Precoded Cluster Hopping for Multibeam GEO Satellite Communication Systems

Beam hopping (BH) and precoding are two trending technologies for high-throughput satellite (HTS) systems. While BH enables the flexible adaptation of the offered capacity to the heterogeneous demand, precoding aims at boosting the spectral efficiency. In this study, we consider an HTS system that employs BH in conjunction with precoding in an attempt to bring the benefits of both in one. In particular, we propose the concept of cluster hopping (CH), where a set of adjacent beams are simultaneously illuminated with the same frequency resource. On this line, we propose an efficient time–space illumination pattern design, where we determine the set of clusters that shall be illuminated simultaneously at each hopping event along with the dwelling time. The CH time–space illumination pattern design formulation is shown to be theoretically intractable due to the combinatorial nature of the problem and the impact of the actual illumination design on the resulting interference. For this, we make some design decisions on the beam–cluster design that open the door to a less complex still well-performing solution. Supporting results based on numerical simulations are provided which validate the effectiveness of the proposed CH concept and a time–space illumination pattern design with respect to benchmark schemes.

Estimation of a Heterogeneous Demand Function with Berkson Errors

Berkson errors are commonplace in empirical microeconomics. In consumer demand this form of measurement error occurs when the price an individual pays is measured by the (weighted) average price paid by individuals in a group (e.g., a county), rather than the true transaction price. We show the importance of Berkson errors for demand estimation with nonseparable unobserved heterogeneity. We develop a consistent estimator using external information on the true price distribution. Examining gasoline demand in the U.S., we document substantial within-market price variability. Accounting for Berkson errors is quantitatively important. Imposing the Slutsky shape constraint reduces sensitivity to Berkson errors.

Dynamic and Heterogeneous Demand for Urban Green Space by Urban Residents: Evidence from the Cities in China

With the increasing number of people starting to attach importance to urban green space (UGS), estimating urban residents’ demand for UGS has become critical. Previous studies have estimated residents’ demands for UGS in different cities using the Rosen–Roback model. However, there has been little analysis of the renters’ requirements for UGS or the dynamic and heterogeneous demands for UGS by residents in cities with varying characteristics. In this study, the prefecture-level cities in China were selected as the object of study for their wide representation of various city characteristics, and the Rosen–Roback model was used to address the abovementioned issues. Residents’ demand for UGS in 285 prefecture-level cities was assessed from 2010 to 2017. The results confirm our hypothesis that when UGS increases by 1%, house prices will increase by about 3% and rents will increase by about 7%, and the demand continues to grow. We also analyzed the heterogeneous demands for UGS by urban residents in different types of cities from three aspects: population density, economic level, and the effects of the National Forest City policy. Finally, we provided suggestions based on empirical results for planning and managing UGS.