10,132 research outputs found

    An indoor variance-based localization technique utilizing the UWB estimation of geometrical propagation parameters

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    A novel localization framework is presented based on ultra-wideband (UWB) channel sounding, employing a triangulation method using the geometrical properties of propagation paths, such as time delay of arrival, angle of departure, angle of arrival, and their estimated variances. In order to extract these parameters from the UWB sounding data, an extension to the high-resolution RiMAX algorithm was developed, facilitating the analysis of these frequency-dependent multipath parameters. This framework was then tested by performing indoor measurements with a vector network analyzer and virtual antenna arrays. The estimated means and variances of these geometrical parameters were utilized to generate multiple sample sets of input values for our localization framework. Next to that, we consider the existence of multiple possible target locations, which were subsequently clustered using a Kim-Parks algorithm, resulting in a more robust estimation of each target node. Measurements reveal that our newly proposed technique achieves an average accuracy of 0.26, 0.28, and 0.90 m in line-of-sight (LoS), obstructed-LoS, and non-LoS scenarios, respectively, and this with only one single beacon node. Moreover, utilizing the estimated variances of the multipath parameters proved to enhance the location estimation significantly compared to only utilizing their estimated mean values

    Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

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    Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

    Target motion estimation via a multistatic FSR

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    The focus of this paper is on the estimation of the kinematic parameters of moving targets via a MIMO Forward Scatter Radar (FSR) system. A sub-optimum estimation technique is considered that exploits the information concerning the time instants at which the target crosses the individual baselines to retrieve the motion parameters. The accuracy of such technique is firstly investigated from a theoretical point of view and then the effectiveness of the proposed approach is demonstrated by applying it to live MIMO FSR data. Shown results prove the practical applicability of the proposed technique

    Visible Light Communications towards 5G

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    5G networks have to offer extremely high capacity for novel streaming applications. One of the most promising approaches is to embed large numbers of co-operating small cells into the macro-cell coverage area. Alternatively, optical wireless based technologies can be adopted as an alternative physical layer offering higher data rates. Visible light communications (VLC) is an emerging technology for future high capacity communication links (it has been accepted to 5GPP) in the visible range of the electromagnetic spectrum (~370–780 nm) utilizing light-emitting diodes (LEDs) simultaneously provide data transmission and room illumination. A major challenge in VLC is the LED modulation bandwidths, which are limited to a few MHz. However, myriad gigabit speed transmission links have already been demonstrated. Non line-of-sight (NLOS) optical wireless is resistant to blocking by people and obstacles and is capable of adapting its’ throughput according to the current channel state information. Concurrently, organic polymer LEDs (PLEDs) have become the focus of enormous attention for solid-state lighting applications due to their advantages over conventional white LEDs such as ultra-low costs, low heating temperature, mechanical flexibility and large photoactive areas when produced with wet processing methods. This paper discusses development of such VLC links with a view to implementing ubiquitous broadcasting networks featuring advanced modulation formats such as orthogonal frequency division multiplexing (OFDM) or carrier-less amplitude and phase modulation (CAP) in conjunction with equalization techniques. Finally, this paper will also summarize the results of the European project ICT COST IC1101 OPTICWISE (Optical Wireless Communications - An Emerging Technology) dealing VLC and OLEDs towards 5G networks
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