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Register a new userPerformance Tradeoffs of Joint Radar-Communication Networks
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Added by
Ping Ren
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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This letter considers a network where nodes share a wireless channel to work in turn as pulse radars for target detection and as transmitters for data exchange. Radar detection range and network throughput are studied using stochastic geometry tools. We derive closed-form expressions that identify the key tradeoffs between radar and communication operations. Results reveal interesting design hints and stress a marked sensitivity of radar detection to communication interference.
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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.
Joint radar and communication (JRC) technology has become important for civil and military applications for decades. This paper introduces the concepts, characteristics and advantages of JRC technology, presenting the typical applications that have benefited from JRC technology currently and in the future. This paper explores the state-of-the-art of JRC in the levels of coexistence, cooperation, co-design and collaboration. Compared to previous surveys, this paper reviews the entire trends that drive the development of radar sensing and wireless communication using JRC. Specifically, we explore an open research issue on radar and communication operating with mutual benefits based on collaboration, which represents the fourth stage of JRC evolution. This paper provides useful perspectives for future researches of JRC technology.
Mobile network is evolving from a communication-only network towards the one with joint communication and radio/radar 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. In this paper, we provide a comprehensive survey for systems and technologies that enable JCAS in PMN, with a focus on works in the last ten years. Starting with 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 for three types of JCAS systems, with particular attention to their differences on the 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, and 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 eight topics: mutual information, waveform optimization, antenna array design, clutter suppression, sensing parameter estimation, pattern analysis, networked sensing under cellular topology, and sensing-assisted secure communication. This paper provides a comprehensive picture for the motivation, methodology, challenges, and research opportunities of realizing PMN. The PMN is expected to provide a ubiquitous radio sensing platform and enable a vast number of novel smart applications.
This work characterises the effect of mutual interference in a planar network of pulsed-radar devices. Using stochastic geometry tools and a strongest interferer approximation, we derive simple closed-form expressions that pinpoint the role played by key system parameters on radar detection range and false alarm rate in the interference-limited region. The fundamental tradeoffs of the system between radar performance, network density and antenna directivity are captured for different path-loss exponents in the no-fading and Rayleigh-fading cases. The discussion highlights practical design hints for tuning the radar parameters. The accuracy of the model is verified through network simulations, and the role of random noise on detection in sparse, non interference-limited networks is characterised.
In this paper, we propose multi-input multi-output (MIMO) beamforming designs towards joint radar sensing and multi-user communications. We employ the Cramer-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios. We then propose minimizing the CRB of radar sensing while guaranteeing a pre-defined level of signal-to-interference-plus-noise ratio (SINR) for each communication user. For the single-user scenario, we derive a closed form for the optimal solution for both cases of point and extended targets. For the multi-user scenario, we show that both problems can be relaxed into semidefinite programming by using the semidefinite relaxation approach, and prove that the global optimum can always be obtained. Finally, we demonstrate numerically that the globally optimal solutions are reachable via the proposed methods, which provide significant gains in target estimation performance over state-of-the-art benchmarks.
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