Universal Software Radio Peripheral/GNU Radio-Based Implementation of a Software-Defined Radio Communication System

Challenges involved in space communications across wireless channels call for new approaches to radio systems. Due to the growing need for frequency change in modern wireless systems, an adaptive radio system has the highest demand. Software-defined radios (SDR) offer this type of adaptivity as well as compatibility with other standard platforms such as USRP/GNU radio. Despite limitations of this approach due to hardware components, viable modeling and simulation as well as deployable systems are possible using this platform. This chapter presents a detailed implementation procedure for a USRP/GNU radio-based SDR communication system that can be used for practical experiments as well as an academic lab in this field. In this experiment the USRP has been configured to receive signal from a local radio station using the BasicRX model daughterboard. The programmable USRP executes Python block code implemented in the GNU Radio Companion (GRC) on Ubuntu OS.

Energy-Efficient Design of LTE Systems

There has been a paradigm shift in the field of mobile communication, with an overwhelming increase in data usage. As more and more users are migrating to smartphones, the amount of data being transmitted has increased. However, huge amounts of data and signal propagation are bound to be detrimental to the ecological balance. Long-term evolution (LTE), due to its flexibility and backward compatibility, has emerged as the network of choice for 4G and beyond. In this chapter, the significance of core technologies for LTE network is highlighted, along with the inherent advantage of reducing the energy consumption of cellular network. An energy-efficient design of LTE is proposed that blends the technologies proposed by 3GPP such as adaptive OFDMA with that of MU-MIMO.

Thinking eHealth

This chapter explains eHealth; discusses experiences, health management strategies, and healthcare models to address overweight and obesity in young population; and focuses on mathematical background of individual health status monitoring system to empower young people to manage their health. The proposed system uses symptoms observed with mobile sensing devices to define individual physical and psychological status. It has flexible logical inference system providing positive psychological influence on young people since full acceptance of recommendations towards healthy lifestyles is reached and correct interpretation is guaranteed. Models and algorithms are developed based on the composition inference rule in fuzzy logic that makes health status identification process faster and obtained results more precise and efficient comparing to traditional identification algorithms.

Real-Time Digital Signal Processing-Based Algorithm for Universal Software Radio Peripheral to Detect GPS Signal

Software-defined radios (SDR) are gradually becoming a practical option for implementing RF communication systems due to their low cost, off-the-shelf availability, and flexibility. Although the analog limitations of the hardware devices in these systems create barriers to some applications, creative algorithms in digital signal processing (DSP) can improve the results. In some cases, this improvement is essential to establishing a robust and reliable communication. The universal software radio peripheral (USRP) is a popular hardware that can be used alongside the SDR. Among many capabilities of USRP and its changeable daughter boards is receiving GPS signals. The GPS satellites transmit data on two main frequencies, L1 (1575.42 MHz) and L2 (1227.60 MHz). In this chapter, the focus is on describing a detailed implementation of the real-time DSP-based algorithm for USRP to detect GPS signal, namely the L1 band that transmits at 1575.42 MHz.

Hyperspectral Microwave Atmospheric Sounder (HyMAS) Graphical User Interface Design

The hyperspectral microwave atmospheric sounder (HyMAS), for weather and climate missions, is capable of all-weather sounding equivalent to hyperspectral infrared sounders (in which clouds decrease the accuracy of the results) in clear air with vertical resolution of approximately 1 km. This will improve both the vertical and horizontal resolutions of the atmosphere. Through the use of independent RF antennas that sample the volume of the Earth's atmosphere through various levels of frequencies, thereby producing a set of dense, spaced vertical weighting functions, hyperspectral microwave is achieved. This yields surface precipitation rate and water path retrievals for small hail, soft hail, or snow pellets, snow, rainwater, etc. with high accuracies. One of HyMAS requirements is a graphical user interface (GUI). Hyperspectral measurements allow the user to determine the Earth's temperature with vertical resolution exceeding 1km (1093.61 yards).

