Papers › Attention-based SIC Ordering and Power Allocation for Non-orthogonal Multiple Access Networks
Attention-based SIC Ordering and Power Allocation for Non-orthogonal Multiple Access Networks
Liang Huang, Bincheng Zhu, Runkai Nan, Kaikai Chi, Yuan Wu
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Non-orthogonal multiple access (NOMA) emerges as a superior technology for enhancing spectral efficiency compared to orthogonal multiple access. In NOMA networks, successive interference cancellation (SIC) plays a crucial role in decoding user signals sequentially. The challenge lies in the joint optimization of SIC ordering and power allocation, due to the factorial nature of ordering combinations. This study introduces an innovative solution, the Attention-based SIC Ordering and Power Allocation (ASOPA) framework, targeting an uplink NOMA network with dynamic SIC ordering. ASOPA aims to maximize weighted proportional fairness by employing deep reinforcement learning, strategically decomposing the problem into two manageable subproblems: SIC ordering optimization and optimal power allocation. Our approach utilizes an attention-based neural network, which processes instantaneous channel gains and user weights to determine the SIC decoding sequence for each user. Once the SIC ordering is established, the power allocation subproblem transforms into a convex optimization problem, enabling efficient calculation. Extensive simulations validate ASOPA's efficacy, demonstrating a performance closely paralleling the exhaustive method, with over 97% confidence in normalized network utility. Notably, ASOPA maintains a low execution latency of approximately 50 milliseconds in a ten-user NOMA network, aligning with static SIC ordering algorithms. Furthermore, ASOPA demonstrates superior performance in various NOMA network configurations, including scenarios with imperfect channel state information, multiple base stations, and multiple-antenna setups. Such results underscore ASOPA's robustness and effectiveness, highlighting its ability to excel across various NOMA network environments. The complete source code for ASOPA is accessible at https://github.com/Jil-Menzerna/ASOPA.
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