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Tareq Y Alnaffouri - One of the best experts on this subject based on the ideXlab platform.

  • sensor placement and resource allocation for energy harvesting iot networks
    Digital Signal Processing, 2020
    Co-Authors: Osama M Bushnaq, Anas Chaaban, Sundeep Prabhakar Chepuri, Geert Leus, Tareq Y Alnaffouri
    Abstract:

    Abstract Optimal sensor selection for source parameter estimation in energy harvesting Internet of Things (IoT) networks is studied in this paper. Specifically, the focus is on the selection of the sensor locations which minimizes the estimation error at a fusion center, and to optimally allocate power and bandwidth for each selected sensor subject to a prescribed spectral and energy budget. To do so, measurement accuracy, communication link quality, and the amount of energy harvested are all taken into account. The sensor selection is studied under both analog and Digital Transmission schemes from the selected sensors to the fusion center. In the Digital Transmission case, an information theoretic approach is used to model the Transmission rate, observation quantization, and encoding. We numerically prove that with a sufficient system bandwidth, the Digital system outperforms the analog system with a possibly different sensor selection. The design problem of interest is a Boolean non convex optimization problem, which is solved by relaxing the Boolean constraints. To efficiently round the obtained relaxed solution, we propose a randomized rounding algorithm which generalizes the existing algorithm.

  • sensor placement and resource allocation for energy harvesting iot networks
    arXiv: Signal Processing, 2019
    Co-Authors: Osama M Bushnaq, Anas Chaaban, Sundeep Prabhakar Chepuri, Geert Leus, Tareq Y Alnaffouri
    Abstract:

    The paper studies optimal sensor selection for source estimation in energy harvesting Internet of Things (IoT) networks. Specifically, the focus is on the selection of the sensor locations which minimizes the estimation error at a fusion center, and to optimally allocate power and bandwidth for each selected sensor subject to a prescribed spectral and energy budget. To do so, measurement accuracy, communication link quality, and the amount of energy harvested are all taken into account. The sensor selection is studied under both analog and Digital Transmission schemes from the selected sensors to the fusion center. In the Digital Transmission case, an information theoretic approach is used to model the Transmission rate, observation quantization, and encoding. We numerically prove that with a sufficient system bandwidth, the Digital system outperforms the analog system with a possibly different sensor selection. Two source models are studied in this paper: static source estimation for a vector of correlated sources and dynamic state estimation for a scalar source. The design problem of interest is a Boolean non convex optimization problem, which is solved by relaxing the Boolean constraints. We propose a randomized rounding algorithm which generalizes the existing algorithm. The proposed randomized rounding algorithm takes the joint sensor location, power and bandwidth selection into account to efficiently round the obtained relaxed solution.

Peter Vary - One of the best experts on this subject based on the ideXlab platform.

  • Analysis of Digital Modulation with Unequal Power Allocation
    2015
    Co-Authors: Peter Vary
    Abstract:

    Abstract — In Digital Transmission systems for speech, audio, and video signals source encoders extract parameters which are quantized and converted into a Digital representation. As the individual bits of this representation exhibit different bit error sensitivities, usually channel coding with unequal error protection (UEP) is applied. However, some Transmission systems do not include channel coding for several reasons. In this situation a concept called modulation with unequal power allocation (MUPA) can be applied which achieves UEP by allocating different Transmission power to the modulation symbols according to the individual bit error sensitivities. The average transmitted energy per bit remains unaffected. In this contribution we present a new detailed analysis of BPSK-MUPA and 16-QAM-MUPA, and discuss the performance improvements in terms of parameter SNR compared to systems with constant symbol energy. I

  • Design and Evaluation of Hybrid Digital-Analog Transmission Outperforming Purely Digital Concepts
    IEEE Transactions on Communications, 2014
    Co-Authors: Matthias Rüngeler, Johannes Bunte, Peter Vary
    Abstract:

