The rapid proliferation of delay-sensitive Internet of Things (IoT) applications in 6G networks necessitates innovative solutions for computation-intensive tasks in mobile edge computing (MEC) systems. This paper addresses the critical challenge of spatio-temporal task heterogeneity induced by user mobility in multi-server MEC-enabled IoT networks. A cooperative MEC framework is proposed with joint optimization of transmit power allocation, computation offloading, and central processing unit (CPU)-cycle frequencies. The formulated mixed-integer nonlinear programming problem is decomposed into two tractable subproblems: the transmit power control subproblem solved by LambertWfunction-based closed-form solutions; the task offloading and CPU-cycle frequency allocation subproblem solved by a novel deep reinforcement learning (DRL)-based method named D3SAC. Combining dueling double deep Q-network with soft actor-critic algorithms, our proposed D3SAC method dynamically adapts to user mobility while maintaining energy efficiency and computational latency. Simulation results demonstrate over 52% energy reduction compared with maximum-power transmission schemes. Compared to existing DRL baselines, our proposed method achieves superior task completion rate and CPU-cycle frequencies utilization efficiency while maintaining balanced workload distribution.

This letter rethinks the probabilistic broadcast gossip scheme to achieve fast distributed average consensus in wireless networks. The consensus attainment in this scheme is heavily influenced by the broadcast probability of each node, which directly affects the convergence rate. To reduce communication costs for achieving consensus, we formulate an optimization problem to determine the optimal broadcast probability for each node. This problem involves a challenging nonconvex spectral radius term in the objective function. To address this challenge, we introduce an enhanced majorization-minimization-based approach that leverages a novel surrogate function to effectively upper bound the spectral radius function. Simulation results show that the proposed method provides substantial performance improvements over existing heuristic methods for broadcast probability optimization.

Hybrid-field channel estimation in extremely large-scale multiple-input and multiple-output systems presents significant challenges due to the coexistence of far-field and near-field scatterers. This letter proposes an alternating direction method of multipliers-based joint dictionary learning and channel estimation method to address these challenges. By leveraging extensive channel measurement data, a hybrid-field dictionary is learned to effectively capture the unique characteristics of the hybrid-field channels. Simulation results demonstrate that our proposed method outperforms existing predefined far-field and near-field dictionary-based schemes, highlighting its potential for Internet of Things networks.

Achieving fast and accurate average consensus is pivotal for numerous collaborative tasks in wireless sensor networks (WSNs). Toward this end, this article explores the design of asymmetric mixing matrices for achieving faster average consensus rate in WSNs. We first demonstrate that the optimal symmetric mixing matrices may achieve much slower consensus rate compared to an asymmetric mixing matrix, and formulate the design of the asymmetric mixing matrix as a nonconvex spectral radius minimization problem. To address this challenge, a locally optimal iterative spectral norm-based method is proposed. Furthermore, to reduce the computational complexity while maintaining an acceptable level of performance gains, we also introduce two suboptimal methods based on the Frobenius norm and the numerical radius upper bounds, respectively. Through extensive simulation results across both fixed and random network topologies, we demonstrate that our proposed asymmetric schemes outperform existing benchmark optimal symmetric and best constant schemes in terms of the spectral radius and the consensus time performance.

Considering reconfigurable intelligent surfaces (RISs), we study a multi-cluster multiple-input-single-output (MISO) non-orthogonal multiple access (NOMA) downlink communication network. In the network, RISs assist the communication from the base station (BS) to all users by passive beamforming. Our goal is to minimize the total transmit power by jointly optimizing the active beamforming matrices at the BS and the reflection coefficient vector at the RISs. Because of the constraints on the RIS reflection amplitudes and phase shifts, the formulated quadratically constrained quadratic problem is highly non-convex. For the aforementioned problem, the conventional semidefinite programming (SDP) based algorithm has prohibitively high computational complexity and deteriorating performance. Here, we propose an effective second-order cone programming (SOCP)-alternating direction method of multipliers (ADMM) based algorithm to obtain the locally optimal solution. To reduce the computational complexity, we also propose a low-complexity zero-forcing based suboptimal algorithm. It is shown through simulation results that our proposed SOCP-ADMM based algorithm achieves significant performance gain over the conventional SDP based algorithm. Furthermore, when the target transmission rates of central and cell-edge users are 0.5 bps/Hz, our proposed NOMA RIS-aided system with 32 RIS elements has about 2.5 dB performance gain over the conventional massive multiple-input-multiple-output system with 64 transmit antennas.

