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On the capacity and scheduling of a multi-sector cell with co-channel interference knowledge

2010· article· en· W1994561857 on OpenAlexaff
Patrick Mitran, Catherine Rosenberg, John Sydor, Jun Luo, Samat Shabd

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsCommunications Research Centre CanadaUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceScheduling (production processes)Computer networkChannel capacityCo-channel interferenceChannel allocation schemesInterference (communication)Space-division multiple accessWirelessChannel (broadcasting)White spacesScheduleHandoverTelecommunications linkCognitive radioElectronic engineeringTelecommunicationsMathematical optimizationEngineeringMathematics

Abstract

fetched live from OpenAlex

We study the capacity of a single-hop wireless cell comprised of M fixed directional antennas concentrically arranged around a common axis and each reusing the same channel. The premise for this architecture is that co-channel interference between the fixed directional beams can be identified and reported, allowing joint conflict-free scheduling, i.e., it becomes possible to compute the schedule over all the M sectors together. We are primarily interested in studying the capacity gains that can be realized as a function of the number of sectors, the parameters of the directional antennas and other system parameters. Our analytical and numerical results yield several useful engineering insights on the selection of antenna parameters, the impact of node placement, and the impact of physical layer parameters. This study demonstrates how Space Division Multiple Access (SDMA) can yield excellent capacity provided inter-cell co-channel coexistence is achieved. Such coexistence techniques are now being considered for cognitive radio TV White Space systems and by evolving standards such as the IEEE 802.16 h.

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 score0.501
Threshold uncertainty score0.213

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 head0.023
GPT teacher head0.227
Teacher spread0.205 · 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

Citations5
Published2010
Admission routes1
Has abstractyes

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