Since broadcast communication is considered the highly appropriate technique for the dissemination of safety messages in vehicular ad hoc networks (VANETs), it is vital to design a high-efficient medium access control (MAC) protocol for vehicles accessing wireless channel. However, the characteristics of VANETs, such as high-velocity vehicles, highly dynamic topology and very unstable communication link, lower the performance of existing MAC protocols. In this paper, we propose a novel Capture-aware TDMA-based MAC (CT-MAC) protocol, which can better utilize the channel resource than the existing MAC protocols by setting the optimal frame length with taking capture effect into account. To obtain the realistically optimal frame length, the closed form expression of probability of capture effect is derived under Nakagami-m fading channel which is proper to model the small-scale fading channel in VANETs. Besides, a discrete Markov chain is introduced to analyze the impact of capture effect on channel utilization. The theoretical analyses and the simulation results show that the reliability of broadcast and the efficiency of channel utilization are significantly improved.

In this article, we investigate the long-term energy minimization for nonorthogonal multiple access (NOMA)-based mobile edge computing (MEC) systems with user mobility, continuous tasks arrival, and time-varying channel when the reconfiguration energy cost caused by dynamic voltage frequency scaling (DVFS) technology and the effect of imperfect successive interference cancelation (SIC) decoding in NOMA transmission are taken into account. We formulate the considered problem as a nonconvex optimization problem. To solve it, we decompose it into a computation resource and transmit power optimization subproblem, and an offloading ratio and transmission time optimization subproblem. We first show that when the offloading ratio and transmission time are given, the optimal local CPU frequency, the optimal computation resource allocation in the base station, and the optimal transmit power can be theoretically derived. Then, based on the above theoretical derivation, a soft actor–critic (SAC)-based deep reinforcement learning (DRL) algorithm is proposed to learn the near-optimal offloading ratio and transmission time for users. Simulation results show that the proposed algorithms can significantly improve the system performance.

In this paper, we investigate the resource allocation of mobile edge computing (MEC) in cognitive capacity harvesting networks (CCHNs) when non-orthogonal multiple-access (NOMA) technique is adopted. Different from traditional studies for NOMA-MEC networks, we aim at minimizing the total cost of CCHN while satisfying the quality-of-service (QoS) of secondary users (SUs). We adopt the mechanism of time division multiple access (TDMA) when several NOMA groups use the same spectrum, and consider both the waiting delay and transmission delay during data offloading with the optimization of transmission order of NOMA groups. We formulate the considered problem as a mixed integer non-linear programming (MINLP). We show that the transmit power and the allocated computing resource for each SU can be derived when the transmission time and transmission order of the NOMA groups are given. Based on this, the considered problem can be decomposed into a transmission time and order optimization subproblem, a cellular resource block (CRB) selection subproblem and a cognitive radio (CR) router selection subproblem. To solve the transmission time and order optimization subproblem, we first simplify the delay constraint via theoretic analysis, and then propose a binary segmentation (B-Seg) algorithm and a transmission order adjustment (TOA) algorithm to find the optimal transmission time and transmission order of NOMA groups, respectively. To solve the CRB selection subproblem and the CR router selection subproblem, a bigger requirement first (BRF) algorithm and a game-based iteration (GBI) algorithm are respectively proposed. Simulation results show that the proposed algorithms can significantly improve the system performance.

In this paper, we investigate the sum-rate maximization of cellular users (CUs) and device-to-device (D2D) pairs for the D2D communications underlaying cellular networks over the Nakagami-m fading channel. We consider a resource-abundant scenario where the number of CUs is more than that of D2D pairs, and discuss the joint optimization of signal to interference noise ratio (SINR) thresholds and CU-D2D pairing. We formulate the problem as a mixed integer non-linear programming (MINLP), which is NP-hard in general. To solve the problem, we propose a two-step approach. First, we add virtual D2D pairs to make the number of D2D pairs equal to that of CUs, and derive the near-optimal SINR threshold for each possible CU-D2D pairing by the Simulated Annealing (SA) algorithm. Second, the optimal CU-D2D pairing can be obtained by Hungarian (HG) algorithm. Simulation results show that: 1) the proposed SINR policy outperforms the other policies by about 11.3% –63.4%; 2) compared with several state-of-the-art resource allocation policies, HG allocation can achieve an average sum-rate improvement of 10.9% –72.8%.

This paper explores a system that integrates non-orthogonal multiple access (NOMA) with mobile edge computing (MEC), supported by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in energy splitting (ES) mode. We aim to address the challenge of minimizing energy consumption over the long-term by modeling it as a non-convex optimization problem. To simplify the resolution of this complex issue, we introduce a decomposition approach using a multi-agent deep reinforcement learning (DRL) technique. Specifically, we decompose the problem into the sub-problems of computational resource and power allocation, and phase shifting, amplitude coefficient, offloading ratio, and time optimization. Based on theoretical derivations, we determine the optimal computational resource and power allocation for the given phase shifting, amplitude coefficient, offloading ratio, and time. Then, we propose a multi-agent twin delayed deep deterministic policy gradient (TD3) algorithm to jointly optimize phase shifting, amplitude coefficient, offloading ratio, and time for each time slot. Simulation outcomes demonstrate that the decomposition-based multi-agent TD3 (DB-MATD3) algorithm achieves faster convergence compared to conventional centralized DRL approaches. Compared with other existing methods, the proposed task offloading strategy, which employs the ES-based STAR-RIS, achieves energy savings of 28.7% to 40.8%.