impact of noise and achieves outstanding sensing-assisted beamforming performance, especially at the low signal-to-noise ratio region.

Dual-functional radar-communication (DFRC) has been widely concerned in future communication systems. By aggregating communication and detection resources, DFRC enables improved communication data rate as well as continual real-time sensing capability. However, the spectral efficiency of the communication channel will be inevitably compromised since the transceiver design has to take into account both of the communication and radar oriented beampatterns. In this paper, the reconfigurable intelligent surface (RIS) panels are deployed to assist the design of multi-antenna DFRC transceivers, where the RIS panels introduce additional degree-of-freedom to accommodate the dual-functional beampattern. First, applying the operator-valued free probability theory, we derive the closed-form expression of the asymptotic achievable rate of the multi-RIS-assisted MIMO DFRC systems in presence of general Rician fading. Then, we propose an alternating optimization (AO) algorithm to jointly optimize the transmit signals of the DFRC transmitter and the phase shifts of the reflecting elements of the RIS panels, which achieves an optimal tradeoff between the achievable rate and the desired radar beampattern. Simulation results verify the accuracy of the asymptotic expression of the achievable rate. In addition, the deployment of RISs and the proposed AO algorithm are proven to improve both the detection and communication performance.

In this paper, we investigate the multiple access technique for the aerial networks, where the multi-antenna base stations are carried by unmanned aerial vehicles (UAVs) to serve ground users. The uplink signals are transmitted by the users simultaneously, then detected and recovered by the aerial networks. On one hand, compared to the case that each UAV individually detects the signals of serving users, we aim to improve the quality of the recovered signals by using cooperative techniques that leverage the signals from multiple UAVs. On the other hand, the fully-centralized cooperation requires signaling exchange between UAVs, which incurs huge signaling overhead and latency, and is infeasible for aerial networks. Therefore, we propose a two-stage distributed minimum mean squared error (MMSE) detection with one-shot signal combining. Specifically, the users' signals are first locally detected by each UAV via the MMSE detector, and then weighted combined at the central UAV in a one-shot manner, where the combining weights are designed to minimize the MSE between the combined signals and the original signals. When only imperfect channel state information is locally available at each UAV, by using the random matrix theory, these combining weights are shown to depend on the long-term channel statistics and thus, greatly reduce the interaction overhead and latency. Numerical results show that the proposed scheme outperforms the non-cooperative detection in terms of the achievable spectral efficiency. Meanwhile, with a much smaller interaction overhead, the proposed scheme achieves comparable spectral efficiency as the fully centralized signal detection.

With the widespread connectivity of everyday devices realized by the advent of the Internet of Things (IoT), communication between users of different devices has become increasingly close. In practical scenarios, obstacles present between the transceiver may cause a deterioration in the quality of the received signals. Therefore, the reconfigurable intelligent surface (RIS) is employed to create virtual Line-of-Sight (LoS) channels in an IoT network. Specifically, this article aims at maximizing the sum-rate of the RIS-assisted multiuser multiple-input–single-output (MU-MISO) communication systems by jointly optimizing the phase shift matrix of the RIS and transmit beamforming. To solve the formulated nonconvex problem, a piecewise-deep reinforcement learning (DRL) algorithm is proposed in this article. Unlike the existing alternative optimization (AO) algorithms, the proposed algorithm avoids falling into the local optimal by using an exploration mechanism. Moreover, piecewise-DRL can reduce the action dimension, allowing the algorithm to obtain faster convergence. Simultaneously, this algorithm also ensures that the parameters of the two-part networks are updated to generate a larger system sum-rate by unsupervised joint optimization. Simulations in various circumstances reveal that the proposed approach is more robust and presents better stability and faster convergence than previous state-of-the-art algorithms while obtaining competitive performance.

Due to the flexible deployment of unmanned aerial vehicles (UAVs) and the wide-area coverage of satellites, the space–air–ground (SAG) communication network can provide flexible and pervasive connectivity, especially in remote areas. In this work, we investigate the uplink transmission in a SAG network, where the nonorthogonal multiple access mechanism is adopted at the UAVs to enhance the number of access from ground user equipments (UEs) and a low-earth orbit satellite offers the wireless backhaul for UAVs. In particular, the energy efficiency (EE) of the considered network is maximized by optimizing the user association (UA), power allocation (PA), and UAV 3-D trajectory jointly with the consideration of the movement of the satellite. To tackle the formulated problem, by leveraging the block coordinate descent (BCD) method, we develop a joint UA, PA, and UAV trajectory (namely, JUPT) optimization algorithm, i.e., the original problem is decomposed into three subproblems, and the subproblems are solved iteratively until convergence. Specifically, we propose to include the virtual UEs in the system and develop a low-complexity matching algorithm to effectively solve the UA problem. A successive convex approximation (SCA)-based Dinkelbach algorithm is then adopted to address the PA problem. Later, with the introduction of the auxiliary variables, the UAV 3-D trajectory subproblem is iteratively solved by the SCA method. Our numerical results demonstrate the superiority of the proposed JUPT algorithm, which obtains significantly higher EE compared to the benchmark schemes. Moreover, the rapid convergence of the JUPT algorithm is verified.

To facilitate the development of Internet of Things (IoT) services, future networks are expected to simultaneously provide sensing functionality and support low-power communications. In this article, we investigate the system sum-rate maximization problem in an integrated sensing and reconfigurable intelligent surface (RIS) backscatter communication system, where the base station (BS) simultaneously detects backscattered signals from multiple IoT devices and senses targets based on the echo signals. We formulate a joint transmit beamforming, RIS phase shifts, and receive beamforming design problem under the Cramér–Rao bound (CRB) constraint for target angle estimation. To solve the nonconvex problem, we then propose a fractional programming (FP)-based alternating optimization algorithm. In particular, the FP technique is first employed to transform the formulated problem into a more tractable form, and the exact penalty method and manifold optimization are then utilized to address the CRB constraint and constant-modulus constraint, respectively. Numerical results have shown that the proposed design significantly improves the system sum rate and illustrates the tradeoff between the communication and sensing performance.

The low earth orbit (LEO) satellites have received extensive attention as an essential supplement to the terrestrial network for supporting global Internet of Things (IoT) services. Considering the rapid growth of IoT devices and the significant satellite-to-ground latency, proposing low-latency, low-overhead access protocols for LEO satellite IoT systems is challenging. In this article, we propose a multibeam random access (RA) framework and deploy the deep reinforcement learning (DRL) algorithm to control the nonorthogonal multiple access (NOMA) aided RA strategy. First, we divide the satellite coverage region into multiple beams and assume that the adjacent beams share parts of regions. Hence, the devices in the sharing region are allowed to transmit packets in two periods allocated for the two beams. Then, packets in multiple beams can be decoded jointly by an interslot successive interference cancelation (SIC) decoder. In addition, we consider the heterogeneity among devices and assign different power levels for heterogeneous types of devices, which enables power-domain NOMA and the intraslot SIC decoder in this system to mitigate the collision resolution. To maximize the average throughput, the deep deterministic policy gradient (DDPG) algorithm is adopted to achieve an online decision to optimize the RA protocol where the packet repetition strategies of devices are adjusted dynamically. The simulation results show that the proposed scheme outperforms the traditional benchmark schemes with significant throughput gain.

In this paper, physical layer security techniques are investigated for cooperative multi-input multi-output (C-MIMO), which operates as an underlaid cognitive radio system that coexists with a primary user (PU). The underlaid secrecy paradigm is enabled by improving the secrecy rate towards the C-MIMO receiver and reducing the interference towards the PU. Such a communication model is especially suitable for implementing Industrial Internet of Things (IIoT) systems in the unlicensed spectrum, which can trade off spectral efficiency and information secrecy. To this end, we propose an eigenspace-adaptive precoding (EAP) method and formulate the secrecy rate optimization problem, which is subject to both the single device power constraint and the interference power constraint. This precoder design is enabled by decomposing the original optimization problem into eigenspace selection and power allocation sub-problems. Herein, the eigenvectors are adaptively selected by the transmitter according to the channel conditions of the underlaid users and the PUs. In addition, a simplified EAP method is proposed for large-dimensional C-MIMO transmission, exploiting the additional spatial degree of freedom for a low-complexity secrecy precoder design. Numerical results show that by transmitting signal and artificial noise in the properly selected eigenspace, C-MIMO can eliminate the secrecy outage and outperforms the fixed eigenspace precoding methods. Moreover, the proposed simplified EAP method for the large-dimensional C-MIMO can significantly improve the secrecy rate.

We investigate the application of reinforcement learning (RL) on online fountain codes, and propose two schemes to reduce the full-recovery overhead with limited feedback. First, we use RL in determining the optimal degree of coded symbols for a given number of feedback, and propose the RL-based degree determination (RL-DD), with the help of theoretical analysis of the relationship between recovery rate and buffer occupancy. Then we propose online fountain codes with no build-up phase using sectioned distribution (OFCNB-SD), where the encoder sends symbols whose degrees are sampled from different sections of an overall distribution, and the decoder is improved to utilize coded symbols that are not immediately decodable. We present theoretical analysis of OFCNB-SD, and introduce RL-based sectioned distribution (RL-SD) scheme where the sectioning of the overall distribution is optimized with RL. Simulation results show that our proposed schemes could achieve lower full-recovery overhead with limited feedback compared to existing schemes.

