Sparse MIMO for ISAC: New Opportunities and Challenges

TL;DR

Sparse MIMO technology, by removing traditional antenna spacing restrictions, offers larger virtual apertures and enhanced capabilities for integrated sensing and communication in future 6G networks.

Contribution

This paper provides a comprehensive overview of sparse MIMO architectures, their advantages, challenges, and design issues for 6G ISAC applications.

Findings

Sparse MIMO significantly enlarges array apertures compared to conventional MIMO.

Numerical results show improved sensing and communication performance with sparse MIMO.

Identifies key design challenges such as beam pattern synthesis and array optimization.

Abstract

Multiple-input multiple-output (MIMO) has been a key technology of wireless communications for decades. A typical MIMO system employs antenna arrays with the inter-antenna spacing being half of the signal wavelength, which we term as compact MIMO. Looking forward towards the future sixth-generation (6G) mobile communication networks, MIMO system will achieve even finer spatial resolution to not only enhance the spectral efficiency of wireless communications, but also enable more accurate wireless sensing. To this end, by removing the restriction of half-wavelength antenna spacing, sparse MIMO has been proposed as a new architecture that is able to significantly enlarge the array aperture as compared to conventional compact MIMO with the same number of array elements. In addition, sparse MIMO leads to a new form of virtual MIMO systems for sensing with their virtual apertures considerably larger than physical apertures. As sparse MIMO is expected to be a viable technology for 6G, we provide in this article a comprehensive overview of it, especially focusing on its appealing advantages for integrated sensing and communication (ISAC) towards 6G. Specifically, assorted sparse MIMO architectures are first introduced, followed by their new benefits as well as challenges. We then discuss the main design issues of sparse MIMO, including beam pattern synthesis, signal processing, grating lobe suppression, beam codebook design, and array geometry optimization. Last, we provide numerical results to evaluate the performance of sparse MIMO for ISAC and point out promising directions for future research.