A Proactive Machine Learning Based Neural-chaos Architecture For Physical Layer Security Through Papr Reduction

Abstract

Proactive Physical Layer Security Has Evolved With The Emergence Of Web 3.0 Which Shifts Control From Centralized Cloud Servers Toward Distributed Edge Nodes, Blockchain Miners/validators, And Peer-to-peer Communication. Many Of These Nodes Rely On Low-power Radios And Operate In Highly Dynamic Environments. One Of The Most Pressing Challenges Is To Implement Proactive Security Through Peak To Average Power Ration (PAPR) Reduction. PAPR Reduction Techniques Such As Clipping, Selective Mapping, And Tone Reservation Ensure That Edge Devices Handle OFDM Waveforms Efficiently, Allowing Decentralized Nodes To Participate Reliably In Consensus Protocols And Data Exchange Without Frequent Downtime Or Excessive Energy Consumption. One Of The Major Challenges That Multiplexed Data Suffers From Is High Value Of PAPR Which Make Data Transmission Perceptible. The Proposed Work Uses A Neural Selective Mapping (N-SLM) Technique And Attains Lower PAPR Compared To Previously Existing Work, Thereby Increasing The Security Of Distributed Wireless Networks.

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