Recent advances in random matrix theory have spurred the adoption of eigenvalue-based detection techniques for cooperative spectrum sensing in cognitive radio. These techniques use the ratio between the largest and the smallest eigenvalues of the received signal covariance matrix to infer the presence or absence of the primary signal. The results derived so far are based on asymptotical assumptions, due to the difficulties in characterizing the exact eigenvalues ratio distribution. By exploiting a recent result on the limiting distribution of the smallest eigenvalue in complex Wishart matrices, in this paper we derive an expression for the limiting eigenvalue ratio distribution, which turns out to be much more accurate than the previous approximations also in the non-asymptotical region. This result is then applied to calculate the decision sensing threshold as a function of a target probability of false alarm. Numerical simulations show that the proposed detection rule provides a substantial improvement compared to the other eigenvalue-based algorithms.

We propose a novel cooperative positioning algorithm that fuses information from satellites and terrestrial wireless systems, suitable for GPS-challenged scenarios. The algorithm is fully distributed over an unstructured network, does not require a fusion center, does not rely on fixed terrestrial infrastructure, and is thus suitable for ad-hoc deployment. The proposed message passing algorithm, named hybrid sum-product algorithm over a wireless network (H-SPAWN), is described and analyzed. A novel parametric message representation is introduced, to reduce computational and communication overhead. Through simulation, we show that H-SPAWN improves positioning availability and accuracy, and outperforms hybrid positioning algorithms based on conventional estimation techniques.

In this paper we consider signal detection in cognitive radio networks, under a non-parametric, multi-sensor detection scenario, and compare the cases of known and unknown noise level. The analysis is focused on two eigenvalue-based methods, namely Roy's largest root test, which requires knowledge of the noise variance, and the generalized likelihood ratio test, which can be interpreted as a test of the largest eigenvalue vs. a maximum-likelihood estimate of the noise variance. The detection performance of the two considered methods is expressed by closed-form analytical formulas, shown to be accurate even for small number of sensors and samples. We then derive an expression of the gap between the two detectors in terms of the signal-to-noise ratio of the signal to be detected, and we identify critical settings where this gap is significant (e.g., low number of sensors and signal strength). Our results thus provide a measure of the impact of noise level knowledge and highlight the importance of accurate noise estimation.

In this paper we introduce and design sparse constellations for Direct-to-Satellite Internet of Things (DtS-IoT). DtS-IoT does not require a ground infrastructure, because the devices are directly connected to Low Earth Orbit satellites acting as orbiting gateways. The key idea of sparse constellations is to significantly reduce the number of in-orbit DtS-IoT satellites by (i) a proper dimensioning of the delivery delay anyway present in resource-constrained IoT services, and (ii) an optimal positioning of the orbiting gateways. First, we analyze LoRa/LoRaWAN and NB-IoT standards and derive realistic constraints on the maximum gap time between two consecutive passing-by satellites. Then, we introduce and optimize an algorithm to design quasi-optimal topologies for sparse IoT constellations. Finally, we apply our design to both global and regional coverage and we analyze the trade-off between latency, number of orbit planes and total number of satellites. Results show that sparse constellations can provide world-wide IoT coverage with only 12.5% and 22.5% of the satellites required by traditional dense constellations considering 3-hour and 2-hour gaps. Also, we show that region-specific coverage of Africa and Europe can be achieved with only 4 and 3 satellites for LoRa/LoRaWAN and NB-IoT, respectively.

In this paper, a new variant of the McEliece cryptosystem, based on quasi-cyclic low-density parity-check (QC-LDPC) codes, is studied. In principle, such codes can substitute Goppa codes, originally used by McEliece; their adoption, however, is subject to cryptanalytic evaluation to ensure sufficient system robustness. The authors conclude that some families of QC-LDPC codes, based on circulant permutation matrices, are inapplicable in this context, due to security issues, whilst other codes, based on the "difference families" approach, can be able to ensure a good level of security against intrusions, even if very large lengths are needed.