Physical layer loading algorithms for indoor wireless multicarrier systems
Bibliographic record
Abstract
The demand for wireless networks has been growing rapidly over the recent past due to improved reliability, higher supported data rates, seamless connectivity between users and the access point, and low deployment costs relative to wireline infrastructure. This increase in demand started with the popular IEEE 802.11b wireless local area network standard. Many recent wireless network standards are now employing multicarrier modulation in their design. Multicarrier modulation reduces the system's susceptibility to the frequency-selective fading channel, due to multipath propagation, by transforming it into a collection of approximately flat subchannels. As a result, this makes it easier to compensate for the distortion introduced by the channel. However, standardized wireless modems, such as the ETSI HiperLAN/2 and the IEEE 802.11a standards, employ the same operating parameters across all subcarriers, and thus do not exploit all the advantages offered by the multicarrier framework. This dissertation investigates techniques to further enhance system throughput performance by tailoring several operating parameters on a per-subcarrier basis. These parameters are subcarrier modulation schemes, power levels, and equalizer lengths. The idea of tailoring modulation schemes and power levels, known as bit allocation and power allocation, has been studied for many years and for many applications. This work proposes two novel discrete bit allocation algorithms that strive to reach the optimal solution in a low computational complexity fashion, while constrained to a specified error performance. A novel power allocation algorithm is proposed that satisfies regulatory requirements by obeying a frequency interval power constraint. Investigation of the third parameter, subcarrier equalizer lengths, has not been conducted before in the literature. Two algorithms are proposed that vary the lengths of the subcarrier equalizers such that the overall distortion is reduced to some specified amount, while the number of equalizer taps used by the system are kept small. Finally, the use of bit allocation is extended to the case when multiple antennas are employed by the wireless modems. Four algorithms are proposed that perform generalized antenna selection diversity at both the transmitter and receiver, in tandem with discrete bit allocation. Results show that employing two transmit and two receive antennas with discrete bit allocation can achieve an average increase in throughput of up to 33% when compared to a system without bit allocation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".