A Novel Two-Step Channel Estimation Method for RIS-Assisted mmWave Systems
Abstract
:1. Introduction
- In addition to the cascaded channel estimation, we further estimate the reflected channels of RIS-BS and UE-RIS. Remarkably, a novel two-step channel estimation method using MMPGA, LS estimator, ResNet, and MTRnet is introduced for the RIS-assisted mmWave systems.
- The MMPGA-LS-ResNet is proposed to estimate the cascaded channel of UE-RIS-BS. The MMPGA-LS optimizes the crossover strategy and mutation strategy compared with the common evolution algorithm. As a result, the proposed MMPGA-LS is capable of reducing the estimation error. Then, ResNet is applied to further reduce the cascaded channel error. Relying on the designed network architecture, including the multiple cross-layer operations and layers, the proposed ResNet learns the relationship between the output of MMPGA-LS and the channel of UE-RIS-BS effectively.
- Furthermore, the proposed MTRnet is introduced for estimating the reflected channels of RIS-UE and UE-RIS. Compared with the single regression model, the MTRnet integrates the multi-task learning model and ResNet. As a result, the proposed MTRnet with multiple output layers achieves the reflected channel estimation within fewer optimization models compared with that based on the single regression model.
- A series of experimental results have validated the superiority of the novel two-step channel estimation method. For the cascaded channel estimation performance, the MMPGA-LS achieves a lower NMSE compared with a genetic algorithm (GA) [29] and particle swarm algorithm (PSO) [30]. Besides, the proposed ResNet also obtains a lower NMSE compared with convolutional recurrent neural network (CRNN) [17] and CNN [15]. Additionally, the proposed MTRnet based on the multi-task learning ability still outperforms some single-learning models in terms of a lower NMSE. Besides, the proposed method also obtains a low NMSE in the RIS with the formulation of the uniform planar array.
2. Channel Estimation System Model
3. The Novel Two-Step Channel Estimation Method
3.1. MMPGA-LS-ResNet-Based Cascaded Channel Estimation
3.1.1. Population Initialization
3.1.2. Population Classification
3.1.3. Crossover
3.1.4. Adaptive Mutation
3.1.5. Population Reservation
3.1.6. ResNet
- Initialize ;
- Classify based on fitness ranking;
- Execute crossover operation shown in Figure 2;
- Execute adaption mutation operation shown in Figure 3;
- Reserve with the higher fitness;
- Select the best one with the highest fitness;
- Substitute into Equations (1)–(6) and LS algorithm;
- based on Equation (31);
- Substitute into the proposed ResNet shown in Figure 4;
- Train the network parameters in the ResNet;
- Attain the optimization model of ResNet;
- .
3.2. MTRnet-Based Reflected Channel Estimation
3.3. Implementation of the Novel Two-Step Method
- Generate population ;
- Attain via the proposed MMPGA;
- Obtain by LS estimator;
- ;
- Substitute into the proposed ResNet;
- Generate via the optimization model of ResNet;
- ;
- Substitute into the proposed MTRnet;
- Achieve via the optimization model of MTRnet;
- , .
- Initialize population;
- Classify via the descending order criterion of fitness;
- Execute the crossover operation based on Equations (24) and (25);
- Adopt the adaptive mutation strategy based on Equations (26) and (27);
- Select with higher fitness;
- Replace with ;
- Replace with ;
- Estimate ;
- Construct ;
- Predict via the optimization model of ResNet.
- Construct ;
- Predict and by the optimization model of MTRnet;
- Evaluate the channel estimation performance based on Equations (39) and (40).
4. Simulation Results and Discussion
4.1. Comparisons with Reported Methods
4.2. Numerical Results of The Proposed Two-Step Mehod
4.3. Discussion of the Proposed Method
5. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Layers | Description | |
---|---|---|
Convolution | ||
Convolution | ||
Convolution | ||
Convolution | ||
Cross-layers | - |
Sub-Tasks | Layers | Description |
---|---|---|
Sub-task I | hidden | P neurons |
Sub-task II | hidden | P neurons |
Sub-task III | hidden | neurons |
Sub-task IV | hidden | P neurons |
Sub-task V | hidden | neurons |
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Yu, J. A Novel Two-Step Channel Estimation Method for RIS-Assisted mmWave Systems. Sensors 2024, 24, 5362. https://doi.org/10.3390/s24165362
Yu J. A Novel Two-Step Channel Estimation Method for RIS-Assisted mmWave Systems. Sensors. 2024; 24(16):5362. https://doi.org/10.3390/s24165362
Chicago/Turabian StyleYu, Jiarun. 2024. "A Novel Two-Step Channel Estimation Method for RIS-Assisted mmWave Systems" Sensors 24, no. 16: 5362. https://doi.org/10.3390/s24165362