Efficient packet data service in a spread spectrum OFDM cellular system with 2‐dimensional radio resource allocation
Bibliographic record
Abstract
Abstract This paper describes and evaluates a novel 2‐dimensional radio resource allocation scheme for high‐throughput cellular packet data access. The proposed system employs spread spectrum orthogonal frequency division multiplexing with adaptive time and frequency allocation (SS‐OFDM‐F/TA) of radio resources. In order to achieve high spectral efficiency of downlink packet transmissions to multiple mobile users, multi‐user diversity available in the multi path fading environment is exploited through the use of adaptive modulation and coding, best serving sector selection, suitable scheduling of packet transmissions and hybrid ARQ employing soft‐packet combining and incremental redundancy. The data throughput, packet delay and fairness of the system with slow and fast best sector selection are examined. A method of grouping disjoint sub‐bands for use in highly frequency‐selective environments is described and evaluated. Packet re‐transmission options including a proposal of several asynchronous re‐transmission algorithms and constraining the maximum re‐transmission interval are also considered. It is demonstrated that the proposed spread spectrum OFDM system is capable of reaching a spectral efficiency of about 1.7 b/sec/Hz/sector with single receive and transmit antennas, and seems to provide a promising solution for high‐throughput best‐effort delay‐tolerant data services in future cellular radio systems. Copyright © 2004 AEI
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".