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Record W7014867190

Reconfigurable Multiple Access Schemes for URLLC-mMTC Coexisting Networks

2023· dissertation· en· W7014867190 on OpenAlexafffund

Bibliographic record

VenueeScholarship@McGill (McGill) · 2023
Typedissertation
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsKey (lock)Scheme (mathematics)Data transmissionSet (abstract data type)Focus (optics)
DOInot available

Abstract

fetched live from OpenAlex

Since the fifth generation (5G) wireless networks, the focus of machine-type communication (MTC) has shifted to emerging applications such as the Industrial Internet of Things (IIoT) and smart cities. Massive MTC (mMTC) and ultra-reliable low-latency communication (URLLC), two distinct MTC service classes targeting scalability and reliability with low latency, are expected to be merged to support diversified and more stringent quality of service (QoS) requirements in MTC beyond 5G.With limited radio resources, supporting these contrasting requirements for URLLC-mMTC coexisting networks is challenging.Multiple access schemes should consider traffic characteristics and QoS requirements to adaptively prioritize devices with urgent traffic demands while providing massive connections.Additionally, as autonomous robots are increasingly utilized in various verticals, devices with mobility are likely to cooperate as clusters or operate in out-of-coverage areas.As a result, multi-hop relay connections should be enabled to address the coverage problem and support information exchanges in different network structures.In this thesis, we propose access schemes that combine grant-based (GB) and grantfree (GF) access techniques and equip massive multiple-input-multiple-output (mMIMO) antenna array at the access point (AP) to provide reliable and low-latency transmissions while increasing device connections, targeting different cases of URLLC-mMTC coexisting networks under manifold traffic and network configurations.First, we develop an access scheme to support scalable URLLC networks that contain a large number of devices with various latency requirements.The optimization problem is formulated to maximize the system throughput while minimizing packet delay violations.Considering that the objective function is hard to derive from unknown event-based traffic statistics and different delay constraints, both single-and multi-agent reinforcement-learning (RL)-based algorithms are designed to exploit the traffic correlation and delay status to grant resources for devices with high traffic demands and stringent delay requirements and let other devices contend for transmissions.Also, an action correction mechanism is introduced to ensure better convergence and safe operation of the RL agents.Second, we propose an access scheme for critical mMTC networks, which supports URLLC services for a varying fraction of massively connected devices, and each device has regular and delay-sensitive alarm traffic simultaneously instead of a homogenous traffic type.Additionally, unlike the previous single-hop network, we consider device clusters where devices transmit to the AP by two-hop relay connections through cluster leaders, and we allocate resources dynamically to mitigate inter-cluster interference.Then, the First and foremost, I would like to express my sincere gratitude to my supervisor, Professor Tho Le-Ngoc, for his tireless guidance,

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0010.002
Open science0.0040.000
Research integrity0.0010.001
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 head0.042
GPT teacher head0.290
Teacher spread0.247 · 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.

Study designNot applicable
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
Published2023
Admission routes2
Has abstractyes

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