Paper Info
Title
Improved sparse multiuser detection based on modulation-alphabets exploitation.
Abstract
In this paper, we focus on sporadic random-access communications and consider compressed-sensing (CS) techniques to perform the multiuser detection (MUD). Since all the users do not necessarily transmit information, MUD consists in detecting the transmitting users (activity detection) and their corresponding transmitted data (data detection). The main results presented here rely on the exploitation of the user signal alphabet knowledge within the detection step. To this aim, several modifications of the group orthogonal matching pursuit (GOMP) algorithm were proposed, differing in the way the modulation alphabet knowledge is considered within the detection stage. These modifications can be extended to any greedy-based CS-MUD. To overcome the error floor occurring at high SNR with a higher number of active users, we then propose an iterative ℓ1 minimization-based MUD algorithm that alternates between activity and data detection. Compared to the existing GOMP-based CS-MUD, the proposed modified GOMP algorithms exhibit a significant gain with almost the same complexity. The iterative ℓ1 minimization-based MUD algorithm has a higher complexity but outperforms all the others without any observed error-floor.
Year
DOI
Venue
2017
10.1016/j.dsp.2017.08.011
Digital Signal Processing
Keywords
Field
DocType
Compressed sensing,Group orthogonal matching pursuit,ℓ1-Minimization,Sparsity,Multiuser detection,Sporadic communication
Matching pursuit,Pattern recognition,Data detection,Computer science,Multiuser detection,Modulation,Activity detection,Minification,Artificial intelligence,Compressed sensing,Alphabet
Journal
Volume
ISSN
Citations 
71
1051-2004
0
PageRank 
References 
Authors
0.34
25
4
Name
Order
Citations
PageRank
Malek Messai1205.01
Abdeldjalil Aïssa-El-Bey216225.10
Karine Amis37517.77
Frédéric Guilloud4348.66