Message-Passing Receiver Design for Joint Channel Estimation and Data Decoding in Uplink Grant-Free SCMA Systems

Abstract

The conventional grant-based network relies on the handshaking between base station and active users to achieve dynamic multi-user scheduling, which may cost large signaling overheads as well as system latency. To address those problems, the grant-free receiver design is considered in this paper based on sparse code multiple access (SCMA), one of the promising air interface technologies for 5G wireless networks. With the presence of unknown multipath fading, the proposed receiver blindly performs joint channel estimation and data decoding without knowing the user activity in the network. Based on the framework of belief propagation (BP), we formulate a messagepassing receiver for uplink SCMA that performs joint estimation iteratively. However, the direct application of BP for the multivariable detection problem is complex. Motivated by the idea of approximate inference, we use expectation propagation to project the intractable distributions into Gaussian families such that a linear complexity decoder is obtained. Simulation results show that the proposed receiver can detect active users in the network with a high accuracy and can achieve an improved bit-error-rate performance compared with existing methods.

On the Fundamental Limits of MIMO Massive Multiple Access Channels

“…In [12], the achievability bound for the random-access code is proposed and the tradeoff between the energy-per-bit and spectral efficiency for var,ious MAC regimes in the massive multiple access is studied. Moreover, some recent works on the coding aspects of massive random access can be found in [13]- [18].…”

DNN-Aided Block Sparse Bayesian Learning for User Activity Detection and Channel Estimation in Grant-Free Non-Orthogonal Random Access

“…Otherwise, this user is detected as active. The hyper-parameter γ k is assumed to follow a Gamma distribution [27], [28]. The noise precision λ = 1/σ 2 w is unknown at the receiver but assumed with a prior probability p(λ).…”

Iterative Joint Channel Estimation, User Activity Tracking, and Data Detection for FTN-NOMA Systems Supporting Random Access

“…Its extension to FTN signaling was then considered in [41], where the generalized approximate MPA was employed. For NOMA relying on random access, the authors of [42] and [43] assumed the user activity to be static and constructed factor graph models for simultaneously solving the channel estimation as well as data and user activity detection problem.…”

“…Therefore, joint user activity tracking, channel estimation and data detection has to be designed for FTN-NOMA systems relying on random access. Instead of relying on precision parameter of [42] and [43] to capture the user activity, we introduce a binary variable to represent the user activity. By employing the autoregressive moving average (ARMA) model of [44] for approximating the correlated noise samples imposed by FTN signaling, we arrive at the factorization problem of the joint a posteriori distribution.…”

“…• In contrast to [42], [43] assuming static user activity in random access, we study dynamically fluctuating user activity and channel envelopes. We intrinsically amalgamate FTN signaling with random access NOMA systems and demonstrate that their throughput gains may be combined.…”