Incorporating wireless caching in non-orthogonal multiple access (NOMA) networks is a promising technique to reduce the delivery latency and improve the quality of service. In this paper, we study a cache content placement optimization problem in a cellular NOMA downlink wireless caching network. Our goal is to minimize the average transmit power under the cache capacity constraints. The optimization problem is a non-linear integer programming, which is non-deterministic polynomial-time hard. To efficiently solve the problem, an alternating upper plane method based on quadratic knapsack problem (QKP) is proposed. To deal with the general situation that the sizes of files and cache capacities are not integers, an alternating method based on semidefinite relaxation is also proposed. Finally, a constrained concave-convex procedure-based iterative method is proposed to further reduce the computational complexity. Simulation results show that our proposed methods are superior to the schemes which cache the most popular files until the cache is full.

In this paper, we investigate an unmanned aerial vehicle (UAV) enabled wireless powered communication network, where the UAV with constant power supply first charges all users by transmitting wireless energy to all users simultaneously, and after that all users send their own information to the UAV. Our target is the joint optimization of the time allocation as well as the position of the UAV to make the uplink sum achievable rate for all users as large as possible. To solve this non-convex problem, we first derive the analytic optimal solution of the time allocation, which is expressed as the function of UAV position. The original problem, after substituting the derived optimal time allocation, is reformulated as a new optimization problem whose optimization variable is only UAV position. We propose a sequential unconstrained convex minimization-based algorithm to obtain the globally optimal solution. The simulation results demonstrate that the performance of our proposed algorithm matches with that obtained by two-dimensional exhaustive search. To decrease the complexity, a Dinkelbach-based algorithm to obtain the locally optimal solution is also proposed. The simulation results show that the performances of our proposed two algorithms are superior to the schemes without time allocation optimization and/or position optimization.

In[1], the title of the paper should be: “Joint Position and Time Allocation Optimization of UAV Enabled Wireless Powered Communication Networks.”

Cooperative non-orthogonal multiple access (NOMA) systems inherit advantages of the NOMA protocol and the cooperative relay. In this paper, we propose cooperative NOMA systems in multiple-input-multiple-output channels. The whole transmission is divided into two phases. In the first phase, the base station broadcasts signals using the NOMA protocol to a central user and a cell-edge user. In the second phase, the central user helps the base station cooperatively relay signals intended for the cell-edge user. Our objective is to maximize achievable rate from the base station to the cell-edge user under transmit power constraints and achievable rate constraint from the base station to the central user. The difficulty of this problem is the joint beamforming of the base station and the central user in the second phase. We propose a constrained convex-concave procedure (CCCP)-based algorithm. To reduce computational complexity, we also propose a closed-form search-based suboptimal algorithm. Simulation results demonstrate that our proposed cooperative NOMA system with CCCP-based algorithm outperforms the conventional NOMA scheme. When achievable rate constraint to the central user is low, our proposed cooperative NOMA system with the closed-form search-based suboptimal algorithm outperforms the NOMA scheme.

In this letter, we consider a cellular downlink multiple-input-multiple-output nonorthogonal multiple access (NOMA) secure transmission network, which consists of a base station, a central user, and a cell-edge user. The base station and two users are all equipped with multiple antennas. The central user is an entrusted user and the cell-edge user is a potential eavesdropper. We focus on secure beamforming optimization problem, which maximizes achievable secrecy rate of the central user subject to transmit power constraint at the base station and transmission rate requirement at the cell-edge user. The optimization problem is nonconvex. We employ majorization-minimization method to iteratively optimize a sequence of valid surrogate functions for the nonconvex optimization problem. Furthermore, in each iteration, we derive the semi-closed form solution to optimize the valid surrogate functions. Simulation results demonstrate that our proposed NOMA scheme outperforms the zero-forcing-based NOMA scheme and conventional orthogonal multiple access scheme.