Markov Modeling for Data Block Transmission of OFDM Systems over Fading Channels
Bibliographic record
Abstract
Orthogonal frequency-division multiplexing (OFDM) is a promising technique for high data rate wireless access networks. Modeling OFDM systems for the analysis of network performance is very challenging, because of the complexity of the modulation/coding schemes and the wideband wireless channel fading in both the time and frequency domains. In this paper, a novel packet-level model based on a two-dimensional Markov chain is proposed for OFDM systems over time-varying (Nakagami-m fading), frequency-selective channels. First, the level cross rate (LCR) of the amplitude of channel frequency response is derived. Then, we develop a methodology to map the received signal-to-noise ratio (SNR) of the subcarriers into a finite number of channel states with different packet error rate (PER). The proposed model presents directly the performance of the OFDM systems and incorporates the time- and frequency-domain correlations of the fading channels. Channel coding is also considered in evaluating PER. Simulations have verified that the statistics of the BER presented by our model are consistent with those of waveform simulations. The proposed Markov model can be an effective tool to study and optimize upper-layer protocols of OFDM-based wireless networks, via both analysis and simulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".