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Record W2147446656 · doi:10.1109/wts.2009.5068970

An efficient multiuser scheduling scheme for MIMO-CDMA wireless systems

2009· article· en· W2147446656 on OpenAlexaff
Elmahdi Driouch, Wessam Ajib

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsComputer scienceBase stationScheduling (production processes)BeamformingComputational complexity theoryTelecommunications linkMIMOCode division multiple accessWirelessWireless networkTime division multiple accessMathematical optimizationDistributed computingAlgorithmComputer networkMathematicsTelecommunications

Abstract

fetched live from OpenAlex

In multiuser CDMA wireless systems where the base station is equipped with multiple antennas, the base station takes advantage from the spatial and code separability between the served users in order to enhance the performance of the system. However, this enhancement is constrained by the design of an appropriate scheduling scheme which is responsible of choosing the best users to serve. In this paper, we propose efficient scheduling algorithms for the downlink of multiantenna CDMA wireless systems using zero forcing beamforming. Our proposition maximizes the system sum rate and keeps the computational complexity low. We make use of a graph theoretical approach to represent the system as an undirected weighted graph. As a second step, we formulate the scheduling problem as the maximum weight k-colorable subgraph problem. We propose two heuristic solutions to find the users to serve in each time slot in an acceptable polynomial time. Finally we evaluate the efficiency of the proposed schemes by mean of simulations and the results show the near-optimal performance of the proposed schedulers with very low computational complexity compared to the optimal exhaustive search over all the possible users combinaisons.

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 score0.608
Threshold uncertainty score0.755

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 head0.009
GPT teacher head0.240
Teacher spread0.231 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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