Correlated MTC user activity detection

Abstract

Wireless communications have undergone significant evolution, progressing from human-type communications (HTC) to machine-type communications (MTC), driven by escalating demands for seamless connectivity. Unlike HTC, which primarily facilitates human interactions, MTC enables autonomous interactions among machine-type devices (MTDs), thereby forming extensive networks such as the Internet of Things (IoT). This paradigm shift introduces new challenges, particularly in managing MTC's correlated and bursty traffic patterns, stemming from the event-driven behaviors of MTDs in response to environmental changes.

This thesis addresses a critical aspect of MTC: the enhancement of active user detection through advanced signal processing techniques, specifically compressive sensing with a focus on Sparse Bayesian learning (SBL). The sparse nature of MTC traffic, where only a few MTDs are active simultaneously, necessitates efficient resource allocation and accurate identification of active devices. Traditional orthogonal pilot sequences, which are effective in HTC, are less suitable in MTC due to their impractical resource consumption. Compressive sensing-based methods, including SBL, leverage the sparse characteristics of MTC traffic to optimize resource utilization and improve detection accuracy under scenarios with non-orthogonal pilot sequences.

The primary objective of this research is to augment the effectiveness of SBL by exploiting temporal and spatial correlations among MTDs. By integrating these correlations into the detection framework, the thesis aims to achieve more precise identification of active users within MTC environments. This approach not only enhances the robustness of active user detection but also contributes to advancing resource allocation strategies tailored to the unique dynamics of autonomous MTC networks.