Double-Sided Information Aided Temporal-Correlated Massive Access

TL;DR

This paper introduces a double-sided information aided algorithm for joint activity detection and channel estimation in temporal-correlated massive access, leveraging information from both previous and next frames to improve performance.

Contribution

It proposes a novel double-sided information (DSI) approach within the AMP framework for better activity detection and channel estimation in temporally correlated massive access.

Findings

Superior performance over existing methods

Effective use of information from adjacent frames

Enhanced accuracy in activity detection and channel estimation

Abstract

This letter considers temporal-correlated massive access, where each device, once activated, is likely to transmit continuously over several consecutive frames. Motivated by that the device activity at each frame is correlated to not only its previous frame but also its next frame, we propose a double-sided information (DSI) aided joint activity detection and channel estimation algorithm based on the approximate message passing (AMP) framework. The DSI is extracted from the estimation results in a sliding window that contains the target detection frame and its previous and next frames. The proposed algorithm demonstrates superior performance over the state-of-the-art methods.