Sparsity Learning-Based Multiuser Detection in Grant-Free Massive-Device Multiple Access

Abstract

In this work, we study the multiuser detection (MUD) problem for a grant-free massive-device multiple access (MaDMA) system, where a large number of single-antenna user devices transmit sporadic data to a multi-antenna base station (BS). Specifically, we put forth two MUD schemes, termed random sparsity learning multiuser detection (RSL-MUD) and structured sparsity learning multiuser detection (SSL-MUD) for the time-slotted and non-time-slotted grant-free MaDMA systems, respectively. In the time-slotted RSL-MUD scheme, active users generate and transmit data packets with random sparsity. In the non-time-slotted SSL-MUD scheme, we introduce a sliding-window-based detection framework, and the user signals in each observation window naturally exhibit structured sparsity. We show that by exploiting the sparsity embedded in the user signals, we can recover the user activity state, the channel, and the user data in a single phase, without using pilot signals for channel estimation and/or active user identification. To this end, we develop a message-passing based statistical inference framework for the BS to blindly detect the user data without any prior knowledge of the identities and the channel state information (CSI) of the active users. Simulation results show that our RSL-MUD and SSL-MUD schemes significantly outperform their counterpart schemes in both reducing the transmission overhead and improving the error behavior of the system.

Double-Sparsity Learning-Based Channel-and-Signal Estimation in Massive MIMO With Generalized Spatial Modulation

“…To solve (13), our previous work proposed to factorize the noisy product Y by exploiting either the channel sparsity [19], [20] or the signal sparsity [21]. Sparse matrix factorization techniques, such as the K-SVD algorithm [25], the SPAMS algorithm [26], the ER-SpUD algorithm [27], and the bilinear generalized approximate message passing (BiG-AMP) algorithm [28], can be used to produce the estimates of H and X simultaneously.…”

“…Sparse matrix factorization techniques, such as the K-SVD algorithm [25], the SPAMS algorithm [26], the ER-SpUD algorithm [27], and the bilinear generalized approximate message passing (BiG-AMP) algorithm [28], can be used to produce the estimates of H and X simultaneously. It has been shown in [19], [20], and [21] that the blind estimation approach suffers from phase and permutation ambiguities.…”

“…We show that although the basic idea is borrowed from the affine sparse matrix factorization (ASMF) algorithm developed in [19], new initialization and and re-initialization methods are necessary to ensure a good algorithm performance for the considered double-sparsity model. We also show that our proposed scheme significantly outperforms the other blind detection schemes [20], [21] (that exploit either the channel sparsity or the signal sparsity, but not both) and the state-of-the-art training-based schemes for massive MIMO systems with GSM [22] 1 .…”

Joint User Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA

“…Fig. 1 shows the example of the standard Throughout the paper, we make the following assumptions [34], [36].…”

“…Another approach is to rely on the Gaussian approximation that is widely used in the design of approximate message passing algorithms [33], [34], [37]. However, simply approximating (24) by a Gaussian distribution can lead to a substantial information loss and consequently incurs evident performance degradation, as demonstrated by the numerical results in Section VI.…”