    Efficient Digital Transmission of analog speech, audio or video requires source coding which introduces unavoidable quantization errors. The bit stream produced by the source encoder needs to be protected against Transmission errors by channel coding. The split of a given gross bit rate between source and channel coding is a compromise taking into account the design target of the worst case channel. Thus, even in clear channel conditions the quality of the decoded source signal is limited because of the quantization errors. Hybrid Digital-analog (HDA) codes address this limitation by additionally transmitting the quantization error with quasi-analog methods (discrete-time, quasi-continuous-amplitude) with neither increasing the transmit power, nor the occupied frequency bandwidth on the radio channel. However, the decoding complexity of existing HDA codes is infeasible for the required block lengths. In this paper, the decoding complexity problem is solved by a new design approach. These HDA codes benefit from well-known Digital codes. Furthermore, theoretical bounds are derived and explicit guidelines for the design of superior HDA systems are given. By experimental verification it is shown that the HDA concept may outperform conventional purely Digital Transmission systems at all channel qualities while additionally eliminating the quality saturation effect.

Osama M Bushnaq - One of the best experts on this subject based on the ideXlab platform.

  • sensor placement and resource allocation for energy harvesting iot networks
    Digital Signal Processing, 2020
    Co-Authors: Osama M Bushnaq, Anas Chaaban, Sundeep Prabhakar Chepuri, Geert Leus, Tareq Y Alnaffouri
    Abstract:

    Abstract Optimal sensor selection for source parameter estimation in energy harvesting Internet of Things (IoT) networks is studied in this paper. Specifically, the focus is on the selection of the sensor locations which minimizes the estimation error at a fusion center, and to optimally allocate power and bandwidth for each selected sensor subject to a prescribed spectral and energy budget. To do so, measurement accuracy, communication link quality, and the amount of energy harvested are all taken into account. The sensor selection is studied under both analog and Digital Transmission schemes from the selected sensors to the fusion center. In the Digital Transmission case, an information theoretic approach is used to model the Transmission rate, observation quantization, and encoding. We numerically prove that with a sufficient system bandwidth, the Digital system outperforms the analog system with a possibly different sensor selection. The design problem of interest is a Boolean non convex optimization problem, which is solved by relaxing the Boolean constraints. To efficiently round the obtained relaxed solution, we propose a randomized rounding algorithm which generalizes the existing algorithm.

  • sensor placement and resource allocation for energy harvesting iot networks
    arXiv: Signal Processing, 2019
    Co-Authors: Osama M Bushnaq, Anas Chaaban, Sundeep Prabhakar Chepuri, Geert Leus, Tareq Y Alnaffouri
    Abstract:

    The paper studies optimal sensor selection for source estimation in energy harvesting Internet of Things (IoT) networks. Specifically, the focus is on the selection of the sensor locations which minimizes the estimation error at a fusion center, and to optimally allocate power and bandwidth for each selected sensor subject to a prescribed spectral and energy budget. To do so, measurement accuracy, communication link quality, and the amount of energy harvested are all taken into account. The sensor selection is studied under both analog and Digital Transmission schemes from the selected sensors to the fusion center. In the Digital Transmission case, an information theoretic approach is used to model the Transmission rate, observation quantization, and encoding. We numerically prove that with a sufficient system bandwidth, the Digital system outperforms the analog system with a possibly different sensor selection. Two source models are studied in this paper: static source estimation for a vector of correlated sources and dynamic state estimation for a scalar source. The design problem of interest is a Boolean non convex optimization problem, which is solved by relaxing the Boolean constraints. We propose a randomized rounding algorithm which generalizes the existing algorithm. The proposed randomized rounding algorithm takes the joint sensor location, power and bandwidth selection into account to efficiently round the obtained relaxed solution.

M Morelli - One of the best experts on this subject based on the ideXlab platform.

