Cognitive Radio : A Solution for Issues in Network Convergence

While planning for strategic communication no nation or organisation can ignore the problems of dynamic radio resource allocation and interference. Among lot number of technologies as a solution Cognitive Radio (CR) is the best one, which helps to overcome the problem of interference, and also allow efficient and dynamic radio resource allocation. Applying Cognitive Radio Networks (CRN) to strategic communications as a solution, this roadmap has been proposed by nations and organizations. For fast network deployment it is mandatory to overcome traffic problem in spectrum, increase communication reshaping mechanism and also strategic radio should act as multi-functional Radio-Frequency(RF) Unit, these are the themes for which CRN is the choice. Wireless Sensor Networks(WSNs) present day have many challenges, if it get clubbed with CR, many problems can be solved. Theme of our research is to empower CRN, so that it will help to solve above problems and also help to manage traffic in network convergence services without ignoring or compromising security

Telekomunikacja i Techniki Informacyjne, 2012, nr 1-2

kwartalni

Quantum Reinforcement Learning for Dynamic Spectrum Access in Cognitive Radio Networks

Abstract This thesis proposes Quantum Reinforcement Learning (QRL) as an improvement to conventional reinforcement learning-based dynamic spectrum access used within cognitive radio networks. The aim is to overcome the slow convergence problem associated with exploration within reinforcement learning schemes. A literature review for the background of the carried out research work is illustrated. Review of research works on learning-based assignment techniques as well as quantum search techniques is provided. Modelling of three traditional dynamic channel assignment techniques is illustrated and the advantage characteristic of each technique is discussed. These techniques have been simulated to provide a comparison with learning based techniques, including QRL. Reinforcement learning techniques are used as a direct comparison with the Quantum Reinforcement Learning approaches. The elements of Quantum computation are then presented as an introduction to quantum search techniques. The Grover search algorithm is introduced. The algorithm is discussed from a theoretical perspective. The Grover algorithm is then used for the first time as a spectrum allocation scheme and compared to conventional schemes. Quantum Reinforcement Learning (QRL) is introduced as a natural evolution of the quantum search. The Grover search algorithm is combined as a decision making mechanism with conventional Reinforcement Learning (RL) algorithms resulting in a more efficient learning engine. Simulation results are provided and discussed. The convergence speed has been significantly increased. The beneficial effects of Quantum Reinforcement Learning (QRL) become more pronounced as the traffic load increases. The thesis shows that both system performance and capacity can be improved. Depending on the traffic load, the system capacity has improved by 9-84% from a number of users supported perspective. It also demonstrated file delay reduction for up to an average of 26% and 2.8% throughput improvement