Real-Time FMCW Radar X-Band Signal Acquisition and Visualization

The recent developments in the remote sensing technologies have resulted in large amounts of data transmitted from spaceborne sensors. To keep up with the volume, speed, and variety of these data, new data acquisition and visualization systems need to be developed. This chapter focuses on some design and development considerations for a real-time data acquisition and visualization of X-band in a frequency-modulated continuous wave (FMCW) radar. Relevant issues such as high-speed network, parallel data processing system, and large-scale storage system are discussed. Ideally, the acquisition system should be capable of concurrent processing at low cost and visualization technique should be in the same time scale with other conventional 2D visualization of X-band weather radars. Benefits of this type of radar are that it is not just safe and inexpensive, but also serves as a means in filling in gaps of higher-powered pulse-doppler radars when used in conjunction with them.

Digital Communications and a Smart World

This chapter is devoted to digital communications in a smart world. The author examines turbo codes that are currently introduced in many international standards and implemented in numerous advanced communication systems, applied in a smart world, and evaluate the process of extrinsic information transfer (EXIT). The convergence properties of the iterative decoding process, associated with a given turbo-coding scheme, are estimated using the analysis technique based on so-called EXIT charts. This approach provides a possibility to predict the bit-error rate (BER) of a turbo code system with only the extrinsic information transfer chart. The idea is to consider the associated soft-input soft-output (SISO) stages as information processors, which map input a priori log likelihood ratios (LLRs) onto output extrinsic LLRs. Compared with other methods, the suggested approach provides insight into the iterative behavior of linear turbo systems with substantial reduction in numerical complexity.

Smart Ads for Better User Targeting

Currently, there is a problem of contextual advertising. Advertisers want to be able to clearly target the audience because user experience and revenue depend on the relevance of the displayed ads. Today's technology makes it easier not only to collect a wealth of information but also to ensure that it accurately reflects on your target audience, industry, and ad campaigns. Contextual advertising supports much of the web's ecosystem today online but not offline. The task was to develop a system prototype and scale it, which gives the ability to display advertising based on user interests in real life based on the best techniques of contextual advertising.

Research of Multichannel User Data to Identify the Degree of Similarity

This chapter introduces the method to assess similarity based on Facebook Graph API and user movements. All movements of users are collected and analyzed. The chapter presents an additional method for analyzing user-generated images on Instagram Graph API. The chapter presents a two-step multiparameter algorithm that generates recommendations based on user social activity and movements. A flexible mechanism for the calculations of time that one spends on a variety of social activities to more accurately identify the relationships between users is presented. To reduce the load on the application, the algorithms of data analysis and transfer optimization are proposed. The ultimate result of the study is to build a platform based on the “client-server” model and includes a mobile app on the iOS platform and server, which would be set up on the “LAMP” platform (L - Linux operating system, A - Apache web server, M - MySQL database, P - PHP programming language). The given result can be used and applied in various spheres of our lives to identify different relationships between people.

Centrality Metrics-Based Connected Dominating Sets for Real-World Network Graphs

The authors investigate the use of centrality metrics as node weights to determine connected dominating sets (CDS) for a suite of 60 real-world network graphs of diverse degree distribution. They employ centrality metrics that are neighborhood-based (degree centrality [DEG] and eigenvector centrality [EVC]), shortest path-based (betweenness centrality [BWC] and closeness centrality [CLC]) as well as the local clustering coefficient complement-based degree centrality metric (LCC'DC), which is a hybrid of the neighborhood and shortest path-based categories. The authors target for minimum CDS node size (number of nodes constituting the CDS). Though both the BWC and CLC are shortest path-based centrality metrics, they observe the BWC-based CDSs to be of the smallest node size for about 60% of the real-world networks and the CLC-based CDSs to be of the largest node size for more than 40% of the real-world networks. The authors observe the computationally light LCC'DC-based CDS node size to be the same as the computationally heavy BWC-based CDS node size for about 50% of the real-world networks.