Distributed Optimization for Massive Connectivity

TL;DR

This paper introduces a fast, distributed algorithm for joint activity detection and channel estimation in massive IoT networks, significantly improving convergence speed and computational efficiency over existing methods.

Contribution

It proposes a novel distributed algorithm for solving Group Lasso problems in IoT connectivity, enabling parallel processing and faster convergence.

Findings

Algorithm achieves faster convergence than state-of-the-art methods.

Significant reduction in computation time demonstrated.

Effective in large-scale IoT connectivity scenarios.

Abstract

Massive device connectivity in Internet of Thing (IoT) networks with sporadic traffic poses significant communication challenges. To overcome this challenge, the serving base station is required to detect the active devices and estimate the corresponding channel state information during each coherence block. The corresponding joint activity detection and channel estimation problem can be formulated as a group sparse estimation problem, also known under the name "Group Lasso". This letter presents a fast and efficient distributed algorithm to solve such Group Lasso problems, which alternates between solving small-scaled problems in parallel and dealing with a linear equation for consensus. Numerical results demonstrate the speedup of this algorithm compared with the state-of-the-art methods in terms of convergence speed and computation time.