The reconfigurable intelligent surface (RIS) is useful to effectively improve the coverage and data rate of end-to-end communications. In contrast to the well-studied coverage-extension use case, in this paper, multiple RIS panels are introduced, aiming to enhance the data rate of multi-input multi-output (MIMO) channels in presence of insufficient scattering. Specifically, via the operator-valued free probability theory, the asymptotic mutual information of the large-dimensional RIS-assisted MIMO channel is obtained under the Rician fading with Weichselberger’s correlation structure, in presence of both the direct and the reflected links. Although the mutual information of Rician MIMO channels scales linearly as the number of antennas and the signal-to-noise ratio (SNR) in decibels, numerical results show that it requires sufficiently large SNR, proportional to the Rician factor, in order to obtain the theoretically guaranteed linear improvement. This paper shows that the proposed multi-RIS deployment is especially effective to improve the mutual information of MIMO channels under the large Rician factor conditions. When the reflected links have similar arriving and departing angles across the RIS panels, a small number of RIS panels are sufficient to harness the spatial degree of freedom of the multi-RIS assisted MIMO channels.

With the high flexibility of supporting resource-intensive and time-sensitive applications, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is proposed as an innovational paradigm to support the mobile users (MUs). As a promising technology, digital twin (DT) is capable of timely mapping the physical entities to virtual models, and reflecting the MEC network state in real-time. In this paper, we first propose an MEC network with multiple movable UAVs and one DT-empowered ground base station to enhance the MEC service for MUs. Considering the limited energy resource of both MUs and UAVs, we formulate an online problem of resource scheduling to minimize the weighted energy consumption of them. To tackle the difficulty of the combinational problem, we formulate it as a Markov decision process (MDP) with multiple types of agents. Since the proposed MDP has huge state space and action space, we propose a deep reinforcement learning approach based on multi-agent proximal policy optimization (MAPPO) with Beta distribution and attention mechanism to pursue the optimal computation offloading policy. Numerical results show that our proposed scheme is able to efficiently reduce the energy consumption and outperforms the benchmarks in performance, convergence speed and utilization of resources.

In the uplink machine-type communication (MTC) system, a combination of grant-free transmission and non-orthogonal multiple access (NOMA) emerges to reduce the control overhead and transmission latency. In the grant-free scenario, the base station needs to identify the active devices and estimate the channel state information before the data detection. However, due to the lack of a scheduling process, the user activity detection (UAD) and channel estimation (CE) are both challenging, especially when short non-orthogonal preambles are adopted. In this paper, by exploiting the framework of the compressive sensing-based algorithm, we propose a novel deep learning architecture, namely UAD and CE Neural Network (UAD-CE-NN), to effectively solve the joint UAD and CE problem for grant-free NOMA. In the proposed scheme, the user activity and channel state information hidden in the received data signals are also exploited to aid the preamble for higher detection accuracy. Specifically, UAD-CE-NN is composed of two stages: we first build a preamble detection neural network for a tentative UAD-CE; a data detection neural network is then deployed to exploit the data signals. Compared with the conventional schemes, the proposed scheme obtains much higher accuracy for both the UAD and CE, especially when short preamble sequences are employed.

With the increasing data traffic in the fifth-generation (5G) communication system, the 5G new radio extended to unlicensed bands (5G NR-U) has become a promising approach to relieve the heavy pressure on the cellular system. To achieve the efficient coexistence with the WiFi system and improve the efficiency of temporal, spectral and spatial resource utilization, we first divide the transmission space into two subspaces by leveraging spatial reuse, where the data transmitted by cellular user equipments (UEs) falls into one subspace and the data transmitted by Internet of Things (IoT) devices coexisting with WiFi users through power control is in the other subspace. Then, the coexistence among cellular UEs, IoT devices, and WiFi users is formulated as an optimization model with the aim of maximizing the cellular system throughput via the joint power and subchannel allocation under the interference constraint. Although the resulting optimization problem is a mixed-integer nonlinear programmming, we decompose it into two subproblems and develop an alternating iterative approach to effectively solve them. Also, the closed-form allocations of the power and subchannels are obtained. Simulation results confirm that the proposed scheme can improve the cellular system performance and guarantee the coexistence in the unlicensed band.

In various location-based Internet of Things (IoT) services, it is required to localize a large number of energy-limited devices simultaneously and accurately. In order to achieve this goal, a reconfigurable intelligent surface (RIS)-assisted positioning method for multiple IoT devices is proposed, where the signals transmitted by the users reach the base station (BS) along the direct path and the reflection path via the RIS. The difference in the propagation delay of the two paths is essential in the proposed triangulation-based localization framework, which is estimated via the cross-correlation function of the received signals. Based on the orthogonality of the transmitted signals, the optimization of the multiantenna BS and the RIS, with the goal of minimizing the total transmission power of the IoT devices, is constructed. For the orthogonal signal case, the nonconvex optimization problem for the RIS is recast into a convex problem via the semidefinite relaxation (SDR). For the nonorthogonal signal case, the zero-forcing (ZF) combining vectors at the BS are adopted to eliminate interferences among multiple users, and the block coordinate descent (BCD) algorithm is used to decouple the combining vectors and the RIS phases. Numerical results show that by using the proposed optimization method, decimeter-level positioning accuracy can be achieved with low-power consumption, and significant power gain can be achieved compared to the unoptimized RIS-assisted localization.

To support the ubiquitous connectivity requirement of sixth generation communication, unmanned aerial vehicles (UAVs) play a key role as a major part of the future communication networks. One major issue in UAV communications is the interference resulting from spectrum sharing and line-of-sight links. Recently, the application of the coordinated multipoint (CoMP) technology has been proposed to reduce the interference in the UAV-terrestrial heterogeneous network (HetNet). In this paper, we consider a three-dimensional (3D) multilayer UAV-terrestrial HetNet, where the aerial base stations (ABSs) are deployed at multiple different altitudes. Using stochastic geometry, we develop a tractable mathematical framework to characterize the aggregate interference and evaluate the coverage probability of this HetNet. Our numerical results show that the implementation of the CoMP scheme can effectively reduce the interference in the network, especially when the density of base stations is relatively large. Furthermore, the system parameters of the ABSs deployed at higher altitudes dominantly influence the coverage performance of the considered 3D HetNet.

Despite the great promises that the resistive random access memory (ReRAM) has shown as the next generation of non-volatile memory technology, its crossbar array structure leads to a severe sneak path interference to the signal read back from the memory cell. In this paper, we first propose a novel belief propagation (BP) based detector for the sneak path interference in ReRAM. Based on the conditions for a sneak path to occur and the dependence of the states of the memory cells that are involved in the sneak path, a Tanner graph for the ReRAM channel is constructed, inside which specific messages are updated iteratively to get a better estimation of the sneak path affected cells. We further combine the graph of the designed BP detector with that of the BP decoder of the polar codes to form a joint detector and decoder. Tailored for the joint detector and decoder over the ReRAM channel, effective polar codes are constructed using the genetic algorithm. Simulation results show that the BP detector can effectively detect the cells affected by the sneak path, and the proposed polar codes and the joint detector and decoder can significantly improve the error rate performance of ReRAM.

Beamforming design is an important technique for enhancing the performance of integrated sensing and communication (ISAC) systems. However, related research based on the hybrid analog-digital (HAD) architecture is still limited. In this paper, we investigate the partially-connected hybrid beamforming design for multi-user ISAC systems. Instead of the commonly used beampattern related metric, the Cramér-Rao bound (CRB) is employed as the sensing performance metric for direction of arrival (DOA) estimation. We aim to minimize the CRB while satisfying the signal-to-interference-plus-noise ratio (SINR) constraints for individual communication users by jointly optimizing the digital and analog beamformers. Subsequently, we propose an alternating optimization based framework, which is significantly different from the conventional methods based on the approximation of the optimal fully-digital beamformer with a hybrid one. We also consider an alternative formulation of optimizing the SINR of radar echo signals. Based on optimal receive beamformer design, we transform the SINR based joint transmitter and receiver optimization problem to a series of problems sharing a similar form with the CRB based transmitter optimization problem, which can be efficiently solved via the proposed algorithm. Simulation results show that the proposed designs provide significant performance gains in DOA estimation over the existing beampattern approximation based design.

Caching popular contents at edge networks (such as small-cell base stations) has been proposed to deal with the ever-growing mobile traffic. At the meantime, recommendation system is able to shape user demands for further prompting caching gain. In this paper, we study a multi-antenna multi-cell edge network employing transmit beamforming with caching-aware recommendation and user association. We first establish a framework for the joint problem of beamforming, user association, content caching and recommendation to minimize the content transmission delay of mobile users, by specifying a set of necessary conditions for all four component functions of the network. The resulting optimization problem corresponds to a non-convex, multi-timescale, and mixed-integer programming problem, which is hard to handle. To deal with the difficulty in solving the joint optimization problem by the direct formulation, we equivalently decompose it into three sub-problems. Then, we develop a computationally-efficient iterative algorithm to obtain the sub-optimal solution, where the three subproblems are tackled iteratively. Simulation results are conducted to demonstrate that the proposed algorithm can obtain lower transmission delay than baseline schemes.

