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Energy efficient hybrid NOMA-OMA IoT systems: A hybrid PPO approach
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OA
AI
T
Trang H. T. NguyenH
Heejung YuT
Taejoon Kim DOI：10.23919/jcn.2025.000119.png)
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Abstract
En 中文
Consider a hybrid orthogonal multiple access (OMA) and non-OMA (NOMA)—HMA multicasting for Internet-of-things (IoT) systems, where a group of interested IoT devices is assigned to subchannels, some using NOMA and the others using OMA. Since multicasting is heavily constrained by the weakest channel gain of involved IoT devices, an advanced resource allocation strategy is required to enhance energy efficiency. This paper investigates a simultaneous optimization of power allocation and subchannel assignment to maximize energy efficiency in HMA, not resorting to alternating optimization (AO). Specifically, an optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, posing a significant challenge in finding a globally optimal solution. To address this, we propose two novel algorithms: the first is designed for small search spaces, using the Big-M method for linearization of a bilinear term followed by an iterative and threshold-based rounding, and the second is tailored for large search spaces, utilizing a hybrid proximal policy optimization (HPPO) technique to train hybrid action space. The simulation results confirm that both the proposed algorithms surpass other AO-based benchmarks by 7.1% and 5.6%, respectively, in terms of energy efficiency.
Keywords:
Deep reinforcement learning
energy efficiency
hybrid NOMA-OMA
IoT
resource allocation
Journal
J
IF:
3.2
Papers:
47
Citations:
0
Organization
Cited Papers