Self-Organizing mmWave Networks : A Power Allocation Scheme Based on Machine Learning

TL;DR

This paper proposes a scalable, self-organizing power allocation scheme for ultra dense mmWave networks using clustering and Q-learning, ensuring consistent QoS across various network sizes.

Contribution

It introduces a novel self-organizing power allocation method combining clustering and reinforcement learning for dense mmWave networks.

Findings

Ensures QoS for all users regardless of network size

Demonstrates scalability and self-organizing capabilities

Improves network management in ultra dense deployments

Abstract

Millimeter-wave (mmWave) communication is anticipated to provide significant throughout gains in urban scenarios. To this end, network densification is a necessity to meet the high traffic volume generated by smart phones, tablets, and sensory devices while overcoming large pathloss and high blockages at mmWaves frequencies. These denser networks are created with users deploying small mmWave base stations (BSs) in a plug-and-play fashion. Although, this deployment method provides the required density, the amorphous deployment of BSs needs distributed management. To address this difficulty, we propose a self-organizing method to allocate power to mmWave BSs in an ultra dense network. The proposed method consists of two parts: clustering using fast local clustering and power allocation via Q-learning. The important features of the proposed method are its scalability and self-organizing capabilities, which are both important features of 5G. Our simulations demonstrate that the introduced method, provides required quality of service (QoS) for all the users independent of the size of the network.