TV White Space Research Articles

Field Measurement Test on Protection Ratio For DVB-T2 Interfered By TV White Space Devices

TV white space (TVWS) refers to the unused UHF and VHF spectrum owned by primary users, such as TV (DVB-T2) broadcasters in a certain location. The protection of primary users' interference from white space devices has been the major implementation issue. Protection ratio setting is commonly used to limit the amount of transmission power that white space devices can permit. In the present study, the simulation and experiment for the proposed protection ratio values are compared. Simulation using Monte Carlo has been conducted. Field measurements were implemented to see whether the protection ratio value could be used to assist the government in implementing TVWS. The outcomes showed that the simulation-based protection ratio was 1 dB greater than the field test result. The results indicate that the simulation model can be used as a reference for the government to estimate the value of the protection ratio and facilitate better efficient implementation

Development of Database Accessibility for Frequency Scheduling of TVWS Broadband Connectivity in Rural Areas Using Dynamic Spectrum Access Technique

As a result of the switchover from analogue to digital transmission, Television White Space (TVWS) presented itself as good opportunity to supplement the existing licensed spectrum to ease the spectrum scarcity. Rural communities were usually not connected due to poor returns to the internet providers to provide broadband access to the areas. This research has prepared the framework and feasibility study for deploying broadband internet services using Television White Space (TVWS) technology in Ugbawka, a rural area in Enugu state, Nigeria. In this work, a Network Ping was run on five websites using three major internet service providers as backhaul to establish facts of poor or even non-existent internet services. Using Ping Plotter 5 Pro, accessing ieee.com using MTN yielded a Round Trip (RT) average of 846.433 ms, with a loss of 83.8% packet over 10mins count. Radio Frequency (RF) Explorer and Carlson transceiver, Customer Premise Equipment (CPE) were used for field trials to determine availability of TVWS Frequencies. A database app was developed by writing some codes in the Basic for Android (B4A) Intermediate Development Environment (IDE). Empirical outdoor propagation model was developed with a 2.15 pathloss exponent while the indoor propagation model gave a pathloss exponent of 3.47 . An algorithm was developed, titled, TVWS Optimization Quadrature Amplitude Algorithm, (TOQA), where throughput of this project performed better by giving 60Mbps and 70 Mbps at Signal-Noise-Ratio (SNR) of 5dB while the conventional algorithm gave 30Mbps and 25 Mbps at same SNR value. The Bit Error Rate was lower than the conventional models used, giving the TOQA values of 10-3 at SNR of 5 dB and 10-6 at SNR of 30 dB while the conventional method gave 10-1 and 10-3 respectively at the same SNR values. With an Average of 56.2% network latency recorded in Ugbawka, TVWS will make a great impact on Internet connectivity if deployed.

Performance Comparison between Handover Decision Algorithms for Horizontal Handover in Long Term Evolution Networks

Comparative Analyses of Handover Decision Algorithms (HODA) for horizontal handovers in Long Term Evolution Network (LTE-A) in terms of Key Performance Indicators (KPIs) were conducted using system level simulation in MATLAB. The KPIs analyzed were the users’ throughput, spectral efficiency, cell –edge Spectral efficiency, handover success rate , call drop Rate, Cumulative Distribution Function (CDF) of Received Signal Reference Power (RSRP), Average RSRP over Primary Component Carrier (PCC) and Secondary Carrier (SCC) and interruption time. . From the results obtained, the algorithm based on spectrum aggregation of carriers from wireless communication spectrum and television white space band referred to as the proposed-TVWS algorithm showed remarkable performance improvements in all the chosen KPIs when compared with MIF and CONV except in handover success rate at UEs speeds less than 70 km/hr of which MIF-HODA algorithm seemed to perform better. Therefore, the proposed-TVWS has yielded baseline certainty in the increase of wireless network quality of service in addition to the seamless network connectivity for the mobile users.

