Channel estimation and optimal resource allocation for reconfigurable intelligent surfaces and stacked intelligent metasurfaces
Thesis event information
Date and time of the thesis defence
Place of the thesis defence
OP auditorium (L10)
Topic of the dissertation
Channel estimation and optimal resource allocation for reconfigurable intelligent surfaces and stacked intelligent metasurfaces
Doctoral candidate
Master of Science (Technology) Nipuni Uthpala Ginige
Faculty and unit
University of Oulu Graduate School, Faculty of Information Technology and Electrical Engineering, Centre for Wireless Communications - Radio Technologies
Subject of study
Communications Engineering
Opponent
Professor Ertugrul Basar, Tampere University
Custos
Professor Nandana Rajatheva, University of Oulu
Channel estimation and optimal resource allocation for reconfigurable intelligent surfaces and stacked intelligent metasurfaces
This thesis focuses on designing efficient channel estimation and fairness optimization algorithms for beyond diagonal reconfigurable intelligent surfaces (BD-RIS)-assisted and stacked intelligent metasurface (SIM)-assisted systems.
The first part of this thesis proposes novel joint channel estimation and prediction strategies with low overhead and high accuracy for two reconfigurable intelligent surfaces (RIS) architectures in a BD-RIS-assisted multiple-input multiple-output (MIMO) system operating in correlated fast-fading environments with channel aging. The channel estimation procedure utilizes the Tucker2 decomposition with bilinear alternative least squares. The channel prediction framework is based on a convolutional neural network combined with an auto-regressive predictor. Insightful simulation results demonstrate that our proposed approach is robust to channel aging and exhibits a high estimation accuracy, low pilot overhead, and low computational complexity.
Next, in this thesis, we propose a novel tensor-based joint channel estimation and inter-layer channel coefficients calibration protocol exploiting the PARATUCK2 decomposition and the alternating least squares method. This protocol can be applied to an SIM-assisted multi-user multiple-input single-output (MISO) system. Numerical results prove the proposed scheme's superiority in comparison with state-of-the-art schemes.
Finally, this thesis focuses on developing two max-min fairness algorithms for a SIM-assisted multi-user MISO system, based on instantaneous channel state information (CSI) and statistical CSI. We propose an alternating optimization algorithm for the instantaneous CSI case that jointly optimizes power allocation using geometric programming (GP) and wave-based beamforming coefficients using the gradient descent-ascent method. Since deriving an exact expression for the average minimum achievable rate is analytically intractable for the statistical CSI case, we derive a tight upper bound and thereby formulate a stochastic optimization problem. This problem is then solved, capitalizing on an alternating approach combining GP and gradient descent algorithms, to obtain the optimal policies. Our numerical results show significant improvements in the system's minimum achievable rate and fairness compared to the benchmark schemes.
The first part of this thesis proposes novel joint channel estimation and prediction strategies with low overhead and high accuracy for two reconfigurable intelligent surfaces (RIS) architectures in a BD-RIS-assisted multiple-input multiple-output (MIMO) system operating in correlated fast-fading environments with channel aging. The channel estimation procedure utilizes the Tucker2 decomposition with bilinear alternative least squares. The channel prediction framework is based on a convolutional neural network combined with an auto-regressive predictor. Insightful simulation results demonstrate that our proposed approach is robust to channel aging and exhibits a high estimation accuracy, low pilot overhead, and low computational complexity.
Next, in this thesis, we propose a novel tensor-based joint channel estimation and inter-layer channel coefficients calibration protocol exploiting the PARATUCK2 decomposition and the alternating least squares method. This protocol can be applied to an SIM-assisted multi-user multiple-input single-output (MISO) system. Numerical results prove the proposed scheme's superiority in comparison with state-of-the-art schemes.
Finally, this thesis focuses on developing two max-min fairness algorithms for a SIM-assisted multi-user MISO system, based on instantaneous channel state information (CSI) and statistical CSI. We propose an alternating optimization algorithm for the instantaneous CSI case that jointly optimizes power allocation using geometric programming (GP) and wave-based beamforming coefficients using the gradient descent-ascent method. Since deriving an exact expression for the average minimum achievable rate is analytically intractable for the statistical CSI case, we derive a tight upper bound and thereby formulate a stochastic optimization problem. This problem is then solved, capitalizing on an alternating approach combining GP and gradient descent algorithms, to obtain the optimal policies. Our numerical results show significant improvements in the system's minimum achievable rate and fairness compared to the benchmark schemes.
Last updated: 6.10.2025