Active Beam Learning for Full-Duplex Wireless Systems

TL;DR

This paper introduces an active beam learning approach for full-duplex wireless systems that uses probing measurements and neural networks to design transmit and receive beams, effectively suppressing self-interference and maximizing spectral efficiency.

Contribution

The novel active beam learning method avoids explicit channel estimation by using probing measurements and neural networks to design beams in full-duplex systems.

Findings

Effectively suppresses self-interference

Achieves near-maximal SNR with minimal probing

Robust to measurement noise and channel structure

Abstract

In this paper, we present a novel active beam learning method for in-band full-duplex wireless systems, that aims to design transmit and receive beams which suppress self-interference and maximize the sum spectral efficiency. Rather than rely on explicit estimation of the downlink, uplink, and/or self-interference channels like in most existing work, our method instead actively probes all three channels through measurements of SNR and INR over a fixed number of time slots. Then, once this probing concludes, all collected probing measurements are used to design transmit and receive beams which serve downlink and uplink in a full-duplex fashion. We realize this active beam learning scheme through a network of LSTMs and DNNs, which learns to design each probing beam pair and subsequently extract and record valuable information from each probing measurement such that near-optimal serving beams can be designed following the probing stage. Simulation indicates that our method reliably suppresses self-interference while delivering near-maximal SNR on the downlink and uplink with merely 3-10 probing time slots, while exhibiting robustness to measurement noise and the structure of the self-interference channel.