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Record W2969291518 · doi:10.1109/twc.2019.2935202

Perfect Self-Interference Cancellation Based on Mode-Switching for Differential Channel-Unaware Two-Way Relay Networks

2019· article· en· W2969291518 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2019
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRelayComputer scienceSingle antenna interference cancellationPairwise error probabilityChannel (broadcasting)Relay channelTopology (electrical circuits)Interference (communication)Antenna (radio)TelecommunicationsAlgorithmElectronic engineeringFadingMathematicsPower (physics)PhysicsEngineering

Abstract

fetched live from OpenAlex

We consider channel-unaware two-way relay networks in which two single-antenna nodes exchange information through multiple single-antenna half-duplex amplify-and-forward relays. For these networks, we develop a novel self-interference (SI) cancellation scheme that does not invoke channel information neither at the relays nor at the nodes. Unlike its previous counterparts, the scheme proposed herein enables perfect SI cancellation even when the relays have a single antenna each. This was not possible previously without employing an even number of antennas at each relay. In the first phase of the proposed scheme, the network is operated in a one-way relaying mode, whereas in the second phase it is operated in a two-way relaying mode. In the latter mode, the vectors received by the nodes at the end of the first phase are used to completely eliminate SI, without estimating the channel as in existing methodologies. To analyze the effectiveness of the proposed scheme, we derive upper bounds on the pairwise error probability for the Alamouti and SP(2) codes. Using these bounds, we investigate the dependence of the system performance on the data rate. In addition, we show that for all data rates, the proposed scheme outperforms previously proposed ones. Theoretical results are confirmed by simulations.

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 categoriesMeta-epidemiology (narrow)
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 score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.016
GPT teacher head0.250
Teacher spread0.234 · 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