Throughput Maximization In Noma Enabled Cognitive Iot Systems

Abstract

Computer And Wireless Networks Have Undergone A Paradigm Shift With The Advent Of Big Data. One Of The Most Serious Challenges Of The Present Day Networks In The Medium Access Control Domain Is The Rapidly Increasing Number Of Users And Limited Bandwidth. Hence Multiplexing Techniques Which Would Utilize The Available Bandwidth Resources Effectively Are Being Searched For. Non-Orthogonal Multiple Access (NOMA) Has Emerged As One Of The Promising Options For Future Generation Multiplexing Solutions. In NOMA, The Signals Are Separated In The Power Domain Which Allows The Simultaneous Transmission At The Same Time And Frequency Range Thereby Augmenting The Spectral Efficiency. However, Due To Multipath Fading Effects, Power Level Separation Is Often Complex To Implement In Real Time Situations. In This Paper, NOMA Based Transmission Mechanism Has Been Proposed Which Employs Decision Feedback Equalization Along With Successive Signal Detection Based On Power Level Separation. To Emulate A Real Life Scenario, Different Path Gains Have Been Considered For Near, Average And Far Users In A Typical Cellular Network. The Performance Of The Proposed System Has Been Evaluated In Terms Of The Bit Error Rate Both Without And With The Proposed System Being Used. It Has Been Shown That Employing The Proposed System, The Error Performance Of Average And Far Users Almost Coincide With That Of The Near Users Thereby Indicating The Fact That All Users In The NOMA Based System Can Be Detected With Almost Identical Accuracy.

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