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Optimally extracting the advantages available from reconfigurable intelligent surfaces (RISs) in wireless communications systems requires estimation of the channels to and from the RIS. The process of determining these channels is complicated when the RIS is composed of passive elements without any sensing or data processing capabilities, and thus, the channels must be estimated indirectly by a noncolocated device, typically a controlling base station (BS). In this article, we examine channel estimation for passive RIS-based systems from a fundamental viewpoint. We study various possible channel models and the identifiability of the models as a function of the available pilot data and behavior of the RIS during training. In particular, we will consider situations with and without line-of-sight propagation, single-antenna and multi-antenna configurations for the users and BS, correlated and sparse channel models, single-carrier and wideband orthogonal frequency-division multiplexing (OFDM) scenarios, availability of direct links between the users and BS, exploitation of prior information, as well as a number of other special cases. We further conduct simulations of representative algorithms and comparisons of their performance for various channel models using the relevant Cramér-Rao bounds.
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This content will become publicly available on July 1, 2026
Distributed Graph-Based Learning for User Association and Beamforming Design in Multi-RIS Multi-Cell Networks
We propose a novel graph neural network (GNN) architecture for jointly optimizing user association, base station (BS) beamforming, and reconfigurable intelligent surface (RIS) phase shift in a multi-RIS aided multi-cell network. The proposed architecture represents BSs and users as nodes in a bipartite graph where the same type of nodes shares the same neural networks for generating messages and updating its representations, allowing for distributed implementation. In addition, we utilize a composite reflected channel estimation integrated between layers of the GNN structure to significantly reduce the signaling overhead and complexity required for channel estimation in a multi-RIS network. To avoid BS overload, load balancing is regularized in the training of the GNN and we further develop a collision avoidance algorithm to ensure strict load balancing at every BS. Numerical results show that the proposed GNN architecture is significantly more efficient than existing approaches. The results further demonstrate its strong scalability with network size and achieving a throughput performance approaching that of a centralized traditional optimization algorithm, without requiring individual RIS-reflected channels estimation and without the need for re-training or fine-tuning.
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- Award ID(s):
- 2403285
- PAR ID:
- 10614780
- Publisher / Repository:
- IEEE Transactions on Wireless Communications
- Date Published:
- Journal Name:
- IEEE Transactions on Wireless Communications
- Volume:
- 24
- Issue:
- 7
- ISSN:
- 1536-1276
- Page Range / eLocation ID:
- 6118 to 6134
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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