Asymptotic performance analysis and design of wireless communication systems in generalized fading and noise
Bibliographic record
Abstract
One of the most important challenges for communication system designers is to combat the detrimental effects of fading and noise. Fading refers to random changes in the channel gain due to shadowing and multi-path propagation of the transmitted signal. Several statistical distributions have been proposed to model the fading phenomenon. On the other hand, various techniques, such as multi-antenna transmission and/or reception (e.g., space-time coding) have been proposed to improve the performance of wireless communication systems in fading channels. Clearly, the performance and consequently, the design of these systems depend on the considered fading model. Since the wireless environment is intrinsically dynamic and may also experience the keyhole effect, i.e., not fully correlated but rank deficient fading, it is important to have a comprehensive performance analysis framework which is applicable to a wide range of fading models. In this thesis, we provide such a framework for the analysis of single-antenna transmission with receive diversity and space-time coding in generalized non-keyhole and keyhole fading channels. The presented analysis provides valuable new insights into system design and can be used to obtain tight asymptotic upper bounds for the bit, symbol, and frame error probabilities. The second subject studied in this thesis is non-Gaussian noise. Noise (which in our definition includes interference) in communication systems has been traditionally modelled as Gaussian. This is mainly motivated by the tractability of the design and analysis of communication systems in Gaussian noise and is justified by the central limit theorem. However, wireless communication systems are often impaired by non-Gaussian noise and interference as well. Examples of non-Gaussian noise include co-channel interference, man-made or natural impulsive noise, and ultra-wideband interference. In this thesis, we analyze the performance of single-antenna transmission and space-time codes impaired by non-Gaussian noise and interference. Our general and easy-to-evaluate results reveal the effects of these types of noise and pave the way for designing robust detection techniques that perform close to optimum for a wide range of practical noise and interference environments. As an example, we propose an adaptive Lp -norm metric for robust detection in non-Gaussian noise which outperforms previously reported metrics.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".