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 environment. The considered spectrum sensing techniques are: sequential energy and cyclosationary feature based detectors. An Universal Software Radio Peripheral platform with GNU-Radio is employed for implementation purpose. We analyzed the performances of both spectrum sensing methods by measuring the detection probabilities as a function of SNR for a given false alarm probability. As predicted theoretically, experimental measurements show that the cyclostationnary feature detector performs better than the sequential energy detector. However sequential energy detector can be used for reduction of sensing time in the presence of strong signals.

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High Peak to Average Power Ratio (PAPR) is a critical issue in multicarrier communication systems using Orthogonal Frequency Division Multiplexing (OFDM), as in the Second Generation Terrestrial Digital Video Broadcasting (DVB-T2) system. This problem can result in large performance degradation due to the nonlinearity of the High Power Amplifier (HPA) or in its low power efficiency. In this paper, we evaluate the performance of different Tone Reservation-based techniques for PAPR reduction in DVB-T2 context. Also, we propose an iterative TR-based technique called "One Kernel One Peak" (OKOP). Simulation results and performance comparison of these techniques in terms of gain in PAPR reduction, mean power variation, and complexity will be given. Finally, we describe the implementation of a PAPR reduction algorithm in the DVB-T2 modulator.

283-284International audienceThis 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 algorithms for OSA (opportunistic spectrum access) on real radio environment using USRP N210 platforms

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