7 research outputs found
Design, Analysis, Implementation and Evaluation of Real-time Opportunistic Spectrum Access in Cloud-based Cognitive Radio Networks
Opportunistic spectrum access in cognitive radio network is proposed for remediation of spectrum under-utilization caused by exclusive licensing for service providers that are intermittently utilizing spectrum at any given geolocation and time. The unlicensed secondary users (SUs) rely on opportunistic spectrum access to maximize spectrum utilization by sensing/identifying the idle bands without causing harmful interference to licensed primary users (PUs). In this thesis, Real-time Opportunistic Spectrum Access in Cloud-based Cognitive Radio Networks (ROAR) architecture is presented where cloud computing is used for processing and storage of idle channels. Software-defined radios (SDRs) are used as SUs and PUs that identify, report, analyze and utilize the available idle channels. The SUs in ROAR architecture query the spectrum geolocation database for idle channels and use them opportunistically. The testbed for ROAR architecture is designed, analyzed, implemented and evaluated for efficient and plausible opportunistic communication between SUs
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Multi-channel Stochastic Resource Allocation and Dynamic Access Scheduling
Modern communication systems often have the ability to transmit signals on multiple communication mediums (e.g., RF, visible light) or interfaces (e.g., MAC layer protocols) at the same time. While each channel has different characteristics, a centralized controller with channel condition information will be able to schedule the resource allocated to each channel to achieve various optimization criteria. In this thesis, we focus on two usage scenarios: Indoor hybrid free space optical (FSO)-WiFi femtocells and multi-channel satellite communication (SATCOM). For the Indoor hybrid free space optical (FSO)-WiFi femtocells, a smart network controller is designed to determine which channel/interface to use for a specific user/time slot combination to maximize some pre-specified objectives such as load balance. In particular, this problem is modeled as a dynamic scheduling problem, which is a Markov decision process problem that is solved using a deep-Q reinforcement learning (RL) framework. For the SATCOM scenario, a smart network controller is proposed to transmit information securely on different channels to mitigate jamming and eavesdropping attacks. The proposed approaches combine elements from game theory and information theory to provide provably secure protocols from an information theoretic viewpoint
Bowdoin Orient v.116, no.1-27 (1986-1987)
https://digitalcommons.bowdoin.edu/bowdoinorient-1980s/1007/thumbnail.jp