Hypernetwork-Based Adaptive Self-Interference Cancellation for Full-Duplex Wireless Communication Systems

Full-duplex (FD) systems have emerged as a promising avenue to optimize temporal and spectral resource utilization by enabling simultaneous data transmission and reception on a single frequency. Nonetheless, the presence of robust self-interference (SI) signals at the receiver presents a critical challenge that necessitates effective SI cancellation strategies. Recent advancements have introduced neural networks (NN) to address this challenge, offering computational advantages over conventional polynomial models. This paper delves into the realm of leveraging Hyper Neural Networks (hyperNet) for SI cancellation (SIC), thereby exploring their unique attributes to enhance the efficiency of FD systems. The proposed method utilizes a dynamic hyperNet model to learn the non-linear characteristics of the SI channel and cancel it from the received signal. The efficacy of the proposed SIC technique is assessed using datasets derived from a MATLAB simulation platform that accurately replicates the transmission process observed in real-world scenarios. The simulation results demonstrate the superior performance of the proposed hyperNet in comparison to other state-of-art NN based SIC methods. Our proposed hyperNet-based SIC technique demonstrates its ability to autonomously adapt to more complex characteristics of the varying self-interference channel.