Existing multi-task models for object recognition and segmentation have verified the effectiveness of joint optimization of two semantic tasks. However, learning discriminative representations with insufficient training data and redundant contextual information from the background remains challenging. Semantic attributes are designed as powerful and informative mid-level features that 1) share information across categories to model the interclass correlation and that 2) can be localized in the object region to benefit foreground extraction. This paper introduces a novel attribute-aware feature encoding (AFE) module to a multi-task network for object recognition and segmentation with the aim of improving both semantic tasks by regularizing feature encoding with auxiliary attribute learning. Intuitively, attribute learning in our method not only provides extra supervision signals to capture interclass correlation in object classification but also refines the output of object segmentation via weakly supervised attribute localization. The experimental results on two public benchmarks show that our method yields remarkable improvement in both semantic tasks and auxiliary attribute estimation over existing methods.

The Satellite communication network is not limited by geographical factors and is an indispensable part of global interconnection. This paper analyzes a hybrid of cloud computing and edge computing LEO satellite network architecture, where the three-tier network is composed of ground users, LEO satellites, and remote cloud servers. Users can not only forward computing tasks to remote cloud servers through LEO satellites for processing, but also directly offload computing tasks to LEO satellites for processing. Based on this, we study the problem of user computing offloading in LEO satellite network and construct a joint optimization problem of offloading decision and computing resource allocation, which aims to reduce the user processing delay and energy consumption when the total computing resources of edge nodes are limited. This problem is a mixed-integer nonlinear problem, which is difficult to be solved in finite time. With the increase in the number of users, the complexity of the solution is very high. Therefore, we reconstruct the problem based on the linear reconstruction technique and relax the binary variables to transform the original non-convex problem into a convex problem. The simulation results show that the algorithm can effectively reduce user delay and energy consumption compared to the case when the tasks are processed locally.

Recently, online fountain codes attract much attention as the online property is enhanced by introducing feedback. In this paper, we propose two improvements to Heuristic Table Lookup based on Overhead (HTLO), i.e., Integration based Overhead Calculation (IO) and Flexible Selection Strategy (FSS), and introduce our new feedback strategy IO-FSS-HTLO. In the proposed scheme, the degree of coded symbols and corresponding feedback points are selected to achieve lower overhead when the number of feedback transmissions is limited. Results show that IO-FSS-HTLO could achieve better overhead performance under limited feedback scenarios compared to HTLO.

With the explosively increasing video traffic, ensuring the smooth playback of a video has been a challenging problem especially in the fifth generation (5G) mobile communication system. To improve the quality of experience (QoE) of a video playback, the real-time prediction of the video stuck can be a help. In this paper, we firstly select eight features from different layers to reflect the quality of video playback. Then, two models, long and short term memory (LSTM)-based Prediction Model and Gated recurrent unit(GRU)-based Prediction Model, are proposed to predict the stuck state of playback. Finally, to evaluate the effectiveness of the two proposed prediction models, we present the simulation results of accuracy and loss of the two models. Besides, comparison between traditional methods and the proposed one are provided with performance gain in terms of the accuracy, recall, confusion matrix as well as F1-score.

Low earth orbit (LEO) satellite networks can break through geographical restrictions and achieve global wireless coverage, which is an indispensable choice for future mobile communication systems. In this article, we present a hybrid cloud and edge computing LEO satellite (CECLS) network with a three-tier computation architecture, which can provide ground users with heterogeneous computation resources and enable ground users to obtain computation services around the world. With the CECLS architecture, we investigate the computation offloading decisions to minimize the sum energy consumption of ground users, while satisfying the constraints in terms of the coverage time and the computation capability of each LEO satellite. The considered problem leads to a discrete and nonconvex since the objective function and constraints contain binary variables, which makes it difficult to solve. To address this challenging problem, we convert the original nonconvex problem into a linear programming problem by using the binary variables relaxation method. Then, we propose a distributed algorithm by leveraging the alternating direction method of multipliers (ADMMs) to approximate the optimal solution with low computational complexity. Simulation results show that the proposed algorithm can effectively reduce the total energy consumption of ground users.

The interest in Facial Expression Recognition (FER) is increasing day by day due to its practical and potential applications, such as human physiological interaction diagnosis and mental disease detection. This area has received much attention from the research community in recent years and achieved remarkable results; however, a significant improvement is required in spatial problems. This research work presents a novel framework and proposes an effective and robust solution for FER under an unconstrained environment; it also helps us to classify facial images in the client/server model along with preserving privacy. There are a lot of cryptography techniques available but they are computationally expensive; on the other side, we have implemented a lightweight method capable of ensuring secure communication with the help of randomization. Initially, we perform preprocessing techniques to encounter the unconstrained environment. Face detection is performed for the removal of excessive background and it detects the face in the real-world environment. Data augmentation is for the insufficient data regime. A dual-enhanced capsule network is used to handle the spatial problem. The traditional capsule networks are unable to sufficiently extract the features, as the distance varies greatly between facial features. Therefore, the proposed network is capable of spatial transformation due to the action unit aware mechanism and thus forwards the most desiring features for dynamic routing between capsules. The squashing function is used for classification purposes. Simple classification is performed through a single party, whereas we also implemented the client/server model with privacy measurements. Both parties do not trust each other, as they do not know the input of each other. We have elaborated that the effectiveness of our method remains unchanged by preserving privacy by validating the results on four popular and versatile databases that outperform all the homomorphic cryptographic techniques.

Online fountain codes (OFC) have attracted much attention for their good intermediate performance, which is important for receivers with low-complexity requirement. However, low-complexity receivers generally have limited buffer size to store coded symbols that have not been fully decoded yet, as well as limited power budget for feedback transmissions. In this paper, we propose improved transmission schemes for online fountain codes to reduce the buffer occupancy and feedback transmissions. Firstly, we analyze the relationship between buffer occupancy and overhead as well as the relationship between recovery rate and overhead for online fountain codes. Motivated by the analysis, we propose the weighted online fountain codes (WOFC) which can adapt to various buffer sizes by adjusting the weight to control the probability that a coded symbol can be fully processed immediately, and analyze its performance. Then we further propose weighted online fountain codes with low feedback (WOFC-LF), which utilize the proposed analysis to estimate the recovery rate, and reduce feedback transmissions. Simulation results verify the effectiveness of the analysis for both OFC and WOFC, and demonstrate the superior performance of WOFC-LF with limited buffer size and feedback transmissions.

In this paper, we investigate the network utility maximization problem in a dual-functional radar-communication multi-unmanned aerial vehicle (multi-UAV) network where multiple UAVs serve a group of communication users and cooperatively sense the target simultaneously. To balance the communication and sensing performance, we formulate a joint UAV location, user association, and UAV transmission power control problem to maximize the total network utility under the constraint of localization accuracy. We then propose a computationally practical method to solve this NP-hard problem by decomposing it into three sub-problems, i.e., UAV location optimization, user association and transmission power control. Three mechanisms are then introduced to solve the three sub-problems based on spectral clustering, coalition game, and successive convex approximation, respectively. The spectral clustering result provides an initial solution for user association. Based on the three mechanisms, an overall algorithm is proposed to iteratively solve the whole problem. We demonstrate that the proposed algorithm improves the minimum user data rate significantly, as well as the fairness of the network. Moreover, the proposed algorithm increases the network utility with a lower power consumption and similar localization accuracy, compared to conventional techniques.

Content caching is recognized as a promising solution to release the heavy burden of backhaul links and decrease the content transmission latency in Fog radio access networks (Fog-RANs). However, the content caching design is still a challenging problem with considering the user request patterns, the content delivery strategies, and the limited caching capacity. Recommendation has the capability of reshaping users' content requests for further prompting caching gain. The joint recommendation, caching, beamforming holds the potential to improve the system performance of Fog-RANs. In this paper, a joint recommendation, caching, and beamforming scheme is proposed for multi-cell multi-antenna recommendation aware Fog-RANs. Aiming at minimizing the content transmission latency, we formulate a joint recommendation, caching, and beamforming optimization problem. The minimization problem is a very challenging two-timescale mixed integer nonlinear programming problem, which is hard to solve in general. By exploring structural properties of the problem, we propose an alternative optimization algorithm with low complexity through decomposing the original problem into three sub-problems. Extensive simulations show that our proposed method can significantly reduce the content transmission delay.

Grant-free Non-Orthogonal Multiple Access (NOMA) techniques are able to reduce the signaling overhead and the transmission latency in multi-user communications system. However, most of the existing code-domain grant-free NOMA schemes reuse the spreading signatures designed for the grant-based scenarios. Considering the sparsity and randomness nature of user activities in the uplink transmissions, we propose a deep learning-based signature design, where the non-equal user activation probabilities are exploited to optimize the code-domain NOMA signature. In addition, the conventional grant-free NOMA signatures are not specifically designed over finite Galois field, which hinders the implementation of the encoder/decoder using practical hardware. To address these challenges, we utilize the quantized deep learning framework for the NOMA signature training, which jointly optimizes the sequence generation and the quantization. The numerical results reveal that the obtained signatures outperform the conventional ones especially when the users has unequal activation probabilities.

