The expected growth in the mobile video demand over the broadband cellular networks is one of the key factors driving the wireless industry to develop fifth generation of network technology. This scenario is fueling the need for group-oriented services (i.e., multicast and broadcast) in order to efficiently manage the radio resources, and consequently, grant different groups of users simultaneous access to the same multimedia content with differentiated quality of service (QoS). The evolved Multimedia Broadcast Multicast Service (eMBMS), standardized by the Third Generation Partnership Project (3GPP), is one of the technologies likely to be extended to 5G systems with the aim of addressing Point-to-Multipoint services. In addition, Non-Orthogonal Multiplexing Access (NOMA) techniques are being also considered as a driver to increase the efficient use of the spectrum in multi-user environments with asymmetric data delivery. The present article proposes the joint use of subgrouping multicast techniques and NOMA, in an eMBMS-like scenarios. Performance is evaluated in envisaged 5G environments, where different quality video services are delivered to a group of users interested in the same contents.

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In cognitive radios (CRs), secondary users (SUs) transmit alongside primary users (PUs). In order to avoid interference SU perform spectrum sensing and adaptive transmission. Reliable detection in wide geographical regions needs to perform collaborative sensing. The state of the art for efficient cooperative sensing is linear statistics combination. Spatial-spectral joint detection also provides multiband cooperative sensing to access opportunistically several bands at a time. Convex maximization is able to solve only an approximation of the optimization within a restricted solution domain, due to its non-convex nature. In this paper, we demonstrate that convex constraints are counterproductive and we propose an alternative optimization technique based on genetic algorithms. The genetic programming performs direct search of the optimal solution one step before the reformulations needed previously. We demonstrate that, by operating directly on the objective and abstracting from the convexity, the collaborative multiband sensing is optimized consistently with the problem formulation

Both satellite transmissions and DVB applications over satellite present peculiar characteristics that could be taken into consideration in order to further exploit the optimality of the transmission. In this paper, starting from the state-of-the-art, the optimization of the APSK constellation through asymmetric symbols arrangement is investigated for its use in satellite communications. In particular, the optimization problem is tackled by means of Genetic Algorithms that have already been demonstrated to work nicely with complex non-linear optimization problems like the one presented hereinafter. This work aims at studying the various parameters involved in the optimization routine in order to establish those that best fit this case, thus further enhancing the constellation

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