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Record W2600286306 · doi:10.1109/vtcfall.2016.7880944

Cooperative versus Full-Duplex Communication in Cellular Networks: A Comparison of the Total Degrees of Freedom

2016· article· en· W2600286306 on OpenAlexaff
Amr El‐Keyi, Halim Yanıkömeroğlu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsCarleton University
Fundersnot available
KeywordsDuplex (building)Base stationTelecommunications linkMIMOUpper and lower boundsTransceiverComputer scienceNode (physics)TelecommunicationsComputer networkMathematicsWirelessPhysicsBeamformingAcousticsChemistry

Abstract

fetched live from OpenAlex

In this paper, we compare the potential gain that can be obtained from separate-antenna full-duplex transceivers in cellular networks with that obtained from cooperative operation of half-duplex base stations. The gain is characterized in terms of the total degrees of freedom (DoF). In particular, we consider a system composed of two adjacent MIMO base stations. We consider a single time-frequency resource unit that is used by each base station to communicate with one MIMO user. For the full-duplex case, we assume that each node has a configurable transceiver that can allocate some antennas to the uplink and the remainder to the downlink. We provide an upper bound on the total DoF of the full-duplex system and derive the optimal antenna allocation at each node. We compare the derived upper bound for the full-duplex transceivers with the achievable DoF in the case of half-duplex cooperative multipoint transmission. Our results indicate that the achievable DoF in the cooperative case is always greater than or equal to the upper bound on the DoF of the full-duplex system. We further investigate the case of full-duplex cooperative multipoint transmission and show that the maximum DoF gain due to full-duplex operation cannot exceed 12.5% of the DoF of the half-duplex cooperative system.

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: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.334

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 science0.0010.000
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 head0.025
GPT teacher head0.249
Teacher spread0.224 · 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
Published2016
Admission routes1
Has abstractyes

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