TV White Space Based Wireless Broadband Internet Connectivity: A Case Study With Implementation Details and Performance Analysis

Abstract

Even at the current age of sophisticated communication systems, remote rural communities with low user density throughout the world lack broadband internet service of good quality. Traditional ways fall short to meet the demand of those regions due to technological or geographical challenges as well as less economic viability. Since the unlicensed operation of spare TV (Television) spectra was approved by the authorities in many countries of the world, various applications of those spectra, known as TV White Space (TVWS), have been researched. In many literature and implementations, TVWS has been found to be feasible in providing quality broadband internet service to deprived rural communities. Penetration through obstacles, good non-line-of-sight connectivity, long-distance transmission, wider horizontal beam-width, and abundance of unused TV channels in remote rural regions are some of the promising features of TVWS which can be leveraged to connect rural communities to broadband internet and related digital services. We made a comprehensive effort to evaluate TVWS based broadband internet connectivity through laboratory testing and outdoor testing with a measurement campaign at Stillwater, Oklahoma and by providing live internet service to a client in the rural area of Tillman county, Oklahoma, USA. Radio frequency (RF) coverage heatmaps have been created over the geographical map using the received signal data measured. Also, profiles of received power, signal to noise ratio (SNR) and throughput have been analyzed against antenna directionality. The purpose of this study is to provide the details of our TVWS trial and lessons learned from it, followed by performance analysis, comparison of results with theory and future directions.

Coherence-based sufficient condition for support recovery using block generalized orthogonal matching pursuit

“…This criterion specifically applies when the number of selected support elements in the method is equal to one Q=1 [10] [11]. Coherence statistics are valuable for resolving various signal processing issues, such as support detection and vector quantization [12] [18].…”