Coordinated Pilot Transmissions for Detecting the Signal Sparsity Level in Massive IoT Networks

• Published in: IEEE transactions on communications 2024-03, Vol.72 (3), p.1612-1624
• Main Authors: Lopez, Onel L. A., Brante, Glauber, Souza, Richard Demo, Juntti, Markku, Latva-Aho, Matti
• Format: Article
• Language: English
• Subjects: Channel estimation; compressed sensing; Data processing; Fading; Feature extraction; grant-free random access; Inference algorithms; Internet of Things; Massive IoT; Matching pursuit algorithms; Mathematical analysis; Mud; multi-user detection; Multiuser detection; Networks; Random access; Random variables; signal sparsity level; Sparsity; Symbols
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2023.3337242

Grant-free protocols exploiting compressed sensing multi-user detection (MUD) are appealing for solving the random access problem in massive Internet of Things (IoT) networks with sporadic device activity. Such protocols would greatly benefit from prior deterministic knowledge of the sparsity level, i.e., the instantaneous number of simultaneously active devices K . Aiming at this, herein we introduce a framework relying on coordinated pilot transmissions (CPTs) for detecting K . Specifically, the proposed CPT mechanism includes a downlink (DL) phase for channel state information acquisition that resolves fading uncertainty in the uplink (UL) transmission phase using shared UL pilot symbols for channel compensation. We propose a signal sparsity level detector and analytically assess its accuracy when network channels are subject to Rayleigh fading. We show that the variance of the estimator increases with K , and its distribution approximates that of the sum of a Student's t and Gaussian random variable. The numerical results evince the need for carefully configuring the duration of the DL and UL phases. Indeed, we show that relatively short DL phases are preferable in highly sparse networks given the total CPT duration is fixed. Finally, we discuss and exemplify with some early results the potential of the proposed CPT framework for MUD, and highlight relevant research directions.