Perceptive Mobile Network Based on Joint Communication and Radio Sensing

University of Technology Sydney. Faculty of Engineering and Information Technology.Radio networks have been evolving from communication-only wireless connectivity to a network for services, which will enable new business models and user experiences for emerging industrial applications. Many of these applications, including automotive, industrial automation, public safety and security tasks, will require information retrieval relating to mobile devices and objects through radio sensing. Radio sensing here refers to the process of information extraction for objects of interest in the surrounding environment that is covered by radio signals. We call the evolutionary mobile network with both communication and radio sensing functions as a perceptive mobile network. Such joint functions can be promoted as one of the core components in future 5G/6G standards. The parametric values regarding moving objects, human movement, and any change in the environment surrounding the user equipment are embedded with the wireless signal and this enables the possibility of using the cellular signal for information extraction. As both wireless communication and radar system exhibit similar receiver front-end architecture at high frequency, it triggers the concepts of joint communication and radio sensing (JCAS) operation. In that circumstance, a unified platform can introduce shared hardware between two functions, which eventually implies reduced size, cost and weight. The main purpose of this doctoral study is to analyse the radio sensing capability of a mobile network and design the framework for joint operation. The thesis aims to design advanced signals and protocols that allow communications and sensing to be better implemented jointly and benefit from each other efficiently. An additional goal is to investigate the existing sensing parameter estimation processes and their suitability in signal processing for JCAS operation. The thesis provides a general framework for the envisioned perceptive mobile networks that enable radio sensing using downlink and uplink mobile signaling, by considering future mobile network architecture and components, practical sophisticated communication signal format, and complicated signal propagation environment. The thesis discusses the required modifications and upgrades to existing mobile networks to facilitate JCAS functionalities. One and multi-dimensional compressive sensing techniques are successfully employed for estimating the parameters of the sensed scene, following the state of the art, by applying orthogonal frequency-division multiplexing (OFDM) based multi-user multiple-input multiple-output (MIMO) signal model. The simulated results presented here demonstrate reasonable performance in radio sensing using perceptive mobile networks. The research works shown in this thesis indicate the feasibility of the perceptive mobile network and provide a way to proceed

Study of the cyclostationarity properties of various signals of opportunity

Global Navigation Satellite Systems (GNSS) offer precise position estimation and navigation services outdoor but they are rarely accessible in strong multipath environments, such as indoor environments. Fortunately, several Signals of Opportunity (SoO), (such as RFID, Wi-Fi, Bluetooth, digital TV signals, etc.) are readily available in these environments, creating an opportunity for seamless positioning. Performance evolution of positioning can be achieved through contextual exploitation of SoO. The detection and identification of available SoO signals or of the signals which are most relevant to localization and the signal selection in an optimum way, according to designer defined optimality criteria, are important stages to enter such contextual awareness domain. Man-made modulated signals have certain properties which vary periodically in time and this time-varying periodical characteristics trigger what is known as cyclostationarity. Cyclostationarity analysis can be used, among others, as a tool for signal detection. Detected signals through cyclostationary features can be exploited as SoO. The main purpose of this thesis is to study and analyze the cyclostationarity properties of various SoO. An additional goal is to investigate whether such cyclostationarity properties can be used to detect, identify and distinguish the signals which are present in a certain frequency band. The thesis is divided into two parts. In the literature review part, the physical layer study of several signals is given, by emphasizing the potential of SoO in positioning. In the implementation part, the possibility of signals detection through cyclostationary features is investigated through MATLAB simulations. Cyclostationary properties obtained through FFT accumulation Method (FAM) and statistical performance of detection are studied in the presence of stationary additive white Gaussian noise (AWGN). Besides that, the performance in signal detection using cyclostationary-based detector is also compared to the performance with the energy-based detectors, used as benchmarks. The simulated result suggest that cyclostationary features can certainly detect the presence of signals in noise, but simple cases, such as one type of signal only and AWGN noise, are better addressed via traditional energy-based detection. However, cyclostationary features can exhibit advantages in other types of noises and in the presence of signal mixtures which in fact may fulfil one of the preliminary requirements of cognitive positioning

Study of the cyclostationarity properties of various signals of opportunity

Global Navigation Satellite Systems (GNSS) offer precise position estimation and navigation services outdoor but they are rarely accessible in strong multipath environments, such as indoor environments. Fortunately, several Signals of Opportunity (SoO), (such as RFID, Wi-Fi, Bluetooth, digital TV signals, etc.) are readily available in these environments, creating an opportunity for seamless positioning. Performance evolution of positioning can be achieved through contextual exploitation of SoO. The detection and identification of available SoO signals or of the signals which are most relevant to localization and the signal selection in an optimum way, according to designer defined optimality criteria, are important stages to enter such contextual awareness domain. Man-made modulated signals have certain properties which vary periodically in time and this time-varying periodical characteristics trigger what is known as cyclostationarity. Cyclostationarity analysis can be used, among others, as a tool for signal detection. Detected signals through cyclostationary features can be exploited as SoO. The main purpose of this thesis is to study and analyze the cyclostationarity properties of various SoO. An additional goal is to investigate whether such cyclostationarity properties can be used to detect, identify and distinguish the signals which are present in a certain frequency band. The thesis is divided into two parts. In the literature review part, the physical layer study of several signals is given, by emphasizing the potential of SoO in positioning. In the implementation part, the possibility of signals detection through cyclostationary features is investigated through MATLAB simulations. Cyclostationary properties obtained through FFT accumulation Method (FAM) and statistical performance of detection are studied in the presence of stationary additive white Gaussian noise (AWGN). Besides that, the performance in signal detection using cyclostationary-based detector is also compared to the performance with the energy-based detectors, used as benchmarks. The simulated result suggest that cyclostationary features can certainly detect the presence of signals in noise, but simple cases, such as one type of signal only and AWGN noise, are better addressed via traditional energy-based detection. However, cyclostationary features can exhibit advantages in other types of noises and in the presence of signal mixtures which in fact may fulfil one of the preliminary requirements of cognitive positioning