Practical implementation of sensing receiver in cognitive radios

Abstract

Due to the underutilization of scarce radio spectrum resources, there is a major shift from an exclusive use model to a spectrum sharing model. This model has been successful in unlicensed bands where wireless standards such as Bluetooth and WLAN IEEE802.11 have flourished. In order to extend this model to licensed bands, a smart radio is required. The cognitive radio is a smart radio that leverages its knowledge of the local environment in order to operate with acceptable interference to primary users in the band. The brain of the cognitive radio is encapsulated in the sensing receiver. Using the sensing receiver, the cognitive radio observes the activity in a band and adapts its communication link parameters so that the interference is minimal. The sensing receiver attributes include wideband operation and agility. The receiver must be able to make fast decisions accurately of the spectrum activities. To date, a practical sensing receiver has been a major stumbling block in the realization of cognitive radio. In our research, we demonstrate that a practical sensing receiver is in fact feasible. Our research objectives are: (1) to develop a sensing receiver based on current technology, and (2) to augment our solution with an associated algorithm that makes fast and accurate sensing possible. Our proposed solution is a Dedicated Sensing Receiver (DSR) that employs an adaptive algorithm to enable the cognitive radio to effectively operate in a competitive environment. The DSR algorithm divides the frequency band into coarse sensing bins which are further divided into fine sensing bins. We derive a mathematical model that is optimized for minimizing sensing time. The optimization algorithm generates the optimum number of coarse and fine sensing bins based on environment conditions and the radio implementation. Our results illustrate the DSR’s ability to reduce open spectrum recognition time by over 10X while operating over a broad range of frequencies. This drastic improvement opens the door for the cognitive radio technology to be used in commercial applications in the near future and more importantly, it allows for the cognitive radio to succeed in time sensitive applications such as voice or video. We have also developed an end to end simulation platform to model the algorithm and the dedicated sensing receiver performance.