1,435 research outputs found

    Indoor radio channel characterization and modeling for a 5.2-GHz bodyworn receiver

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    [Abstract]: Wireless local area network applications may include the use of bodyworn or handportable terminals. For the first time, this paper compares measurements and simulations of a narrowband 5.2-GHz radio channel incorporating a fixed transmitter and a mobile bodyworn receiver. Two indoor environments were considered, an 18-m long corridor and a 42-m2 office. The modeling technique was a site-specific ray-tracing simulator incorporating the radiation pattern of the bodyworn receiver. In the corridor, the measured body-shadowing effect was 5.4 dB, while it was 15.7 dB in the office. First- and second-order small-scale fading statistics for the measured and simulated results are presented and compared with theoretical Rayleigh and lognormal distributions. The root mean square error in the cumulative distributions for the simulated results was less than 0.74% for line-of-sight conditions and less than 1.4% for nonline-of-sight conditions

    AROMA: Automatic Generation of Radio Maps for Localization Systems

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    WLAN localization has become an active research field recently. Due to the wide WLAN deployment, WLAN localization provides ubiquitous coverage and adds to the value of the wireless network by providing the location of its users without using any additional hardware. However, WLAN localization systems usually require constructing a radio map, which is a major barrier of WLAN localization systems' deployment. The radio map stores information about the signal strength from different signal strength streams at selected locations in the site of interest. Typical construction of a radio map involves measurements and calibrations making it a tedious and time-consuming operation. In this paper, we present the AROMA system that automatically constructs accurate active and passive radio maps for both device-based and device-free WLAN localization systems. AROMA has three main goals: high accuracy, low computational requirements, and minimum user overhead. To achieve high accuracy, AROMA uses 3D ray tracing enhanced with the uniform theory of diffraction (UTD) to model the electric field behavior and the human shadowing effect. AROMA also automates a number of routine tasks, such as importing building models and automatic sampling of the area of interest, to reduce the user's overhead. Finally, AROMA uses a number of optimization techniques to reduce the computational requirements. We present our system architecture and describe the details of its different components that allow AROMA to achieve its goals. We evaluate AROMA in two different testbeds. Our experiments show that the predicted signal strength differs from the measurements by a maximum average absolute error of 3.18 dBm achieving a maximum localization error of 2.44m for both the device-based and device-free cases.Comment: 14 pages, 17 figure

    A 3D pyramid network for short ranged high data rate communications at 60 GHz

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    Indoor wireless communications and applications

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    Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter

    ns-3 Implementation of the 3GPP MIMO Channel Model for Frequency Spectrum above 6 GHz

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    Communications at mmWave frequencies will be a key enabler of the next generation of cellular networks, due to the multi-Gbps rate that can be achieved. However, there are still several problems that must be solved before this technology can be widely adopted, primarily associated with the interplay between the variability of mmWave links and the complexity of mobile networks. An end-to-end network simulator represents a great tool to assess the performance of any proposed solution to meet the stringent 5G requirements. Given the criticality of channel propagation characteristics at higher frequencies, we present our implementation of the 3GPP channel model for the 6-100 GHz band for the ns-3 end-to-end 5G mmWave module, and detail its associated MIMO beamforming architecture

    Enhanced indoor location tracking through body shadowing compensation

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    This paper presents a radio frequency (RF)-based location tracking system that improves its performance by eliminating the shadowing caused by the human body of the user being tracked. The presence of such a user will influence the RF signal paths between a body-worn node and the receiving nodes. This influence will vary with the user's location and orientation and, as a result, will deteriorate the performance regarding location tracking. By using multiple mobile nodes, placed on different parts of a human body, we exploit the fact that the combination of multiple measured signal strengths will show less variation caused by the user's body. Another method is to compensate explicitly for the influence of the body by using the user's orientation toward the fixed infrastructure nodes. Both approaches can be independently combined and reduce the influence caused by body shadowing, hereby improving the tracking accuracy. The overall system performance is extensively verified on a building-wide testbed for sensor experiments. The results show a significant improvement in tracking accuracy. The total improvement in mean accuracy is 38.1% when using three mobile nodes instead of one and simultaneously compensating for the user's orientation

