Statistical and intuitive nonlinear signal processing designs for multiuser interference mitigation in UWB systems
Bibliographic record
Abstract
Ultra-wideband (UWB) wireless technologies have the potential to greatly improve the quality of short-range communications between unlicensed wireless devices. By exploiting huge bandwidth, UWB devices can overcome the spectral congestion and performance impairments experienced by narrowband devices operating in common unlicensed frequency bands. On the other hand, strict constraints on the signal power spectral density must be enforced to limit spectral emissions, so while UWB is unlicensed, the properties of transmitted signals must be regulated. In the impulse radio (IR) class of UWB systems, a number of pulses are used to transmit one information symbol and the pulses are randomly time-shifted within a frame structure to avoid collisions with other users. Low power spectral density is maintained by transmitting many pulses and the receiver integrates the pulses to achieve sufficient energy for low error rates. Impulse radio systems employing such time-hopping (TH) and repetition code designs are characterized by low duty cycle transmission, with transmitted signal energy occupying only a small fraction of the available transmission time. Although the TH scheme is intended to prevent catastrophic interference between multiuser TH-UWB transmissions, multiple-user interference (MUI) can impair uncoordinated transmissions in applications where several UWB devices are located at close range. In this talk, we show that the signal processing adopted in conventional UWB receivers does not fully exploit the TH code and has poor performance in the presence of significant MUI. Statistical signal processing based on the properties of MUI in TH-UWB is discussed, and two classes of techniques for mitigation of MUI are presented. The first class uses statistical characterizations of the MUI-plus-AWGN disturbance in the received THUWB signals as a basis for statistical receiver processing structures. The second class uses intuitive interference-sensing techniques specifically developed for the low duty cycle TH-UWB signal format, which control the processing of pulse replicas at the receiver. Both methods lead to greatly improved bit error rate performance compared to the conventional receiver processing. Both types of interference mitigation can be powerfully and efficiently used in a single receiver, and composite receiver designs that provide superior performance are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".