Adaptive channel resource allocation improves wireless access performance
QoS-Aware RACH Preamble Slicing via Quota-Projected Branching Deep Reinforcement Learning
Networking and Internet Architecture
Summary
Wireless devices need a way to share access to communication channels without causing delays or collisions. The paper presents a smart method that uses artificial intelligence to split limited access resources among different types of traffic, like urgent and non-urgent data. Their approach ensures fair and efficient use of the channels, reducing connection failures and delays. They tested their method against others and found it generally performs better, especially when the network is busy.
random access channel (RACH)quality of service (QoS)deep reinforcement learningbranching dueling Double DQNpreamble allocationresource schedulingcontention-based accesstwo-step and four-step random accesscollision managementaccess delay
Authors
Jiulin Guo, Jiahan Xu, Jiashuo Zhang, Heng Yang, Yizhen Sun, Yutong Xie, Shanshan Li, Zhenyu Liu, Lei Zhang
Abstract
Quality-of-service (QoS)-aware random access requires adaptive allocation of a finite random access channel (RACH) preamble budget across heterogeneous traffic and access procedures. This paper proposes QP-BD3QN-RACH, a quota-projected branching deep reinforcement learning controller for mixed two-step (2RA) and four-step (4RA) contention-based random access. Four action branches correspond to the delay-sensitive and delay-tolerant 2RA/4RA preamble pools. A branching dueling Double DQN selects pool-specific multipliers, and deterministic quota projection converts them to nonnegative integer allocations that preserve the preamble budget. With five actions per branch, the controller represents 625 pre-projection branch-action tuples using 20 branch-action outputs. Evaluation covers five arrival loads, cross-method comparison under nominal seed 42, six-seed sensitivity of QP-BD3QN-RACH, and targeted ablations. Across the five-load grid, its mean direction-aligned differences relative to four comparators are positive: 5.74 to 8.21 percentage points for success/collision, 1.23 to 1.92 percentage points for fallback, 0.35 to 0.68 percentage points for blocking, and 0.128 to 0.456 decision intervals for successful-access delay. Load-wise results exhibit metric-dependent tradeoffs, particularly under intermediate and overload conditions.