Intelligent Reflecting Surfaces for Compute-and-Forward
Mahdi Jafari Siavoshani, Seyed Pooya Shariatpanahi, Naeimeh, Omidvar

TL;DR
This paper explores how intelligent reflecting surfaces (IRSs) can significantly improve the computation rate in compute-and-forward wireless systems by optimizing IRS parameters to enhance signal quality and decoding capabilities.
Contribution
It introduces the use of IRSs in compute-and-forward systems, formulates an optimization problem for IRS phase shifts, and proposes an efficient alternating optimization method.
Findings
IRS design significantly boosts computation rate
Proposed AO approach effectively optimizes IRS parameters
Numerical results confirm IRS's potential for future wireless networks
Abstract
Compute-and-forward is a promising strategy to tackle interference and obtain high rates between the transmitting users in a wireless network. However, the quality of the wireless channels between the users substantially limits the achievable computation rate in such systems. In this paper, we introduce the idea of using intelligent reflecting surfaces (IRSs) to enhance the computing capability of the compute-and-forward systems. For this purpose, we consider a multiple access channel(MAC) where a number of users aim to send data to a base station (BS) in a wireless network, where the BS is interested in decoding a linear combination of the data from different users in the corresponding finite field. Considering the compute-and-forward framework, we show that through carefully designing the IRS parameters, such a scenario's computation rate can be significantly improved. More…
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.
