2026· International Research Journal of Multidisciplinary Scope· 0 citations· 30 references
TL;DR
Results indicate that the proposed AO-SCA framework provides an effective and practical solution for fairness-aware power allocation in downlink MN-NOMA systems, and provides a balanced fairness-efficiency tradeoff.
Abstract
Mixed Numerology Non-Orthogonal Multiple Access (MN-NOMA) is a promising technique for beyond-5G wireless networks, but practical deployments face two major challenges: inter-numerology interference (INI) caused by overlapping subcarriers and residual interference due to imperfect successive interference cancellation (SIC). Most existing studies address these two issues separately. This paper proposes a QoS-aware power allocation framework that jointly mitigates INI and imperfect SIC in downlink MN-NOMA systems. The resource allocation problem is formulated with a logarithmic utility function, that aims to balance the overall spectral efficiency and user fairness. The resulting optimization problem is highly non-convex because of the coupled interference terms. To solve it efficiently, an alternating optimization and successive convex approximation (AO-SCA) framework is developed, where the original problem is iteratively transformed into tractable convex sub problems. Simulation results demonstrate clear performance gains over Equal Power Allocation (EPA) and Fixed Power Allocation (FPA) schemes. The proposed framework improves spectral efficiency, particularly in the low-to-moderate SNR region, while maintaining reliable performance under practical interference conditions. Unlike schemes that favor only strong-channel users, the proposed method provides a balanced fairness-efficiency tradeoff, maintaining a Jain's fairness index of approximately 0.67 while reducing outage probability to near-zero levels at SNR values above 30 dB. These results indicate that the proposed AO-SCA framework provides an effective and practical solution for fairness-aware
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
In single-input single-output downlink systems, the common and private streams in rate-splitting multiple access (RSMA) fully overlap in the power domain, causing strong inter-private interference. Under imperfect successive interference cancellation (SIC), this interference significantly increases users’ decoding error probability. To overcome this limitation, we propose a semi-rate-splitting multiple access (SRMA) scheme that distributes private streams across two distinct bandwidth/time regions rather than fully overlapping them. To evaluate the proposed SRMA, we formulate two design problems: 1) maximizing the minimum ergodic capacity (EC) among users and 2) minimizing the system connection outage probability (COP). Both problems jointly optimize the rate-split ratio, power allocation coefficient, and partitioned-resource factor. To address the non-convexity in these problems, we employ successive convex approximation and analytical reformulation to relax them into tractable convex problems with closed-form solutions. Monte-Carlo simulations demonstrate that SRMA outstandingly improves COP performance while achieving EC levels comparable to RSMA and outperforming conventional non-orthogonal multiple.
Thai-Hoc Vu, Anh-Tu Le, Miroslav Voznak· IEEE Wireless Communications...· 0 citations
Validation of the APG algorithm's resilience revealed that it outperformed benchmark algorithms in terms of energy efficiency and execution time, demonstrating its usefulness for challenging optimization tasks, particularly those involving bursty communication.
K. A. Bonsu, Ebenezer Baidoo Baidoo Bediako, K. Darkwah et al.· Applied Mathematics and Stat...· 0 citations
This paper investigates joint subcarrier and power allocation for a multi-user Orthogonal Frequency Division Multiplexing (OFDM)-based Integrated Sensing and Communication (ISAC) system in Vehicle-to-Everything (V2X) environments. The goal is to maximize a weighted sum of the capped effective radar Signal-to-Noise Ratio (SNR) and aggregate communication rate, under per-user constraints on minimum effective radar SNR, communication rate, and range resolution. The problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) model. To address its non-convexity, we develop a Block Coordinate Descent–based Joint Resource Allocation (BCD-JRA) algorithm that alternates between nonlinear power allocation and mixed-integer subcarrier assignment and is used as a benchmark in our study. To support real-time V2X operation, we further propose a low-complexity two-stage heuristic, termed Phased Constraint Satisfaction and Greedy Allocation (PSGA). PSGA first allocates the minimum resources needed to satisfy the Quality of Service (QoS) constraints, and then greedily assigns remaining resources based on marginal utility gains while accounting for effective radar SNR capping. The simulation results show that PSGA attains utility close to the BCD-JRA benchmark with millisecond-level latency and satisfies all QoS constraints in the reported experiments.
Jiahao Zheng, Xinhao Chen, Linyu Huang et al.· IEEE Transactions on Wireles...· 0 citations
A phase-controlled hybridization mechanism that integrates the exploration dynamics of moth-flame optimization with the exploitation capability of the whale optimization algorithm (WOA) for fairness-driven power allocation in a two-user PD-NOMA UFMC system is proposed.
Gopal K sharma, Vineeta Saxena Nigam, Rakesh K. Arya· Physica Scripta· 0 citations
Integrated Sensing and Communication (ISAC) is emerging as a key technology for next-generation wireless networks, enabling simultaneous communication and sensing functionalities. This paper focuses a RIS-assisted full-duplex (FD) ISAC system, in which a multi-antenna base station (BS) concurrently performs multi-user uplink and downlink transmission while also carrying out radar sensing. To maximize the joint uplink–downlink sum rate, an optimization problem is formulated under practical constraints, such as radar detection SINR, self-interference, BS transmit power, user power budgets, and RIS unit-modulus conditions. To address the nonconvexity of this problem, a two-stage hybrid optimization approach is developed. In the first stage, the augmented Lagrangian technique decomposes the complex problem into simpler subproblems involving beamforming, power allocation, and RIS phase optimization, leading to a feasible initial solution. The second stage employs a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework to refine this solution adaptively, enabling the system to respond effectively to variations in the channel environment, mobility patterns, and interference levels. The proposed hybrid framework achieves optimal resource allocation while maintaining feasibility, robustness, and adaptability. Analytical results confirm its convergence behavior, and extensive simulation results confirm that the proposed scheme consistently outperforms conventional optimization and single-agent DRL baselines in sum-rate maximization, interference mitigation, and sensing accuracy, confirming its effectiveness for RIS-assisted full-duplex ISAC systems.
S. Waqas, Fenghua Huang, Fakhar Abbas et al.· IEEE Transactions on Wireles...· 0 citations