Capacity, Coverage and Power Profile Performance Evaluation of a Novel Rural Broadband Services Exploiting TVWS From High Altitude Platform

The coverage and capacity of exploiting TV White Spaces (TVWS) from High Altitude Platforms (HAPs) with multiple antenna payloads using uniform rectangular phased array (URA) antennas are explored in this article. A scenario is suggested in which a HAP and TVWS base station are installed on the HAP within a HAP coverage area with a radius of 100 km and a cell radius of 10.5 km. A scenario is suggested in which TVWS base stations are installed on a HAP providing coverage to an area of 100 km radius. This area is consisted of a number of cells where each of them has a radius of 10.5 km. The formation of HAP cells with various reuse patterns has been established. An analysis of coverage, capacity and power profile function for TVWS link from 25×25 URA HAP arrays is presented. To minimize the sidelobe levels in the antenna array synthesis, window function has been assumed. Power profile within a cell using URA is highlighted. Channel to Interference (CIR) performance contour, coverage probability and channel capacity have been determined. The channel capacity for the theoretical Shannon bound and practical truncated Shannon bound are determined and compared. Results show that a maximum channel capacity of 53 Mbps can be attained from the proposed model.

Software-Defined Radio-Based Evaluation Platform for Highly Mobile IEEE 802.22 System

Wide-area communication systems using TV white space (TVWS) are promising technologies capable of full-coverage and cost-effective vehicular and transportation communications owing to their excellent propagation characteristics. IEEE 802.22 was developed as a TVWS-based fixed communication standard to support a wide coverage area of 10–30 km. To cope with the various applications in mobile communication, the transmission performance needs to be enhanced and evaluated. Thus, it becomes necessary to develop an experimental platform to enable the flexible implementation of transmitter and receiver algorithms. In this regard, we propose a software-defined radio (SDR)-based evaluation platform enabling the bench testing of physical layer (PHY) performance for highly mobile IEEE 802.22 communication systems. Using this platform, radio frequency signals can be transmitted and received using reconfigurable hardware and software, with the received baseband signals being recorded and stored by the SDR device. The stored data were post-processed using a MATLAB-based program to evaluate the transmission performance. The performance achieved by SDR-based evaluation system is similar to computer simulations, thereby demonstrating the feasibility of highly mobile IEEE 802.22 communication systems. Moreover, the proposed evaluation platform is available for future field experiments to encourage the development and evaluation of novel PHY algorithms.

A Menu-driven Interface for a Geolocation Database for Wireless Spectrum Sharing

Spectrum sharing is of paramount importance in television white space (TVWS) wireless communications. Secondary users must ensure that their operation in any TV channel does not interfere with the transmission of the primary users and other secondary users. This paper presents a menu-driven interface (MDI) which a secondary user inquirer may utilize to determine the TV channels available. The MDI utilizes a reinforcement learning-based algorithm and an interference indicator to determine the availability of each TV channel for secondary use. The algorithm utilized a set of parameters to provide accurate data on the availability of the channel. These parameters include time, location, and the receiving antenna parameters. The algorithm was able to produce an accuracy of 94.69% in determining the availability of the channel based on the broadcasting time of primary users (PUs) and an accuracy of 84.38% based on four tested locations.

Improved Resource Allocation Model for Reducing Interference Among Secondary Users in TV White Space for Broadband Services

In recent years, the Television White Space has attracted the interest of many researchers due to its propagation characteristics obtainable between 470MHz and 790MHz spectrum bands. However, aggregate interference increase when secondary users in the wireless network increase. The aggregate interference on the side of Primary Users has been extensively scrutinized. Therefore, resource allocation (power and spectrum) is crucial when designing the Television White Space network to avoid interferences from Secondary Users to Primary Users and among Secondary Users themselves. This paper proposes a resource allocation model that uses joint power and spectrum hybrid Particle Swarm Optimization, Firefly, and Genetic algorithm for reducing the aggregate interference among Secondary Users. The algorithm is integrated with the admission control algorithm so that; there is a possibility to remove some of the Secondary Users in the network whenever the Signal to Noise Interference Ratio threshold for Secondary and Primary Users is not met. We considered the infeasible system whereby all Secondary and Primary Users may not be supported simultaneously. Metrics such as Primary User Signal-to-noise ratio, sum throughput, and secondary user signal-to-noise ratio less than the threshold used to compare the performance of the proposed algorithm and the results show that the PSOFAGA with effective link gain ratio admission control has the best performance compared to particle swarm optimization, genetic algorithm, firefly algorithm, and PSOFAGA algorithm.

