Spectrum Management and Cognitive Radios

The ever increasing demand for new services from users who want high-quality broadband services while on the move, is straining the efficiency of current spectrum allocation paradigms, leading to an overall feeling of spectrum scarcity. In order to circumvent this problem, two possible solutions are being investigated: (i) implementing new technologies capable of accessing the temporarily/locally unused bands, without interfering with the licensed services, like Cognitive Radios; (ii) release some spectrum bands thanks to new services providing higher spectral efficiency, e.g., DVB-T, and allocate them to new wireless systems. These two approaches are promising, but also pose novel coexistence and interference management challenges to deal with. In particular, the deployment of devices such as Cognitive Radio, characterized by the inherent unplanned, irregular and random locations of the network nodes, require advanced mathematical techniques in order to explicitly model their spatial distribution. In such context, the system performance and optimization are strongly dependent on this spatial configuration. On the other hand, allocating some released spectrum bands to other wireless services poses severe coexistence issues with all the pre-existing services on the same or adjacent spectrum bands. In this thesis, these methodologies for better spectrum usage are investigated. In particular, using Stochastic Geometry theory, a novel mathematical framework is introduced for cognitive networks, providing a closed-form expression for coverage probability and a single-integral form for average downlink rate and Average Symbol Error Probability. Then, focusing on more regulatory aspects, interference challenges between DVB-T and LTE systems are analysed proposing a versatile methodology for their proper coexistence. Moreover, the studies performed inside the CEPT SE43 working group on the amount of spectrum potentially available to Cognitive Radios and an analysis of the Hidden Node problem are provided. Finally, a study on the extension of cognitive technologies to Hybrid Satellite Terrestrial Systems is proposed.

Agroecological management of insect pests on sugarbeets and lettuce cultivated in organic farming

Two-year field trials were conducted in northern Italy with the aim of developing a trapcrop-based agroecological approach for the control of flea beetles (Chaetocnema tibialis (Illiger), Phyllotreta spp. (Chevrolat) (Coleoptera: Chrysomelidae)) and Lygus rugulipennis Poppius (Hemiptera: Miridae), key pests of sugar beet and lettuce, respectively. Flea beetle damage trials compared a trap cropping treatment, i.e., a sugar beet plot with a border of Sinapis alba (L.) and Brassica juncea (L.) with a control treatment, i.e., a sugar beet plot with bare soil as field border. Sugar beets grown near trap crops showed a significant decrease (≈40%) in flea beetle damage compared to control. Moreover, flea beetle damage varied with distance from the edge of the trap plants, being highest at 2 m from the edge, then decreasing at higher distances. Regarding L. rugulipennis on lettuce two experiments were conducted. A semiochemical-assisted trap cropping trial was supported by another test evaluating the efficacy of pheromones and trap placement. In this trial, it was found that pheromone baited traps caught significantly more specimens of L. rugulipennis than unbaited traps. It was also found that traps placed at ground level produced larger catches than traps placed at the height of 70 cm. In the semiochemical-assisted trap cropping experiment, a treatment where lettuce was grown next to two Alfa-Alfa borders containing pheromone baited traps was compared with a control treatment, where lettuce was grown near bare soil. This experiment showed that the above-mentioned strategy managed to reduce L. rugulipennis damage to lettuce by ≈30%. From these studies, it appears that trap crop-based strategy, alone or with baited traps, made it possible to reduce crop damage to economically acceptable levels and to minimize the need for insecticide treatments, showing that those strategy could be implemented in organic farming as a means of controlling insect pests.