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Record W2158711336 · doi:10.1109/lcomm.2008.080708

Adaptive MIMO Beamforming Algorithm Based on Gradient Search of the Channel Capacity in OFDM-SDMA Systems

2008· article· en· W2158711336 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Communications Letters · 2008
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsOrthogonal frequency-division multiplexingBeamformingSpace-division multiple accessComputer scienceMIMOMIMO-OFDMWSDMAAlgorithmTelecommunications linkPrecodingChannel (broadcasting)MultiplexingWireless broadbandAdaptive beamformerWirelessElectronic engineeringTelecommunicationsWireless networkEngineering

Abstract

fetched live from OpenAlex

This letter proposes an adaptive beamforming algorithm for uplink access in broadband wireless networks employing orthogonal frequency-division multiplexing with space-division multiple access (OFDM-SDMA) technologies. The proposed algorithm seeks, iteratively, the optimal transmit weight vectors that directly maximize the OFDM-SDMA channel capacity for each user in the system, using gradient search of the channel capacity. The analysis and simulation show that the capacity of OFDM-SDMA systems with the transmit weights obtained using the proposed adaptive beamforming algorithm is substantially higher than the one based on conventional approaches such as eigen-beamforming.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.856
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0080.001
Research integrity0.0000.001
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 head0.124
GPT teacher head0.281
Teacher spread0.157 · 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