Scholarlink

<p>This paper provides a Markov Chain model for analysing Successive Interference Cancellation (SIC) in IoT networks with pure ALOHA cases of bursty traffic. The attractiveness of pure Aloha as a random access method for large scale IoT networks stems from the ease of implementation and very low communication overheads. However, the network performance degradation resulting from high density deployments and bursty traffic patterns can be attributed to the high occurrence rates of packet collisions and subsequent long delays. Recent advances in successive interference cancellation (SIC) have provided improved methods for resolving collisions. However, the majority of the literature assumes poisson traffic, idealized decoding or limited performance metrics which do not capture realistic characteristics of IoT traffic. This paper aims to address these challenges through development of a Markov chain-based performance modeling framework for SIC enabled pure Aloha under bursty traffic patterns. The arrival process of packets is modeled as a markov modulated poisson process (MMPP), and multi-level SIC is applied to decode overlapping packet transmissions. Through application of the above-mentioned framework, a wide range of performance metrics may be evaluated including system throughput, packet success probability, average packet delay, queue length evolution, and SIC decoding success rate. Simulation results indicate that the proposed framework provides a significant improvement in terms of throughput and packet success probability over both conventional Pure ALOHA and slotted ALOHA systems with regard to delay and queue congestion, especially at higher burstiness levels and heavier traffic loads. Additionally, the results of this study demonstrate that moderate SIC depths represent an efficient trade off between increased performance gains and increased decoding complexity. Therefore, this research establishes a practical and replicable basis for studying and optimizing SIC assisted random access protocols in future low latency and highly reliable IoT networks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Markov Chain-Based Performance Modeling of Successive Interference Cancellation in Pure ALOHA for IoT Networks Under Bursty Traffic Conditions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月12日

查看全文
  • Mahesh Manchanda,
  • Keshav Kaushik,
  • Renu Kumawat

摘要

This paper provides a Markov Chain model for analysing Successive Interference Cancellation (SIC) in IoT networks with pure ALOHA cases of bursty traffic. The attractiveness of pure Aloha as a random access method for large scale IoT networks stems from the ease of implementation and very low communication overheads. However, the network performance degradation resulting from high density deployments and bursty traffic patterns can be attributed to the high occurrence rates of packet collisions and subsequent long delays. Recent advances in successive interference cancellation (SIC) have provided improved methods for resolving collisions. However, the majority of the literature assumes poisson traffic, idealized decoding or limited performance metrics which do not capture realistic characteristics of IoT traffic. This paper aims to address these challenges through development of a Markov chain-based performance modeling framework for SIC enabled pure Aloha under bursty traffic patterns. The arrival process of packets is modeled as a markov modulated poisson process (MMPP), and multi-level SIC is applied to decode overlapping packet transmissions. Through application of the above-mentioned framework, a wide range of performance metrics may be evaluated including system throughput, packet success probability, average packet delay, queue length evolution, and SIC decoding success rate. Simulation results indicate that the proposed framework provides a significant improvement in terms of throughput and packet success probability over both conventional Pure ALOHA and slotted ALOHA systems with regard to delay and queue congestion, especially at higher burstiness levels and heavier traffic loads. Additionally, the results of this study demonstrate that moderate SIC depths represent an efficient trade off between increased performance gains and increased decoding complexity. Therefore, this research establishes a practical and replicable basis for studying and optimizing SIC assisted random access protocols in future low latency and highly reliable IoT networks.