Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Indoor wireless communications and applications

Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter

A Localization System for Optimizing the Deployment of Small Cells in 2-Tier Heterogeneous Wireless Networks

Due to the ever growing population of mobile device users and expansion on the number of devices and applications requiring data usage, there is an increasing demand for improved capacity in wireless cellular networks. Cell densification and 2-tier heterogeneous networks (HetNets) are two solutions which will assist 5G systems in meeting these growing capacity demands. Small-cell deployment over existing heterogeneous networks have been considered by researchers. Different strategies for deploying these small-cells within the existing network among which are random, cell-edge and high user concentration (HUC) have also been explored. Small cells deployed on locations of HUC offloads traffic from existing network infrastructure, ensure good Quality of Service (QoS) and balanced load in the network but there is a challenge of identifying HUC locations. There has been considerable research performed into techniques for determining user location and cell deployment. Currently localization can be achieved using time dependent methods such as Time of Arrival (ToA), Time Difference of Arrival (TDoA), or Global Positioning Systems (GPS). GPS based solutions provide high accuracy user positioning but suffer from concerns over user privacy, and other time dependent approaches require regular synchronization which can be difficult to achieve in practice. Alternatively, Received Signal Strength (RSS) based solutions can provide simple anonymous user data, requiring no extra hardware within the mobile handset but often rely on triangulation from adjacent Base Stations (BS). In mobile cellular networks such solutions are therefore often only applicable near the cell edge, as installing additional BS would increase the complexity and cost of a network deployment. The work presented in this thesis overcomes these limitations by providing an observer system for wireless networks that can be used to periodically monitor the cell coverage area and identify regions of high concentrations of users for possible small cell deployment in 2-tier heterogeneous networks. The observer system comprises of two collinear antennas separated by λ/2. The relative phase of each antenna was varied using a phase shifter so that the combined output of the two antennas were used to create sum and difference radiation patterns, and to steer the antenna radiation pattern creating different azimuth positions for AoA estimation. Statistical regression analysis was used to develop range estimation models based on four different environment empirical pathloss models for user range estimation. Users were located into clusters by classifying them into azimuth-range classes and counting the number of users in each class. Locations for small cell deployment were identified based on class population. BPEM, ADEM, BUEM, EARM and NLOS models were developed for more accurate range estimation. A prototype system was implemented and tested both outdoor and indoor using a network of WiFi nodes. Experimental results show close relationship with simulation and an average PER in range estimation error of 80% by applying developed error models. Based on both simulation and experiment, system showed good performance. By deploying micro-, pico-, or femto-cells in areas of higher user concentration, high data rates and good quality of service in the network can be maintained. The observer system provides the network manager with relative angle of arrival (AoA), distance estimation and relative location of user clusters within the cell. The observer system divides the cell into a series of azimuthal and range sectors, and determines which sector the users are located in. Simulation and a prototype design of the system is presented and results have shown system robustness and high accuracy for its purpose

Location Estimation in Wireless Communication Systems

Localization has become a key enabling technology in many emerging wireless applications and services. One of the most challenging problems in wireless localization technologies is that the performance is easily affected by the signal propagation environment. When the direct path between transmitter and receiver is obstructed, the signal measurement error for the localization process will increase significantly. Considering this problem, we first propose a novel algorithm which can automatically detect and remove the obstruction and improve the localization performance in complex environment. Besides the environmental dependency, the accuracy of target location estimation is highly sensitive to the positions of reference nodes. In this thesis, we also study on the reference node placement, and derive an optimum deployment scheme which can provide the best localization accuracy. Another challenge of wireless localization is due to insufficient number of reference nodes available in the target\u27s communication range. In this circumstance, we finally utilize the internal sensors in today\u27s smartphones to provide additional information for localization purpose, and propose a novel algorithm which can combine the location dependent parameters measured from sensors and available reference nodes together. The combined localization algorithm can overcome the error accumulation from sensor with the help of only few number of reference nodes

Doctor of Philosophy

dissertationThe wireless radio channel is typically thought of as a means to move information from transmitter to receiver, but the radio channel can also be used to detect changes in the environment of the radio link. This dissertation is focused on the measurements we can make at the physical layer of wireless networks, and how we can use those measurements to obtain information about the locations of transceivers and people. The first contribution of this work is the development and testing of an open source, 802.11b sounder and receiver, which is capable of decoding packets and using them to estimate the channel impulse response (CIR) of a radio link at a fraction of the cost of traditional channel sounders. This receiver improves on previous implementations by performing optimized matched filtering on the field-programmable gate array (FPGA) of the Universal Software Radio Peripheral (USRP), allowing it to operate at full bandwidth. The second contribution of this work is an extensive experimental evaluation of a technology called location distinction, i.e., the ability to identify changes in radio transceiver position, via CIR measurements. Previous location distinction work has focused on single-input single-output (SISO) radio links. We extend this work to the context of multiple-input multiple-output (MIMO) radio links, and study system design trade-offs which affect the performance of MIMO location distinction. The third contribution of this work introduces the "exploiting radio windows" (ERW) attack, in which an attacker outside of a building surreptitiously uses the transmissions of an otherwise secure wireless network inside of the building to infer location information about people inside the building. This is possible because of the relative transparency of external walls to radio transmissions. The final contribution of this dissertation is a feasibility study for building a rapidly deployable radio tomographic (RTI) imaging system for special operations forces (SOF). We show that it is possible to obtain valuable tracking information using as few as 10 radios over a single floor of a typical suburban home, even without precise radio location measurements