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Record W2170089590 · doi:10.1109/vtc.2002.1002801

A time space division multiple access (TSDMA) protocol for multihop wireless networks with access points

2003· article· en· W2170089590 on OpenAlexaff
Chi‐Hsiang Yeh, Hairong Zhou, H.T. Mouftah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsQueen's University
Fundersnot available
KeywordsTime division multiple accessComputer networkComputer scienceChannel access methodWireless ad hoc networkWireless networkMulti-frequency time division multiple accessAccess controlThroughputMedia access controlMultiple Access with Collision Avoidance for WirelessHidden node problemWirelessChannel (broadcasting)Wi-Fi arrayTelecommunicationsOrthogonal frequency-division multiplexingMIMO-OFDM

Abstract

fetched live from OpenAlex

We propose time division multiple access with circular reservation (TDMA/CR), a time/space division multiple access (TSDMA)-based medium access control (MAC) protocol for wireless mobile networks with control units, including wireless LANs, cellular networks with ad hoc relaying capability, and ad hoc networks with access points or clusterheads. Different from the MAC protocol of IEEE 802.11, TDMA/CR is centralized and can utilize the computation capability of base stations or access points to increase network throughput, reduce latency, and provide QoS guarantees. We evaluate the performance of TDMA/CR and show that the utilization achievable by the wireless-tree (or splitting) channel access mechanisms of TDMA/CR is about 40% to 46% and the channel access delay is small and bounded. Moreover, TDMA/CR can achieve considerably higher throughput (e.g., by a factor of about 3 or higher) due to its support for variable-radius transmissions in ad hoc wireless networks.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.028
GPT teacher head0.311
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2003
Admission routes1
Has abstractyes

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