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Record W2753610083 · doi:10.1109/jsyst.2017.2741976

Distributed Transmission Scheduling and Power Allocation in CoMP

2017· article· en· W2753610083 on OpenAlex
Shu Fu, Haibo Zhou, Jian Qiao, Liang Liang, Yunjian Jia, Bin Wu

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

VenueIEEE Systems Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsComputer scienceOrthogonalizationScheduling (production processes)PrecodingBase stationDistributed computingFair-share schedulingComputer networkMathematical optimizationChannel (broadcasting)AlgorithmMathematicsQuality of serviceMIMO

Abstract

fetched live from OpenAlex

The performance of wireless networks can be largely enhanced by coordinated multipoint (CoMP). To design an efficient CoMP in multiuser multiple-input multiple-output scenario, conventional transmission scheduling and power allocation are usually performed in a static and centralized manner. In this paper, we focus on dynamic and distributed transmission scheduling and power allocation. We first determine the coordinated base-station sets (defined as CBSs) candidates in each subband by the channel energy (i.e., square Frobenius norm of channel matrix) of each user. Each CBS candidate contains a set of coordination base-stations and edge users. By chordal distance, we can measure the orthogonality between space spanned of users in the same CBS candidate. Then, we propose two heuristic user scheduling algorithms based on channel energy and chordal distance between users to determine the set of users served by each CBS candidate. The first algorithm is based on an open problem, which reveals the philosophy of user scheduling with orthogonalization threshold guarantee. The second one deals with user scheduling by selecting a set of edge users with the largest total channel energy and orthogonalization threshold guarantee. With the total channel energy per CBS candidate, the CBSs and their served edge users can be determined. Then, water-filling power allocation is further applied to CBSs with block diagonalization precoding. Algorithm performance is demonstrated by extensive simulations.

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 score0.855
Threshold uncertainty score0.444

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 head0.013
GPT teacher head0.248
Teacher spread0.234 · 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