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Record W4386883085 · doi:10.1109/lwc.2023.3317440

Beamforming Design for Novel Relay-Assisted Multi-User Multi-Tag Symbiotic Radios

2023· article· en· W4386883085 on OpenAlexaff
Zhaoxi Fang, Zongda Wu, Wei Ni, Xin Wang, Ekram Hossain

Bibliographic record

VenueIEEE Wireless Communications Letters · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Manitoba
FundersNatural Science Foundation of Zhejiang Province
KeywordsBeamformingRelayComputer scienceComputer networkTelecommunications

Abstract

fetched live from OpenAlex

This letter considers a novel relay-assisted, multi-user multi-tag uplink symbiotic radio system, in which a relay node forwards multiple users’ signals to a base station (BS), meanwhile powering multiple backscatter tags. We put forth a new joint transmit and receive beamforming design problem to minimize the relay’s transmit power while satisfying the signal-to-interference-plus-noise ratio (SINR) constraints of both the primary users and the tags. An alternating optimization algorithm based on semi-definite programming (SDP) and zero-forcing is proposed to solve this non-convex power minimization problem. The proposed approach is also extended to jointly optimize the transmit/receive beamforming to maximize the minimal SINR of the tags under a given relay power budget. Numerical results show that the proposed beamforming designs significantly outperform the benchmarks.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.420
Threshold uncertainty score1.000

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 head0.074
GPT teacher head0.287
Teacher spread0.213 · 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.

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

Citations3
Published2023
Admission routes1
Has abstractyes

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