Fluid Antenna System Liberating Multiuser MIMO for ISAC via Deep Reinforcement Learning
Xidian University · Yonsei University · +2 more institutions
Abstract
The aim of this paper is to enhance the performance of an integrated sensing and communications (ISAC) system in the multiuser multiple-input multiple-output (MIMO) downlink in which a two-dimensional (2D) fluid antenna system (FAS) with multiple activated ports is employed at the base station (BS) to maximize the sum-rate of the downlink users subject to a sensing constraint. The unique feature of this setup is that the locations of the antenna ports at the FAS can be optimized jointly with the precoding design to achieve a higher sum-rate. The required optimization problem is however NP-hard. To overcome this, we start by considering the perfect channel state information (CSI) scenario where all the port CSI…
Citation impact
- FWCI
- 24.01
- Percentile
- 100%
- References
- 53
Authors
7Topics & keywords
- Reinforcement learning
- Computer science
- Telecommunications link
- Precoding
- Base station
- Artificial neural network
- MIMO
- Autoencoder