Optimization of transmitted-reference receivers in the ultra-wide bandwidth system
Bibliographic record
Abstract
This thesis contributes the research and development of novel receiver \noptimization approaches conducted in ultra-wide bandwidth (UWB) systems. \nThe ultimate goal of the improved receiver technology is to simplify the receiver \nstructures at the cost of a tolerable performance degradation or improve the \nreceiver performances at the cost of a tolerable complexity. Recently, UWB \ntechnology has become more and more attractive due to its increased performance. \nAn advanced scheme that can provide a further improvement is \nstrongly recommended and highly demanded. This research project focuses \non the design of outstanding receivers suitable for the UWB system with \ntransmitted-reference signaling. Two types of improved receivers are investigated. \nThe first one is based on the optimization of inter-pulse time delay Td \nin the traditional transmitted-reference receivers where one data pulse is transmitted \nTd seconds delay after one reference pulse in a bit duration. The second \none is based on the joint optimization of the number of reference symbols \nand the integration interval length in the generalized transmitted-reference receivers where Nd data symbols are transmitted after Nr reference symbols \nin a data packet. For both improved receivers, simulation and theoretical approaches \nare used to provide the optimization results. The numerical results \nshow that the improved receivers by using different optimization approaches \noutperform the non-improved receivers significantly for most practical cases. \nAn up to 4.2dB performance improvement can be achieved consequently. \nThe principal conclusion from this thesis is that all the optimization \nschemes presented herein can be successfully applied to the design of receivers \nin the UWB transmitted-reference systems that the data decision can be obtained \nby thresholding the correlator output of the reference information with \nthe data information.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".