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Record W3187882620 · doi:10.1109/icc42927.2021.9500428

RIS-Assisted Spatial Modulation and Space Shift Keying for Ambient Backscattering Communications

2021· article· en· W3187882620 on OpenAlex
Anirban Bhowal, Sonia Aı̈ssa, Rakhesh Singh Kshetrimayum

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsKeyingWirelessComputer scienceElectronic engineeringModulation (music)Bit error rateTelecommunicationsElectrical engineeringChannel (broadcasting)EngineeringPhysicsAcoustics

Abstract

fetched live from OpenAlex

In wireless communications, reconfigurable intelligent surfaces (RIS) are emerging as a promising technology that is made possible by the advent of software controlled metamaterial sheets for controlling the wireless channels dynamically. In future applications, IoT devices will have small sizes and limited power supply. To make these devices spectrally and energy efficient in accordance with the advanced 5G and 6G specifications, we propose ambient backscattering (ABSc) technique along with spatial modulation (SM) and space shift keying (SSK) for data transfer assisted with RIS. We also conduct a thorough performance analysis of these schemes in terms of outage probability, and bit error rate, validated by Monte-Carlo simulations, and provide comparative results that illustrate the merits of the proposed techniques. In particular, it is shown that RIS-empowered SM and SSK along with ABSc perform much better than conventional communications.

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: Methods · Consensus signal: none
Teacher disagreement score0.772
Threshold uncertainty score0.492

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 communication0.0000.000
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 head0.031
GPT teacher head0.269
Teacher spread0.238 · 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