An Efficient Adaptive Channel Estimation in a Massive MIMO-OFDM Communication Network Based on Minimization of Error Entropy
Bibliographic record
Abstract
The integration of Massive MIMO, OFDM, and NOMA technologies represents a powerful solution for next-generation wireless communication networks.However, these systems face significant challenges, including accurate channel estimation under low Signal-to-Noise Ratio (SNR) conditions, slow convergence and limited adaptability of conventional algorithms such as LMS and NLMS, inter-user interference, and the complexity of modeling frequency-selective fading in large-scale antenna arrays.This study proposes an adaptive channel estimation framework based on the Minimum Error Entropy (MEE) criterion.Unlike traditional methods that rely on second-order statistics, the MEE approach utilizes higher-order statistics, making it more effective in modeling non-Gaussian and impulsive noise commonly encountered in real-world communication channels.The adaptive nature of the filter also allows it to respond dynamically to timevarying channel conditions.Simulation results demonstrate that the proposed MEE-based estimator achieves a remarkably low Mean Squared Error (MSE) of approximately 2 * 10 -4 and an average Bit Error Rate (BER) of around 9 * 10 -4 , outperforming conventional estimators in both accuracy and robustness.The simulation results show that Leveraging Kernel Density Estimation (KDE) for improved error modeling and coefficient adaptation, the proposed method offers a scalable and efficient solution for reliable channel estimation in Massive MIMO-OFDM-NOMA systems.These results highlight the potential of the proposed framework to significantly enhance spectral efficiency and communication reliability in future 5G/6G 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".