Joint Communication and Radar Sensing in 5G Mobile Network by Compressive Sensing

© 2019 IEEE. There is growing interest in integrating communication and radar sensing into one system. However, very limited results are reported on how to realize sensing using complicated mobile signals when joint communication and radar sensing (JCAS) is applied to mobile networks. This paper studies radar sensing using one-dimension (1D) to 3D compressive sensing (CS) techniques, referring to signals compatible with latest fifth generation (5G) new radio (NR) standard. We demonstrate that radio sensing using both downlink and uplink 5G signals can be realized with reasonable performance using these CS techniques, and highlight the respective advantages and disadvantages of these techniques.

Framework for a Perceptive Mobile Network using Joint Communication and Radar Sensing

In this paper, we develop a framework for a novel perceptive mobile/cellular network that integrates radar sensing function into the mobile communication network. We propose a unified system platform that enables downlink and uplink sensing, sharing the same transmitted signals with communications. We aim to tackle the fundamental sensing parameter estimation problem in perceptive mobile networks, by addressing two key challenges associated with sophisticated mobile signals and rich multipath in mobile networks. To extract sensing parameters from orthogonal frequency division multiple access (OFDMA) and spatial division multiple access (SDMA) communication signals, we propose two approaches to formulate it to problems that can be solved by compressive sensing techniques. Most sensing algorithms have limits on the number of multipath signals for their inputs. To reduce the multipath signals, as well as removing unwanted clutter signals, we propose a background subtraction method based on simple recursive computation, and provide a closed-form expression for performance characterization. The effectiveness of these methods is validated in simulations.Comment: 14 pages, 12 figures, Journal pape

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

Joint Compressed Sensing and Manipulation of Wireless Emissions with Intelligent Surfaces

Programmable, intelligent surfaces can manipulate electromagnetic waves impinging upon them, producing arbitrarily shaped reflection, refraction and diffraction, to the benefit of wireless users. Moreover, in their recent form of HyperSurfaces, they have acquired inter-networking capabilities, enabling the Internet of Material Properties with immense potential in wireless communications. However, as with any system with inputs and outputs, accurate sensing of the impinging wave attributes is imperative for programming HyperSurfaces to obtain a required response. Related solutions include field nano-sensors embedded within HyperSurfaces to perform minute measurements over the area of the HyperSurface, as well as external sensing systems. The present work proposes a sensing system that can operate without such additional hardware. The novel scheme programs the HyperSurface to perform compressed sensing of the impinging wave via simple one-antenna power measurements. The HyperSurface can jointly be programmed for both wave sensing and wave manipulation duties at the same time. Evaluation via simulations validates the concept and highlight its promising potential.Comment: Published at IEEE DCOSS 2019 / IoT4.0 workshop (https://www.dcoss.org/workshops.html). Funded by the European Union via the Horizon 2020: Future Emerging Topics - Research and Innovation Action call (FETOPEN-RIA), grant EU736876, project VISORSURF (http://www.visorsurf.eu

Cooperative Passive Coherent Location: A Promising 5G Service to Support Road Safety

5G promises many new vertical service areas beyond simple communication and data transfer. We propose CPCL (cooperative passive coherent location), a distributed MIMO radar service, which can be offered by mobile radio network operators as a service for public user groups. CPCL comes as an inherent part of the radio network and takes advantage of the most important key features proposed for 5G. It extends the well-known idea of passive radar (also known as passive coherent location, PCL) by introducing cooperative principles. These range from cooperative, synchronous radio signaling, and MAC up to radar data fusion on sensor and scenario levels. By using software-defined radio and network paradigms, as well as real-time mobile edge computing facilities intended for 5G, CPCL promises to become a ubiquitous radar service which may be adaptive, reconfigurable, and perhaps cognitive. As CPCL makes double use of radio resources (both in terms of frequency bands and hardware), it can be considered a green technology. Although we introduce the CPCL idea from the viewpoint of vehicle-to-vehicle/infrastructure (V2X) communication, it can definitely also be applied to many other applications in industry, transport, logistics, and for safety and security applications

Beyond 5G RIS mmWave Systems: Where Communication and Localization Meet

Upcoming beyond fifth generation (5G) communications systems aim at further enhancing key performance indicators and fully supporting brand-new use cases by embracing emerging techniques, e.g., reconfigurable intelligent surface (RIS), integrated communication, localization, and sensing, and mmWave/THz communications. The wireless intelligence empowered by state-of-the-art artificial intelligence techniques has been widely considered at the transceivers, and now the paradigm is deemed to be shifted to the smart control of radio propagation environment by virtue of RISs. In this paper, we argue that to harness the full potential of RISs, localization and communication must be tightly coupled. This is in sharp contrast to 5G and earlier generations, where localization was a minor additional service. To support this, we first introduce the fundamentals of RIS mmWave channel modeling, followed by RIS channel state information acquisition and link establishment. Then, we deal with the connection between localization and communications, from a separate and joint perspective

5G PRS-Based Sensing: A Sensing Reference Signal Approach for Joint Sensing and Communication System

The emerging joint sensing and communication (JSC) technology is expected to support new applications and services, such as autonomous driving and extended reality (XR), in the future wireless communication systems. Pilot (or reference) signals in wireless communications usually have good passive detection performance, strong anti-noise capability and good auto-correlation characteristics, hence they bear the potential for applying in radar sensing. In this paper, we investigate how to apply the positioning reference signal (PRS) of the 5th generation (5G) mobile communications in radar sensing. This approach has the unique benefit of compatibility with the most advanced mobile communication system available so far. Thus, the PRS can be regarded as a sensing reference signal to simultaneously realize the functions of radar sensing, communication and positioning in a convenient manner. Firstly, we propose a PRS based radar sensing scheme and analyze its range and velocity estimation performance, based on which we propose a method that improves the accuracy of velocity estimation by using multiple frames. Furthermore, the Cramer-Rao lower bound (CRLB) of the range and velocity estimation for PRS based radar sensing and the CRLB of the range estimation for PRS based positioning are derived. Our analysis and simulation results demonstrate the feasibility and superiority of PRS over other pilot signals in radar sensing. Finally, some suggestions for the future 5G-Advanced and 6th generation (6G) frame structure design containing the sensing reference signal are derived based on our study