Joint communication and positioning based on soft channel parameter estimation
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RESEARCH
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Joint communication and positioning based on soft channel parameter estimation Kathrin Schmeink*, Rebecca Adam and Peter Adam Hoeher
Abstract A joint communication and positioning system based on maximum-likelihood channel parameter estimation is proposed. The parameters of the physical channel, needed for positioning, and the channel coefficients of the equivalent discrete-time channel model, needed for communication, are estimated jointly using a priori information about pulse shaping and receive filtering. The paper focusses on the positioning part of the system. It is investigated how soft information for the parameter estimates can be obtained. On the basis of confidence regions, two methods for obtaining soft information are proposed. The accuracy of these approximative methods depends on the nonlinearity of the parameter estimation problem, which is analyzed by so-called curvature measures. The performance of the two methods is investigated by means of Monte Carlo simulations. The results are compared with the Cramer-Rao lower bound. It is shown that soft information aids the positioning. Negative effects caused by multipath propagation can be mitigated significantly even without oversampling. 1 Introduction Interest in joint communication and positioning is steadily increasing [1]. Synergetic effects like improved resource allocation and new applications like locationbased services or a precise location determination of emergency calls are attractive features of joint communication and positioning. Since the system requirements of communication and positioning are quite different, it is a challenging task to combine them: Communication aims at high data rates with little training overhead. Only the channel coefficients of the equivalent discretetime channel model, which includes pulse shaping and receive filtering in addition to the physical channel, need to be estimated for data detection. In contrast, positioning aims at precise position estimates. Therefore, parameters of the physical channel like the time of arrival (TOA) or the angle of arrival (AOA) need to be estimated as accurately as possible [2,3]. Significant training is typically spent for this purpose. In this paper, a joint communication and positioning system based on maximum-likelihood channel parameter estimation is suggested [4]. The estimator exploits the fact that channel and parameter estimation are closely related. The parameters of the physical channel and * Correspondence: [email protected] Information and Coding Theory Lab Faculty of Engineering, University of Kiel Kaiserstrasse 2, 24143 Kiel, Germany
the channel coefficients of the equivalent discrete-time channel model are estimated jointly by utilizing a priori information about pulse shaping and receive filtering. Hence, training symbols that are included in the data burst aid both communication and positioning. On the one hand, in [5-7], it is proposed to use a priori information about pulse shaping and receive filtering in order to improve the estimat
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