Digital Beamforming Robust to Time-Varying Carrier Frequency Offset

• URL: http://arxiv.org/abs/2103.04948v1
• Date: Mon, 8 Mar 2021 18:08:56 GMT
• Title: Digital Beamforming Robust to Time-Varying Carrier Frequency Offset
• Authors: Shuang Li, Payam Nayeri, and Michael B. Wakin
• Abstract summary: We present novel beamforming algorithms that are robust to signal corruptions arising from a time-variant carrier frequency offset. We propose two atomic-norm-minimization (ANM)-based methods to design a weight vector that can be used to cancel interference when there exist unknown time-varying frequency drift in the pilot and interferer signals.
• Score: 21.18926642388997
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Adaptive interference cancellation is rapidly becoming a necessity for our modern wireless communication systems, due to the proliferation of wireless devices that interfere with each other. To cancel interference, digital beamforming algorithms adaptively adjust the weight vector of the antenna array, and in turn its radiation pattern, to minimize interference while maximizing the desired signal power. While these algorithms are effective in ideal scenarios, they are sensitive to signal corruptions. In this work, we consider the case when the transmitter and receiver in a communication system cannot be synchronized, resulting in a carrier frequency offset that corrupts the signal. We present novel beamforming algorithms that are robust to signal corruptions arising from this time-variant carrier frequency offset. In particular, we bring in the Discrete Prolate Spheroidal Sequences (DPSS's) and propose two atomic-norm-minimization (ANM)-based methods in both 1D and 2D frameworks to design a weight vector that can be used to cancel interference when there exist unknown time-varying frequency drift in the pilot and interferer signals. Both algorithms do not assume a pilot signal is known. Noting that solving ANM optimization problems via semi-definite programs can be a computational burden, we also present a novel fast algorithm to approximately solve our 1D ANM optimization problem. Finally, we confirm the benefits of our proposed algorithms and show the advantages over existing approaches with a series of experiments.

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