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—In the Software Radio context, the parametrization is becoming an important topic especially when it comes to multi-standard designs. This paper capitalizes on the Common Operator technique to present new common structures for the FFT and FEC decoding algorithms. A key benefit of exhibiting common operators is the regular architecture it brings when… (More)
In the Software Radio context, the parametrization is becoming an important topic especially when it comes to multi-standard designs. This paper capitalizes on the Common Operator technique to present a new common structure for the FFT and Viterbi algorithms. A key benefit of exhibiting common operators is the regular architecture it brings when implemented… (More)
In cognitive radio, the secondary users are able to sense the spectral environment and use this information to opportunistically access the licensed spectrum in the absence of the primary users. In this paper, we present an experimental study that evaluates the performance of two different spectrum sensing techniques to detect primary user signals in real… (More)
This paper proposes a flexible architecture for the FFT and Viterbi algorithms based on the Common Operator (CO) technique. The FFT and Viterbi structural similarities are investigated to build a common architecture for both algorithms where area is traded against throughput. FPGA implementation and experimental results are discussed in this paper.
Spectrum sensing is a fundamental problem in cognitive radio systems. Its main objective is to reliably detect signals from licensed primary users to avoid harmful interference. As a first step toward building a large-scale cognitive radio network testbed, we propose to investigate experimentally the performance of three blind spectrum sensing algorithms.… (More)
This demonstration presents a proof-of-concept for opportunistic spectrum access. It particularly focuses on reinforcement learning algorithm called UCB (Upper Confidence Bound) designed by the machine learning community to solve the MAB problem (Multi-Armed Bandit). The demonstrator shows the first worldwide implementation of reinforcement learning… (More)