Jul 2026· 2026 International Conference on Emerging Trends in Information, Communication & Systems (ICETICS)· pp. 1-6· 0 citations· 16 references
Abstract
Power spectrum allocation in Device to Device (D2D) communication using Non-Orthogonal Multiple Access (NOMA) presents a challenging optimization problem due to subchannel pairing, continuous power control, and Successive Interference Cancellation (SIC) ordering. These interdependent parameters result in a mixed-integer, non-convex problem subject to requirement of Quality of Service (QoS) constraints. Existing schemes exhibit limitations, as Deep Q-Networks (DQN) approach restricts from limited action space, leading to suboptimal transmit power allocation and reduced energy efficiency. However, Deep deterministic policy gradient (DDPG) scheme often unstables near SIC threshold. To handle these limitations, this research paper addresses Quantum enhanced DDPG (QDDPG) scheme, which integrates hybrid actor-critic with a feasibility aware projection to enforce SIC and QoS constraints. QDDPG reaches a return of 0.97 in 350 episodes, however DDPG and DQN reach to 0.84 and 0.62, respectively. With 60 D2D pairs, QDDPG attains a sum rate of 9.6 versus 8.7 in DDPG and 7.4 in DQN. Energy efficiency equals 5.8 bits/J at 10 pairs in QDDPG, and 4.7 bits/J and 4.1 bits/J in DDPG and DQN, respectively. These results indicate that the proposed QDDPG shows consistent performance improvements over DDPG and DQN schemes under the considered network conditions.
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.
S. Dhotre, S. Nalbalwar, A. Nandgaonkar· International Research Journ...· 0 citations
A maximum matching algorithm for channel allocation with a faster convergence rate that divides the entire set of cellular users and D2D groups into overlapping clusters based on channel gains and utilizes the Kuhn-Munkres algorithm for the best channel allocation to the D2D groups within the same cluster.
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
Simulation results demonstrate that the APS NOMA scheme outperforms both the FPS NOMA and Orthogonal Multiple Access schemes, reducing OP significantly across a range of SNRs, making it highly effective for reliable and energy-efficient communication in future wireless networks.
S. Ajibowu, O. Adeleke, M. Asafa et al.· Nigerian Journal of Technolo...· 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 focused review of power allocation strategies in NOMA is presented, with emphasis on the progression from static and optimization-based dynamic schemes to data-driven Artificial Intelligence (AI) and Machine Learning (ML) driven approaches.
Lekshmi Nair M, Neelakantan Pc· International Journal of Com...· 0 citations
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