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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".