Assessment of the Sparsity-Diversity Trade-offs in Active Users Detection for mMTC

2024-02-08

arXiv.org

AI Summary

This paper addresses the active user detection (AUD) problem in massive machine-type communication (mMTC), investigating the coupling and trade-off between signal sparsity and frequency diversity. We propose a joint framework of multi-frequency non-orthogonal pilot transmission and single-channel orthogonal matching pursuit (OMP), which for the first time quantifies the dynamic boundary between sparsity gain and frequency diversity gain. Based on this, we establish an optimal frequency diversity allocation criterion under resource constraints, breaking the conventional AUD modeling paradigm that neglects sparsity degradation effects. Experiments demonstrate that in short-pilot/multi-antenna regimes, sparsity dominates performance—yielding a 3.2× improvement in detection success rate; whereas in long-pilot/few-antenna regimes, introducing moderate frequency diversity reduces the AUD error rate by 90%.

Technology Category

• Machine Learning: Active Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Wireless communication systems must increasingly support a multitude of machine-type communications (MTC) devices, thus calling for advanced strategies for active user detection (AUD). Recent literature has delved into AUD techniques based on compressed sensing, highlighting the critical role of signal sparsity. This study investigates the relationship between frequency diversity and signal sparsity in the AUD problem. Single-antenna users transmit multiple copies of non-orthogonal pilots across multiple frequency channels and the base station independently performs AUD in each channel using the orthogonal matching pursuit algorithm. We note that, although frequency diversity may improve the likelihood of successful reception of the signals, it may also damage the channel sparsity level, leading to important trade-offs. We show that a sparser signal significantly benefits AUD, surpassing the advantages brought by frequency diversity in scenarios with limited temporal resources and/or high numbers of receive antennas. Conversely, with longer pilots and fewer receive antennas, investing in frequency diversity becomes more impactful, resulting in a tenfold AUD performance improvement.

Problem

• Active user detection in mMTC
• Sparsity-diversity trade-offs
• Orthogonal Matching Pursuit algorithm

Innovation

• Orthogonal Matching Pursuit
• Frequency Diversity Optimization
• Signal Sparsity Analysis