Abstract
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 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.
Highlights
P ARAMETER estimation and classification in the presence of noise is a ubiquitous problem in wireless signal processing
Scientific publications that provide details on the implementation of Machine Learning (ML) applied to wireless communication systems (Table 1) are relatively difficult to find despite the widespread use of ML
A real-world issue overlooked by many of these simulation-based works is that online training requires the transmission of weight updates or error values across the wireless channel, which results in noisy learning and loads the network layer with an additional task to coordinate between devices
Summary
P ARAMETER estimation and classification in the presence of noise is a ubiquitous problem in wireless signal processing. Machine Learning (ML) models have a successful performance history of predicting these unknown parameters in real-world wireless systems with few assumptions about data. ML-based wireless signal processing solutions tout a number of advantages. These include the ability to implement real-world Cognitive Radio (CR) networks with dynamic spectrum access capabilities [6], Wireless Sensor Networks (WSN) with less re-programming and longer service life [7], Self Organizing Networks (SON) with robust routing protocols [9], and more secure Internet of Things (IoT) networks that fully utilize resources to stay online longer [11].
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