Reservoir Computing-Based Digital Self-Interference Cancellation for In-Band Full-Duplex Radios - Zhikai Liu; Haifeng Luo; Tharmalingam Ratnarajah (IEEE Transactions on Machine Learning in Communications and Networking, 2024)
Digital self-interference cancellation (DSIC) has become a pivotal strategy for implementing in-band full-duplex (IBFD) radios to overcome the hurdles posed by residual self-interference that persist after propagation and analog domain cancellation. This work proposes a novel reservoir computing-based DSIC (RC-DSIC) technique and compares it with traditional polynomial-based (PL-DSIC) and various existing neural network-based (NN-DSIC) approaches. We begin by delineating the structure of the RC and exploring its capability to address the DSIC task, highlighting its potential advantages over current methodologies. Subsequently, we examine the computational complexity of these approaches and undertake extensive simulations to compare the proposed RC-DSIC approach against PL-DSIC and existing NN-DSIC schemes. Our results reveal that the RC-DSIC scheme attains 99.84% of the performance offered by PL-based DSIC algorithms while requiring only 1.51% of the computational demand. Compared to many existing NN-DSIC schemes, the RC-DSIC method achieves at least 99.73% of its performance with no more than 36.61% of the computational demand. This performance justifies the viability of RC-DSIC as an effective and efficient solution for DSIC in IBFD, striking it is a better implementation method in terms of computational simplicity.