Narrowband interference detection and mitigation for indoor ultra-wideband communication systems
Bibliographic record
Abstract
In February 2002, the FCC (2002 a, b) issued a ruling that ultra-wideband (UWB) could be used for data communications as well as for radar and safety applications. UWB system is constrained to have a maximum power transmission of -41 dBm and a bandwidth ranging from 3.1-10.6 GHz. UWB co-exists and does not interfere with the existing narrowband or wideband communication systems in the same spectrum. However, due to its low power in the same bandwidth, UWB is affected by the so-called narrowband (NB) interference. This thesis presents a method to estimate and detect narrowband signals in radio impulse receiver with the intention to eliminate the NB interference. Narrowband bandwidth is very small compared to the bandwidth of UWB therefore the interference can be considered as a single tone. To detect such a tone using conventional techniques is not feasible at least up to this time for UWB as current technology can not support such high data rates. Alternatives way to track down the narrowband signal include using a power spectral density estimation technique called spectrogram. For all cases, the spectrogram at specific frequency range where the narrowband active statistically be larger than its overall average power. Here, a threshold detector is built which reports detection at the frequency range where the narrowband is located if the spectrogram exceeds a threshold value. Upon completing of successful NB detection, the NB signal in the UWB system will be estimated in digital form and cancelled in analog form. The pipelined LMS algorithm is used to estimate the NB signal; the algorithm is implemented using a built-in IP core from the Altera DSP library which can be simulated in either Matlab platform or in FPGA boards. The design correctness has been validated by means of Monte-Carlo simulation and hardware implementation using standard UWB IEEE standard channel models, Time Hopping-Pulse Position Modulation and the rake receiver technique.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".