sparse recovery
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2022 ◽  
Vol 421 ◽  
pp. 126923
Author(s):  
Bin Zhao ◽  
Pengbo Geng ◽  
Wengu Chen ◽  
Zhu Zeng
Keyword(s):  

2022 ◽  
Vol 13 (3) ◽  
pp. 279-289
Author(s):  
Xingyu Lu ◽  
Jianchao Yang ◽  
Ke Tan ◽  
Weimin Su ◽  
Hong Gu

2021 ◽  
Author(s):  
Rong Fu ◽  
Tianyao Huang ◽  
Lei Wang ◽  
Yimin Liu

2021 ◽  
Author(s):  
Shah Mahdi Hasan ◽  
Kaushik Mahata ◽  
Md Mashud Hyder

Grant-Free Non Orthogonal Multiple Access (NOMA) offers promising solutions to realize uplink (UL) massive Machine Type Communication (mMTC) using limited spectrum resources, while reducing signalling overhead. Because of the sparse, sporadic activities exhibited by the user equipments (UE), the active user detection (AUD) problem is often formulated as a compressive sensing problem. In line of that, greedy sparse recovery algorithms are spearheading the development of compressed sensing based multi-user detectors (CS-MUD). However, for a given number of resources, the performance of CS-MUD algorithms are fundamentally limited at higher overloading of NOMA. To circumvent this issue, in this work, we propose a two-stage hierarchical multi-user detection framework, where the UEs are randomly assigned to some pre-defined clusters. The active UEs split their data transmission frame into two phases. In the first phase an UE uses the sinusoidal spreading sequence (SS) of its cluster. In the second phase the UE uses its own unique random SS. At phase 1 of detection, the active clusters are detected, and a reduced sensing matrix is constructed. This matrix is used in Phase 2 to recover the active UE indices using some sparse recovery algorithm. Numerical investigations validate the efficacy of the proposed algorithm in highly overloaded scenarios.


2021 ◽  
Author(s):  
Shah Mahdi Hasan ◽  
Kaushik Mahata ◽  
Md Mashud Hyder

Grant-Free Non Orthogonal Multiple Access (NOMA) offers promising solutions to realize uplink (UL) massive Machine Type Communication (mMTC) using limited spectrum resources, while reducing signalling overhead. Because of the sparse, sporadic activities exhibited by the user equipments (UE), the active user detection (AUD) problem is often formulated as a compressive sensing problem. In line of that, greedy sparse recovery algorithms are spearheading the development of compressed sensing based multi-user detectors (CS-MUD). However, for a given number of resources, the performance of CS-MUD algorithms are fundamentally limited at higher overloading of NOMA. To circumvent this issue, in this work, we propose a two-stage hierarchical multi-user detection framework, where the UEs are randomly assigned to some pre-defined clusters. The active UEs split their data transmission frame into two phases. In the first phase an UE uses the sinusoidal spreading sequence (SS) of its cluster. In the second phase the UE uses its own unique random SS. At phase 1 of detection, the active clusters are detected, and a reduced sensing matrix is constructed. This matrix is used in Phase 2 to recover the active UE indices using some sparse recovery algorithm. Numerical investigations validate the efficacy of the proposed algorithm in highly overloaded scenarios.


2021 ◽  
pp. 108432
Author(s):  
Jian Huang ◽  
Yuling Jiao ◽  
Xiliang Lu ◽  
Yueyong Shi ◽  
Qinglong Yang ◽  
...  

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