Performance Optimization of LS/LMMSE Using Swarm Intelligence in 3D MIMO-OFDM Systems
Bibliographic record
Abstract
The capability of Least square (LS) and least minimum mean squared error (LMMSE) channel estimation techniques are limited due to one or two factors (inherent additive Gaussian noise and Inter Carrier Interference, higher computational complexity).These factors tend to be severe when the system grows in terms of numbers of transmitting and receiving antennas, channel parameters, noise etc. Accurate channel parameters estimation using these techniques is still not possible even with smaller Multi input multi output (MIMO) systems at higher signal to noise ratios (SNR) due to complex nature of channel parameters.Swarm Intelligence consisting of agents spread in search space having limited capabilities and random behaviour when interacts with each other and within their own locality are capable of finding solution for a complex problem.When the constructive behaviour of such particles in particle swarm optimization (PSO) within the search space limited to some constraint is applied to optimize the performance of 3D-Pilot Aided Channel Estimation (3D-PACE) of MIMO-OFDM system, results showed that the bit error rate (BER) is significantly decreased.The channel parameters at the receiver obtained using LS and LMMSE are further optimized using PSO with proper and careful setting of PSO initial parameters.
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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 | 0.001 | 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 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".