TY - GEN
T1 - In-Band, Full-Duplex Self-Interference Mitigation Using Sparse Tap-Delay Models with Quantized and Power Constrained Weights
AU - Herschfelt, Andrew
AU - Chiriyath, Alex
AU - Molnar, Alyosha Christopher
AU - Landon, David G.
AU - Bliss, Daniel W.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - In-band, full-duplex (IBFD) radio frequency (RF) users generate self-interference that obstructs receiver operations. This interference may be mitigated using several techniques, including antenna isolation, circulators, digital mitigation, and analog mitigation at the carrier frequency. We apply an analog mitigation technique that uses a sparse tap-delay model to reduce power and computational complexity. We demonstrate that optimizing the weights of the resulting equalizer achieves sufficient self-interference mitigation, even under realistic hardware limitations. The performance is limited by i) how many delay taps are chosen, ii) how closely this discrete model matches the actual channel, iii) the dynamic range of the weighting coefficients, and iv) the timing precision of the system. We compare the achievable performance of this technique under different combinations of these limitations. We propose a sparse, constrained optimization solution that offers sufficient self-interference mitigation and significantly reduces the power consumption.
AB - In-band, full-duplex (IBFD) radio frequency (RF) users generate self-interference that obstructs receiver operations. This interference may be mitigated using several techniques, including antenna isolation, circulators, digital mitigation, and analog mitigation at the carrier frequency. We apply an analog mitigation technique that uses a sparse tap-delay model to reduce power and computational complexity. We demonstrate that optimizing the weights of the resulting equalizer achieves sufficient self-interference mitigation, even under realistic hardware limitations. The performance is limited by i) how many delay taps are chosen, ii) how closely this discrete model matches the actual channel, iii) the dynamic range of the weighting coefficients, and iv) the timing precision of the system. We compare the achievable performance of this technique under different combinations of these limitations. We propose a sparse, constrained optimization solution that offers sufficient self-interference mitigation and significantly reduces the power consumption.
KW - channel matching filter
KW - constrained optimization
KW - full-duplex
KW - In-band
KW - self-interference mitigation
KW - sparse channel estimation
UR - http://www.scopus.com/inward/record.url?scp=85107744782&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107744782&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF51394.2020.9443416
DO - 10.1109/IEEECONF51394.2020.9443416
M3 - Conference contribution
AN - SCOPUS:85107744782
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1260
EP - 1264
BT - Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Y2 - 1 November 2020 through 5 November 2020
ER -