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Beamforming and Power Control for Wireless Network Virtualization in Uplink MIMO Systems

2024· article· en· W4402159170 on OpenAlexafffund
Ahmed Almehdhar, Ben Liang, Min Dong, Gary Boudreau, Yahia Ahmed

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsEricsson (Canada)Ontario Tech UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBeamformingMIMOComputer scienceTelecommunications linkPower controlComputer networkWireless networkWirelessPower (physics)Telecommunications

Abstract

fetched live from OpenAlex

We consider wireless network virtualization (WNV) in an uplink multiple-input multiple-output system, where multiple service providers (SPs) operate in virtually isolated networks managed by an infrastructure provider (InP) that owns the communication equipment. Service isolation is achieved at the physical layer by exploiting a large number of antennas at the base stations. We formulate this WNV as a non-convex optimization problem for the InP, jointly considering the uplink receive beamforming at the BS and the transmit power of the SPs' subscribing user devices. We decompose the problem into two subproblems and derive closed-form solutions to both. We then adopt an alternating optimization approach to combine the closed-form solutions to solve the original problem. Our simulation results show that the proposed method provides strong service isolation among the SPs while retaining efficiency similar to or better than centralized beamforming without virtualization, and it substantially outperforms traditional WNV with strict resource separation.

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 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: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.216
Teacher spread0.210 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2024
Admission routes2
Has abstractyes

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