User Activity Detection for Irregular Repetition Slotted Aloha based MMTC

TL;DR

This paper introduces a Bayesian user activity detection algorithm for IRSA in massive machine-type communications, improving detection accuracy and efficiency, and analyzing its impact on system throughput with practical simulations.

Contribution

A novel Bayesian UAD algorithm for IRSA that exploits activity sparsity and structure, achieving near CRB performance with significantly shorter pilots than classical methods.

Findings

The proposed UAD algorithm achieves CRB on channel estimation.

Accurate user activity detection with as few as 20 pilot symbols.

Insights into how system parameters affect UAD errors and throughput.

Abstract

Irregular repetition slotted aloha (IRSA) is a grant-free random access protocol for massive machine-type communications, where a large number of users sporadically send their data packets to a base station (BS). IRSA is a completely distributed multiple access protocol: in any given frame, a small subset of the users, i.e., the active users, transmit replicas of their packet in randomly selected resource elements (REs). The first step in the decoding process at the BS is to detect which users are active in each frame. To this end, a novel Bayesian user activity detection (UAD) algorithm is developed, which exploits both the sparsity in user activity as well as the underlying structure of IRSA-based transmissions. Next, the Cramer-Rao bound (CRB) on the mean squared error in channel estimation is derived. It is empirically shown that the channel estimates obtained as a by-product of the proposed UAD algorithm achieves the CRB. Then, the signal to interference plus noise ratio achieved by the users is analyzed, accounting for UAD, channel estimation errors, and pilot contamination. The impact of these non-idealities on the throughput of IRSA is illustrated via Monte Carlo simulations. For example, in a system with 1500 users and 10% of the users being active per frame, a pilot length of as low as 20 symbols is sufficient for accurate user activity detection. In contrast, using classical compressed sensing approaches for UAD would require a pilot length of about 346 symbols. The results also reveal crucial insights into dependence of UAD errors and throughput on parameters such as the length of the pilot sequence, the number of antennas at the BS, the number of users, and the signal to noise ratio.