| Title | Multi-Agent Reinforcement Learning-Based User Pairing in Multi-Carrier NOMA Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Wang, Shaoyang, Lv, Tiejun, Zhang, Xuewei |
| Conference Name | 2019 IEEE International Conference on Communications Workshops (ICC Workshops) |
| Keywords | channel capacity, channel conditions, Communication systems, composability, convolutional neural nets, convolutional neural network, cooperative game, cyber physical systems, deep deterministic policy gradient, end-to-end low complexity method, exhaustive search method, game theory, hard channel capacities, learning (artificial intelligence), MC-NOMA, multi-access systems, multi-agent systems, multiagent deep reinforcement learning, multiagent reinforcement learning-based user pairing, multicarrier NOMA systems, multicarrier nonorthogonal multiple access systems, optimisation, optimization problems, pubcrawl, Resiliency, resource allocation, resource allocation problems, search problems, soft channel capacities, telecommunication computing, transmission capability, Trustworthy Systems, user paring network, wireless channels |
| Abstract | This paper investigates the problem of user pairing in multi-carrier non-orthogonal multiple access (MC-NOMA) systems. Firstly, the hard channel capacity and soft channel capacity are presented. The former depicts the transmission capability of the system that depends on the channel conditions, and the latter refers to the effective throughput of the system that is determined by the actual user demands. Then, two optimization problems to maximize the hard and soft channel capacities are established, respectively. Inspired by the multiagent deep reinforcement learning (MADRL) and convolutional neural network, the user paring network (UP-Net), based on the cooperative game and deep deterministic policy gradient, is designed for solving the optimization problems. Simulation results demonstrate that the performance of the designed UP-Net is comparable to that obtained from the exhaustive search method via the end-to-end low complexity method, which is superior to the common method, and corroborate that the UP-Net focuses more on the actual user demands to improve the soft channel capacity. Additionally and more importantly, the paper makes a useful exploration on the use of MADRL to solve the resource allocation problems in communication systems. Meanwhile, the design method has strong universality and can be easily extended to other issues. |
| DOI | 10.1109/ICCW.2019.8757016 |
| Citation Key | wang_multi-agent_2019 |
Multi-Agent Reinforcement Learning-Based User Pairing in Multi-Carrier NOMA Systems