Novel Resource Allocation Algorithm for TV White Space Networks Using a Hybrid of Firefly Algorithm, Genetic Algorithm, and Ant Colony Optimization Algorithm

Novel Resource Allocation Algorithm for TV White Space Networks Using a Hybrid of Firefly Algorithm, Genetic Algorithm, and Ant Colony Optimization Algorithm

Development of a Material for Gauging Sustainable Transitions Towards Adoption and Implementation of Television White Space Communications Systems

A television white space (TVWS) is an unused spectrum band in the TV broadcasting band. This unused band of frequency in TV channels can be used for different applications that benefit the people. TVWS, with its accompanying communications technologies, has promising uses and advantages in wireless systems. TVWS can replace or co-exist with other communications systems. However, gauging the technological transitions for the adoption of the TVWS system is sometimes overlooked. This paper developed a set of questions that could gauge and evaluate the level of adoption and implementation of a country's TVWS communications based on Davis's proposed technological transition architecture framework. The technological assessment developed in this paper leads to a better understanding of the niche, regime, and landscape considerations in implementing TVWS systems.

Investigation of Radio Frequency Licensed Spectrum Utilization in Nigeria: A Study of Rumuokwuta, Port Harcourt, Nigeria

This study was carried out to investigate the spectrum utilization of the licensed Radio Frequency (RF) spectrum in Rumuokwuta, Port Harcourt. An outdoor measurement of spectrum occupancy was carried out in a high-rise building situated at Rumuokwuta urban area in Port Harcourt, Nigeria using RF explorer spectrum analyzer and a personal computer laptop system. Spectrum activities in the band of 240-960 MHz were monitored for 24 hours. The frequency band was subdivided into 24 sub bands each with a span size of 30 MHz. Scanning of bands was made efficient using a python script that scans a range, analyzed the frequencies and signal strengths for 112 data points, saves data in CSV file format, scans the next range until the 24 ranges were scanned. The process was repeated to achieve 15 iterations. With a noise floor of - 110dBm, a threshold of -95dBm was used to determine the presence of signal, hence the spectrum occupancy of measured bands. Results showed that out of the 24 investigated sub bands; only one band was completely occupied with spectrum occupancy of 100%. 12 bands were partially occupied while 11 were completely free. The average spectrum occupancy for the whole band was obtained as 11.64%. This showed good location for dynamic spectrum access and cognitive radio deployment, especially in Television White Space (TVWS).

Detecting Byzantine attacks in Cognitive Radio Networks : A two-layered approach using Hidden Markov Model and machine learning

Cognitive Radio Networks (CRN) are gaining popularity due to their ability to harness the TV whitespace. CRNs are expected to usher in a new wireless technology to cater to the ever growing population of wireless mobile devices while the current ISM range of wireless technologies is increasingly becoming insufficient. CRNs uses the principle of collaborative spectrum sensing (CSS) where unlicensed users, called Secondary Users (SU) keep sensing a licensed band belonging to the incumbent user called the Primary User (PU). Such a collaborative sensing mechanism enables an increased detection accuracy of the incumbent’s presence (or absence) as all the SUs send their local sensing reports to a Fusion Centre (FC), where these sensing reports are aggregated to arrive at a final sensing decision. However, this collaborative sensing introduces vulnerabilities which can be used to carry out an attack called the Byzantine Attack (a.k.a. Spectrum Sensing Data Falsification (SSDF) attack). We present a two-layer model framework to classify Byzantine attackers in a CRN. The first layer, Processing layer, uses Hidden Markov Model (HMM) to get a probabilistic relationship between the PU’s states and the SUs’ sensing reports. This generates the required dataset for the next layer. The second layer, Decision layer, uses several ML algorithms to classify the SUs into Byzantine attackers and normal SUs. Extensive simulation results confirm that the learning classifiers perform well across various testing parameters. Finally, a comparison analysis of the proposed method with an existing non-ML technique shows that the ML approach is more robust especially under high presence of malicious users.

