Sparse MIMO for ISAC: New Opportunities and Challenges

TL;DR

The paper addresses enabling ISAC in 6G by moving from conventional compact MIMO to sparse MIMO, thereby enlarging both physical and virtual array apertures without adding elements. It surveys architectures such as USA and NUSA (including MRA, MoA, NA, CPA) and shows that virtual co-arrays can yield $O(M^2)$ sensing DoF, enabling finer spatial resolution and an expanded near-field region. It identifies five main design issues—beam pattern synthesis, signal processing, grating-lobe suppression, beam codebook design, and array geometry optimization—and provides guidance and examples, including near-field processing and optimized training. Simulation results demonstrate gains in angular resolution, sensing accuracy, and spectral efficiency, especially in near-field regimes, underscoring the practical potential of sparse MIMO for ISAC. The work also outlines promising future directions such as sparse IRS/RIS integration and robust beam control to meet 6G requirements.

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.

Figures (6)

• Figure 1: An illustration of sparse MIMO ISAC systems, where the BS and UE are equipped with USAs, with their inter-antenna spacings being $\eta_{\rm BS}d_0$ and $\eta_{\rm UE}d_0$, respectively. Note that for general sparse MIMO ISAC systems, the BS and UE can adopt different SA architectures as will be discussed in Section II.
• Figure 2: Illustration of different array architectures, all with $6$ elements, and their virtual arrays formed by the nonnegative parts of difference co-arrays.
• Figure 3: The far-field beam patterns of different array architectures, all with $M=16$ antenna elements.
• Figure 4: The near-field beam focusing patterns of various array architectures, all with $M=128$ antenna elements. The desired beam focusing location is $(r_0,\theta_0)=(200 \ \rm m,0^o)$, labelled as a black asterisk in the figure.
• Figure 5: NRMSE of estimated target's DOAs versus the receive SNR, by using the ZF-MUSIC method.