Self-Interference Cancellation in In-band Full Duplex Systems (IBFD) Using Radial Basis Function Neural Networks (RBFNN)

Authors

Khaled Gharaibeh
Yarmouk University image/svg+xml
DOI: https://doi.org/10.52549/ijeei.v14i3.8090
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Published: 05-09-2026 • Issue: Vol. 14 No. 3: September 2026 • Section: Regular paper

Abstract

In-band Full Duplex (IBFD) wireless systems have attracted attention for use in emerging wireless systems as they provide significant enhancement to system capacity over traditional techniques. In IBFD, transmission and reception occur simultaneously and on the same frequency band. Thus, the main challenge in system design is self-interference (SI) where the strong transmit signal leaking to the receiver interferes with the weak received signal. SI typically includes a nonlinear component resulting from the transmitter Power Amplifier (PA); therefore, the resulting SI is highly dependent on transmit power. In this paper, an SI cancellation (SIC) scheme for IBFD systems is presented and verified. The proposed nonlinear SIC scheme is based on using a Radial Basis Function Neural Network (RBFNN) to model the nonlinear SI. A practically motivated feature representation which includes instantaneous power of the transmit signal is introduced, enabling efficient modelling of amplitude-dependent and memory-dependent nonlinearities. The proposed RBFNN canceller offers localized approximation capability and inherently stable training structure, making it suitable for addressing the SI dependence on transmit power. Simulation results demonstrate that the RBFNN-based SI canceller outperforms model-based cancellers such as the Memory Polynomial (MP) cancellers by 10 dB across a transmit power range of 30 dB.
Keywords: Self-interference cancellation; In-band full duplex; RBF neural networks; Nonlinear modeling; Power amplifier; OFDM.

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Author Biographies

Khaled Gharaibeh

Khaled M Gharaibeh received his B.S. and M.S. in Electrical Engineering in 1995 and 1998 respectively, both from Jordan University of Science and Technology, Irbid, Jordan and his PhD in Electrical Engineering in 2004 from North Carolina State University, Raleigh, NC. He is a Senior member of the Institute of Electrical and Electronics Engineering (IEEE), Microwave Theory and Techniques Society (MTTS), Jordan Engineers Association and the honor society Eta Kappa Nu. His research interests are nonlinear system identification, behavioral modeling of nonlinear RF circuits, Passive intermodulation Cancellation and wireless communications.

How to Cite

[1]
K. Gharaibeh, “Self-Interference Cancellation in In-band Full Duplex Systems (IBFD) Using Radial Basis Function Neural Networks (RBFNN)”, IJEEI, vol. 14, no. 3, pp. 675–689, Sep. 2026, doi: 10.52549/ijeei.v14i3.8090.