Optimized CNN and Adaptive RBFNN for channel Estimation and Hybrid Precoding approaches for Multi User Millimeter wave Massive MIMO
Bibliographic record
Abstract
Abstract The Millimetre Wave (mmWave) communication satisfy the demand for high data rates due to the characteristic of wide bandwidth. Using massive Multiple-Input Multiple-Output (MIMO) technology, significant propagation loss of mmWave communication is effectively compensated. However, it is challenging to provide a specialised Radio Frequency (RF) chain for each antenna due to constrained physical area with closely spaced antennas and prohibitive power consumption in mmWave massive MIMO systems. This paper presents novel approaches for effective channel estimation and hybrid precoding in mmWave communication systems. To address the challenges of channel estimation, a Convolutional Neural Network (CNN) is utilized, and network parameters are optimized using Enhanced Whale Optimization Algorithm (EWOA). The proposed CNN-based channel estimation method aims to accurately estimate the channel in mmWave systems with enhanced efficiency and reduced complexity. By training CNN using EWOA optimization algorithm, the network parameters are fine-tuned to improve accuracy and generalization capability of channel estimation process. Furthermore, hybrid precoding is achieved using Adaptive Radial Basis Function Neural Networks (Adaptive RBFNN) which enables efficient precoding while minimizing complexity. Moreover, the Adaptive RBFNN approach determines the optimal precoding weights based on Channel State Information (CSI), resulting in improved performance and reduced computational overhead. The performance analysis is validated using MATLAB/Simulink software and offers in providing effectual and reliable mmWave communication systems, facilitating the realization of high-speed and high-capacity wireless networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".