Abstract
In this paper, we propose a self referencing scheme for the reduced complexity parallel least mean square (RC-pLMS) adaptive beamforming algorithm as means of robustness against possible interruptions in the reference signal and its hardware implementation. The RC-pLMS is a single stage, non-blind, least mean square (LMS) algorithm with modified input vectors formed as a linear combination of the current and the previous input sample. In this context, its convergence and its stability are critically dependent on the availability of its reference signal and are known to severally degrade when discontinued. Thus, for robustness against the pre-mentioned and with respect to the RC-pLMS accelerated convergence and low residual error profile, we propose the use of it’s filtered output, as an alternative learning sequence, whenever the original reference signal is discontinued, i.e. self-referencing. The proposed self referencing approach is evaluated in infinite and finite precision modes on software and on hardware, i.e. Field Programmable Gate Array (FPGA), respectively. Hardware and software simulation validates the RC-pLMS robustness against different reference signal obstruction scenarios, through the use of the proposed self-referencing approach, while maintaining an accelerated convergence behavior, a low complexity architecture and a high precision beam pointing accuracy.
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References
Srar, J.A., Chung, K., Mansour, A.: Adaptive array beamforming using a combined LMS-LMS algorithm. IEEE Trans. Anten. Propagat. 58(11), 3545–3557 (2010)
Aboulnasr, T., Mayyas, K.: A robust variable step-size LMS-type algorithm: analysis and simulations. IEEE Trans. Signal Process. 45(3), 631–639 (1997)
Sayed, A.H., Kailath, T.: A state-space approach to adaptive RLS filtering. IEEE Signal Process. Magaz. 11, 18–60 (1994)
Xiubing, Z., et al.: A new modified robust variable step size LMS algorithm. In: Proceedings of the 4th IEEE Conference on Industrial Electronics and Applications (ICIEA 2009), pp. 2699–2703. IEEE (2009)
Shengkui, Z., Zhihong, M., Suiyang, K.: A fast variable step-size LMS algorithm with system identification. In: Proceedings of the 2nd IEEE Conference on Industrial Electronics and Applications (ICIEA 2007), pp. 2340–2345. IEEE (2007)
Kwong, R.H., Johnston, E.W.: A variable step size LMS algorithm. IEEE Trans. Signal Process. 40(7), 1633–1642 (1992)
Lee, S., Lim, J.-S., Sung, K.: A low-complexity AFF-RLS algorithm using a normalization technique. IEICE Electron. Exp. 6, 1774–1780 (2009)
Paleologu, C., Benesty, J., Ciochina, S.: A robust variable forgetting factor recursive least-squares algorithm for system identification. IEEE Signal Process. Lett. 15, 597–600 (2008)
Albu, F., Kadlec, J., Coleman, N., Fagan, A.: The Gauss-Seidel fast affine projection algorithm. In: IEEE Workshop on Signal Processing Systems, San Diego, CA, pp. 109–114. IEEE (2002)
Mansour, A., Mesleh, R., Abaza, M.: New challenges in wireless and free space optical communications. Opt. Lasers Eng. 89, 95–108 (2017)
Hong, W., et al.: Multibeam antenna technologies for 5G wireless communications. IEEE Trans. Anten. Propagat. 65, 6231–6249 (2017)
Kim, D., Park, S., Kim, T., Minz, L., Park, S.: Fully digital beamforming receiver with a real-time calibration for 5G mobile communication. IEEE Trans. Anten. Propag. 67, 3809–3819 (2019)
Akkad, G., Mansour, A., ElHassan, B., Inaty, E.: A multi-stage parallel LMS structure and its stability analysis using transfer function approximation. In: Proceedings of the 28th European Signal Processing Conference (EUSIPCO), Amsterdam, August 2020, pp. 1–5 (2020)
Sarma, R.K., Khan, M.T., Shaik, R.A., Hazarika, J.: A novel time-shared and LUT-less pipelined architecture for LMS adaptive filter. IEEE Trans. Very Large Scale Integr. Syst. 28(1), 1–10 (2019)
Zhao, W., Lin, J.Q., Chan, S.C., So, H.K.: A division-free and variable-regularized LMS-based generalized sidelobe canceller for adaptive beamforming and its efficient hardware realization. IEEE Access 6, 64470–64485 (2018)
Albu, F., Kadlec, J., Coleman, N., Fagan, A.: Pipelined implementations of the a priori error-feedback LSL algorithm using logarithmic arithmetic. In: Proceedings of the 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing, Orlando, FL, May 2002, pp. III-2681–III-2684 (2002)
Akkad, G., Mansour, A., ElHassan, B., Srar, J., Najem, M., LeRoy, F.: Low complexity robust adaptive beamformer based on parallel RLMS and Kalman RLMS. In: Proceedings of the 27th European Signal Processing Conference (EUSIPCO), A Coruna, September 2019, pp. 1–5 (2019)
Akkad, G., Mansour, A., ElHassan, B.A., Inaty, E., Ayoubi, R., Srar, J.A.: A pipelined reduced complexity two-stages parallel LMS structure for adaptive beamforming. In: IEEE Transactions on Circuits and Systems I: Regular Papers, pp. 1–13 (2020)
Yedavalli, P.S., Riihonen, T., Wang, X., Rabaey, J.M.: Far-field RF wireless power transfer with blind adaptive beamforming for internet of things devices. IEEE Access 5, 1743–1752 (2017)
Akkad, G., Mansour, A., ElHassan, B., Inaty, E., Ayoubi, R.: Two stages parallel LMS structure a pipelined hardware architecture. In: Proceedings of the 28th European Signal Processing Conference (EUSIPCO), Amsterdam, August 2020, pp. 1–5 (2020)
Shanbhag, N.R., Parhi, K.K.: Relaxed look-ahead pipelined LMS adaptive filters and their application to ADPCM coder. IEEE Trans. Circuits Syst. II Analog Digital Signal Process. 40(12), 753–766 (1993)
Acknowledgment
The authors are grateful to AID - DGA (l’ Agence de l’ Innovation de Defense a la Direction Generale de l’ Armement – Minitere des Armees) & ANR (Agence Nationale de le Recherche en France) for supporting our ANR-ASTRID – Project (ANR-19-ASTR-0005-03).
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Akkad, G., Mansour, A., ElHassan, B., Inaty, E., Ayoubi, R. (2022). A Self Referencing Technique for the RC-pLMS Adaptive Beamformer and Its Hardware Implementation. In: Saponara, S., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2021. Lecture Notes in Electrical Engineering, vol 866. Springer, Cham. https://doi.org/10.1007/978-3-030-95498-7_11
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