How Many Simultaneous Beamformers are Needed for Integrated Sensing and Communications?
Kareem M. Attiah, and Wei Yu

TL;DR
This paper derives bounds on the minimum number of beamformers needed in an integrated sensing and communications system to optimize both tasks, considering interference cancellation capabilities and measurement of sensing performance.
Contribution
It establishes theoretical bounds on the number of beamformers required for ISAC systems, accounting for interference cancellation and different sensing performance metrics.
Findings
Maximum of K + sqrt(L(L+1)/2) beamformers needed with interference cancellation.
Maximum of sqrt(K^2 + L(L+1)/2) beamformers without interference cancellation.
For N_tr targets, the bounds scale linearly with K and N_tr when interference is canceled.
Abstract
Consider a downlink integrated sensing and communications (ISAC) system in which a base station employs linear beamforming to communicate to users, while simultaneously uses sensing beams to perform a sensing task of estimating real parameters. How many beamformers are needed to achieve the best performance for both sensing and communications? This paper establishes bounds on the minimum number of downlink beamformers, in which sensing performance is measured in terms of the Cram\'{e}r-Rao bound for parameter estimation and communications performance is measured in terms of the signal-to-interference-and-noise ratios. We show that an ISAC system requires at most beamformers if the remote users have the ability to cancel the interference caused by the sensing beams. If cancelling interference due to the sensing beams is not possible, the bound…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDirection-of-Arrival Estimation Techniques · Distributed Sensor Networks and Detection Algorithms · Radar Systems and Signal Processing
