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Record W2005149067 · doi:10.1109/glocom.2014.7037458

Relay-assisted downlink transmissions to support increased data rates for single antenna users

2014· article· en· W2005149067 on OpenAlexaff
Fadhel Alhumaidi, J. How

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTelecommunications linkComputer scienceComputer networkRelayScheduling (production processes)MultiplexingInterference (communication)TransmitterChannel (broadcasting)WirelessFrame (networking)Wireless networkReal-time computingTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

In this paper, we exploit cooperation between mobile stations and transmissions scheduling in a wireless network to enhance the downlink data rates to multiple single antenna users. The objective is to increase the number of multiplexed signals within a channel use close to what is offered in interference alignment approaches without incurring the corresponding system complexity. Specifically, we divide the communication process into two stages. In the first stage, the transmitter with multiple antennas using time division multiplexing sends to different users in their allocated time slots a number of messages equal to the number of transmit antennas in a MISO frame. In the next stage, every user and its assigned relays form an independent network, and relays using an amplify-and-forward scheme aid the recovery of messages by the user solving linear system of equations. All of these subnetworks utilize the available channel concurrently producing an acceptable level of multiple access interference (MAI) where the MAI is controlled using a clustering technique tailored to location of users and potential relays. Simulation results are provided demonstrating the capacity improvements in the proposed scheme and showing good performance in a low SNR region of operation.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.810
Threshold uncertainty score0.600

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 head0.040
GPT teacher head0.288
Teacher spread0.248 · 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
GenreMethods

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

Citations2
Published2014
Admission routes1
Has abstractyes

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