Optimal Multichannel Slotted ALOHA for Deadline-Constrained Unicast Systems

Zhang, Yijin; Guan, Fengyu; Lo, Yuan-Hsun; Li, Jun

doi:10.1109/jsyst.2018.2805909

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Due to the great success of ALOHA and CSMA, it deserves to investigate how they work in the delayconstrained setting. There are some existing works on delay-constrained ALOHA [15], [43], [44] and delayconstrained CSMA [45], [46]. Deng et al in [15] analyzed the asymptotic performance of ALOHA system for framesynchronized delay-constrained traffic pattern.…”

Section: Introductionmentioning

confidence: 99%

“…Deng et al in [15] analyzed the asymptotic performance of ALOHA system for framesynchronized delay-constrained traffic pattern. Zhang et al in [43], [44] studied the system throughput and optimal transmission probability of ALOHA under the saturated delay-constrained traffic. Campolo et al in [45] analyzed the p-persistent CSMA for broadcasting delay-constrained traffic.…”

Section: Introductionmentioning

confidence: 99%

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Comparing Delay-Constrained ALOHA and CSMA: A Learning-Based Low-Complexity Approximate Approach

Deng

Zhang

et al. 2021

IEEE Open J. Commun. Soc.

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Supporting delay-constrained traffic becomes more and more critical in multimedia communication systems, tactile Internet, networked control systems, and cyber-physical systems, etc. In delay-constrained traffic, each packet has a hard deadline. When it is not delivered before the hard deadline, it becomes useless and will be removed from the system. This feature is completely different from that of traditional delay-unconstrained traffic and brings new challenge to network protocol design. In this work, we study the widely-used (slotted) ALOHA and CSMA wireless access protocols but under the new delay-constrained setting. Our goal is to compare delay-constrained ALOHA and CSMA for different system settings and thus give network operators guidelines on protocol selection. We use two Markov chains to analyze delay-constrained ALOHA and CSMA, respectively. However, the number of states of Markov chains increases exponentially with respect to the number of users in the network. Therefore, we can only compare the exact performance of delay-constrained ALOHA and CSMA for small-scale networks. To address the curse of dimensionality, we design a single-user parameterized ALOHA (resp. CSMA) system, where the parameters are to be learned to approximate the original multi-user ALOHA (resp. CSMA) system. In addition, our low-complexity approach preserves the Markov-chain structure of the systems and thus enables us to compute some other interested performance metrics such as average delivery time. We use our low-complexity approach to reveal the conditions under which ALOHA (resp. CSMA) outperforms CSMA (resp. ALOHA) in the delay-constrained setting via extensive simulations.INDEX TERMS Delay-constrained communications, ALOHA, CSMA, Markov chain, machine learning.

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