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Record W2167192348 · doi:10.1109/icc.2008.899

Optimum Power Distribution for Uplink Channel in a Cooperative Wireless CDMA Network

2008· article· en· W2167192348 on OpenAlexaff
B. Wang, Dongmei Zhao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer networkTelecommunications linkComputer scienceBase stationRelayNetwork packetTransmission (telecommunications)WirelessTransmitter power outputRSSChannel (broadcasting)Interference (communication)Power (physics)TelecommunicationsTransmitter

Abstract

fetched live from OpenAlex

This paper studies power distribution in the uplink channel of a wireless CDMA network, where peer stations cooperatively relay traffic for each other, and the destination, i.e., the base station (BS), combines signals received from both the source station (SS) and the relay station (RS). Two different cooperative schemes are presented. In the first scheme, the SS transmits packets in all time slots, and two RSs forward received signals from the SS to the BS in odd and even time slots alternatively. In the second scheme, the SS transmits in the odd time slots, and a single RS receives from the SS in the odd time slots and forwards to the BS in the even time slots. Two forwarding techniques, i.e., decode-and- forward (DF) and amplify-and-forward (AF), are considered. For each of the cooperation scheme and forwarding technique combinations, an optimization problem is formulated with an objective to minimize the total transmission power subject to the average transmission rate and signal-to-interference-plus- noise ratio (SINR) requirement of the user traffic. Our results show that by appropriately selecting the RSs and forwarding techniques, cooperative communications in a CDMA network can significantly reduce the communication outage probability and save total transmission power.

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: Empirical · Consensus signal: none
Teacher disagreement score0.978
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.041
GPT teacher head0.275
Teacher spread0.233 · 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

Citations16
Published2008
Admission routes1
Has abstractyes

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