Research and processing development on satellite communications has strongly re-emerged in recent years. Following the prosperity of various wireless services provided by satellite communications, the security issue has raised growing concerns since the space information network is susceptible to be eavesdropped by illegal adversaries in such a large-scale wireless network. Recently, the physical-layer security (PLS) has emerged as an alternative security paradigm that explores the randomness of the wireless channel to achieve confidentiality and authentication. The success story of the PLS technique now spans a decade and thrives to provide a layer of defense in satellite communications. With this position, a comprehensive survey of satellite communications is conducted in this article with an emphasis on PLS. We first briefly introduce essential background and the view of the satellite Internet of Things (IoT), as well as discuss related research challenges faced by the emerging integrated network architecture. Then, we revisit the most popular satellite channel model influenced by many factors and list the commonly used secrecy performance metrics. Also, we provide an exhaustive review of state-of-the-art research activity on PLS in satellite communications, which we categorize by different architectures including land mobile satellite communication networks, hybrid satellite-terrestrial relay networks, and satellite-terrestrial integrated networks. In addition, a number of open research problems are identified as possible future research directions.

For the benefit of improved intermediate performance, recently online fountain codes attract much research attention. However, there is a trade-off between the intermediate performance and the full recovery overhead for online fountain codes, which prevents them to be improved simultaneously. We analyze this trade-off, and propose to improve both of these two performance. We first propose a method called Online Fountain Codes without Build-up phase (OFCNB) where the degree-1 coded symbols are transmitted at first and the build-up phase is removed to improve the intermediate performance. Then we analyze the performance of OFCNB theoretically. Motivated by the analysis results, we propose Systematic Online Fountain Codes (SOFC) to further reduce the full recovery overhead. Theoretical analysis shows that SOFC has better intermediate performance, and it also requires lower full recovery overhead when the channel erasure rate is lower than a constant. Simulation results verify the analysis and demonstrate the superior performance of OFCNB and SOFC in comparison to other online fountain codes.

To cope with the exponential growth of demand, ultra dense networks (UDNs) are a promising technology in future mobile networks. With small cells densely deployed in networks, how to allocate wireless resources in UDNs efficiently becomes a challenging research topic. In this paper, we concentrate on the distributed user-centric clustering and base station (BS) mode choose problem in UDNs. We formulate a combinatorial optimization problem, with the throughput maximization and power consumption minimization jointly considered in the optimization object. In order to reduce the complexity of the problem, we decompose the original problem into two subproblems in terms of user-centric clustering and BS mode choose, and then solve those subproblems by the max-sum algorithm in sequence. The proposed algorithm can be conducted in a distributed way, and the computational complexity grows linearly with the network size. Simulation results show that the performance of proposed algorithm approaches the performance of the exhaustive algorithm well, and outperforms the conventional algorithm significantly.

Providing ubiquitous connectivity to diverse device types is the key challenge for 5G and beyond 5G (B5G). Unmanned aerial vehicles (UAVs) are expected to be an important component of the upcoming wireless networks that can potentially facilitate wireless broadcast and support high rate transmissions. Compared to the communications with fixed infrastructure, UAV has salient attributes, such as flexible deployment, strong line-of-sight connection links, and additional design degrees of freedom with the controlled mobility. In this paper, a comprehensive survey on UAV communication toward 5G/B5G wireless networks is presented. We first briefly introduce essential background and the space-air-ground integrated networks, as well as discuss related research challenges faced by the emerging integrated network architecture. We then provide an exhaustive review of various 5G techniques based on UAV platforms, which we categorize by different domains, including physical layer, network layer, and joint communication, computing, and caching. In addition, a great number of open research problems are outlined and identified as possible future research directions.

This paper develops a tractable analysis framework to evaluate the reliability and security performance of cooperative non-orthogonal multiple access (co-NOMA) in cognitive networks, where both a primary base station (PBS) and a NOMA-strong primary user (PU) send confidential messages to multiple uniformly distributed PUs in the presence of randomly located external eavesdroppers. For constricting the interference to the PUs imposed by cognitive femto base stations (CFBSs), a mobile association scheme is introduced. Moreover, an eavesdropper-exclusion zone is introduced around the PBS for improving the secrecy performance of the primary networks. To characterize the security-reliability tradeoff of the considered network, we first derive the activation probability of CFBSs and the conditional probability density function associated with the distance between the relay user and other PUs. Then, the connection outage probability (COP) and the secrecy outage probability (SOP) of each PU with NOMA (co-NOMA) or non-cooperative NOMA (nco-NOMA) are separately derived to obtain the overall COP and SOP in the primary networks. Finally, the tradeoff between COP and SOP with co-NOMA (identified as transmission SOP) is investigated for simultaneously reflecting the security and reliability. Numerical results demonstrate the performance improvements of the proposed co-NOMA scheme in comparison to that of the nco-NOMA scheme in terms of different parameters. Furthermore, the security-reliability tradeoff performance of co-NOMA is shown.

To satisfy the rapidly growing demands of wireless communications, new structures have been proposed for the fifth-generation (5G) mobile communication networks, such as cloud radio access networks (C-RANs), which have advantages including high energy efficiency, large network capacity, and high flexibility. This paper concentrates on the problem of remote radio head (RRH) activation, user association, and power allocation in C-RANs. To tackle the problem with l0norm, we transform it into a mixed-integer nonlinear programming (MINLP) problem. Instead of solving it by centralized solvers, we propose a novel algorithm based on Benders decomposition, which can obtain the optimal solution of the MINLP problem. To solve the primal problem in Benders decomposition efficiently, we adopt the alternating direction method of multipliers (ADMM) to achieve a parallel implementation. To further reduce the complexity of solving the MINLP problem, a distributed two-stage iterative algorithm combining the ADMM and the max-sum algorithm is also proposed. The simulation results demonstrate that the first proposed algorithm can obtain the optimal solution, and the second proposed algorithm outperforms conventional algorithms significantly.

Intuitively, we can think of object recognition and attribute prediction as correlated tasks. However, they appeared to conflict in a simple two-branch multi-task framework (a category branch and an attribute branch) with a shared backbone part (convolutional layers and pooling layers). The performance dropped along with the iterative training steps. This result might have been caused by the noncoherent feature distribution between the object recognition features and the attribute prediction features. Recognition features are discriminative for different categories and are not sensitive to intracategory variations, while attribute prediction features are discriminative for different attributes, although these attributes can exist in objects from the same category. Thus, a conflict occurs when we force the network to learn the two kinds of distinct features simultaneously. To address this problem, we propose the category and attribute prediction network (CAP-net), in which a category-constrained attribute prediction structure is introduced to transfer the object recognition knowledge and avoid the conflict between two features. The CAP-net parameters can be learned easily with a regularization method. Extensive experimental results show that the CAP-net outperforms the state-of-the-art methods on object recognition and attribute prediction tasks.

Modulation classification of communication signal is one of the key technologies for realizing non-cooperative communication tasks, multi system communication interconnection and software radio. Therefore, when the decision process cannot wait for more data to increase certainty, how to effectively classify the modulation type in a short time is an unavoidable and challenging topic. In this paper, we make a performance comparison of traditional feature-based neural network and deep neural network (DNN) with complex digital modulation signal datasets. The results indicate that DNN has a stronger ability to extract classification features. Then we demonstrate two novel architectures based on DNN, which disentangle more meaning hidden features from the short-time signal and perform superiorly under limited signal length. Finally, we test the generalization ability of neural network models to signal-to-noise radio (SNR).

Unmanned Aerial Vehicles (UAVs) communications have received increasing attention in both military and civilian applications due to low cost and ease of deployment. Security is an unavoidable yet challenging issue during the data transmission process of communication networks. In this paper, we concentrate on the resource allocation in secure relay network assisted by a UAV in the presence of multiple eavesdroppers. Our target is to maximize the secrecy rate by jointly designing the transmit beamformer and artificial noise subject to the transmit power constraint of UAV. The resulting optimization problem is highly intractable and the key observation is that the original optimization problem can be equivalently transformed into a two- level problem. In particular, the inner-level problem can be solved by exploiting Semi-Definite Relaxation (SDR) and Charnes-Cooper transformation techniques, and the outer-level problem is handled by performing one-dimensional algorithm. Also, the tightness of the rank-relaxation is analyzed. Finally, simulation results are provided to validate the performance of our proposed scheme.

