发表机构
Embry-Riddle Aeronautical University; The Johns Hopkins University Applied Physics Laboratory（安柏瑞德航空大学; 约翰斯·霍普金斯大学应用物理实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对射频损伤下IBFD MIMO系统的自干扰问题，提出基于两阶段回声状态网络的信号恢复方案，优于BSS、LSTM和GRU，实现高效非线性自干扰消除。
AI 中文摘要
带内全双工（IBFD）多输入多输出（MIMO）系统能够在同一频段上同时进行发送和接收，从而提升下一代无线网络的频谱效率。然而，IBFD-MIMO 系统易受自干扰（SI）影响，该干扰可能压过感兴趣信号（SOI）。在此场景下，可采用盲源分离（BSS）算法来消除 SI 并实现联合感知与通信（JSAC），但 BSS 算法大多假设理想化的线性和准平稳信号模型，这在现实射频（RF）损伤（如 I/Q 不平衡、载波频率偏移（CFO）、相位噪声和功率放大器非线性）下并不成立。本文提出了一种基于两阶段回声状态网络（ESN）的方案，在这些现实条件下优于 BSS。一个冻结的 ESN 被离线训练以表征静态 SI 路径，而一个自适应 ESN 通过递归最小二乘在线更新，利用稀疏导频符号跟踪时变的 SOI 路径。我们评估了所提方案在不同块大小下的 SOI 恢复性能和获取速度，并将其与其他循环神经网络（RNN），如长短期记忆（LSTM）和门控循环单元（GRU）进行比较。仿真结果表明，所提方法在效率和 SOI 恢复方面均优于 BSS、LSTM 和 GRU，证明了 ESN 在现实 IBFD MIMO 系统中用于实时非线性自干扰消除的可行性。
英文摘要
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.
Comments6 pages, 5 figures. This work has been accepted by IEEE Milcom 2026
