Compressed Sensing Reception of Bursty UWB Impulse Radio is Robust to Narrow-Band Interference
Bibliographic record
Abstract
We have recently proposed a novel receiver for Ultra-Wide-band Impulse-Radio communication in bursty applications like Wireless Sensor Networks. The receiver, based on the principle of Compressed Sensing (CS), exploits the sparsity of the transmitted signal to achieve reliable demodulation. It acquires a modest number of projections of the received signal using analog correlators, and performs a joint decoding of the time of arrival and the data bits from these under-sampled measurements via an efficient quadratic program. In this paper we examine the robustness of this receiver to strong narrow-band interference (NBI) from primary licensed systems like WiMAX. First, by choosing frequency selective test functions in the front-end correlators, we ensure that the interferer can corrupt only a small fraction of the CS measurements. Then we implement a 'digital notch' by identifying and dropping those affected measurements during the quadratic programming reconstruction. The method is easily extended to multiple interferers without additional cost or complexity. We show that by implementing such a 'digital notch' the receiver becomes extremely robust to NBIs. For example its performance is negligibly affected even when the WiMAX customer premise equipment is at a distance comparable to that of the UWB transmitter and the base station is only ten times farther off, both very practical scenarios.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".