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Record W7051355006

Optimal link adaptation for multicarrier communication systems

2014· other· en· W7051355006 on OpenAlexafffund

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2014
Typeother
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPower (physics)Transmission (telecommunications)Set (abstract data type)Process (computing)NucleofectionConstraint (computer-aided design)
DOInot available

Abstract

fetched live from OpenAlex

Link adaptation is the terminology used to describe techniques that improve multicarrier communication systems performance by dynamically adapting the transmission parameters, i.e., transmit power and number of bits per subcarrier, to the changing quality of the wireless link. The research literature has focused on single objective optimization techniques to optimize the multicarrier communication systems performance, e.g., maximizing the throughput/capacity or minimizing the transmit power subject to a set of constraints. In this dissertation, we adopt a novel optimization concept, namely multiobjective optimization, where our objective is to simultaneously optimize the conflicting and incommensurable throughput and power objectives. More specifically, in Chapters 2 and 3, we propose novel algorithms that jointly maximize the multicarrier system throughput and minimize its total transmit power subject to quality-of-service, total transmit power, and maximum allocated bits per subcarrier constraints. The proposed algorithms require prior knowledge about the importance of the competing objective functions in terms of pre-determined weighting coefficients, or they can adapt the weighting coefficients during the solution process while meeting the constraints, in order to reduce the computational complexity. Simulation results show significant performance gains in terms of the achieved throughput and transmit power when compared to single optimization approaches, at the cost of no additional complexity. Motivated by the obtained results, in Chapter 4 the problem is extended to the cognitive radio environment where the multicarrier unlicensed/secondary user, with limited sensing capabilities, needs to satisfy additional constraints for the leaked interference to existing licensed/primary users. In Chapter 5, a multiobjective optimization problem is formulated to balance between the SU capacity and the leaked interference to existing primary users, where the effect of the imperfect channel-state-information on the links from the secondary user transmitter to the primary users receivers is considered. Simulation results show improvements of the energy efficiency of the secondary user when compared to its counterparts of the works in the literature, with reduced computational complexity. In Chapter 6 we investigate the optimal link adaptation problem to optimize the energy efficiency of secondary users while considering the effect of imperfect channelstate- information on the links between the secondary user transmitter and receiver pairs and the limited sensing capabilities of the secondary user. The proposed link adaptation algorithm guarantees minimum required rate for the secondary user and statistical interference constraints to the existing primary users. Finally, conclusions and possible extensions to the optimal link adaptation problem is discussed in Chapter 7.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.028
GPT teacher head0.269
Teacher spread0.241 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes2
Has abstractyes

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