Sep 2026· IEEE Transactions on Mobile Computing· Vol 25, pp. 13782-13799· 1 citation· 55 references
Abstract
Mobile Edge Computing (MEC) is promising to enable low delay services with which users can offload computing intensive and delay sensitive tasks to the edge. Considering a multi-cell MEC (MC-MEC) network without sufficient resources to serve all users, user selection and non-orthogonal multiple access (NOMA) should be introduced. Then, to maximize the delay-aware average user service satisfaction degree (DA-AveUSD), user selection and resource allocation are jointly optimized (DA-JUSRA), which is modeled as a mixed integer nonlinear programming (MINLP) problem and proven to be NP-hard. To solve this problem, it is decomposed into two independent subproblems, i.e., the power allocation (PA) problem and the user selection, subchannel scheduling and computing resource allocation (USC) problem. Next, a convex evolutionary alternating optimization (CEAO) algorithm is proposed, which alternately applies the convex optimization method and the Karush-Kuhn-Tucker (KKT)-embedding enhanced elite genetic algorithm (KKT-embedding E2GA) to solve the PA and the USC problem, respectively. Simulations show that compared to the optimal exhaustive search algorithm, the proposed CEAO algorithm converges rapidly within a few iterations, with a gap in DA-AveUSD of less than 1% to the optimum performance. Next, compared to existing user selection schemes, DA-JUSRA with CEAO can enhance DA-AveUSD by more than 50% and yield a higher optimal load.
Non-orthogonal multiple access (NOMA) is a kind of 5G and 6G radio access technology, which not only enhances spectrum efficiency but also enables several users at the same time to access the network and share the same frequency resource. This paper studies the problem of jointly optimizing power allocation and channel resource assignment in the downlink multi-carrier NOMA system, with the aim of maximizing the weighted sum rate under individual quality-of-service (QoS) constraints, per-user minimum rate requirements, and total transmit power budget. We cast the problem as a mixed-integer non-linear programming (MINLP) task and decompose it into two tractable subproblems: A low-complexity channel allocation step using a bipartite matching framework, followed by an successive convex approximation (SCA) solution to the power control step with Lagrangian duality. A closed-form expression for the optimal power ratio under fixed channel assignment is derived to achieve efficient iteration between the two stages. To further reduce the computational burden for dense deployment of the network, we combined the iterative scheme with a DRL module based on the deep deterministic policy gradient (DDPG) algorithm to enable the system to respond to changes in channel state without having to solve the optimization problem at each time slot. Simulation results show that when deployed in a 3GPP-compliant urban macro-cell environment, the proposed joint scheme can achieve 38 percent more sum throughput than orthogonal frequency-division multiple access (OFDMA) baselines, a 22 percent increase over fixed NOMA power allocation, and converges within 15 iterations under moderate user density. The energy efficiency gain is 3.62 bits/J/Hz when combining the DRL-based dynamic policy, and the practical feasibility of the proposed framework for next-generation network deployment is verified.
Yuming Fu, Xiaofeng Chang, Wanze Gan· Digital Signal and Computer...· 0 citations
This paper considers priority-aware partial computation offloading in an uplink mobile edge computing (MEC) network. Devices assigned to different groups occupy orthogonal subbands, whereas devices within each group use power-domain non-orthogonal multiple access (NOMA) with successive interference cancellation. Task-input size determines the transmitted and processed workload, while queue backlog and application urgency determine the service weight. The Gaussian multiple-access-channel rate region is convex, but the complete allocation problem is not jointly convex in the adopted variables because the offloaded workload is coupled with reciprocal transmission rate and reciprocal edge-CPU allocation. A structure-exploiting block-coordinate projected-gradient method is developed. It combines exact finite-candidate offloading updates, an exact edge-CPU allocation bounded below by deadline feasibility and above by local-path saturation, and an analytical projected power step with Armijo backtracking. For eight users at 23 dBm, pairwise group-based NOMA reduces the weighted delay–energy cost and device energy by 6.18% and 23.44%, respectively, relative to orthogonal access. Queue-aware weighting reduces upper-backlog-quartile delay by 2.69 ms (95% confidence half-width: 0.78 ms) while increasing lower-quartile delay by 8.34 ms (half-width: 2.07 ms). In a paired 15-iteration ablation, generic projected block-coordinate updates have a cost ratio of 1.0098 (half-width: 0.0086) relative to the structured method. A hybrid deep deterministic policy-gradient policy, evaluated over five training seeds, has an 11.77% higher cost while requiring 0.84% of the median online decision time. Of 432 allocations, 392 satisfy the residual-qualified stopping tests and 40 are explicitly reported as iteration-safeguard terminations.
