Summary
Full duplex MIMO wireless systems send and receive signals simultaneously on the same frequency to boost network speed. However, these systems face the problem of self-interference, where their own transmissions drown out incoming signals. Traditional methods to separate signals assume simple conditions that don't hold when the radio hardware has imperfections. The authors propose using a special kind of recurrent neural network called an echo state network, trained partly offline and partly online, to better cancel out interference and recover the signals of interest. Their method works better and faster than previous approaches, making it more practical for real-world wireless devices.
What this means in practice
- •For wireless communication engineers: Enhance real-time self-interference cancellation to improve signal recovery in full duplex MIMO radios under realistic hardware impairments.
- •For telecommunications hardware developers: Design improved full duplex MIMO radio units that use echo state networks for efficient joint sensing and communication.$Commercial implications: Enables development of advanced radio hardware with built-in neural network based interference cancellation, improving spectral efficiency.
Authors
Conrad Prisby, Siyao Li, Chengtao Xu, Thomas Yang
Abstract
In-band full-duplex (IBFD) multiple-input multiple-output (MIMO) systems enable simultaneous transmission and reception on the same frequency band, improving spectral efficiency for next-generation wireless networks. However, IBFD-MIMO systems are susceptible to self-interference (SI), which may overpower signals of interest (SOI). In this scenario, blind source separation (BSS) algorithms can be adopted to remove SI and perform joint sensing and communication (JSAC), but BSS algorithms mostly assume an idealized linear and quasi-stationary signal model, which does not hold under realistic radio frequency (RF) impairments, such as I/Q imbalance, carrier frequency offset (CFO), phase noise, and power amplifier nonlinearity. This paper proposes a two-stage echo state network (ESN)-based scheme that is superior to BSS under these realistic conditions. A frozen ESN is trained offline to characterize the static SI path, while an adaptive ESN, updated online via recursive least squares, tracks the time-varying SOI path using sparse pilot symbols. We evaluate the proposed scheme's SOI recovery performance and acquisition speed with different block sizes, comparing it against other recurrent neural networks (RNN), such as long short-term memory (LSTM) and gated recurrent unit (GRU). Simulation results show that the proposed approach outperforms BSS, LSTM, and GRU in both efficiency and SOI recovery, demonstrating the viability of ESNs for real-time, nonlinear self-interference cancellation in realistic IBFD MIMO systems.