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Record W2964009355 · doi:10.1109/vtcfall.2015.7391126

SCMA for Open-Loop Joint Transmission CoMP

2015· article· en· W2964009355 on OpenAlexaff
Usa Vilaipornsawai, Hosein Nikopour, Alireza Bayesteh, Jianglei Ma

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsHuawei Technologies (Canada)
Fundersnot available
KeywordsComputer scienceRobustness (evolution)Channel state informationScheduling (production processes)Telecommunications linkSpectral efficiencyComputer networkOverhead (engineering)MultiplexingWirelessChannel (broadcasting)EngineeringTelecommunications

Abstract

fetched live from OpenAlex

Sparse Code Multiple Access (SCMA), a non-orthogonal multiple access scheme, has been introduced as a key 5G technology to improve spectral efficiency. In this work, we propose SCMA to enable open-loop coordinated multipoint (CoMP) joint transmission (JT). The scheme combines CoMP techniques with multi-user SCMA (MU-SCMA) in downlink. This scheme provides open-loop user multiplexing and JT in power and code domains, with robustness to mobility and low overhead of channel state information (CSI) acquisition. The combined scheme is called MU-SCMA- CoMP, in which SCMA layers and transmit power of multiple transmit points (TPs) are shared among multiple users while a user may receive multiple SCMA layers from multiple TPs within a CoMP cluster. The benefits of the proposed scheme includes: i) drastic overhead reduction of CSI acquisition, ii) significant increase in throughput and coverage, and iii) robustness to channel aging. Various algorithms of MU-SCMA-CoMP are presented, including the detection strategy, power sharing optimization, and scheduling. System level evaluation shows that the proposed schemes provide significant throughput and coverage gains over OFDMA for both pedestrian and vehicular users.

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 categoriesnone
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 score0.442
Threshold uncertainty score0.274

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 head0.059
GPT teacher head0.277
Teacher spread0.218 · 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
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

Citations15
Published2015
Admission routes1
Has abstractyes

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