Jamil K. J. Bataineh, Ahlam Jawarneh, K. Hayajneh et al.· Italian National Conference...· 0 citations
Mobile edge computing (MEC) enables resource-constrained user devices (UDs) to obtain low-latency computing services by offloading computational tasks to the network edge. Non-orthogonal multiple access-enabled mobile edge computing (NOMA-MEC) systems feature asymmetric states across UDs, dynamic task arrivals, and competition for wireless and edge computing resources. Under these conditions, offloading decisions affect device energy consumption, task delay, and edge computing resource allocation, making long-term system optimization difficult. This study jointly optimizes task offloading and system resource scheduling to minimize the long-term delay–energy cost. The problem is formulated as a partially observable Markov decision process (POMDP) and addressed using a master-refined multi-agent proximal policy optimization (MR-MAPPO) algorithm. MR-MAPPO combines continuous action relaxation, master action refinement, and a behavior cloning auxiliary term to learn policies in a hybrid discrete–continuous action space. A marginal congestion delay term is also introduced to capture the impact of newly admitted tasks on existing edge workloads. Simulation results show that MR-MAPPO outperforms the considered baselines, while ablation studies verify the effects of its key components. Under the main experimental setting, MR-MAPPO reduces the system cost by 17.9% and 22.9% relative to standard MAPPO and particle swarm optimization (PSO), respectively.
Scheduling in multiuser multiple input multiple output (MU-MIMO) systems is essential for efficient resource allocation and overall performance enhancement. In this work, a multiuser scheduling problem is formulated to maximize the product of user equipments'(UEs) aggregate satisfactions, which maintains user fairness. Solving such a combinatorial problem using exhaustive search (EX), which requires evaluating all possible multiuser groups within a massive number of resource blocks (RBs), is prohibitive. Instead, we propose an efficient users'satisfaction based scheduling approach (US-SA). In our US-SA, a low dimension sub-grouping matrix is constructed {at each frame}, which is used to schedule the best multiuser group in each time slot; satisfied users are eliminated from the scheduling process. Our US-SA performs close to the optimal EX method in terms of satisfaction, transmitted data amount, spectral efficiency, latency, and fairness with lower computational cost. Moreover, our experiments demonstrate that the proposed scheme outperforms competing techniques.
Co-channel interference (CCI) management through inter-system consensus, where CCI is kept below a predetermined threshold, has been studied as an approach to proactive spectrum sharing among multiple local 5G systems. In our prior work, we established a resource allocation method that exploits the fact that CCI varies depending on user equipment (UE) positions when beamforming is steered to track each UE. However, the previous study did not incorporate a mechanism to guarantee compliance with interference constraints that keep CCI below a specified level. Moreover, since frequency access opportunities for individual UEs were not guaranteed, there was a concern that some UEs could be left without any allocated resources. In this paper, we formulate the mobility-prediction-based resource allocation as a 0-1 integer linear program (ILP) and introduce both an interference constraint and a minimum allocation constraint as hard constraints. The proposed method is positioned as a hard-constraint scheduler that prioritizes interference compliance and prevention of zero-allocation UEs, while the remaining degrees of freedom are used for SNR-based throughput maximization. The effectiveness and throughput–fairness tradeoff of the proposed method are demonstrated through computer simulations.
Power-domain non-orthogonal multiple access (PD-NOMA) combined with universal filtered multicarrier (UFMC) offers a promising waveform architecture for 5G and beyond, providing high spectral efficiency and improved out-of-band emission suppression. However, the performance of PD-NOMA UFMC systems critically depends on optimal power allocation, which poses a challenging non-convex optimization problem with fairness constraints. Although metaheuristic methods have been explored in PD-NOMA, their application to UFMC-based systems remains limited. This study proposes a phase-controlled hybridization mechanism that integrates the exploration dynamics of moth-flame optimization (MFO) with the exploitation capability of the whale optimization algorithm (WOA) for fairness-driven power allocation in a two-user PD-NOMA UFMC system. A composite weighted objective formulation is designed to jointly minimize bit error rate (BER), maximize achievable rate, and enhance user fairness. The simulation results show that the hybrid WOA–MFO approach converges faster and achieves approximately 12.16% improvement in the best fitness compared to the conventional WOA. In terms of communication performance, the hybrid framework significantly reduces the transmit power required to achieve the target performance levels. For the BER benchmark, the required transmit power is reduced by 6.33 dBm for the Far user and 4.66 dBm for the Near user. For the achievable rate target, the required transmit power is reduced by 2.29 dBm and 4.81 dBm for the Far and Near users, respectively. Similarly, reductions of 2.5 dBm and 4.8 dBm are observed for the Far and Near users at the outage probability threshold. For the fairness requirement based on Jain’s fairness index, the hybrid approach achieves the target level with approximately 2.4 dBm lower transmit power compared with the baseline WOA. These results indicate that the hybrid WOA–MFO approach provides an efficient and balanced solution for computational performance and fairness-driven power allocation in PD-NOMA UFMC systems.
Gopal K sharma, Vineeta Saxena Nigam, Rakesh K. Arya· Physica Scripta· 0 citations
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
