Neural Network Cognitive Engine for Autonomous and Distributed Underlay Dynamic Spectrum Access
Fatemeh Shah-Mohammadi, Andres Kwasinski

TL;DR
This paper introduces a neural network-based cognitive engine for autonomous, distributed underlay dynamic spectrum access, enabling secondary users to predict and control interference on primary networks without information exchange.
Contribution
It presents a novel neural network cognitive engine that predicts primary link adaptation, allowing secondary users to manage interference and optimize throughput autonomously.
Findings
Accurately predicts primary link modulation and coding modes.
Maintains interference within prescribed limits.
Enables higher secondary user throughput.
Abstract
Two key challenges in underlay dynamic spectrum access (DSA) are how to establish an interference limit from the primary network (PN) and how cognitive radios (CRs) in the secondary network (SN) become aware of the interference they create on the PN, especially when there is no exchange of information between the two networks. These challenges are addressed in this paper by presenting a fully autonomous and distributed underlay DSA scheme where each CR operates based on predicting its transmission effect on the PN. The scheme is based on a cognitive engine with an artificial neural network that predicts, without exchanging information between the networks, the adaptive modulation and coding configuration for the primary link nearest to a transmitting CR. By managing the effect of the SN on the PN, the presented technique maintains the relative average throughput change in the PN within…
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