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Record W2099982081 · doi:10.1109/icbn.2005.1589680

Increasing the spectral efficiency in microcell hotspots using M-ary CDMA: capacity analysis and simulation results

2005· article· en· W2099982081 on OpenAlexaff
Aminata A. Garba, Naveen Mysore, Jan Bajcsy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsMicrocellComputer scienceCode division multiple accessBit error rateComputer networkBase stationDecoding methodsPower controlWirelessThroughputSpectral efficiencyTransmission (telecommunications)Electronic engineeringAlgorithmTelecommunicationsPower (physics)Channel (broadcasting)Engineering

Abstract

fetched live from OpenAlex

We consider a single "hotspot" microcell where many wireless CDMA users transmit data concurrently using power control. As opposed to traditionally considered binary CDMA transmission or conventional M-ary CDMA, we explore the potential benefits of using generalized M-ary spreading codes. We compute the symmetric sum capacity when single-user detection and decoding are used at the receiver (base station) and observe that the use of appropriate M-ary CDMA can lead to more than threefold increase in transmission rates. It turns out that even using one extra CDMA chip level (i.e., zero) can potentially improve the aggregate data throughput in a microcell by 20-50 percent. Using the insights from this analysis, we generate pseudo-random M-ary spreading codes and explore turbo coded architecture for corresponding M-ary CDMA transmission. Simulation results indicate that the proposed system can support up to 60 to 70 users at bit error rates of 10/sup -4/, with a power control error of up to 1 dB at SNR's encountered in traditional microcells.

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)0.002
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 score0.119
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
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 head0.044
GPT teacher head0.307
Teacher spread0.263 · 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
Published2005
Admission routes1
Has abstractyes

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