Joint Activity Detection and Channel Estimation for Massive Random Access Using SBL and SCA

TL;DR

This paper introduces a covariance learning-based sparse Bayesian learning method with successive convex approximation for joint device activity detection and channel estimation in massive random access scenarios, demonstrating improved performance.

Contribution

The paper proposes a novel CL-SCA approach combining SCA and empirical Bayesian estimation for efficient JADCE in mMTC, outperforming existing methods.

Findings

The proposed CL-SCA method accurately detects active devices.

It provides superior channel estimates compared to existing techniques.

Simulation results confirm its efficiency and robustness.

Abstract

In massive machine-type communication (mMTC) applications, a key challenge is joint device activity detection and channel estimation (JADCE) under grant-free random access, as a massive number of devices with sporadic traffic seek to connect to the base station. We address JADCE for massive random access using a covariance learning-based sparse Bayesian learning (SBL) approach. Specifically, we first use the successive convex approximation (SCA) framework to partially linearize the scaled negative log-likelihood function (LLF) of the data, then minimize it to estimate the sparse vector of devices' signal powers. After identifying active devices from these power estimates, empirical Bayesian estimation is used to obtain channel estimates. Simulation results demonstrate the efficiency and performance superiority of the proposed CL-SCA method compared to other existing methods.