An Efficient Algorithm for Device Detection and Channel Estimation in Asynchronous IoT Systems

TL;DR

This paper introduces a low-complexity, real-time algorithm for device detection and channel estimation in asynchronous IoT systems, addressing delays and uncoordinated transmissions.

Contribution

It formulates the detection and estimation problem as a group LASSO and proposes a block coordinate descent algorithm with closed-form updates for efficient implementation.

Findings

Algorithm effectively detects active devices and estimates delays and channels.

Low computational complexity suitable for real-time IoT applications.

Addresses asynchronous transmission issues in massive device scenarios.

Abstract

A great amount of endeavour has recently been devoted to the joint device activity detection and channel estimation problem in massive machine-type communications. This paper targets at two practical issues along this line that have not been addressed before: asynchronous transmission from uncoordinated users and efficient algorithms for real-time implementation in systems with a massive number of devices. Specifically, this paper considers a practical system where the preamble sent by each active device is delayed by some unknown number of symbols due to the lack of coordination. We manage to cast the problem of detecting the active devices and estimating their delay and channels into a group LASSO problem. Then, a block coordinate descent algorithm is proposed to solve this problem globally, where the closed-form solution is available when updating each block of variables with the other blocks of variables being fixed, thanks to the special structure of our interested problem. Our analysis shows that the overall complexity of the proposed algorithm is low, making it suitable for real-time application.