New Architecture for Dynamic Spectrum Allocation in Cognitive Heterogeneous Network using Self Organizing Map

TL;DR

This paper proposes a hybrid hierarchical architecture using self-organizing maps for dynamic spectrum allocation in cognitive heterogeneous networks, aiming to optimize radio resource use and reduce interference.

Contribution

It introduces a novel hybrid architecture combining centralized and distributed approaches with a Hebbian learning-based framework for spectrum sensing and allocation.

Findings

Enhanced spectrum efficiency demonstrated

Reduced interference through dynamic interaction

Maintained QoS in the network

Abstract

This paper introduces the Hybrid Architecture of Dynamic Spectrum Allocation in the hierarchical network combining centralized and distributed architecture to get optimum allocation of radio resources. It can limit the interference by interacting dynamically and enhance the spectrum efficiency while maintaining the desired QoS in the network. This paper presented dynamic framework for the interaction. The proposed architecture employed simple learning rule based on hebbian learning for sensing the primary network and allocating the spectrum.