Estimating tropical rain attenuation on the Earth-satellite path using radar data

Radar-return echoes, known as ‘reflectivity’, are exploited in the course of estimating rain attenuation along a slant path. Relevant radar gates or ‘range bins’ are identified to correlate a specific satellite path. The reflectivity value of each range bin is converted to rainfall rate using established radar reflectivity values – rainfall rates, (Z–R relation). Specific attenuation is then derived for all associated range bins. The attenuation for each bin is the product of specific attenuation and its effective path length. The summation of attenuation endured by all range bins is inferred as the attenuation along the slant path. In this study, an X-band slant path rain attenuation was estimated using 2.85 GHz (S-band) Terminal Doppler Weather Radar (TDWR) data. A technique to estimate rain attenuation by exploitation of radar information is elaborated in this article. Comparisons between the radar-derived attenuation estimations and actual satellite signal measurements are also presented. The findings were verified by comparing the generated values to the directly measured rain attenuation from the Razak satellite (RazakSAT). Radar reflectivity data were obtained from Kuala Lumpur International Airport (KLIA) radar station operated by the Malaysian Meteorology Department (MMD). Preliminary findings using the most recent Z–R relation (i.e. the generated radar-derived rain attenuation estimations) appear to show lower values than the actual measurements

User-centric learning for multiple access selections

We are in the age where business growth is based on how user-centric your services or goods is. Current research on wireless system is more focused on ensuring that user could achieve optimal throughput with minimal delay, disregarding what user actually wants from the services. Looking from con-nectivity point of view, especially in urban areas these days, there are multiple mobile and wireless access that user could choose to get connected to. As people are looking toward machine automa-tion, we understand that the same could be done for allowing users to choose services based on their own requirement. This paper looks into unconventional, non-disruptive approach to provide mobile services based on user requirements. The first stage of this study is to look for user association from three new perspectives. The second stage involved utilizing a reinforcement learning algorithm known as q-learning, to learn from feedbacks to identify optimal decision in reaching user-centric requirement goal. The outcome from the proposed deployment has shown significant improvement in user association with learning aware solution © BEIESP

Estimation of satellite link’s fade margin using non-meteorological technique and worst month analysis

Satellite technology is shifting to higher frequencies such as Q or V-band to cater to greater bandwidth and higher data rates applications such as videoconferencing, internet of things (IoT) and telemedicine. The main challenge in deploying high-frequency bands in heavy precipitation areas is severe rain attenuation. In this paper, a frequency scaling technique was developed to estimate the fade margin at a higher frequency. The worst month analysis was also conducted since the analysis is also important in determining dependable fade margin. The result was evaluated and analyzed using root mean square error (RMSE) and percentage error. The proposed model offers the smallest RMSE and lowest percentage error when compared to all existing prediction models. A dependable fade margin acquired from high-accuracy rain attenuation estimation is very important. This is to apply the best mitigation technique in overcoming rain attenuation in the satellite-Earth link so that, the best system performance can be delivered