Accelerating Coordinate Descent via Active Set Selection for Device Activity Detection for Multi-Cell Massive Random Access
Ziyue Wang, Ya-Feng Liu, Zhilin Chen, Wei Yu

TL;DR
This paper introduces an active set selection strategy to accelerate coordinate descent algorithms for device activity detection in multi-cell massive MIMO systems, significantly improving computational efficiency by exploiting sparsity.
Contribution
A novel active set selection method is proposed to enhance coordinate descent for device detection, leveraging sparsity to improve speed and efficiency.
Findings
The active set CD algorithm outperforms traditional CD in speed.
The method effectively exploits sparsity in device activity.
Simulation results confirm improved computational efficiency.
Abstract
We propose a computationally efficient algorithm for the device activity detection problem in the multi-cell massive multi-input multi-output (MIMO) system, where the active devices transmit their signature sequences to multiple BSs in multiple cells and all the BSs cooperate to detect the active devices. The device activity detection problem has been formulated as a maximum likelihood maximization (MLE) problem in the literature. The state-of-the-art algorithm for solving the problem is the (random) coordinate descent (CD) algorithm. However, the CD algorithm fails to exploit the special sparsity structure of the solution of the device activity detection problem, i.e., most of devices are not active in each time slot. In this paper, we propose a novel active set selection strategy to accelerate the CD algorithm and propose an efficient active set CD algorithm for solving the considered…
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Taxonomy
TopicsEnergy Harvesting in Wireless Networks · Advanced MIMO Systems Optimization · Cooperative Communication and Network Coding
