Subcarrier availability in OFDM systems with imperfect carrier synchronization in deep fading noisy doppler channels
Bibliographic record
Abstract
In this thesis, we investigate the performance of a multi-user OFDM system under imperfect synchronization which is caused due to noise, Doppler shift and frequency selective fades in the channel. Analytical result indicates that the SNR degrades as the average power of the channel impairments such as AWGN, carrier frequency offset due to Doppler frequency and fading gain is increased.The SNR degradation leads to imperfect synchronization and hence decreases the total number of subcarriers available for allocation. Based on Monte Carlo analysis, 22% loss in the number of allocatable subcarriers is noticed under imperfect synchronization as compared to perfect synchronization. We utilize empirical modelling to characterize the available number of subcarriers as a Poisson random variable. In addition, we determine the percentage decrease in the total number of allocatable subcarriers under varying channel parameters such as AWGN, Doppler frequency and fading gain. The results indicate 19% decrease in the number of available subcarriers as average AWGN power is increased by 10dB; 44% decrease as the Doppler frequency is varied between 10Hz to 100Hz; and 56% decrease as the fading gain is varied between 0dB to -30dB. Furthermore, the evaluation of an adaptive subcarrier allocation algorithm under imperfect synchronization. Hence, radio resource allocation for multicarrier systems should consider the percentage loss in the available subcarriers under imperfcet synchronization.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".