Name
Title | ||
|---|---|---|
Learning Driven Resource Allocation and SIC Ordering in EH Relay Aided NB-IoT Networks |
Abstract | ||
|---|---|---|
Integrating the energy-harvesting (EH) relay and non-orthogonal multiple access (NOMA) technologies into narrow band internet of things (NB-IoT) networks can efficiently improve the energy and spectrum efficiency of the network and the quality-of-service of edge users. Therefore, we consider an EH relay aided NOMA NB-IoT network in this letter. To reduce the rate variance among NB-IoT devices, we aim to maximize the proportional fairness of data rate across all NB-IoT devices through jointly optimizing the communication resource allocation and successive interference cancellation (SIC) ordering subject to the minimum data rate requirements. Considering the non-convexity of this optimization problem, we propose a deep reinforcement learning based online optimization algorithm to obtain the sub-optimal solution. Simulation results demonstrate that the proposed algorithm can efficiently improve the proportional fairness and the total throughput among NB-IoT devices, in comparison with orthogonal multiple access techniques. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LCOMM.2021.3077635 | IEEE Communications Letters |
Keywords | DocType | Volume |
Non-orthogonal multiple access (NOMA),resource allocation,deep reinforcement learning | Journal | 25 |
Issue | ISSN | Citations |
8 | 1089-7798 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Ping Qian | 1 | 529 | 49.54 |
| Chao Yang | 2 | 87 | 22.49 |
| Huimei Han | 3 | 9 | 2.18 |
| Yuan Wu | 4 | 538 | 61.11 |
| Limin Meng | 5 | 76 | 17.53 |