Channel estimation and self-interference cancelation in full-duplex communication systems

• Published in: IEEE transactions on vehicular technology Vol. 66; no. 1; pp. 321 - 334
• Main Authors: Masmoudi, Ahmed, Tho Le-Ngoc
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analog to digital conversion; Analog to digital converters; Baseband; Cancellation; Channel estimation; Computer simulation; Full-duplex communication; Interference; Interference cancellation; maximum likelihood; MIMO communication; multi-input–multi-output (MIMO); Noise; Overloading; Radio frequency; radio-frequency (RF) imperfections; self-interference (SI) suppression; Signal processing; Signal to noise ratio; subspace method; Transceivers; Transmitters
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2016.2540538

This paper presents a two-stage self-interference (SI) cancellation for full-duplex multi-input-multi-output (MIMO) communications systems. By exploiting the SI channel sparsity, a compressed-sensing-based SI channel estimation technique is developed and used in the first SI-cancellation radio-frequency (RF) stage to reduce the SI power prior to the receiver low-noise amplifier (LNA) and the analog-to-digital converter (ADC) to avoid overloading. Subsequently, a subspace-based algorithm is proposed to jointly estimate the coefficients of both the residual SI and intended channels and transceiver impairments for the second SI-cancellation baseband stage to further reduce the residual SI. Unlike other previous works, the intended signal is taken into consideration during the estimation process to reduce the overhead. It is demonstrated that the SI channel coefficients can be perfectly estimated with no knowledge of the intended signal, and only a few training symbols are needed for ambiguity removal in intended-channel estimation. Simulation results show that the proposed algorithms outperform the least square (LS) algorithms and offer the remarkable signal-to-residual-SI-and-noise ratio (SINR) approaching the signal-to-noise ratio (SNR).