Deploying Deep Neural Network (DNN) models of varying capabilities to support collaborative inference for User Equipment (UE) tasks is becoming increasingly common in Mobile Edge Computing (MEC) systems. In this paper, we aim to minimize energy consumption in a dynamic Non-Orthogonal Multiple Access (NOMA)-based MEC system assisted by a Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) under complex Non-Line-of-Sight (NLoS) conditions, while meeting latency requirements and preserving inference accuracy for UEs. We formulate the problem as a non-convex optimization and address it using a Decomposition-Based Twin Delayed Deep Deterministic Policy Gradient (DB-TD3) approach. The problem is decomposed into two subproblems: 1) computation resource allocation and power optimization, and 2) optimization of the offloading ratio, time fractions allocated for reflection and transmission, phase shift, and transmission time. For the first subproblem, we derive optimal CPU frequencies and transmit power allocation through theoretical analysis. For the second subproblem, the offloading ratio, time fractions allocated for reflection and transmission, phase shift, and transmission time are optimized using the TD3 algorithm. Experimental results demonstrate that the DB-TD3 method significantly improves system efficiency and reduces average energy consumption by 59.3% compared to baseline algorithms. Furthermore, the proposed NOMA with STAR-RIS scheme outperforms other offloading methods, achieving an average energy reduction of 41.3%.

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.