Design of Pre-Rake DS-UWB Downlink with Pre-Equalization
Bibliographic record
Abstract
We consider the design of ultra-wideband (UWB) systems that enable high data rate communications for short-range wireless applications. In particular, we consider the downlink of a direct sequence UWB (DS-UWB) system in which the base station is equipped with multiple antennas and employs pre-rake combining, while each user employs a simple single antenna receiver. We propose the use of multiuser filters for the purpose of pre-equalization at the transmitter in order to mitigate the combined effects of intersymbol interference (ISI) and multiuser interference (MUI) that are generated at the receivers as a result of the wideband nature of the users' channels. For this system, we study the joint design of the transmitter's pre-equalization filters and each receiver's scalar gain under two design criteria. The first design minimizes the total transmitted power from the base station subject to achieving physical layer quality of service requirements of different users. For this design, we show that the calculation of the pre-equalization filters and the receiver gains can be formulated as an efficiently solvable convex optimization problem. In the second design, we consider the minimization of a weighted sum of each user's mean-square error. In order to obtain a computationally tractable solution for this design criterion, we exploit the dual DS-UWB uplink that employs rake combining and post-equalization filters at a central receiver. The numerical studies for each design criterion under realistic models of UWB channel propagation demonstrate the effectiveness of the proposed multiuser pre-equalization filter designs in mitigating ISI and MUI, and thus their ability to enable reliable pre-rake DS-UWB downlink transmission.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".