    광선 추적 기법에 기반한 실내 무선 채널에서의 사용자 쉐도잉 영향

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    학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 김성철.In this dissertation, the effects of user body on radio wave propagation in indoor wireless channels are analyzed. The user who is nearly always being close to mobile device influences very strongly and consistently on propagation channel. Therefore, exclusively focusing on the user body separately from other bodies, the shadowing effects caused by the user are investigated at 2.4 GHz by using the uniform theory of diffraction (UTD) and the ray-tracing technique. First of all, the user body shadowing (UBS) effects on a single ray path are investigated deterministically by using the UTD. The UTD scattering solutions for diffraction at a smooth convex surface are adopted to analyze the effects of user body modeled as a circular cylinder. The UTD-based model for a single ray path is defined as the relative received signal power according to the relative position of user, which is validated by measurements in an anechoic chamber. The validated UTD-based model is combined with the indoor ray-tracing technique in order to examine the UBS effects on multipath channels. Since the ray-tracing provides not only the powers of multipaths but also their angular profiles, it is possible to apply the UTD single path model according to the relationship between the users position and the direction of rays. This combination method is also verified by in-building measurements. In realistic communications, however, the users position can be neither fixed at any one value and nor can its exact value be provided to systems in real time. Thus, a statistical analysis for the UBS is conducted taking into consideration the randomness of users position. First, the K-factor, defined as the ratio of the power in the dominant path and the sum of the powers in the other paths, is proposed as the most significant factor to determine the UBS effects. Because the UBS effects considerably depend on the extent of the dominant path and whether the dominant path is blocked. As a result, the distributions of total power losses caused by the UBS are link-by-link modeled by Nakagami-m distributions. Additionally, the estimated parameter m is proposed as a function of K-factor. Finally, the enhanced UBS stochastic model is proposed based on the bimodal characteristics of UBS. The UBS model based on Nakagami-m distribution has a drawback of inaccuracies for the links with high K-factor because the distribution of total UBS losses for links with high K-factors has a bimodal shape that has two peaks in its histogram. Therefore, the distributions of total UBS losses were classified into unimodal and bimodal groups with the quantitative decision criterion of K-factor. For the unimodal model, Rician distribution is used to achieve the best accuracy, whereas Gaussian mixture model is exploited for the bimodal UBS model. The validity of these proposed models is verified using the ray-tracing simulation in various indoor environments.Chapter 1 Introduction....................................................1 1.1 Indoor Wireless Propagation Channel ........................1 1.2 User Body Effects on Wireless Propagation Channel....2 1.3 Dissertation Outline..................................................4 Chapter 2 User Body Shadowing Effects on Single Ray Path based on UTD..........................5 2.1 Introduction.............................................................5 2.2 Uniform Theory of Diffraction (UTD)............................6 2.3 UTD Solutions at a Smooth Convex Surface................7 2.4 UBS Effects on a Single Ray Path.............................12 2.5 Conclusion............................................................16 Chapter 3 Analysis of UBS Effects on Indoor Wireless Multipath Channels..............17 3.1 Introduction...........................................................17 3.2 Ray-Tracing Technique..........................................18 3.2.1 Image Method.....................................................18 3.2.2 Reliability ...........................................................19 3.3 Application of Single Ray UBS Model to Multipath Channel.........24 3.3.1 Methodology........................................................24 3.3.2 Validation............................................................27 3.4 Link-by-Link Model using Nakagami-m Distribution....29 3.5 Conclusion............................................................35 Chapter 4 Enhanced Statistical Model for UBS based on Bimodal Characteristics............36 4.1 Introduction...........................................................36 4.2 Methodology for Enhancement of UBS Model............39 4.2.1 Bimodal Characteristics of UBS Model...................42 4.2.2 Data Grouping.....................................................43 4.2.3 Other Factors......................................................45 4.3 Ray-Tracing Simulation..........................................48 4.4 Enhanced Statistical UBS Model..............................51 4.4.1 Bimodality in terms of K-factor..............................51 4.4.2 The Unimodal UBS Model....................................54 4.4.3 The Bimodal UBS Model......................................57 4.4.4 Application of the Proposed Model for Other Environments...61 4.5 Conclusion...........................................................64 Chapter 5 Conclusion ..............................................65 5.1 Summary...............................................................65 5.2 Expansion and Application of User Body Effects..........66 5.2.1 Other Frequency Bands.........................................66 5.2.2 Device-to-Device (D2D) Communications................67 5.2.3 Temporal Variation of UBS.....................................71 Bibliography................................................................72 Abstract in Korean.......................................................80Docto
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