Massive multiple-input multiple-output (MIMO) has become a promising solution to provide unprecedented spectral efficiency (SE) to future cellular networks, based on the idea of equipping the base station (BS) with hundreds or thousands of antenna elements operating in a coherent fashion. At the doors of the future fifth generation mobile communication networks (5G), massive MIMO has been widely recognized as one of the cornerstones, given the ambitious key performance indicators of the future standard. The channel state information (CSI) acquisition is one of the core activities in massive MIMO, on which its entire performance depends to a great extent. Pilot contamination has been recognized as the main limiting factor to acquire an accurate CSI, becoming the focus of a large body of research. In this paper, we focus on the pilot contamination problem in massive MIMO systems. An approach is proposed to mitigate this problem based on the use of superimposed (SP) pilots in combination with time-multiplexed (TM) pilot sequences. Specifically, we use the contaminated channel estimates to reduce the amount of interference produced when SP pilots are used. Results show that the amount of interference caused by transmitting pilots alongside the data is substantially reduced when this method is put into place. In turn, the proposed method leads to mitigating the pilot contamination.

The online property of fountain codes enables the encoder to efficiently find the optimal encoding strategy that minimizes the encoding overhead based on the instantaneous decoding state. Therefore, the receiver is able to optimally recover data from losses that differ significantly from the initial expectation. In this paper, we propose a framework to analyze the relationship between overhead and the number of recovered source symbols for online fountain codes based on random graph theory. Motivated by the analysis, we propose improved online fountain codes (IOFCs) by introducing a designated selection of source symbols. Theoretical analysis shows that IOFC has lower overhead compared with the conventional online fountain codes. We verify the proposed analysis via simulation results and demonstrate the tradeoff between full recovery and intermediate performance in comparison to other online fountain codes.

Error control codes are widely applied in modern communication systems to improve the bandwidth-power efficiency and the reliability of data transmissions.Modern error control codes have attracted the interest of scholars and industry partners since Turbo codes were invented.For example, Turbo codes have been used in the 4G cellular mobile systems.Nowadays, LDPC codes and the polar codes are adopted in the 5G standard.The recent development on the theoretic framework of new channel coding theorem for finite code length will provide guidelines for future practical error control codes designs.In the age of IoT, everything will be connected via communication links.It is expected that the next-generation communication systems need to support many scenarios such as wireless communications, optical communications, distributed storage systems, V2X networks, and sensor networks.These scenarios will impose new requirements to the communication systems ranging from lower complexity encoder/decoder, lower delay or latencies, ultrareliable transmission at rates close to the Shannon capacity, low energy consumptions, etc.In addition to the communication systems, error control codes also find emerging applications in security, flash memories, and deep-space probing.This special issue is a collection of 11 papers which explore the performance of error control codes for the next generation communication systems and discuss the opportunities and challenges that they will face.

In legacy long term evolution (LTE) networks, multiple transmission modes are defined to cater to diverse wireless environment and improve the spectrum utilization. However, constrained by user equipment (UE) processing capability on blind detection of downlink control information (DCI), two transmission modes are allowed to be configured to UE simultaneously. In recent 5G standardization, the polar codes have supplanted the tail biting convolution codes (TBCC), becoming the channel coding scheme for downlink control information (DCI). Motivated by its successive decoding property, a novel design of DCI encoding and decoding is proposed in this paper. The proposed scheme could support dynamic configuration of transmission modes with decreasing the complexity of blind detection. Evaluation results from link level simulations show that the performance loss compared to conventional encoding/decoding scheme is generally negligible and the proposed scheme can comply with the false alarm rate (FAR) target of 5G standardization.

The capacity bound of the Gaussian interference channel (IC) has received extensive research interests in recent years. Since the IC model consists of multiple transmitters and multiple receivers, its exact capacity region is generally unknown. One well‐known capacity achieving method in IC is Han‐Kobayashi (H‐K) scheme, which applies two‐layer rate‐splitting (RS) and simultaneous decoding (SD) as the pivotal techniques and is proven to achieve the IC capacity region within 1 bit. However, the computational complexity of SD grows exponentially with the number of independent signal layers, which is not affordable in practice. To this end, we propose a scheme which employs multi‐layer RS at the transmitters and successive simple decoding (SSD) at the receivers in the two‐transmitter and two‐receiver IC model and then study the achievable sum capacity of this scheme. Compared with the complicated SD, SSD regards interference as noise and thus has linear complexity. We first analyze the asymptotic achievable sum capacity of IC with equal‐power multi‐layer RS and SSD, where the number of layers approaches to infinity. Specifically, we derive the closed‐form expression of the achievable sum capacity of the proposed scheme in symmetric IC, where the proposed scheme only suffers from a little capacity loss compared with SD. We then present the achievable sum capacity with finite‐layer RS and SSD. We also derive the sufficient conditions where employing finite‐layer RS may even achieve larger sum capacity than that with infinite‐layer RS. Finally, numerical simulations are proposed to validate that multi‐layer RS and SSD are not generally weaker than SD with respect to the achievable sum capacity, at least for some certain channel gain conditions of IC.

With the development of Virtual Reality (VR) technology, single-viewpoint videos have been replaced by the multi-sviewpoint panoramic video owing to the fact that the latter brings people more immersive experiences. To improve the quality of experience (QoE) of panoramic videos, the video quality assessment (VQA) method need to be investigated. However the design of the quality metric for panoramic videos is a more complicated and harder problem, since users' feelings are affected by more psychological and physiological factors. Traditional VQA methods cannot evaluate the quality of panoramic videos accurately. In this paper, we propose a general objective full-reference quality assessment method for panoramic videos. The proposed method is based on multi-level quality factors, which are calculated with region of interest (ROI) maps. The framework is flexible and expandable, and its objective output has a higher correlation with subjective scores than that of traditional VQA methods and existing panoramic video evaluation methods.

We propose a novel method for joint two-dimensional (2D) direction-of-arrival (DOA) and channel estimation with data detection for uniform rectangular arrays (URAs) for the massive multiple-input multiple-output (MIMO) systems. The conventional DOA estimation algorithms usually assume that the channel impulse responses are known exactly. However, the large number of antennas in a massive MIMO system can lead to a challenge in estimating accurate corresponding channel impulse responses. In contrast, a joint DOA and channel estimation scheme is proposed, which first estimates the channel impulse responses for the links between the transmitters and antenna elements using training sequences. After that, the DOAs of the waves are estimated based on a unitary ESPRIT algorithm using previous channel impulse response estimates instead of accurate channel impulse responses and then, the enhanced channel impulse response estimates can be obtained. The proposed estimator enjoys closed-form expressions, and thus it bypasses the search and pairing processes. In addition, a low-complexity approach toward data detection is presented by reducing the dimension of the inversion matrix in massive MIMO systems. Different cases for the proposed method are analyzed by changing the number of antennas. Experimental results demonstrate the validity of the proposed method.

In this paper, we investigate the linear transceiver optimization for multiple-inputmultiple-output (MIMO) interference networks, where multiple pairs of multi-antenna source and destination nodes communicate simultaneously. Different from most of existing works, we jointly consider three critical issues of the linear transceiver optimization for MIMO interference networks based on multi-objective optimization theory, i.e., signal transmission, energy and security. Specifically, using the modified weighted Tchebycheff method, we investigate three kinds of multi-objective optimization problems (MOOPs): 1) sum mean square error minimization and harvested energy maximization; 2) transmit power minimization and energy harvesting efficiency maximization; 3) transmit power minimization, energy harvesting efficiency maximization, and physical layer security. Based on the Charnes-Cooper transformation and penalty function method, the formulated MOOPs are transformed into convex optimization problems and thus can be effectively solved. The resulting Pareto optimal solutions set reveals the complicated but important relationships among these involved single objective optimization problems, which are usually individually investigated in the literature. Finally, numerical simulation results demonstrate the performance advantages of the proposed algorithm and corroborate the theoretical analysis.

We design novel transmission strategies to maximize the energy efficiency (EE) of the uplink multi-user multipleinput and multiple-output relay channel. In this channel, K multi-antenna users communicate with a multi-antenna base station (BS) through a multi-antenna relay. To achieve the goal of EE maximization, we propose new iterative algorithms to jointly optimize the multi-user precoder and the relay precoder under transmit power constraints for two cases. In the first case, the perfect global channel state information (CSI) is available, while in the second case, the CSI between the relay and the BS is imperfect. To surmount the non-convexity of our formulated EE optimization problems in both cases, we introduce the parameter subtractive function into the proposed algorithms. Then, the EE parameter in the parameter subtractive function is updated by Dinkelbach's algorithm in the perfect CSI case, and by the bisection method in the imperfect CSI case. Moreover, in the perfect CSI case, the relay precoder is optimized by the diagonalization operation and the multi-user precoder is optimized based on the weighted minimum mean square error method. Differently, in the imperfect CSI case, we apply the sign-definiteness lemma to promote the semidefinite programming formulation of the EE optimization problem. Furthermore, we present the numerical results to demonstrate that our proposed iterative algorithms have a good convergence rate in both cases. In addition, we show that our proposed iterative algorithms achieve a higher EE performance than the existing algorithms in both CSI cases.

Owing to the wireless signal power received by the energy harvesting (EH) node is generally higher than that of the information decoding (ID) node in simultaneous wireless information and power transfer (SWIPT) system, the confidential information becomes vulnerable to be wiretapped. Motivated, in this paper, we aim at realizing secure communications for two-user MIMO interference channel with SWIPT. Unfortunately, the formulated secrecy rate maximization problem is non-convex with respect to the covariance matrices of two transmitters, thus an alternative algorithm is proposed to solve the nonconvex optimization problem. Firstly, the orthogonal-projection-based optimization algorithm is performed to completely suppress the interference to ID receiver, then by applying the Taylor series expansion, the covariance matrix of information transmitter is optimized based on the dual optimization. Finally, numerical experiments are conducted to validate the security performance of the proposed algorithm.

