To provide satisfying services for ever-emerging mission-critical applications, the ultra-reliable and low-latency communications (URLLC) need novel design to improve the spectrum efficiency and enhance the robustness. To achieve this, we design a robust rate-splitting and beamforming scheme for the downlink multiuser URLLC system in finite blocklength regime under imperfect channel state information at the transmitter (CSIT) acquisition. Rate-splitting is utilized to deal with the complex inter-user interference and improve the spectrum efficiency. Considering the norm-bounded CSIT error model, we formulate a minimum user rate maximization problem to guarantee the URLLC performance requirements by jointly designing the rate-splitting factors and the common/private beamforming vectors. The corresponding constraints are infinite due to the uncertainty of CSIT and the constraint set is also non-convex. To tackle it, we convert the infinite constraints into finite ones utilizing S-Procedure, and transform the original problem into difference of convex (DC) programming. Efficient approaches based on constrained concave convex procedure and Gaussian randomization are proposed to solve the DC programming and generate initial feasible points. Through extensive simulations, the convergence, robustness and effectiveness proprieties of the design are investigated and confirmed. Compared with the baselines, our design can achieve obvious performance improvement for different blocklength and block error rate requirements.

Along with the deployment of Industrial Internet of Things (IIoT), massive amounts of industrial data have been generated at the network edge, driving the evolution of edge machine learning (ML). But during the ML model training, it may bring privacy leakage by traditional central methods. To address this issue, federated learning (FL) has been proposed as a distributed learning framework for training a global model without uploading raw data to protect data privacy. Since the communication and computing resources are usually limited in IIoT networks, how to reasonably select device and allocate bandwidth is crucial for the FL model training. Therefore, this article proposes a joint edge device selection and bandwidth allocation scheme for FL to minimize the time-averaged cost under the given long-term energy budget and delay constraints in the IIoT system. To tackle with this long-term optimization problem, we construct a virtual energy deficit queue and leverage the Lyapunov optimization theory to transform it into a list of round-wise drift-plus-cost minimization problems first. Then, we design an iterative algorithm to allocate reasonable bandwidth and select appropriate devices to achieve cost minimization while satisfying the energy consumption constraints. Besides, we develop an optimality analysis of the average cost and energy violation for our proposed scheme. Extensive experiments verify that our proposed scheme can achieve superior performance in cost efficiency over other schemes while guaranteeing FL training performance.

This work investigates proactive edge caching for device-to-device (D2D)-assisted wireless networks, where user equipment (UE) can be selected as caching nodes to assist content delivery to reduce the content transmission latency. In doing so, there are two challenges: 1) how to precisely get the user’s preference to cache the proper contents at UEs and 2) how to replace the contents cached at UEs when there are new popular contents emerging. To address these, we develop a user preference learning-based proactive edge caching (UPL-PEC) strategy. In the strategy, we first propose a novel context and social-aware user preference learning method to precisely predict user’s dynamic preferences by jointly exploiting the context correlation among different contents, the influence of social relationships and the time-sequential patterns of user’s content requests. Specifically, the bidirectional long short-term memory networks are adopted to capture the time-sequential patterns of the user’s content requests. And, the graph convolutional networks are developed to capture the high-order similarity representation among different contents from the constructed content graph. To learn the social influence representation, an attention mechanism is designed to generate the social influence weights to users with different social relationship. Based on the learned user preference, a proactive edge caching architecture is proposed to integrate the offline caching content placement and the online caching content replacement policy to continuously cache the popular contents at UEs. Simulation results show that the proposed UPL-PEC strategy outperforms the existing similar caching strategies at about 3.13%–4.62% in terms of the average content transmission latency.

This work investigates proactive edge caching for D2D-assisted wireless networks, where user equipments (UEs) can be selected as caching nodes to assist content delivery. The objective of this work is to achieve a trade-off between the cost for providing caching services and the content transmission latency. Doing so, there are two challenges: 1) Which UEs can be selected as caching nodes; 2) How to place contents on these selected UEs without user’s privacy disclosure. To address these, a novel community detection and attention-weighted federated learning based proactive edge caching (CAFLPC) strategy is proposed. In the strategy, we first group UEs into different communities based on both the mobility and social properties of UEs, and then select important users (IUs) as caching nodes for each community by considering the social importance of UEs. To determine how to place the popular contents in these selected IUs, an attention-weighted federated learning (AWFL) based content popularity prediction framework is proposed. It integrates the attention-weighted federated learning with Bidirectional Long Short Term Memory Network (AWFL_BiLSTM) to achieve a higher content popularity prediction accuracy while protecting user’s privacy. Considering the imbalance of UEs’ active levels and local computing capacities, an attention-weighted aggregation mechanism is proposed to improve the training efficiency and prediction accuracy. Simulations results show that the proposed CAFLPC strategy outperforms the compared existing caching strategies at about 2.2%-35.1% in terms of the transmission latency reduced by per unit cost.

