The availability of flexible radio interfaces capable of adapting their configuration to the time-varying operating environment is the key response to the demand encountered in modern wireless networks for high data rates under strict quality of service (QoS) constraints. To this end, this paper develops a novel link resource adaptation (LRA) scheme for soft-decoded multiantenna (MIMO) bit interleaved coded orthogonal frequency division multiplexing (BIC-OFDM) transmissions employing automatic repeat request (ARQ) mechanisms. As the first step, a simple link performance evaluation model based on the effective signal-to-noise ratio (SNR) mapping concept is derived in a closed-form expression that it is shown to yield better accuracy than previous techniques. Then, an effective LRA strategy is formulated taking advantage of that framework. The aim is maximizing the goodput (GP) metric, that is to say, the number of information bits delivered without error to the user by unit of time, over the available radio resources, such as the power distribution on the subchannels, coding rate, modulation order and the MIMO configuration. The numerical results demonstrate considerable performance gains compared with nonadaptive transmissions, while keeping the computational complexity at affordable levels in view of the specific structure of the GP objective function

We present the NEFOCAST project (named by the contraction of “Nefele”, which is the Italian spelling for the mythological cloud nymph Nephele, and “forecast”), funded by the Tuscany Region, about the feasibility of a system for the detection and monitoring of precipitation fields over the regional territory based on the use of a widespread network of new-generation Eutelsat “SmartLNB” (smart low-noise block converter) domestic terminals. Though primarily intended for interactive satellite services, these devices can also be used as weather sensors, as they have the capability of measuring the rain-induced attenuation incurred by the downlink signal and relaying it on an auxiliary return channel. We illustrate the NEFOCAST system architecture, consisting of the network of ground sensor terminals, the space segment, and the service center, which has the task of processing the information relayed by the terminals for generating rain field maps. We discuss a few methods that allow the conversion of a rain attenuation measurement into an instantaneous rainfall rate. Specifically, we discuss an exponential model relating the specific rain attenuation to the rainfall rate, whose coefficients were obtained from extensive experimental data. The above model permits the inferring of the rainfall rate from the total signal attenuation provided by the SmartLNB and from the link geometry knowledge. Some preliminary results obtained from a SmartLNB installed in Pisa are presented and compared with the output of a conventional tipping bucket rain gauge. It is shown that the NEFOCAST sensor is able to track the fast-varying rainfall rate accurately with no delay, as opposed to a conventional gauge.

In this paper we present results from the NEFOCAST project, funded by the Tuscany Region, aiming at detecting and estimating rainfall fields from the opportunistic use of the rain-induced excess attenuation incurred in the downlink channel by a commercial DVB satellite signal. The attenuation is estimated by reverse-engineering the effects of the various propagation phenomena affecting the received signal, among which, in first place, the perturbations factors affecting geostationary orbits, such as the gravitational attraction from the moon and the sun and the inhomogeneity in Earth mass distribution and, secondly, the small-scale irregularities in the atmospheric refractive index, causing rapid fluctuations in signal amplitude. The latter impairments, in particular, even if periodically counteracted by correction maneuvers, may give rise to significant departures of the actual satellite position from the nominal orbit. A further problem to deal with is the daily and seasonal random fluctuation of the rain height and altitude/size of the associated melting layer. All of the above issues lead to non-negligible random deviations from the dry nominal downlink attenuation, that can be misinterpreted as rain events. In this paper we show how to counteract these issues by employing two differentially-configured Kalman filters designed to track slow and fast changes of the received signal-to-noise ratio, so that the rain events can be reliably detected and the relevant rainfall rate estimated.

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Competing interests EAF is a founder and share holder of VIDA Diagnostics, a company that is commercialising pulmonary image analysis software developed, in part, at the University of Iowa.Provenance and peer review Not commissioned; internally peer reviewed.