Sparse MIMO for ISAC: New Opportunities and Challenges

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 toward future sixth-generation (6G) mobile communication networks, MIMO systems need to 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, in this article, we provide a comprehensive overview of it, especially focusing on its appealing advantages for integrated sensing and communication (ISAC) toward 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.

Authors

Institutions

Publication Details

Journal
IEEE Wireless Communications
Published
2025-03-17
DOI
https://doi.org/10.1109/mwc.001.2400201
Citations
29
Primary Topic
Advanced MIMO Systems Optimization
Type
article
Field-Weighted Citation Impact
21.18
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Sparse MIMO for ISAC: New Opportunities and Challenges

Shi Jin, Linglong Dai, Yifei Yuan, Rui Zhang et al.
29 citations
IEEE Wireless Communications
Advanced MIMO Systems Optimization
21.18
article

Sparse MIMO for ISAC: New Opportunities and Challenges

Shi Jin, Linglong Dai, Yifei Yuan, Rui Zhang, Xinrui Li, Yong Zeng, Hongqi Min
article en
29 citations

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 toward future sixth-generation (6G) mobile communication networks, MIMO systems need to 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, in this article, we provide a comprehensive overview of it, especially focusing on its appealing advantages for integrated sensing and communication (ISAC) toward 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.

IEEE Wireless CommunicationsVol. 32(4)
Chinese University of Hong Kong (HK), China Mobile (China) (CN), Southeast University (CN), Tsinghua University (CN)
Openalex Percentile: Top 0%
Advanced MIMO Systems Optimization
21.18
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.