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Record W4407830027 · doi:10.1109/jsac.2025.3543522

On Precoding for Optical Wireless MISO Broadcasting—An Information Theoretic Perspective

2025· article· en· W4407830027 on OpenAlexaff
Zhenyu Zhang, Anas Chaaban, Steve Hranilovic

Bibliographic record

VenueIEEE Journal on Selected Areas in Communications · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaMcMaster University
Fundersnot available
KeywordsPrecodingComputer sciencePerspective (graphical)Broadcasting (networking)WirelessComputer networkTelecommunicationsZero-forcing precodingMIMOChannel (broadcasting)Artificial intelligence

Abstract

fetched live from OpenAlex

The multiple-input-single-output (MISO) Gaussian broadcast channel (BC) under per-emitter peak-amplitude constraint arises in naturally indoor optical wireless communication (OWC) applications due to the availability of many luminaires and simple photo receivers. In this paper, we study the achievable rate region of the two-user peak-amplitude constrained MISO BC with the goal of increasing the rate region beyond the one achieved using zero-forcing (ZF) precoding. To this end, a novel precoding scheme is proposed that combines a type of discrete-state dirty paper channel (DPC) studied recently and ZF precoding for the MISO BC, termed the DPC-ZF precoding. We derive the achievable rate region of the proposed precoding scheme, and compare it with the one achieved by ZF precoding numerically by simulating a two-user indoor OWC broadcasting system. Numerical results show a significant improvement in the achievable rate region through DPC-ZF precoding compared to ZF precoding, implying the existence of more powerful interference management schemes for OWC broadcasting.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.022
GPT teacher head0.315
Teacher spread0.293 · 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
Published2025
Admission routes1
Has abstractyes

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