Exploiting radio interference for human sensing and communications

Wireless devices are now widely deployed in all indoor spaces including homes, offices, shopping malls, etc. In these spaces, we are ubiquitously enveloped by radio spectrum. The presence of various objects such as furniture, walls and human bodies influences the radio wave propagations in numerous ways owing to reflection and diffraction of the wireless signals. As a result, the signals from multiple propagation paths may overlap in constructive and destructive ways which results in radio interference. Interference is normally construed to be undesirable, since it adversely affects the signal quality at the receiver node. Thus various techniques have been proposed in literature to minimise the effects of interference. In this thesis, on the contrary, we show that radio interference can be used to our advantage in the context of two distinct application domains: (i) device-free human sensing and (ii) multi-hop communications. First, we show that WiFi signals can be used to uniquely identify people. There is strong evidence to suggest that all humans have unique gait patterns. While walking in vicinity of WiFi devices, an individual will interfere with the radio propagations and create unique perturbations in the WiFi spectrum. The unique features that are representative of the gait of the individual are extracted to identify the person. We conduct extensive experiments to demonstrate the proposed system can uniquely identify people with an average accuracy of 93% to 77% from a group comprised of 2 to 6 people. Second, we propose a system that is able to monitor breathing rate in a natural setting where the individual can perform actions such as reading, writing, using their phone, etc. We observe breathing and accompanying actions create both constructive and destructive interference. Certain specific subcarriers carry strong imprints of the subtle chest motions that occur during breathing because of the frequency and spacial diversity of MIMO technology that is employed in the state-of-the-art WiFi devices. Our proposed methods are used to identify those subcarriers and precisely isolate the breathing signals. We implement both previous works on commercial off-the-shelf WiFi devices and exploit Channel State Information (CSI) of WiFi signals to extract the patterns for human identification and breath rate monitoring in RF spectrum. Third, we propose a novel point-to-point communication protocol, which exploit the benefits of constructive interference in order to enhance communication reliability and reduce energy consumption. The proposed protocol attempts to discover the most reliable and energy efficient route between a source and a destination. To achieve this objective, the proposed algorithm identifies direct routes and selects helper nodes to ensure reliable communications while allowing all other devices in the network to power down. During data transmissions, the selected nodes can exploit the benefits of constructive interference to increase received signal strength and enhance reliability. Extensive experiments show the proposed method can save energy consumption by up to 82.5% compared to a state-of-the-art approach whilst achieving similar end-to-end transmission reliability

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

Sparse Signal Processing Concepts for Efficient 5G System Design

As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges and what are the key drivers is part of intensive, ongoing discussions. Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years. In this paper we will describe a variety of scenarios in which signal sparsity arises naturally in 5G wireless systems. Signal sparsity and the associated rich collection of tools and algorithms will thus be a viable source for innovation in 5G wireless system design. We will discribe applications of this sparse signal processing paradigm in MIMO random access, cloud radio access networks, compressive channel-source network coding, and embedded security. We will also emphasize important open problem that may arise in 5G system design, for which sparsity will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces

Massive MIMO for Internet of Things (IoT) Connectivity

Massive MIMO is considered to be one of the key technologies in the emerging 5G systems, but also a concept applicable to other wireless systems. Exploiting the large number of degrees of freedom (DoFs) of massive MIMO essential for achieving high spectral efficiency, high data rates and extreme spatial multiplexing of densely distributed users. On the one hand, the benefits of applying massive MIMO for broadband communication are well known and there has been a large body of research on designing communication schemes to support high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT) is still a developing topic, as IoT connectivity has requirements and constraints that are significantly different from the broadband connections. In this paper we investigate the applicability of massive MIMO to IoT connectivity. Specifically, we treat the two generic types of IoT connections envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC). This paper fills this important gap by identifying the opportunities and challenges in exploiting massive MIMO for IoT connectivity. We provide insights into the trade-offs that emerge when massive MIMO is applied to mMTC or URLLC and present a number of suitable communication schemes. The discussion continues to the questions of network slicing of the wireless resources and the use of massive MIMO to simultaneously support IoT connections with very heterogeneous requirements. The main conclusion is that massive MIMO can bring benefits to the scenarios with IoT connectivity, but it requires tight integration of the physical-layer techniques with the protocol design.Comment: Submitted for publicatio