Nasim Soltani PhD Proposal

Title: Deep Learning for the Physical Layer: From Signal Classification to Decoding

Location: ISEC 532

Committee Members:
Prof. Kaushik Chowdhury (Advisor)
Prof. Stratis Ioannidis
Prof. Robert Nowak

Abstract:
The growth in wireless spectrum usage has created new physical layer applications and intensified the importance of the existing ones. Physical layer applications ranging from device authentication to signal decoding and interpretation are traditionally handled by deterministic signal processing algorithms. Such algorithms, while effective, often require long sequences of data for decision making, or need approximations of the environmental conditions, such as noise models, which may not be always correct in practical conditions. For these reasons, traditional algorithms are not suitable for making quick decisions on the high rate wireless data with higher noise and interference that is a result of crowded spectrum. To this end, deep learning-based methods have been explored extensively by the researchers to substitute for the traditional signal processing algorithms for the physical layer. This thesis explores novel methods in this area in the following parts:

Part I – Signal classification: In this part, we look at two distinct problems of waveform classification and Radio Frequency (RF) fingerprinting. In the first problem, we study two use cases of modulation classification on edge devices, followed by waveform classification and spectrum localization in the Citizen Broadband Radio Service (CBRS) band. In the second problem, we look at RF fingerprinting that is classifying received signals in terms of subtle impairments that each transmitter leaves in its emitted waveform, due to its hardware manufacturing imperfections. We propose methods to overcome the wireless channel effect for RF fingerprinting in both stationary transmitters on a large scale dataset (i.e., 5k WiFi devices), and identical hovering Unmanned Aerial Vehicles (UAVs) that transmit proprietary signals.

Part II – Signal decoding: In this part, we introduce our design of a modular machine learning (ML)-aided Orthogonal Frequency Division Multiplexing (OFDM) receiver that improves the bit error rate (BER) of the traditional receiver. We show how a neural network-based demapper block can be used for secure data transmission. Furthermore, we show how an ML-aided receiver can provide the possibility of reducing communication overhead by obviating the need for the first field of preamble in WiFi signals. We show that reducing the preamble length contributes to higher throughput in WiFi networks, without BER degradation.

Part III – As the proposed work, we will explore the use of active learning for smart sampling of training sets in wireless communications tasks. Active learning reduces the labeling overhead that is often performed using the compute-intensive traditional signal processing algorithms, by intelligently selecting the most informative training samples to be labeled instead of labeling the whole set. We will also design an ML-life cycle control scheme to monitor and update the performance of an ML-aided 5G receiver, when deployed in the field with varying environmental conditions.