| Xu Lei（徐 磊）,Tao Shangjin,Bai Shichao,Zhang Jian,Fang Hongyu,Li Xiaohui.[J].高技术通讯(英文),2021,27(1):10~16 |
|
| Dynamic multi-user detection scheme based on CVA-SSAOMP algorithm in uplink grant-free NOMA |
| |
| DOI：10.3772/j.issn.1006-6748.2021.01.002 |
| 中文关键词: |
| 英文关键词: non-orthogonal multiple access (NOMA), multi-user detection, cross validation, structured sparsity (SP), orthogonal matching pursuit (OMP) |
| 基金项目: |
| Author Name | Affiliation | | Xu Lei（徐 磊） | (School of Electronic and Information Engineering, Anhui University, Hefei 230039, P. R. China) | | Tao Shangjin | (School of Electronic and Information Engineering, Anhui University, Hefei 230039, P. R. China) | | Bai Shichao | (School of Electronic and Information Engineering, Anhui University, Hefei 230039, P. R. China) | | Zhang Jian | (School of Electronic and Information Engineering, Anhui University, Hefei 230039, P. R. China) | | Fang Hongyu | (School of Electronic and Information Engineering, Anhui University, Hefei 230039, P. R. China) | | Li Xiaohui | (School of Electronic and Information Engineering, Anhui University, Hefei 230039, P. R. China) |
|
| Hits: 1453 |
| Download times: 1305 |
| 中文摘要: |
| |
| 英文摘要: |
| In the uplink grant-free non-orthogonal multiple access (NOMA) scenario, since the active us-er at the sender has a structured sparsity transmission characteristic, the compressive sensing recov-ery algorithm is initially applied to the joint detection of the active user and the transmitted data. However, the existing compressed sensing recovery algorithms with unknown sparsity often require noise power or signal-to-noise ratio (SNR) as the priori conditions, which greatly reduces the algo-rithm adaptability in multi-user detection. Therefore, an algorithm based on cross validation aided structured sparsity adaptive orthogonal matching pursuit (CVA-SSAOMP) is proposed to realize multi-user detection in dynamic change communication scenario of channel state information (CSI). The proposed algorithm transforms the structured sparsity model into a block sparse model, and with-out the priori conditions above, the cross validation method in the field of statistics and machine learning is used to adaptively estimate the sparsity of active user through the residual update of cross validation. The simulation results show that, compared with the traditional orthogonal matching pur-suit (OMP) algorithm, subspace pursuit (SP) algorithm and cross validation aided block sparsity adaptive subspace pursuit (CV A-BSASP) algorithm, the proposed algorithm can effectively improve the accurate estimation of the sparsity of active user and the performance of system bit error ratio (BER), and has the advantage of low-complexity. |
|
View Full Text
View/Add Comment Download reader |
| Close |
|
|
|
