Research Article
AMP Inspired Antenna Activity and Signal Detection Algorithm for Generalized Spatial Modulated NOMA
@INPROCEEDINGS{10.1007/978-3-030-37262-0_22, author={Xiang Li and Yang Huang and Wei Heng and Jing Wu and Ke Wang and Gang Wang and Yuan Zhang}, title={AMP Inspired Antenna Activity and Signal Detection Algorithm for Generalized Spatial Modulated NOMA}, proceedings={Ad Hoc Networks. 11th EAI International Conference, ADHOCNETS 2019, Queenstown, New Zealand, November 18--21, 2019, Proceedings}, proceedings_a={ADHOCNETS}, year={2020}, month={1}, keywords={Spatial modulation NOMA Approximate message passing Compressive sensing}, doi={10.1007/978-3-030-37262-0_22} }
- Xiang Li
Yang Huang
Wei Heng
Jing Wu
Ke Wang
Gang Wang
Yuan Zhang
Year: 2020
AMP Inspired Antenna Activity and Signal Detection Algorithm for Generalized Spatial Modulated NOMA
ADHOCNETS
Springer
DOI: 10.1007/978-3-030-37262-0_22
Abstract
The non-orthogonal multiple access technology has been considered as one of potential technologies for the next generation wireless network. Spatial modulation, which improves both spectral and energy efficiency at the same time, has found its potentials in NOMA system. Spatial modulation, together with multiple-input multiple-output technique, could maintain massive connections and provide low latency at the same time. But it also puts forward challenges for multi-user and signal detection. By exploiting the sparsity nature of generalized spatial modulation system, we formulate the active antenna and user signal detection into a general sparse linear-inverse problem. An approximate message passing based algorithm is proposed to detect the antenna activity and transmitted signal simultaneously in the uplink grant-free NOMA scenario. Expect maximum algorithm is utilized to learn the parameters of activity level and noise variance. Simulation results show that proposed scheme outperforms the CS based schemes over a wide range of SNR and sparsity level. Moreover, proposed algorithm achieves convergency in 15 iterations which makes it very practical.