Secure Cloud Controlled Software Defined Radio Network For Bandwidth Allocation

The purpose of this research is to investigate the impact of mobility of wireless devices for opportunistic spectrum access and communications using National Instrument Universal Software Radio Peripherals devices. The overall system utilizes software defined radio networks for frequency allocation, cloud connectivity to maintain up-to-date information, and moving target defense as a security mechanism. Each USRP device sends its geolocation to query the spectrum database for idle channels. The cloud cluster was designed for complex data storage and allocation using a smart load balancer to offer ultra-security to users. This project also explores the advantages of data protection and security through moving target defense. To achieve this, the system would use an array of antennas to split the data into different parts and transmit them across separate antennas. This research provides the design to each of the mentioned projects for the implementation of a fully developed system

Adaptive Threshold-Based RF Spectrum Scanning Through Joint Energy and Bandwidth Detection with USRPs in Cognitive Sensor Networks for ROAR Architecture

Opportunistic spectrum access in cognitive radio networks is regarded as an emerging technology for efficient utilization of under utilized of idle radio frequency spectrum. For opportunistic spectrum access, wireless devices are required to identify idle spectrum through spectrum sensing. The performance study of existing spectrum sensing algorithms often overlooks bandwidth of the detected signal while detecting the signal using peak of the energy spectrum that crosses the pre-specified threshold. This results in high false alarm probability. In this paper, we evaluate an adaptive threshold based RF spectrum sensing approach using USRP Software Defined Radio (SDR) for real-time opportunistic spectrum access in cloud based cognitive radio networks (ROAR) architecture where both signal energy and band-width of the signal are taken into account. We evaluate the performance of the proposed approach using probability of misdetection and false alarms metrics. The proposed approach can be particularized to a scenario with energy based detection or bandwidth based detection. The proposed approach is illustrated through numerical results obtained from experiments