Due to the broadcast feature of the wireless channels, users are easily eavesdropped by eavesdroppers during data transmission, resulting in data leakage. To ensure the Device-to-device (D2D) communications security, in this paper, we propose a UAV-assisted secure communication system where a mobile UAV can transmit the jamming signal to counter the ground eavesdropper to enhance the security of communication. We develop a connection outage probability model to ensure the continuous communication and derive the closed form expression of the connection outage probability for D2D users. We formulated a secrecy rate maximization problem by jointly optimizing UAV’s trajectory and jamming power, in which, the secrecy rate is defined as the difference between the transmission rate and the eavesdropping rate. To solve it, we propose a TD3-based algorithm to optimize the UAV’s trajectory and jamming power. Simulation results illustrate that the overall average secrecy rate of D2D users achieved by our proposed algorithm outperforms other baselines.

We propose a full-duplex cooperative rate-splitting (FD-CRS) scheme in a downlink two-group multicast system. At the transmitter, two distinct messages requested by the two groups respectively are split and then encoded into one common stream and two private streams, based on the principles of rate splitting multiple access (RSMA). The cell-center-users (CCUs) in one group decode the common stream and their own private stream successively, then cooperatively form a distributed beamformer to assist the cell-edge-users (CEUs) in common stream transmission. To make full utilization of the time resources during cooperation, all the CCUs operate in FD mode to enable information receiving and forwarding simultaneously. Moreover, since it is unfair to sacrifice the cooperator’ energy to forward, each CCU is enabled to harvest energy from the received signal by adopting power-splitting protocol. With the objective of minimizing the system transmission power while guaranteeing all the groups’ target rates, an optimization problem is formulated to jointly design the beamformers, message splitting and power-splitting ratio. We reformulate the non-convex problem by using the difference of convex (DC) programming, and then propose an iterative algorithm based on successive convex approximation to solve it to obtain a local minimum. Further, a robust algorithm combining the semi-positive definite relaxation (SDR) technique and penalty function method is developed for the case with imperfect channel state information. Although our proposed FD-CRS scheme adopts the seemingly energy-wasting wireless power transfer technique, the simulation results still confirm the superiority of the proposed scheme, i.e., it outperforms the other baseline schemes in terms of power consumption under various user deployment, network loads and target rates. That is attributed to the comprehensive utilization of FD cooperation gain, spatial multiplexing gain as well as power multiplexing gain.

We propose a cooperative nonorthogonal multicast scheme for multiple-input-single-output (MISO) systems, where the transmitter sends the superimposed signal to two multicast groups. After successfully detecting all signals, the cell-center users (CCUs) in one multicast group help relay signal to the cell-edge users (CEUs) in another group to enhance signal reception. Simultaneous wireless information and power transfer is adopted at CCUs to assist information relaying. In such a system, to minimize the transmission power of the system while satisfying the quality of service requirements of all the users, an optimization problem is formulated to jointly design the transmitter beamformers, distributed CCUs beamformer, as well as the power-splitting (PS) ratios. To solve the nonconvex problem, we first equivalently transform it into a difference of convex (DC) programming. Then, a low-complexity iterative algorithm based on the constrained concave convex procedure (CCCP) is proposed to solve the DC programming one. In addition, a robust joint beamforming and PS scheme is proposed by assuming imperfect channel state information (CSI). The infinite constraints caused by CSI uncertainties are converted into finite ones utilizing the S-procedure. To obtain a rank-one locally optimal solution, the penalty function method and CCCP algorithm are adopted. The simulation results reveal that the proposed cooperative scheme can greatly reduce the transmission power and outperform the baselines within a certain range. Moreover, the robustness and effectiveness of the robust design under imperfect CSI case have been validated.