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Record W3162584477 · doi:10.82308/41382

Cooperative communication for relay and multiple access channels: Design, optimization and performance analysis

2015· article· en· W3162584477 on OpenAlexfundno aff
Ahmad Abu Al Haija

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsnot available
FundersMcGill University
KeywordsRelayComputer networkComputer scienceTelecommunicationsChannel (broadcasting)

Abstract

fetched live from OpenAlex

In modern and future cellular networks such as Long-Term Evolution-Advanced (LTE-A)and 5G systems, cooperative communication can be deployed as an efficient method to improvesystem performance and meet the high-speed data transmission needs of multimediaservices. This thesis studies cooperation in the multiple access channel (MAC) setting,which includes the relay channel (RC) setting as a special case. These cooperative settingsapply to uplink transmission in cellular networks and form the basis to improve the widenetwork performance.The thesis proposes a time-division half-duplex scheme for the MAC with transmittercooperation (MAC-TC). The proposed scheme divides each transmission frame into threephases with variable durations. The two user equipments (UEs) partially exchange theirinformation in the first two phases, then cooperatively and coherently transmit to the basestation (BS) in the third phase, i.e., each UE performs a partial decode-forward (pDF) ofthe other UE information. The BS then performs joint decoding using the signals receivedin all three phases. After designing the scheme for the discrete memoryless channel (DMC),the thesis applies it to the Gaussian channel, derives its optimal resource allocations formaximum throughput and studies the outage performance over fading links. Comparedwith existing schemes, the proposed scheme improves throughput and outage performance,which makes it appealing for deployment in future cellular networks by utilizing the device-to-device (D2D) and infrastructure modes in the uplink transmission.The proposed scheme is near capacity-achieving when the cooperative links are strongerthan the direct links. Compared to existing schemes, this scheme achieves the same orbetter spectral efficiency with simpler signals and shorter decoding delay. By analyzingthe KKT optimality conditions for the maximum transmission rate, the thesis shows thatas the inter-user link qualities increase, the optimal scheme moves from no cooperation topartial then to full cooperation, in which the users fully exchange their information. Theoptimal power allocation to achieve the maximum throughput is analytically derived.The thesis further formulates the outage probability of the cooperative scheme overRayleigh fading channels assuming full channel state information (CSI) at receivers andlimited CSI at transmitters. Results show that cooperation can significantly reduce outageprobabilities and achieve the full diversity order despite additional outages at the UEs. Thethesis considers the RC setting in detail and analytically derives the outage performance for the coherent pDF relaying. Results show that the proposed scheme outperforms the existingschemes since it employs coherent transmission and joint decoding at the destination.Last, the thesis proposes a MAC with a joint source-destination cooperation (MAC-SDC)scheme that also engages the BS in cooperation by employing quantize-forward relaying.In this scheme, the users exchange their information not only through the cooperativelinks as in the MAC-TC, but also via the feedback links from the BS. Results showachievability improvement especially when the cooperative links are marginally strongerthan direct links. Moreover, for the RC setting, the thesis proves that the proposed schemeasymptotically achieves capacity by reaching the cutset bound as BS power increases.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.093
GPT teacher head0.290
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2015
Admission routes1
Has abstractyes

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