Robust L_p-Norm Decoding for BICM-Based Secondary User Systems
Bibliographic record
Abstract
The frequency bands used by secondary user systems such as cognitive radio and ultra-wideband (UWB) systems are subject to various forms of non-Gaussian noise and interference including co-channel and narrowband interference caused by the primary user and other secondary users as well as man-made impulsive noise. For secondary user systems employing popular bit-interleaved coded modulation (BICM) we propose a robust Lp-norm metric for Viterbi decoding to overcome the harmful effects of non-Gaussian noise and interference. For the resulting system, we derive an approximate upper bound on the bit error rate (BER) and a general analytical expression for the asymptotic BER valid for all practically relevant types of fading, including Rayleigh, Ricean, Nakagami-m, Nakagami-q, and Weibull fading, and all types of noise and interference with finite moments. Simulation and analytical results show that, in non-Gaussian noise and interference, the proposed Lp-norm metric yields large performance gains over the conventional L2-norm metric and outperforms erasure decoding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".