Scaling Wideband Massive MIMO Radar via Beamspace Dimension Reduction

TL;DR

This paper introduces a beamspace dimension reduction architecture for wideband massive MIMO radar, significantly decreasing computational complexity while maintaining detection performance, enabling scalable and efficient radar processing.

Contribution

It proposes a windowed beamspace MVDR beamforming architecture that leverages beamspace energy concentration for complexity reduction in massive MIMO radar.

Findings

Achieves detection performance comparable to full-dimensional methods.

Reduces computational complexity from O(N^3) to O(NlogN).

Provides insights into tradeoffs between beamspace window size and FFT resolution.

Abstract

We present an architecture for scaling digital beamforming for wideband massive MIMO radar. Conventional spatial processing becomes computationally prohibitive as array size grows; for example, the computational complexity of MVDR beamforming scales as O(N^3) for an N-element array. In this paper, we show that energy concentration in beamspace provides the basis for drastic complexity reduction, with array scaling governed by the O(NlogN) complexity of the spatial FFT used for beamspace transformation. Specifically, we propose an architecture for windowed beamspace MVDR beamforming, parallelized across targets and subbands, and evaluate its efficacy for beamforming and interference suppression for government-supplied wideband radar data from the DARPA SOAP (Scalable On-Array Processing) program. We demonstrate that our approach achieves detection performance comparable to full-dimensional benchmarks while significantly reducing computational and training overhead, and provide insight into tradeoffs between beamspace window size and FFT resolution in balancing complexity, detection accuracy, and interference suppression.