  • feedforward frequency estimation for psk a tutorial review
    Transactions on Emerging Telecommunications Technologies, 1998
    Co-Authors: M Morelli, U Mengali
    Abstract:

    This paper considers feedforward carrier frequency estimation methods in burst-mode Digital Transmission, with tutorial objectives foremost. Assuming PSK modulation, two scenarios are envisaged in which frequency estimates are derived either from a preamble appended to the data block, or directly from the modulated signal. Several estimation algorithms are considered with different and somewhat contrasting characteristics. The characteristics we focus on are: estimation accuracy, estimation range, minimum operating signal-to-noise ratio (threshold), and implementation complexity. They provide a framework within which current estimation methods can be evaluated. The paper reviews and compares some prominent algorithms proposed in literature, trying to single out the best ones for a given application.

  • data aided frequency estimation for burst Digital Transmission
    IEEE Transactions on Communications, 1997
    Co-Authors: U Mengali, M Morelli
    Abstract:

    Burst Transmission of Digital data is employed in several applications such as satellite time-division multiple access (TDMA) systems and terrestrial mobile cellular radio. We propose a new algorithm for carrier frequency estimation in burst-mode phase shift keying (PSK) Transmissions. The algorithm is data-aided and clock-aided and has a feedforward structure that is easy to implement in Digital form. Its estimation range is large, about /spl plusmn/20% of the symbol rate and its accuracy is close to the Cramer-Rao bound (CRB) for a signal-to-noise ratio (SNR) as low as 0 dB. Comparisons with earlier methods are discussed.

Anas Chaaban - One of the best experts on this subject based on the ideXlab platform.

  • sensor placement and resource allocation for energy harvesting iot networks
    Digital Signal Processing, 2020
    Co-Authors: Osama M Bushnaq, Anas Chaaban, Sundeep Prabhakar Chepuri, Geert Leus, Tareq Y Alnaffouri
    Abstract:

    Abstract Optimal sensor selection for source parameter estimation in energy harvesting Internet of Things (IoT) networks is studied in this paper. Specifically, the focus is on the selection of the sensor locations which minimizes the estimation error at a fusion center, and to optimally allocate power and bandwidth for each selected sensor subject to a prescribed spectral and energy budget. To do so, measurement accuracy, communication link quality, and the amount of energy harvested are all taken into account. The sensor selection is studied under both analog and Digital Transmission schemes from the selected sensors to the fusion center. In the Digital Transmission case, an information theoretic approach is used to model the Transmission rate, observation quantization, and encoding. We numerically prove that with a sufficient system bandwidth, the Digital system outperforms the analog system with a possibly different sensor selection. The design problem of interest is a Boolean non convex optimization problem, which is solved by relaxing the Boolean constraints. To efficiently round the obtained relaxed solution, we propose a randomized rounding algorithm which generalizes the existing algorithm.

  • sensor placement and resource allocation for energy harvesting iot networks
    arXiv: Signal Processing, 2019
    Co-Authors: Osama M Bushnaq, Anas Chaaban, Sundeep Prabhakar Chepuri, Geert Leus, Tareq Y Alnaffouri
    Abstract:

    The paper studies optimal sensor selection for source estimation in energy harvesting Internet of Things (IoT) networks. Specifically, the focus is on the selection of the sensor locations which minimizes the estimation error at a fusion center, and to optimally allocate power and bandwidth for each selected sensor subject to a prescribed spectral and energy budget. To do so, measurement accuracy, communication link quality, and the amount of energy harvested are all taken into account. The sensor selection is studied under both analog and Digital Transmission schemes from the selected sensors to the fusion center. In the Digital Transmission case, an information theoretic approach is used to model the Transmission rate, observation quantization, and encoding. We numerically prove that with a sufficient system bandwidth, the Digital system outperforms the analog system with a possibly different sensor selection. Two source models are studied in this paper: static source estimation for a vector of correlated sources and dynamic state estimation for a scalar source. The design problem of interest is a Boolean non convex optimization problem, which is solved by relaxing the Boolean constraints. We propose a randomized rounding algorithm which generalizes the existing algorithm. The proposed randomized rounding algorithm takes the joint sensor location, power and bandwidth selection into account to efficiently round the obtained relaxed solution.