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Record W1969403444 · doi:10.1002/ett.2853

QoS‐based power allocation for cognitive radios with AMC and ARQ in Nakagami‐<i>m</i> fading channels

2014· article· en· W1969403444 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTransactions on Emerging Telecommunications Technologies · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFadingNakagami distributionComputer scienceQuality of serviceComputer networkAutomatic repeat requestNetwork packetHybrid automatic repeat requestInterference (communication)Link adaptationElectronic engineeringEngineeringTelecommunications linkChannel (broadcasting)

Abstract

fetched live from OpenAlex

Abstract This paper presents power allocation schemes to maximize the effective capacity (EC) of a secondary user (SU) communications link using adaptive modulation and coding (AMC) in an underlay cognitive radio Nakagami‐ m block‐fading environment to meet target quality‐of‐service (QoS) requirements in terms of delay‐outage probability and packet error rate constraints. The SU transmission parameters are chosen such that the primary user imposed interference power constraint (IPC) is satisfied. Three different types of IPCs, namely average interference power, peak interference power and interference power outage, are considered. For each IPC, the analytical solutions for choosing the AMC mode and power allocation in each fading block, and the corresponding SU achievable EC under given QoS requirements are derived. Furthermore, we investigate the performance of a hybrid automatic repeat request (ARQ)/AMC and obtain the closed‐form packet loss rate expression. Illustrative results show the effects of the IPC, fading duration and fading severeness on the SU achievable EC under given QoS requirements. It is shown that for loose delay‐outage requirements, average interference power and interference power outage constraints give higher SU EC than peak interference power constraint. However, for more stringent delay‐outage requirements, the SU achievable EC for the three IPC is significantly reduced. The results also indicate that ARQ is helpful to significantly reduce the packet loss rate for loose delay constraint. However, ARQ increases the delay and is not effective for stringent delay‐outage requirements. Copyright © 2014 John Wiley &amp; Sons, Ltd.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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