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Record W2033819375 · doi:10.1049/iet-com.2013.0027

Robust joint signal and interference alignment in cognitive radio networks with ellipsoidal channel state information uncertainties

2013· article· en· W2033819375 on OpenAlex
Shuai Ma, Huiqin Du, Tharmalingam Ratnarajah, Lei Dong

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

VenueIET Communications · 2013
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCognitive radioEllipsoidJoint (building)Computer scienceInterference (communication)Channel (broadcasting)Interference alignmentChannel state informationCo-channel interferenceSIGNAL (programming language)TelecommunicationsWirelessMIMOPhysicsEngineering

Abstract

fetched live from OpenAlex

The authors propose a distributed robust joint signal and interference alignment design for multiple‐input–multiple‐output cognitive radio (CR) networks where single primary link coexists with multiple secondary links. Considering two practical challenges of interference alignment: imperfect channel state information (CSI) and finite signal‐to‐noise ratio, the proposed scheme aims to minimise both the leakage of interference signals and that of the desired signals, while maintaining interference to the primary user below a permissible level. Under the assumption of the ellipsoidal CSI uncertainties, the joint worst‐case optimisation problem is decomposed and reformulated as semi‐definite programming form by using S ‐lemma, orthogonal relaxation and semi‐definite relaxation. Simulation results verify the effectiveness of the joint design, and robustness of the worst‐case design against channel uncertainties.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.033
GPT teacher head0.227
Teacher spread0.194 · 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