PROJECT TITLE :

Intelligent Spectrum Management Based on Transfer Actor-Critic Learning for Rateless Transmissions in Cognitive Radio Networks - 2018

ABSTRACT:

This Project presents an intelligent spectrum mobility management scheme for cognitive radio networks. The spectrum mobility could involve spectrum handoff (i.e., the user switches to a brand new channel) or stay-and-wait (i.e., the user pauses the transmission for a while until the channel quality improves again). An optimal spectrum mobility management scheme needs to think about its long-term impact on the network performance, like throughput and delay, instead of optimizing solely the short-term performance. We use a Machine Learning theme, called the Transfer Actor-Critic Learning (TACT), for the spectrum mobility management. The proposed theme uses a comprehensive reward operate that considers the channel utilization factor (CUF), packet error rate (PER), packet dropping rate (PDR), and flow throughput. Here, the CUF is set by the spectrum sensing accuracy and channel holding time. The PDR is calculated from the non-preemptive M/G/1 queueing model, and also the flow throughput is estimated from a link-adaptive transmission theme, that utilizes the rateless (Raptor) codes. The proposed scheme achieves a better reward, in terms of the mean opinion score, compared to the myopic and Q-learning based spectrum management schemes.