A survey and comparison of TV white space implementations in Japan, the Philippines, Singapore, the united kingdom, and the united states

A survey and comparison of TV white space implementations in Japan, the Philippines, Singapore, the united kingdom, and the united states

As a Squash Plant Grows

Researching and designing Internet infrastructure solutions in rural and tribal contexts requires reciprocal relationships between researchers and community partners. Methodologies must be meaningful amid local social textures of life. Achieving transdisciplinarity while relating research impacts to partner communities takes care work, particularly where technical capacity is scarce. The Full Circle Framework is an action research full stack development methodology that foregrounds reciprocity among researchers, communities, and sovereign Native nations as the axis for research purpose and progress. Applying the framework to deploy television white space infrastructure in sovereign Native nations in northern New Mexico reveals challenges for rural computing, including the need to design projects according to the pace of rural and tribal government workflows, cultivate care as a resource for overworked researchers and community partners, and co-create a demand for accurate government data around Internet infrastructures in Indian Country and through rural counties.

Reinforcement Learning-Based Television White Space Database

Television white spaces (TVWSs) refer to the unused part of the spectrum under the very high frequency (VHF) and ultra-high frequency (UHF) bands. TVWS are frequencies under licenced primary users (PUs) that are not being used and are available for secondary users (SUs). There are several ways of implementing TVWS in communications, one of which is the use of TVWS database (TVWSDB). The primary purpose of TVWSDB is to protect PUs from interference with SUs. There are several geolocation databases available for this purpose. However, it is unclear if those databases have the prediction feature that gives TVWSDB the capability of decreasing the number of inquiries from SUs. With this in mind, the authors present a reinforcement learning-based TVWSDB. Reinforcement learning (RL) is a machine learning technique that focuses on what has been done based on mapping situations to actions to obtain the highest reward. The learning process was conducted by trying out the actions to gain the reward instead of being told what to do. The actions may directly affect the rewards and future rewards. Based on the results, this algorithm effectively searched the most optimal channel for the SUs in query with the minimum search duration. This paper presents the advantage of using a machine learning approach in TVWSDB with an accurate and faster-searching capability for the available TVWS channels intended for SUs.

TV White Space Network Power Allocation Using Hybrid Grey Wolf Optimizer with Firefly Algorithm and Particle Swarm Optimization

TV white spaces (TVWS) can be utilized by Secondary Users (SUs) equipped with cognitive radio functionality on the condition that they do not cause harmful interference to Primary Users (PUs). Optimization of power allocation is necessary when there is a high density of secondary users in a network in order to reduce the level of interference among SUs and to protect PUs against harmful interference. Grey Wolf Optimizer (GWO) is relatively recent population based metaheuristic algorithm that has shown superior performance compared to other population based metaheuristic algorithms. Recent trend has been to hybridize population based metaheuristic algorithms in order to avoid the problem of getting trapped in a local optimum. This paper presents the design and analysis of performance of a hybrid grey wolf optimizer and Firefly Algorithm (FA) with Particle Swarm Optimization operators for optimization of power allocation in TVWS network power allocation as a continuous optimization problem. Matlab was used for simulation. The hybrid of GWO, FA and PSO (HFAGWOPSO) reduces sum power by 81.42% compared to GWO and improves sum throughput by 16.41% when compared to GWO. Simulation results also show that the algorithm has better convergence rate.

Design and Analysis of a Microstrip Patch Antenna for TV White Space Data Applications

The increasing number of smart and wireless devices demandsfor a more flexible allocation in the higher frequency spectrum. Dynamic spectrum access isone of the major candidates tosolve the problem of spectrum scarcity. TV white spaces(TVWS)provide a means to opportunistically use the underutilized TVfrequency bandsin consideration with the gradual switching of the traditional TV broadcast to digital broadcasting.With smart devices in mini, micro, or nano circuit packaging, one of themajor challenges is in thedesign of compact transceiver antennassuitable for mobiledevices that operates in the Ultra High Frequency (UHF)band.This paper provides a brief overview of TVWS and aproposedmicrostrip patch antenna design.Thegeometric measurements and the antenna parameters were designedand simulated in MATLAB. Results show a resonant performance at the 638 MHz operating frequency and a high front-to-back power ratio in the radiation pattern. The radiation characteristics are almost omni-directional in the azimuth while directional in the elevation plane. There is a minimal radiation at the back hence, for thin and slim devices, this will suit the desired application