Thanks to gigahertz unlicensed spectrum, the millimeter wave (mmWave) communication becomes an important enabling technology to meet the increasing data rate demands of future communication systems. It is also well-known that wireless communications are susceptible to security threatening, especially for wireless two-way relaying networks. Hence, in this work, we propose two secrecy beamforming schemes for large mmWave two-way relaying networks. Firstly, the secrecy rate maximization problem is considered with the constraint of relay power. Then the relay transmit power is optimized to satisfy the secrecy rate requirement of network. Owing to the nonconvexity of original optimization problem, the null-space beamforming is utilized to transform both problems into the standard SOCP problem, which can be solved effectively with the convex optimization technique. Finally, numerical experiments are conducted to show the superior security performance and high energy efficiency of the two proposed secrecy beamforming schemes, respectively.

In this paper, we investigate a rateless code design for a two-time slot two-way relay channel (TWRC), where network coding operation is employed at the relay. Rateless codes are used at both users and the relay nodes as they can cope with various channel conditions. We consider a general TWRC framework, where the two users may use different rateless code generator matrices, thus, the relay cannot directly recover the physical-layer network coded (PNC) message of the two users. We propose a hybrid estimate and forward (EF)/decode and forward (DF) protocol with rateless codes for the TWRC. In the proposed scheme, the relay uses a joint belief propagation decoding process to decode the messages of two users. Depending on whether the relay recovers both users' messages, only one user's message or no users' messages, the relay will generate three different types of signals, namely, NC, soft NC, and soft PNC, respectively. We design the degree distributions of the rateless codes for the three nodes of the TWRC, to make them suitable not only for decoding at two users at high signal-to-noise ratio (SNR), but also for decoding at the relay at low SNR. We show that the proposed hybrid EF/DF protocol with the designed rateless codes outperforms other schemes significantly in terms of bit error rate performance.

Small cell networks have been recognized as a promising technology to realize high spectrum and energy efficiency communications in future wireless networks. However, the capacity of small cell networks is limited by the interference among the links communicating simultaneously. Efficient resource allocation and effective interference management is definitely imperative for small cells. In this paper, we propose an algorithm to optimize the power and subcarrier allocation jointly in order to maximize the weighted sum rate for dense small cell networks. Facing with a large amount of small cells, the optimization problem is in nature a large scale optimization problem. Using advanced decomposition theory, the proposed algorithm can effectively decompose the considered optimization problem into a series of much simpler subproblems which can be efficiently solved in parallel. Finally, simulation results demonstrate that the proposed algorithm enjoys greater performance gain and faster convergence as compared to the existing schemes.

In this paper, we discuss MIMO transceiver design under a new type of power constraint named mixed power constraints. Mixed power constraint is referred to the power model in which for a given set of antennas, several subsets are constrained by sum power constraints while the other antennas are subject to individual power constraints. It includes sum power constraint and per-antenna power constraints as its special cases and it can strike a balance between complexity and performance. In our work, we try to solve the optimization problem in an analytical way instead of relying on some famous software packages e.g., CVX or SeDuMi. Firstly, the specific formula of the optimal signal covariance matrix has been derived. Based on the structure, a low complexity non-iterative solution is given in our work, which can be interpreted as a matrix version water-filling solution. This solution has a much clear engineering meaning and is suitable for practical implementations even for massive MIMO systems. Finally, simulation results demonstrate the accuracy of our theoretical results.

We analyze the achievable rate of the uplink of a single-cell multi-user distributed massive multiple-input-multiple-output (MIMO) system. Each user is equipped with single antenna and the base station (BS) is equipped with a large number of distributed antennas. We derive an analytical expression for the asymptotic ergodic achievable rate of the system under zero-forcing (ZF) detector. In particular, we consider circular antenna array, where the distributed BS antennas are located evenly on a circle, and derive an analytical expression and closed-form bounds for the achievable rate of an arbitrarily located user. Subsequently, closed-form bounds on the average achievable rate per user are obtained under the assumption that the users are uniformly located. Based on the bounds, we can understand the behavior of the system rate with respect to different parameters and find the optimal location of the circular BS antenna array that maximizes the average rate. Numerical results are provided to assess our analytical results and examine the impact of the number and the location of the BS antennas, the transmit power, and the path-loss exponent on system performance. Simulations on multi-cell networks are also demonstrated. Our work shows that circularly distributed massive MIMO system largely outperforms centralized massive MIMO system.

In this paper, the performance of hybrid automatic repeat request with incremental redundancy (HARQ-IR) in a mobile ad hoc network (MANET) is analyzed. Based on the theory of stochastic geometry, both interference and the randomness in nodes' locations are incorporated into our analysis. The outage probability after the kth retransmission is analyzed and the outage performance of HARQ-IR is compared with that of Type-I HARQ and HARQ with chase combining (HARQ-CC). Our analysis reveals that the performance gains of HARQ-IR over Type-I HARQ and HARQ-CC increase with the number of retransmissions while they decrease with the path loss exponent. The network throughput in terms of transmission capacity is also analyzed. The results indicate that a certain small number of retransmissions is sufficient to achieve the maximal transmission capacity. Meanwhile, the optimal intensity of source nodes to maximize the transmission capacity is shown to be within a specific interval.

For the popularity of delivering video service over HTTP Adaptive Streaming (HAS) in the mobile network, developing an automatic method to evaluate customers' quality of experience (QoE) for HAS video service in real time is highly desired for network operators and content providers. This paper proposes a novel QoE evaluation strategy for HAS video service based on the specific features of HAS and data-mining technology. Via capturing the media description files of HAS and the request information of clients, the QoE can be monitored in real time for operators. The evaluation algorithm for the proposed strategy is trained and tested based on the data sets collected from subjective test results. The test results suggest that the predicted Mean Opinion Score (pMOS) measured by our evaluation algorithm has high correlation with the Mean Opinion Score (MOS) measured by the subjective test, and meanwhile the evaluation strategy is easy to implement for operators.

In this paper, we focus on a new small cell discovery signal which improves the detection probability in ultra-dense small cell scenario. Due to the severe inter-cell interference, the detection performance of primary synchronization signal (PSS) and secondary synchronization signal (SSS) is deteriorated in future ultra-dense small cell scenario in the 3rd Generation Partnership Project (3GPP) long term evolution (LTE) system. Thus the current supported 504 cell IDs may not be sufficient. To mitigate the impact of such inter-cell interference problems and improve the cell search performance with increasing number of detect target cells, we propose a novel defined auxiliary secondary synchronization signal (A-SSS) in cell search procedure. Our simulation results show that the detection probability of cell search can be improved effectively with the proposed method.

In this paper, the low information-exchange and robust precoding design for distributed antenna systems is investigated, which is of great importance for the new mobile network architecture namely cloud radio access networks (C-RAN). Relying on an interesting low complexity decomposition algorithm, a distributed robust linear minimum mean-square-error (LMMSE) precoding algorithm is proposed. Exploiting the elegant properties of the decomposition algorithm, the precoder design problem can be decomposed into several subproblems. In addition, all the subproblems can be performed in parallel term. Moreover for the proposed algorithm there is no need of an extra step to calculate the Lagrange multipliers at each iteration. Thus the exchanging information of the algorithm can be significantly reduced. Finally, it is demonstrated by the simulation that the proposed algorithm enjoys both satisfying convergence properties and better performance.

In this paper, the linear minimum mean square error beamformers are designed for relay-assisted cloud radio access network (C-RAN). In C-RAN, the remote radio units are separated from the baseband units to save energy cost. To further enhance network coverage, it is a good choice to duly arrange relay nodes. Regrading the per-antenna power constraints at both the relay nodes and the remote radio heads (RRHs), the beamformer matrices at the relay nodes and RRHs are jointly optimized for the relay assisted C-RAN. Since the considered problem is a non-convex optimization problem and is with multiple variables, it is in general very hard to solve. To make the design suitable for C-RAN exploiting the problem structure, two novel decomposition algorithms are proposed. One algorithm is mainly carried out at the RRHs, another is mainly performed at the relay node. Finally in the simulations, the performance of the proposed algorithms are demonstrated.

The authors propose a low complexity list successive cancellation (LCLSC) decoding algorithm, where the advantages of the successive cancellation (SC) decoding and the list successive cancellation (LSC) decoding are both considered. In the proposed decoding, SC decoding instead of LSC decoding is implemented when all information bits from bad subchannels are received reliably. While the reliability of each information bit is estimated by its likelihood ratio (LR), the bit channel quality is measured via its Bhattacharyya parameter. To achieve this goal, the authors introduce two thresholds: LR threshold and Bhattacharyya parameter threshold. Also, the methods to determine them are both elaborated. The numerical results suggest that the complexity of LCLSC decoding is much lower than LSC decoding and can be close to that of SC decoding, while the error performance is almost equal to that of LSC decoding. Especially, when the code rate is in low region, the advantage of our decoding is more obvious.

