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Optimum Resource Allocation in MU-MIMO OFDMA Wireless Systems

2020· article· en· W3038512991 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsEricsson (Canada)
Fundersnot available
KeywordsComputer scienceScheduling (production processes)MIMOResource allocationOrthogonal frequency-division multiplexingSpatial multiplexingOrthogonal frequency-division multiple accessMulti-user MIMOWirelessFrequency-division multiple accessComputer networkTransmitter power outputMax-min fairnessDistributed computingChannel (broadcasting)Mathematical optimizationTelecommunicationsMathematicsTransmitter

Abstract

fetched live from OpenAlex

With the introduction of Advanced Antenna Systems (AAS) in cellular communication technologies, such as LTE and NR, the same resource can be allocated simultaneously to multiple users via spatial multiplexing. However, this raises new challenges to resource allocation strategy to decide opportunistic co-scheduling on a given resource to increase the system capacity without adversely impacting the user fairness. In addition, the effects of transmit power sharing and inter-user interference on co-scheduling need to be considered in the allocation decision. In this paper, a generic framework for resource allocation considering all these aspects of Multi-User Multi-Input Multi Output (MU-MIMO) in cellular Orthogonal Frequency Division Multiple Access (OFDMA) systems is presented and a scheduling algorithm for optimum resource allocation is provided. The performance of the proposed algorithm is evaluated for a two dimensional AAS using SCM-5G channel model.

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: Empirical · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score0.586

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 head0.011
GPT teacher head0.198
Teacher spread0.187 · 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

Quick stats

Citations6
Published2020
Admission routes1
Has abstractyes

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