Measurement-Adaptive Cellular Random Access Protocols

TL;DR

This paper introduces measurement-adaptive protocols for cellular random access that optimize throughput and user drop rates by leveraging environment measurements, enabling fast, power-efficient adaptation in dynamic wireless networks.

Contribution

It proposes novel self-organizing protocols that adapt transmission parameters based on measurements to improve RACH performance in cellular networks.

Findings

Significant reduction in user dropping rate.

Protocols operate with minimal power and delay overhead.

Fast adaptation to changing network conditions.

Abstract

This work considers a single-cell random access channel (RACH) in cellular wireless networks. Communications over RACH take place when users try to connect to a base station during a handover or when establishing a new connection. Within the framework of Self-Organizing Networks (SONs), the system should self- adapt to dynamically changing environments (channel fading, mobility, etc.) without human intervention. For the performance improvement of the RACH procedure, we aim here at maximizing throughput or alternatively minimizing the user dropping rate. In the context of SON, we propose protocols which exploit information from measurements and user reports in order to estimate current values of the system unknowns and broadcast global action-related values to all users. The protocols suggest an optimal pair of user actions (transmission power and back-off probability) found by minimizing the drift of a certain function. Numerical results illustrate considerable benefits of the dropping rate, at a very low or even zero cost in power expenditure and delay, as well as the fast adaptability of the protocols to environment changes. Although the proposed protocol is designed to minimize primarily the amount of discarded users per cell, our framework allows for other variations (power or delay minimization) as well.