In this paper, the authors investigate the distributed power allocation for the multicell orthogonal frequency division multiple access networks by taking both the energy efficiency and the intercell interference (ICI) mitigation into account. A performance metric termed as throughput contribution is exploited to measure how the ICI is effectively coordinated. To achieve a distributed power allocation scheme for each base station (BS), the throughput contribution of each BS to the network is first given based on a pricing mechanism. Different from the existing works, a biobjective problem is formulated based on the multiobjective optimisation theory, which aims at maximising the throughput contribution of the BS to the network and minimising its total power consumption at the same time. By using the method of the Pascoletti and Serafini scalarisation, the relationship between the varying parameters and the minimal solutions is revealed. Furthermore, to exploit the relationship an algorithm is proposed based on which all the solutions on the boundary of the efficient set can be achieved by adaptively adjusting the involved parameters. With the obtained solution set, the decision maker has more choices in the power allocation schemes in terms of both the energy consumption and the throughput. Finally, the performance of the algorithm is assessed by the simulation results.

In this study, the authors investigate the linear minimum‐mean‐square‐error beamforming design for relay‐assisted cloud radio access network (C‐RAN). A standard C‐RAN architecture separates baseband processing units and wireless radio units in order to save energy cost. To further enhance network coverage, several relay nodes (RNs) are also deployed. Regrading the per‐antenna power constraints at both of the remote radio heads (RRHs) and the RNs in the author's work the beamforming matrices at the RRHs and RNs are ‘jointly’ optimised for the considered relay assisted C‐RAN. The considered optimisation problem is a non‐convex and multiple variable optimisation problem which is in general very hard to solve. In order to make the design suitable for large scale networks exploiting to the problem structure a novel two stage decomposition algorithms are proposed. Finally, a detailed mean‐square‐error performance comparison is given by the simulations.

In this paper, the outage performance of an opportunistic amplify-and-forward cooperative downlink cellular system is analyzed. Different from prior works, the randomness of the network topology is taken into account by modeling the user nodes as a homogeneous Poisson point process. Based on this model, outage probability is derived and the impacts of several system parameters are investigated. Under certain conditions, the closed form expression of outage probability is derived. It is found from our results that the diversity order of this opportunistic cooperative system at high signal-to-noise-ratio (SNR) is one. Moreover, optimal power allocation can be found from our results to minimize the outage probability.

In this paper, outage performance of hybrid automatic repeat request (HARQ) technique in interference dominant mobile ad hoc networks (MANETs) is analyzed. Unlike prior analysis, interference and spatial randomness of the nodes (i.e. the randomness in the number of nodes and nodes' locations) are considered. Based on the theory of point processes, the outage probabilities of two popular HARQ techniques, that are type-I HARQ and HARQ with chase combining (HARQ-CC), are derived in closed forms. The outage performance gain of HARQ-CC over type-I HARQ is also discussed and is found to follow the scaling law of Θ (k2(k+1)/α) where α is the path loss exponent and k is the number of retransmissions. It is also demonstrated that both type-I HARQ and HARQ-CC can significantly improve the communication performance even in interference dominant MANETs. Furthermore, it is revealed that in most cases HARQ-CC is superior over type-I HARQ, however, for a dense network type-I HARQ can provide comparable performance with lower complexity than HARQ-CC and is thus more preferable.

In this paper, the uplink coverage probability for heterogeneous cellular systems with range expansion is analyzed and derived in closed-form. Unlike most of the previous analyses of heterogeneous systems, the randomness of not only the number of mobile users but also their locations is taken into account in the analysis based on the theory of stochastic geometry. With the derived analytical results, the impacts of various system parameters on the uplink performance are investigated in detail. The correctness of the analytical results is also verified by simulations. These analytical results can thus serve as a guidance for the design of the heterogeneous systems.

In this paper, we investigate the joint power allocation for dual-hop amplify-and-forward (AF) MIMO-OFDM cognitive radio (CR) networks. The considered AF MIMO-OFDM CR network coexists with a primary radio (PR) network through underlay spectrum sharing. In order to mitigate the interference to the PR network, environmental learning algorithm is adopted to blindly estimate the null space of the PR user, which are orthogonal to the PR communication channels. With necessary channel state information, under independent transmit power constraints as well as the interference constraints, the power allocation of CR source and relay and subcarrier pairing over two hops are optimized jointly to maximize the CR network throughput. Furthermore, the relay node implements an effective subcarrier permutation policy to enhance the performance further at the cost of affordable complexity. Finally, the performance advantages of the proposed algorithm are demonstrated by the simulation results.

In this study, a unified linear minimum mean‐square‐error (LMMSE) transceiver design framework is investigated, which is suitable for a wide range of wireless systems. The unified design is based on an elegant and powerful mathematical programming technology termed as quadratic matrix programming (QMP). Based on QMP it can be observed that for different wireless systems, there are certain common characteristics which can be exploited to design LMMSE transceivers, for example, the quadratic forms. It is also discovered that evolving from a point‐to‐point multiple‐input–multiple‐output (MIMO) system to various advanced wireless systems such as multi‐cell coordinated systems, multi‐user MIMO systems, MIMO cognitive radio systems, amplify‐and‐forward MIMO relaying systems and so on, the quadratic nature is always kept and the LMMSE transceiver designs can always be carried out via iteratively solving a number of QMP problems. A comprehensive framework on how to solve QMP problems is also given. The work presented in this study is likely to be the first shot for the transceiver design for the future ever‐changing wireless systems.

In this letter, we investigate multiple-input multiple-output (MIMO) communications under energy harvesting (EH) constraints. In our considered EH system, there is one information transmitting (ITx) node, one traditional information receiving (IRx) node and multiple EH nodes. EH nodes can transform the received electromagnetic waves into energy to enlarge the network operation life. When the ITx node sends signals to the destination, it should also optimize the beamforming/precoder matrix to charge the EH nodes efficiently simultaneously. Additionally, the charged energy should be larger than a predefined threshold. Under the EH constraints, in our work both minimum mean-square-error (MMSE) and mutual information are taken as the performance metrics for the beamforming designs at the ITx node. In order to make the proposed algorithms suitable for practical implementation and have affordable overhead, our work focuses on the beamforming designs with partial CSI and this is the distinct contribution of our work. Finally, numerical results are given to show the performance advantages of the proposed algorithms.

In this paper, the performance of hybrid automatic repeat request (HARQ) technique in an ad hoc network is analyzed. Unlike most prior works on the analysis of HARQ, both time-correlated interference and feedback errors are taken into account in the analysis. Based on the theory of point processes, outage probability after the nth retransmission, delay limited throughput and mean transmission time are derived in closed forms. The analytical results are verified by simulations and the impacts of various parameters on the network performance are investigated in detail. It is found that the outage probability obeys the inverse-2/α power law over the number of retransmissions, where α is the path loss exponent. Furthermore, it is demonstrated that the feedback error significantly degrades the delay limited throughput when the ad hoc network becomes dense, while the increment of mean transmission time due to the feedback error is a concave function of the intensity of the network.

In this paper, we consider robust distributed filter-and-forward beamforming design for two-way relaying networks over the frequency selective fading channels, in which two users exchange information with the aid of multiple single-antenna relay nodes. In contrast to the prior works in which the channel state information (CSI) is available, in our work CSI for all relevant links between the users and relay nodes is not perfectly known with stochastic channel errors. Under stochastic channel errors, a robust beamforming design aiming at maximizing the total system signal-to-interference-plus-noise-ratio (SINR) under individual relay power constraints is proposed. Simulation results show that the proposed robust beamformer reduces the sensitivity of the two-way relaying systems to channel estimation errors, and performs better than the algorithm using estimated channels only.

In this paper, we investigate maximum mutual information design for multi-hop amplify-and-forward (AF) multiple-input multiple-out (MIMO) relaying systems with imperfect channel state information, i.e., Gaussian distributed channel estimation errors. The robust design is formulated as a matrix-variate optimization problem. Exploiting the elegant properties of Majorization theory and matrix-variate functions, the optimal structures of the forwarding matrices at the relays and precoding matrix at the source are derived. Based on the derived structures, a water-filling solution is proposed to solve the remaining unknown variables.

In this paper, joint transceiver design for dual-hop amplify-and-forward (AF) MIMO relay systems with Gaussian distributed channel estimation errors in both two hops is investigated. Due to the fact that various linear transceiver designs can be transformed to a weighted linear minimum mean-square-error (LMMSE) transceiver design with specific weighting matrices, weighted mean square error (MSE) is chosen as the performance metric. Precoder matrix at source, forwarding matrix at relay and equalizer matrix at destination are jointly designed with channel estimation errors taken care of by Bayesian philosophy. Several existing algorithms are found to be special cases of the proposed solution. The performance advantage of the proposed robust design is demonstrated by the simulation results.

