Parameter Estimation and Classification via Supervised Learning in the Wireless Physical Layer

Emerging wireless networks possess the potential to achieve levels of connectivity and Quality-of-Service (QoS) that are orders of magnitude higher than today’s networks. Realizing the potential of these networks will require flexible, low cost, and accurate Digital Signal Processing (DSP). Supervised Learning (SL) models employing unknown parameter estimation and classification techniques have experienced widespread use in physical (PHY) layer wireless communication systems since they can achieve low costs via inexpensive forward-pass computations, attain flexible operations due to trainable parameters, and yield accurate results based on the universal approximator attribute. In this survey and tutorial paper, we present a methodical explanation of how SL can be applied to unknown parameter estimation and classification across several different PHY layer components of a wireless communications system. Additionally, via a survey and comparison of popular methods, this paper provides insights on how to perform weight training, weight initialization, loss function regularization, data pre-processing, input feature design, ensemble training, and hyper-parameter validation. In our review of state-of-the-art works, we found significant use of SL algorithms in the following PHY layer applications: Dynamic Spectrum Access (DSA), channel corrections, Automatic Gain Control (AGC), Multiple Input Multiple Output (MIMO) control, Analog to Digital (ADC) conversion, and Automatic Coding and Modulation (ACM). The overarching goal of this survey and tutorial paper is to assist the reader in understanding the motivation and methodologies associated with various SL algorithms applied to PHY layer DSP operations, as well as to provide the reader with the necessary tools and techniques needed for addressing open challenges to be experienced by future wireless networks.