Phase Shift Optimization Algorithm for Achievable Rate Maximization in Reconfigurable Intelligent Surface-Assisted THz Communications
Abstract
:1. Introduction
2. System Model
3. Phase-Shifting Matrix Optimization
3.1. Problem Formulation
3.2. Proposed Proximal Gradient Method
Algorithm 1 Accelerated Proximal Gradient (APG) | |
1: | Input: , |
2: | forq = 0,… Q-1 do |
3: | |
4: | |
5: | Repeat |
6: | |
7: | |
8: | if |
9: | break; |
10: | Else |
11: | (with ) |
12: | End |
13: | End |
14: | |
15: | |
16: | end for |
3.3. Quantization
4. Numerical Results
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Reference | System Model | Main Aspects | Issues |
---|---|---|---|
[25] | RIS-aided multiuser MISO communication system | Proposed an alternating optimization (AO) algorithm for multiuser RIS-aided MIMO that iteratively optimizes the RIS phase shifts and the transmit beamforming vector. The coverage and achievable rate performance are significantly improved when compared to conventional systems without RIS. | To achieve the desired convergence, the AO algorithm requires a large number of iterations, especially when the number of RIS elements is large. Furthermore, the algorithm was designed assuming lower band single antenna users, which, combined with a computational complexity that is strongly dependent on the transmitter array size, makes it difficult to extend to UM-MIMO THz systems. |
[26] | RIS-aided multiuser MISO communication system | Considered an RIS-based downlink multiuser multiantenna system with limited discrete phase shifts, operating in the absence of direct links between the BS and users. Proposed an iterative algorithm in which the transmitter digital beamforming subproblem is solved through zero-forcing with power allocation and the RIS-based analog beamforming is solved by the outer approximation method. It was shown that good sum-rate performances can be achieved with a reasonable sized RIS and a small number of discrete phase shifts. | It assumes single antenna users and the computational complexity can grow prohibitively high for large arrays, which compromises its potential application in UM-MIMO THz systems. |
[22] | RIS-aided MIMO communication system | Derived an iterative algorithm for solving the joint optimization problem of the covariance matrix of the transmitted signal and the RIS elements. The resulting algorithm was shown to achieve similar achievable rate gains to the method from [25] in single-user scenarios but requires fewer iterations. | It was designed to target lower frequency bands, as well as having a computational complexity that is strongly dependent on the number of transmitter antennas. Therefore, applying this approach with the large array of settings envisioned for UM-MIMO THz systems can be very challenging. |
[27] | RIS-aided MIMO THz system | Developed an adaptive gradient descent (A-GD) algorithm for single-user RIS-aided MIMO THz systems. The proposed A-GD algorithm improves the achievable rate performance when compared with other alternative algorithms. The A-GD algorithm considers discrete phase shifts through a final mapping step. | The A-GD algorithm can achieve large gains over an RIS with random phase, shifts but the computational complexity becomes very high when working with large antenna arrays and RISs with a large number of elements. |
Our approach | RIS-aided MIMO THz system | Applied the APG method to a phase shift constrained maximum achievable rate problem. A low complexity algorithm resorting to simple element-wise normalization was derived that can cope with the large problem settings of RIS-aided UM-MIMO systems in the THz band. The proposed algorithm can handle discrete phase shifters with amplitude loss. Numerical evaluation shows that the proposed scheme can achieve competitive performance gains when compared against state-of-the-art solutions, as in [27]. | The approach was designed for single-user scenarios. As future work, it may be extended to multiuser cases. |
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Praia, J.; Pavia, J.P.; Souto, N.; Ribeiro, M. Phase Shift Optimization Algorithm for Achievable Rate Maximization in Reconfigurable Intelligent Surface-Assisted THz Communications. Electronics 2022, 11, 18. https://doi.org/10.3390/electronics11010018
Praia J, Pavia JP, Souto N, Ribeiro M. Phase Shift Optimization Algorithm for Achievable Rate Maximization in Reconfigurable Intelligent Surface-Assisted THz Communications. Electronics. 2022; 11(1):18. https://doi.org/10.3390/electronics11010018
Chicago/Turabian StylePraia, João, João Pedro Pavia, Nuno Souto, and Marco Ribeiro. 2022. "Phase Shift Optimization Algorithm for Achievable Rate Maximization in Reconfigurable Intelligent Surface-Assisted THz Communications" Electronics 11, no. 1: 18. https://doi.org/10.3390/electronics11010018
APA StylePraia, J., Pavia, J. P., Souto, N., & Ribeiro, M. (2022). Phase Shift Optimization Algorithm for Achievable Rate Maximization in Reconfigurable Intelligent Surface-Assisted THz Communications. Electronics, 11(1), 18. https://doi.org/10.3390/electronics11010018