Performance Evaluation of Bluetooth Systems With LDI, Modified LDI, and NSD Receivers
Bibliographic record
Abstract
Bluetooth is a popular short-range low-power radio standard for wireless personal area networks. Bluetooth transmitters employ Gaussian frequency shift keying (GFSK) and simple block codes for error correction. Recently, two new receiver designs for Bluetooth devices, which are the so-called modified limiter-discriminator detector with integrate-and-dump filtering (LDI) and noncoherent sequence detection (NSD), have been proposed in the literature. While the modified LDI receiver is a concatenation of a conventional LDI detector with an improved error-correction decoder, the NSD receiver fully takes into account the memory introduced by the GFSK. Both receivers have been shown to improve the Bluetooth system performance in terms of physical-layer metrics such as bit-error rate and packet-error rate. In this paper, we present a comprehensive performance evaluation considering practically more relevant metrics such as throughput, delay, and delay jitter at the medium-access control layer. To this end, we develop an evaluation framework, which includes the spatial distribution of Bluetooth devices, path loss, fading, realistic data traffic models, scheduling, automatic repeat request, and baseband packet selection. Our numerical and simulation results verify that the newly introduced Bluetooth receivers, especially NSD, offer a significant performance enhancement for Bluetooth systems in terms of practically relevant measures.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".