Name
Title | ||
|---|---|---|
Multi-Task Learning Aided Joint Constellation Design and Multiuser Detection for GF-NOMA |
Abstract | ||
|---|---|---|
This paper aims to investigate the joint optimization of multidimensional constellation design (MCD) and multiuser detection (MUD) for grant-free non-orthogonal multiple access (GF-NOMA). We first formulate the joint optimization problem and derive its explicit expression using variational inference. Due to the intractability of the joint optimization problem, we then resort to deep learning (DL) and approximate the optimal solution in an end-to-end manner. Specifically, we develop a novel variational autoencoder based network, such that the distribution of the multidimensional constellations can be accessed and optimized. We also design a multi-task learning architecture on the decoder side to deal with the complex coupling among signal streams, by taking the MUD process as multiple distinctive yet related tasks. The derivation of the loss function for network training is presented, and simulation results are provided to validate the superior performance of the proposed method over conventional approaches. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICC42927.2021.9500861 | IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021) |
DocType | ISSN | Citations |
Conference | 1550-3607 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhe Ma | 1 | 0 | 0.68 |
| Wen Wu | 2 | 0 | 1.01 |
| Feifei Gao | 3 | 3093 | 212.03 |
| Xuemin Sherman Shen | 4 | 13 | 6.97 |