A novel approach to managing a fully distributed cognitive radio network (CRN) is presented. This approach builds on the concept of history‐based spectrum access, in which cognitive base stations (BSs) independently estimate the system load using history records and adaptively swap their occupied spectrum bands to ensure allocation fairness and high overall throughput. In addition, cognitive BSs monitor primary user (PU) behavior in order to avoid interfering with active PUs. In this work, we address two issues that afflict history‐based access: the first is the high cost of the sensing devices needed at each cognitive BS to be able to independently draw conclusions about the status of the CRN and the second is the unreliability inherent in practical sensing hardware (such as energy detectors). Simulation results show that the proposed technique manages to solve the two abovementioned issues without any noticeable drop in performance and without sacrificing the distributed nature of the protocol.

We present the first fully automated compiler-runtime system that successfully translates and executes OpenMP shared-address-space programs on laboratory-size clusters, for the complete set of regular, repetitive applications in the NAS Parallel Benchmarks. We introduce a hybrid compiler-runtime translation scheme. Compared to previous work, this scheme features a new runtime data flow analysis and new compiler techniques for improving data affinity and reducing communication costs. We present and discuss the performance of our translated programs, and compare them with the performance of the MPI, HPF and UPC versions of the benchmarks. The results show that our translated programs achieve 75% of the hand-coded MPI programs, on average.