This talk explores emerging connections between sensing, modulation and coding through the unifying lens of sparse antenna array geometries. We first explore the versatile uses of index modulation via antenna selection in integrated communications and sensing (ISAC). We demonstrate that judicious antenna subset selection can offer enhanced sensing performance, as well as a balance between spectral and energy efficiency by making clever use of multiple antennas per radio-frequency (RF) chain. We then show that the canonical sensing problem of direction-of-arrival estimation can be naturally interpreted as a so-called analog subspace coding problem. Subspace coding conventionally arises in non-coherent communications, where neither transmitter nor receiver knows the channel, and information is encoded in subspaces rather than vectors. However, connections to sensing have remained unexplored. We bridge this gap and demonstrate how it fosters new research problems at the intersection of coding theory and signal processing, including a new principled approach to noise-robust sparse array design based on optimizing subspace distance. Throughout the talk, we show how sparse array geometries are key to enhancing sensing performance, reducing hardware complexity, and increasing energy efficiency of next-generation multiple-input multiple-output systems equipped with far fewer RF chains than antennas.
Bio: Robin Rajamäki (Member, IEEE) received his D.Sc. degree in electrical engineering in 2021 from Aalto University, Finland. From 2022-2024, he was a postdoctoral scholar at the University of California San Diego. He is currently an assistant professor at Tampere University, Finland. His research interests lie in the intersection of theory and applications of statistical signal processing with a focus on multisensor systems in sensing and wireless communications.