In this paper, we propose two spectrally efficient adaptive partial decode-and-forward (DF) cooperative communication schemes with quantized feedback: adaptive partial repetition DF scheme and adaptive partial coded cooperation DF scheme. We assume that the relay node only has partial channel-state information, which is obtained via an quantized feedback link. We use the so-called mutual information (MI) model to adaptively optimize the amount of message transmitted by the relay node under a given block-error-rate constraint. We develop adaptive algorithms and implementation details for both schemes. Simulation results show that with the quantized feedback, the MI model can predict well the amount of message that needs to be transmitted by the relay node, and that the two proposed schemes can substantially increase the spectral efficiency of the system.

In this paper, we focus on a two-hop amplify-and-forward (AF) cooperative network, in which one source node equipped with multiple antennas communicates with a single-antenna destination, relying on a single-antenna relay. In order to implement beamforming, channel state information (CSI) is feeded back to the source from relay using random vector quantization (RVQ) codebook. Furthermore, one relay with the best CSI is selected in each communication. An upper bound of the outage probability of the whole system is derived and the simulation results show that this bound is very tight.

In this paper, based on analyzing some existing phase lock detectors for quadratic phase-shift keying (QPSK),we propose a novel method of estimating the signal-to-noise ratio (SNR) operating with the lock metric value of classical Mth-power (M=4) phase lock detector. The proposed lock detector-based SNR estimator can perform better than the conventional SNR estimator in terms of mean estimated value (MEV) at medium to high SNR when phase recovery loop is in-lock status. And its normalized mean squared error (NMSE) can reach Cramer-Rao bounds (CRBs) at that SNR. Moreover, the computational complexity of new estimator operating in phase recovery loop is analyzed with less additional multiplications than that of conventional one. This estimation method can also be applied to other similar phase lock detectors.

An improved data-aided (DA) frequency error detector (FED) and a frequency lock detector are proposed under large frequency offset for Digital Video Broadcasting Satellite Second Generation (DVB-S2) system. Computer simulations results show that the proposed error detector can increase the frequency acquisition range and decrease the acquisition time without complexity increase. Its closed-loop normalized frequency root mean square error (RMSE) improves at least 1.5 dB compared with that of conventional error detector. The proposed lock detector shows good lock indication. Its modified version can save more symbols to indicate the locking status.

The partially repeated cooperation system employing Low Density Parity-Check (LDPC) codes achieves space diversity and extra coding gain at the same time. This paper focuses on the power constrained system, in which the total power of the source node and the relay node remains constant. Thus different power allocation schemes result in different repetition lengths at the relay node, and it is interesting to determine the optimal in terms of performance. A modified Gaussian Approximation algorithm is proposed to search for the feasible decoding threshold of the partially repeated regular LDPC codes, with which the outage probability is obtained and used to show the power efficiency of different power allocation schemes. The numerical results show that the partially repeated scheme can achieve a higher power efficiency than the all repeated one. However, the efficiency of the power allocation scheme strongly depends on the channel realizations of the system.

In this paper, an algorithm for removing cycles in quasi-cyclic(QC) LDPC codes is presented. This algorithm can ensure that the code after cycle removal process preserves the quasi-cyclic structure and significantly improves the flexibility in parameter selection (such as the length of the code) of algebraic constructions of QC-LDPC codes. Besides, it has far lower computational complexity than the existing cycle removal algorithm. Experimental results show that this algorithm is very effective in improving the performance of the QC-LDPC codes and can construct code which has better performance than the corresponding binary LDPC code based on IEEE 802.16e standard.

In this paper, we present three algebraic methods for constructing structured nonbinary LDPC codes over finite fields, among which the first method is used to construct quasi-cyclic codes with girth at least 6 based on the automorphisms of finite fields, the second method gives a class of (4,ρ) quasi-cyclic codes with girth at least 8, the third method gives a class of codes with cycles limited. Simulation results show that the constructed codes perform very well over AWGN channel, and they have better performances or far lower computational complexities than the corresponding random Mackay codes or codes algebraically constructed by Lin.

An accurate and simple link quality model is crucial in system evaluation of different link adaptation (LA) solutions, for it acts as a link-to-system (L2S) interface which provides the block-error rate (BLER) performance of a certain modulation coding scheme (MCS) corresponding to the current SINR states. Our previous work in the work of Chen et al. (2007) has verified the good accuracy of the MI-based model for rate compatible LDPC coded OFDM system. In this paper, adaptive modulation and coding (AMC) strategy and two hybrid ARQ schemes of variable retransmission length, partial chase combining and full incremental redundancy, have been designed to check the accuracy of the look-up tables (LUTs) obtained by the MI-based model. The predicted results are quite in accord with the simulation ones, which confirms that the MI- based model provides accurate and simple LUTs for LA design of LDPC codes.

In this paper, we present three algebraic methods for constructing structured nonbinary LDPC codes over finite fields, among which the first method is based on the multiplicative inverses of nonzero elements in finite fields and gives a class of quasi-cyclic codes with girth 6, the second method gives a class of (3, rho) quasi-cyclic codes with girth 8, the third method gives a class of structured codes with cycles limited. The codes given in examples perform well over AWGN channel and have better performances or far lower computational complexities than the corresponding random Mackay codes or codes algebraically constructed by Shu Lin.

Eigen-beamforming technique is introduced in two typical multi-antenna systems: space-time block coding and VBALST. When the channel covariance information is known by the transmitter, optimal power allocation algorithm for eigen-beamforming is proposed based on minimizing the transmission errors. Also, it is indicated in this paper that this algorithm can be simplified when the channel is independent - identically distributed (I.I.D.) channel. By simulation and analysis, eigen-beamforming can greatly improve the performance of multi-antenna system when this power allocation algorithm is adopted.

System evaluation of different resource allocation, power control and link adaptation (LA) solutions require the multi-cell system simulations, which provide important reference to the standardization and system design. Such a system-level simulator is expected to be reliable and quick enough, which requires a very accurate and simple link quality model. The exponential effective-SNR mapping (EESM) model [1, 2] and mutual information-based (MI-based) model [3] were proposed recently, which have been proved to be pretty accurate and independent of fading for 3 GPP Turbo-coded orthogonal frequency division multiplexing (OFDM) system [3, 4]. This paper investigates the accuracies of the above two models for the low-density parity-check (LDPC) codes, in terms of veracity and complexity for different coding rates, block lengths, mobile speeds and modulation schemes. Simulation results verify that both methods show quite good performance in different multi-state channels. The simpler MI-based model performs even better. Especially its coding model is independent of modulation schemes.

IEEE P802.16e standard specifies the construction of low-density parity-check (LDPC) codes with four different code rates, i.e. 1/2, 2/3, 3/4, 5/6, and totally six parity check matrices. However, such a coarse coding rate granularity cannot provide comparable link adaptation performance comparing to turbo codes in HSDPA. In another aspect, more mother codes lead to much higher complexity of encoder implementation due to individual parity check matrices. To reach a balance between the implementation complexity and the fine granularity in link adaptation, this paper investigates the performance of the rate-compatible (RC) scheme, with targeted coding rate varying from 0.1 to 0.9, based on different sets of mother LDPC codes. As a summary, this paper proposes an instruction of mother codes selection and RC schemes for different targeted coding rates, which makes the link adaptation of LDPC codes very convenient for WiMAX systems.

A modified M&M frequency estimation algorithm for DVB-S2 system is proposed in this paper. This estimator provides a larger estimation range compared to the well known Fitz and L&R methods and achieves CRB in the whole SNR operation range of DVB-S2, with a little increase in computational complexity. The enlarged estimation range will reduce the acquisition time of the coarse frequency synchronizer in a two-step carrier frequency recovery scheme. Minimum accumulation lengths to achieve a certain RMS frequency estimation error for the L&R and modified M&M estimator at different SNR are presented. A variable accumulation length scheme based on SNR estimation or coding/modulation scheme employed in system is proposed to minimize the acquisition time in the fine frequency recovery.

A non-uniform signal constellation can be used to obtain shaping gain. One typical way to design non-uniform constellation is make the output signal follow a Gaussian distribution by using equally like signals with unequal spacing. However, for binary turbo coded modulation (BTCM) with M-QAM system, this non-uniform constellation design criteria can be modified based on different impacts of various turbo encoded hits sequence on BER performance. In this paper, author points out that shaping gain for M-QAM with BTCM can be achieved integrated with optimization of mapping rule, performance of the proposed scheme outperforms over BTCM with uniform constellation, especially under low and moderate SNR region. Meanwhile, the effects of code rate, order of QAM constellation on shaping gain are investigated. Numeral results are provided to support the proposed scheme and analysis. The proposed scheme can be applied to high rate data service with hybrid ARQ, such as HSDPA system.

High speed downlink packet access (HSPDA) is proposed as the evolution of WCDMA recently, aiming at providing higher downlink transmission rate with peak rate about 8-10 Mbps. Turbo code combined with high order modulation can provide bandwidth efficient transmission with substantial coding gain over traditional trellis coded modulation (TCM). In this paper, author evaluates the feasibility of the application of binary turbo coded modulation (BTCM) with 16QAM in HSPDA. Some improvements of interleaver design according to label mapping are proposed to enhance the performance. And the impact of rate matching functionality on the performance of BTCM is analyzed. Simulation results show that the improved BTCM scheme with 16QAM can be implemented with low code rate and outperform the conventional scheme.