Design and Analysis of a Microstrip Patch Antenna for TV White Space Data Applications

In the present study, an algorithm for big data encryption has been designed which is concerned with encrypting data in a short time and in a safe manner and difficult to be penetrated by attackers and hackers according to the mechanism used for encryption in this study.The proposed algorithm is a development of an earlier NSCT algorithm. This development is based on adding a key expansion mechanism to generate keys by F-function, and also the number of cycles have been reduced to get less time. The proposed algorithm has been implemented in three stages; the first stage is implemented by FELICS simulation to find out the efficiency of the algorithm. In thesecond stage the proposed algorithm is implemented on the Java language to encrypt the text and to find out the execution time for the encryption. In the last step, the proposed algorithm is implemented on the MATLAB language to encrypt images and to knowthe performance of the algorithm in terms of countering attacks by hackers.After performing these steps and obtaining the results presented in this study, we can say that the proposed algorithm is capable of dealing with big data and that it can be considered safe and free from risks

LPWAN in the TV White Spaces

Low-Power Wide-Area Network (LPWAN) is an enabling Internet-of-Things technology that supports long-range, low-power, and low-cost connectivity to numerous devices. To avoid the crowd in the limited ISM band (where most LPWANs operate) and cost of licensed band, the recently proposed Sensor Network over White Spaces (SNOW) is a promising LPWAN platform that operates over the TV white spaces. As it is a very recent technology and is still in its infancy, the current SNOW implementation uses the Universal Software Radio Peripheral devices as LPWAN nodes, which has high costs (≈$750 USD per device) and large form-factors, hindering its applicability in practical deployment. In this article, we implement SNOW using low-cost, low form-factor, low-power, and widely available commercial off-the-shelf (COTS) devices to enable its practical and large-scale deployment. Our choice of the COTS device (TI CC13x0: CC1310 or CC1350) consequently brings down the cost and form-factor of a SNOW node by 25× and 10×, respectively. Such implementation of SNOW on the CC13x0 devices, however, faces a number of challenges to enable link reliability and communication range. Our implementation addresses these challenges by handling peak-to-average power ratio problem, channel state information estimation, carrier frequency offset estimation, and near-far power problem. Our deployment in the city of Detroit, Michigan, demonstrates that CC13x0-based SNOW can achieve uplink and downlink throughputs of 11.2 and 4.8 kbps per node, respectively, over a distance of 1 km. Also, the overall throughput in the uplink increases linearly with the increase in the number of SNOW nodes.

Cognitive radio based efficient video multicast in TV white space

Cognitive radio based efficient video multicast in TV white space

Improving Throughput in Sub – Nyquist Spectrum Sensing using a Cluster Model for Cognitive Radio Networks

The underutilization of radio spectrum has led to the downfall of Fixed Spectrum Allocation (FSA) since mobile frequency bands are getting overloaded due to rise of users all across the world. These drawbacks could be overcome by a concept called Dynamic Spectrum Allocation (DSA) implemented by an intelligent form of communication termed as Cognitive Radio Networks (CRN). CRN finds its applications adopted in military for battlefield surveillance, medical areas for 24 × 7 patient monitoring and also in TV white spaces. CRN involves SS in an efficient manner to deploy mobile users in TV frequency bands in a wider range through Wideband SS (WSS). WSS provides omnipresent spectrum monitoring for detecting spectral holes in the unutilized TV spectrum. The concept of Sub-Nyquist Sensing in WSS has several advantages in data sampling improving a plethora of application throughputs. The cluster approach in CRN has improved the base station crisis in load handling, throughput performance. In this paper, we focus on improving the traffic Quality of service (QoS) of the users in WSS by employing a cluster CRN network and applying Nyquist Sensing (NS) and Sub-Nyquist sensing (SNS) to find out the significance of SNS in the cluster network.