Equalization for DS-UWB Systems—Part I: BPSK Modulation
Bibliographic record
Abstract
Ultra-wideband wireless transmission has attracted considerable attention both in academia and industry. For high-rate and short-range transmission, direct sequence based ultra-wideband (DS-UWB) systems are a strong contender for consumer market applications. Due to the large transmission bandwidth, the UWB channel is characterized by a long root-mean-square delay spread and the RAKE receiver cannot always overcome the resulting intersymbol interference. We therefore study equalization for DS-UWB systems. This paper is comprised of two parts. In this first part, we consider DS-UWB with binary phase-shift keying (BPSK) modulation, which is the mandatory transmission mode for DS-UWB systems promoted by the UWB Forum industry alliance. We derive matched filter bounds for optimum equalization taking into account practical constraints like receiver filtering, sampling, and the number of RAKE fingers when RAKE preprocessing is applied at the receiver. Our results show that chip-rate sampling is sufficient for close-to-optimum performance. For analysis of suboptimum equalization strategies we further study the distribution of the zeros of the channel transfer function including RAKE combining. Our findings suggest that linear equalization is well suited for the lower data rate modes of DS-UWB systems, whereas nonlinear equalization is preferable for high-data rate modes. Moreover, we devise equalization schemes with widely linear processing, which improve performance while not increasing equalizer complexity. Simulation and numerical results confirm the significance of our analysis and equalizer designs and show that low-complexity (widely) linear and nonlinear equalizers perform close to the pertinent matched filter bound limit.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".