Sub-Carrier/Sub-Channel Allocation in OFDMA Networks
Bibliographic record
Abstract
Introduction OFDM has become the multicarrier transmission technique of choice in broadband transmission over wireless channels. This has been adopted for several wireless access technologies including IEEE 802.11a/g, IEEE 802.16, Digital Video Broadcasting (DVB), and Digital Audio Broadcasting (DAB). What makes OFDM an interesting choice for next generation broadband wireless transmission is its ability in combating frequency selective fading. Instead of transmitting digital symbols sequentially over a single wideband channel, OFDM divides the channel into many narrowband sub-channels or sub-carriers and then simultaneously transmits digital symbols in parallel over these sub-carriers. A transmitted digital symbol over a sub-carrier then experiences a flat fading channel. In a multiuser scenario, a particular sub-carrier at a particular instant may appear differently, in terms of fading characteristics, to different users due to the varying nature of wireless channels and users’ locations. This provides an opportunity to assign certain sub-carriers to users who can utilize them best at that particular moment. The resulting mechanism of such sub-carrier allocation can be viewed as an OFDM-based multiple access scheme called Orthogonal Frequency Division Multiple Access (OFDMA) in which each user is assigned a subset of sub-carriers for exclusive use at any given time. In assigning sub-carriers to users, other resources such as power and modulation format also can be allocated to each assigned sub-carrier. As the number of users increases, there will be more freedom in allocating sub-carriers, transmission power, and modulation format per sub-carrier to different users. To tailor the OFDMA system to users’ needs in terms of desired data rate, maximum available transmission power, or utility, it is natural then to devise a resource allocation scheme that adapts to users’ varying channel conditions on a temporal basis. Adaptive radio resource allocation is thus essential to the performance of OFDMA systems. In recent years, many researchers have tried to explore this idea of adaptively assigning radio resources to users in OFDMA systems in order to optimize a certain metric of interest such as data rate, transmission power, and utility subject to certain constraints.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".