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DTSTAMP:20260521T193948Z
DESCRIPTION:Abstract\n\nThe use of cellular networks for massive machine-ty
 pe communications (mMTC) is attractive due to existing infrastructure. How
 ever\, the large number of user equipments (UEs) in mMTC poses challenges 
 to the random access channel (RACH) in terms of congestion and overloading
 . Existing RACH designs assume single-priority systems with uniform preamb
 le selection\, where each UE randomly selects a preamble from a set. To mi
 tigate congestion\, schemes such as UE backoff and preamble partitioning h
 ave been proposed\, deferring transmissions or assigning subsets of preamb
 les to different UE groups. However\, after the backoff period or within a
  subset\, UEs still select preambles uniformly\, limiting flexibility. As 
 a result\, these methods provide partial relief and fail to meet QoS requi
 rements under heavy network loads. To address this problem\, we consider n
 on-uniform preamble selection within each RACH slot and employ a multi-pri
 ority RACH system\, where UEs are categorized into multiple priority class
 es with different QoS requirements. The system behavior is captured throug
 h access patterns observed at the base station\, with non-uniform preamble
  selection probabilities providing greater flexibility in controlling acce
 ss rates across classes compared to existing methods. We develop an optimi
 zation problem that determines the preamble selection probabilities to max
 imize the RACH throughput of high-priority UEs while ensuring low-priority
  UEs achieve a minimum throughput threshold. Since the optimal solution re
 quires network load knowledge\, we propose two load estimators based on th
 e probability of observed access patterns over multiple RACH slots. An ana
 lytical framework is introduced to compute exact pattern probabilities\, f
 ollowed by a maximum likelihood estimator (MLE) and a reduced-complexity M
 LE (RCMLE). We then integrate the estimation and optimization frameworks\,
  conducting sensitivity analyses of throughput under estimation errors and
  investigating the impact of non-uniform preamble selection on estimation 
 accuracy.\n\nBuilding on this\, we reformulate the estimation problem as a
  multi-armed bandit (MAB) framework\, making it suitable for larger networ
 ks with stochastic UE behavior. When closed-form expressions of throughput
  metrics are not required\, we extend MAB to the optimization problem of d
 etermining preamble selection probabilities\, where empirical approximatio
 ns reduce complexity without requiring network load knowledge. We introduc
 e two action space (AS) formulations and adopt a cross-entropy (CE)-based 
 action selection policy. The framework is further extended to a deep-MAB (
 D-MAB) model that leverages neural networks for scalability in larger netw
 orks. To efficiently explore the resulting AS\, we propose a hierarchical 
 AS generation algorithm. Simulations demonstrate that the proposed framewo
 rks achieve superior performance compared to baselines. The non-uniform pr
 eamble selection scheme consistently improves H-UE throughput while mainta
 ining L-UE fairness\, outperforming uniform preamble selection\, access cl
 ass barring\, and preamble partitioning. The proposed estimators achieve h
 igh accuracy\, with the RCMLE running 46 times faster than the standard ML
 E while incurring only minor degradation under heavy overloading. Building
  on these results\, the MAB–based estimator demonstrates strong scalabilit
 y\, effectively handling larger networks with stochastic UE behavior while
  maintaining low mean absolute error and reducing computational complexity
 . Beyond estimation\, the MAB formulation for optimizing preamble selectio
 n probabilities perform within 5% of the optimal non-uniform solution whil
 e requiring fewer computational resources\, and the D-MAB extension furthe
 r improves scalability by efficiently exploring large ASs. Together\, thes
 e results confirm that the proposed frameworks outperform existing methods
  and provide a scalable solution for priority-aware RACH optimization in m
 MTC scenarios.\n
DTSTART:20260320T140000Z
DTEND:20260320T160000Z
LOCATION:Room 603\, McConnell Engineering Building\, CA\, QC\, Montreal\, H
 3A 0E9\, 3480 rue University
SUMMARY:PhD defence of Ahmed Elmeligy – Priority-Aware Random Access Optimi
 zation in Massive Machine Type Communications Using Non-Uniform Preamble S
 election
URL:/ece/channels/event/phd-defence-ahmed-elmeligy-pri
 ority-aware-random-access-optimization-massive-machine-type-371809
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