A Comprehensive Performance Analysis of MIMO-OFDM Technology Using Different MIMO Configurations and M-QAM Modulation Schemes for LTE Cellular Network
Bibliographic record
Abstract
MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) uses multiple antennas at transmitter and receiver side to facilitate high throughput performance without needing additional bandwidth or transmission power. But frequency synchronization, Inter Carrier Interference (ICI) minimization and antenna diversity are the main challenges for MIMO-OFDM wireless technology. In this paper, a MIMO-OFDM hybrid model is designed with proper frequency synchronization and antenna diversity that minimizes ICI with improved signal strength. Using the model a comprehensive analysis in terms of Bit Error Rate (BER) performance with respect to Signal to Noise Ratio (SNR) and Bit Rate for different M-ary QAM modulation schemes and different MIMO configurations are presented over Rayleigh fading and AWGN channel. After an extensive analysis, it is found that the propose OFDM scheme shows better Bit Error rate (BER) performance for 64-QAM than any other M-ary QAM modulation schemes and the optimum MIMO configuration for this good quality performance is found to be 3×2 configuration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".