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Record W2610512993 · doi:10.1017/9781316212493.011

Sub-Carrier/Sub-Channel Allocation in OFDMA Networks

2017· book-chapter· en· W2610512993 on OpenAlex
Ekram Hossain, Mehdi Rasti, Long Bao Le

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCambridge University Press eBooks · 2017
Typebook-chapter
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversité du Québec à MontréalUniversity of Manitoba
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingFadingWireless broadbandNarrowbandComputer scienceChannel (broadcasting)Electronic engineeringTransmission (telecommunications)Digital broadcastingComputer networkWirelessDigital televisionDigital Video BroadcastingTelecommunicationsBroadbandWireless networkEngineering

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.328
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.179
Teacher spread0.167 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it