Bibliographic record
Abstract
Ultra wideband (UWB) technology has been proposed recently for use in wireless personal area networks. One of the common methods to collect energy in this low transmitting power system is the use of the Rake receiver. In this paper, an adaptive structure and algorithm for a RAKE receiver are proposed for the UWB single-user link with an intended data rate of 120 Mbps. A single band impulse radio system with 1 GHz bandwidth and antipodal PAM modulation technique is considered. The proposed receiver is to be used in an indoor UWB. The indoor environment is represented by a dense multi-path channel model proposed by IEEE P802.15. The multi-path spread caused by the channel is much longer than the symbol time based on the data rate and the binary modulation. To reduce this intense multi-path destruction and the extreme inter-symbol interference, an adaptive structure is used. The receiving starts off with a front end analog filter matching with the signal pulse shape; the signal is then sampled at a rate of 1 Gsps. A simple and effective sliding-window channel estimation method is used to obtain channel parameters. A combination of selective RAKE and LMS adaptive FIR equalizer and a LMS RAKE are then used to recover the transmitted signal. Several numerical examples and simulation results will be presented in comparing these architectures with the conventional selective RAKE receiver using maximal ratio combining (MRC). Results show that the new schemes gather multi-path energy and reject ISI more effectively than the traditional MRC selective RAKE receiver with almost the same level of computation complexity. Due to the simplicity of the algorithm and a reasonable sampling rate, the structure looks promising for practical VLSI implementations
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".