Design Guidelines for Database-Driven Internet of Things-Enabled Dynamic Spectrum Access.

The radio-frequency spectrum shortage, which is primarily caused by the fixed allocation policy, is one of the main bottlenecks to the deployment of existing wireless communication networks, and to the development of new ones. The dynamic spectrum access policy is foreseen as the solution to this problem, since it allows shared spectrum usage by primary licensed and secondary unlicensed networks. In order to turn this policy into reality, the secondary network must be capable of acquiring reliable, real-time information on available bands within the service area, which can be achieved by means of spectrum sensing, spectrum occupancy databases, or a combination of them. This Review presents guidelines related to the design of a framework that can be adopted to foster dynamic spectrum access policies. The framework applies special-purpose Internet of Things (IoT) devices that perform spectrum sensing, subsequently feeding a spectrum occupancy database, which in turn will be used by the secondary network to gather information on location-dependent spectrum availability. The guidelines address technological enablers capable of making the framework feasible, reliable and secure.

Simple and efficient algorithm for improving the MDL estimator of the number of sources.

We propose a simple algorithm for improving the MDL (minimum description length) estimator of the number of sources of signals impinging on multiple sensors. The algorithm is based on the norms of vectors whose elements are the normalized and nonlinearly scaled eigenvalues of the received signal covariance matrix and the corresponding normalized indexes. Such norms are used to discriminate the largest eigenvalues from the remaining ones, thus allowing for the estimation of the number of sources. The MDL estimate is used as the input data of the algorithm. Numerical results unveil that the so-called norm-based improved MDL (iMDL) algorithm can achieve performances that are better than those achieved by the MDL estimator alone. Comparisons are also made with the well-known AIC (Akaike information criterion) estimator and with a recently-proposed estimator based on the random matrix theory (RMT). It is shown that our algorithm can also outperform the AIC and the RMT-based estimator in some situations.