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Mobile network is evolving from a communication-only network towards one with joint communication and radar/radio sensing (JCAS) capabilities, that we call perceptive mobile network (PMN). Radio sensing here refers to information retrieval from received mobile signals for objects of interest in the environment surrounding the radio transceivers, and it may go beyond the functions of localization, tracking, and object recognition of traditional radar. In PMNs, JCAS integrates sensing into communications, sharing a majority of system modules and the same transmitted signals. The PMN is expected to provide a ubiquitous radio sensing platform and enable a vast number of novel smart applications, whilst providing non-compromised communications. In this paper, we present a broad picture of the motivation, methodologies, challenges, and research opportunities of realizing PMN, by providing a comprehensive survey for systems and technologies developed mainly in the last ten years. Beginning by reviewing the work on coexisting communication and radar systems, we highlight their limits on addressing the interference problem, and then introduce the JCAS technology. We then set up JCAS in the mobile network context and envisage its potential applications. We continue to provide a brief review of three types of JCAS systems, with particular attention to their differences in design philosophy. We then introduce a framework of PMN, including the system platform and infrastructure, three types of sensing operations, and signals usable for sensing. Subsequently, we discuss required system modifications to enable sensing on current communication-only infrastructure. Within the context of PMN, we review stimulating research problems and potential solutions, organized under nine topics: performance bounds, waveform optimization, antenna array design, clutter suppression, sensing parameter estimation, resolution of sensing ambiguity, pattern analysis, networked sensing under cellular topology, and sensing-assisted communications. We conclude the paper by listing key open research problems for the aforementioned topics and sharing some lessons that we have learned.

Joint communication and radar/radio sensing (JCAS), also known as dual-function radar communications , enables the integration of communication and radio sensing into one system, sharing a single transmitted signal. The perceptive mobile network (PMN) is a natural evolution of JCAS from simple point-to-point links to a mobile/cellular network with integrated radio-sensing capability. In this article, we present a system architecture that unifies three types of sensing, investigate the required modifications to existing mobile networks, and exemplify the signals applicable to sensing. We also provide a review to stimulate research problems and potential solutions, including mutual information, joint design and optimization for waveform and antenna grouping, clutter suppression, sensing parameter estimation and pattern recognition, and networked sensing under the cellular topology.

Suppression of undesired non-information bearing multipaths, aka clutter, from received signals is a key process for sensing parameter estimation in the perceptive mobile network, a next generation mobile network that integrates radar sensing into communications. In this correspondence, we propose a novel clutter suppression method based on the Gaussian mixture model (GMM) and expectation maximization (EM) estimation, which can achieve fast and effective clutter estimation requiring only a small number of samples. We then apply a one-dimension (1D) compressive sensing (CS) based sensing algorithm to extract useful channel information after removing the estimated clutter. Simulation results are provided for the proposed solution and existing techniques, and validate the effectiveness of the proposed scheme.

Radio sensing can be integrated with communication in what the authors call future perceptive mobile networks. Due to the complicated signal structure, it is challenging to estimate sensing parameters such as delay, angle of arrival, and Doppler when joint communication and radar/radio sensing is applied in perceptive mobile networks. Radio sensing with signals compatible with a fifth-generation (5G) new radio standard using one-dimension (1D) to 3D compressive sensing (CS) techniques under 5G channel conditions is studied. In the case of 1D–3D CS techniques, they formulate the parameter estimation as a sparse signal recovery problem. These algorithms demonstrate respective advantages, but also show shortcomings in dealing with clustered channels. To effectively exploit the cluster structure in multipath channels, they also propose a 2D cluster Kronecker CS algorithm for significantly improved sensing parameter estimation via introducing a prior probability distribution. Simulation results are provided and they focus the respective advantages and disadvantages of these techniques that validate the effectiveness of the proposed algorithms.

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 paper

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.1

In this paper, we consider an alternative low-cost and flexible solution of using an analog antenna array for radio sensing at the base station receiver in the recently proposed perceptive mobile networks. We provide receiver beamforming design, and advanced compressive sensing (CS) signal processing techniques for sensing parameter estimation in a multiuser-MIMO (MU-MIMO) communications system. Simulation results are provided and validate the effectiveness of the proposed solution and sensing algorithms.

We propose signal stripping based approaches for estimating detailed channel composition parameters for sensing in the recently proposed perceptive mobile networks where simultaneous communication and radar sensing is performed. Via reconstructing a (differentiated) channel matrix, we obtain a signal model which can be solved by conventional compressive sensing (CS) techniques. Parameter estimation methods based on a 1-D CS algorithm are proposed and their effectiveness is validated by provided simulation results.

This paper represents broadcast television services which are becoming increasingly digitized and interactive and advances in video compression technology. IPTV technology which is interacting with broadband access network becomes very popular in the consumer level to get the service of live streams. A number of operators and vendors are currently working on IPTV standardization efforts to support wider availability and interoperability of IPTV as a secure, reliable, managed service. The goal of this special issue is to share knowledge and recent advances in IPTV services, the related research, design and deployment challenges and possible approaches for future evolution of the services.

This paper represents QoS models of an IP Network with requirements to implement which monitors the state of resource usage and adaptively adjusts parameters of traffic admission control to overcome prediction or estimation inaccuracy and achieve a balance between QoS and resource utilization. This study also shows the traffic policy, shaping algorithm along with congestion avoidance mechanism. This study shows approaches for providing QoS involve prediction or estimation in case of traffic characterization to determine parameters required of traffic control.

The objective of this paper is to develop the image processing and recognize the faces using PCA-based face recognition technique. MATLAB based programs are implemented to identify the faces using Indian databases and the Face recognition data, University of Essex, UK. For matching unknown images with known images, different techniques like sum of absolute difference (SAD), sum of squared difference (SSD), normalized cross correlation (NCC) etc. can be used which has been shown in this paper. Finally, it has been shown that the performance of PCA-based face recognition system is quite satisfactory instead of having some shortcomings of the system. (Abstract)