State-Based Random Access: A Cross-Layer Approach

TL;DR

This paper introduces state-based algorithms for dynamic control of random access networks, leveraging current network states like channel conditions and queue backlogs to improve performance.

Contribution

It presents novel cross-layer, state-dependent control algorithms for random access networks, demonstrating significant performance improvements over existing methods.

Findings

State-based algorithms outperform traditional control schemes.

Performance gains are consistent across various traffic scenarios.

Approximate network knowledge aids in selecting optimal control functions.

Abstract

In this paper, we propose novel state-based algorithms which dynamically control the random access network based on its current state such as channel states of wireless links and backlog states of the queues. After formulating the problem, corresponding algorithms with diverse control functions are proposed. Consequently, it will be shown that the proposed state-based schemes for control of the random access networks, results in significant performance gains in comparison with previously proposed control algorithms. In order to select an appropriate control function, performances of the state-based control algorithms are compared for a wide range of traffic scenarios. It is also shown that even an approximate knowledge of network statistics helps in selecting the proper state dependent control function.