The Experts below are selected from a list of 73131 Experts worldwide ranked by ideXlab platform

Jeffrey G Andrews - One of the best experts on this subject based on the ideXlab platform.

  • optimizing data aggregation for uplink Machine to Machine Communication networks
    IEEE Transactions on Communications, 2016
    Co-Authors: Derya Malak, Harpreet S Dhillon, Jeffrey G Andrews
    Abstract:

    Machine-to-Machine (M2M) Communication’s severe power limitations challenge the interconnectivity, access management, and reliable Communication of data. In densely deployed M2M networks, controlling and aggregating the generated data is critical. We propose an energy-efficient data aggregation scheme for a hierarchical M2M network. We develop a coverage probability-based optimal data aggregation scheme for M2M devices to minimize the average total energy expenditure per unit area per unit time or simply the energy density of an M2M Communication network. Our analysis exposes the key tradeoffs between the energy density of the M2M network and the coverage characteristics for successive and parallel transmission schemes that can be either half-duplex or full-duplex. Comparing the rate and energy performances of the transmission models, we observe that successive mode and half-duplex parallel mode have better coverage characteristics compared to full-duplex parallel scheme. Simulation results show that the uplink coverage characteristics dominate the trend of the energy consumption for both successive and parallel schemes.

  • optimizing data aggregation for uplink Machine to Machine Communication networks
    arXiv: Information Theory, 2015
    Co-Authors: Derya Malak, Harpreet S Dhillon, Jeffrey G Andrews
    Abstract:

    Machine-to-Machine (M2M) Communication's severe power limitations challenge the interconnectivity, access management, and reliable Communication of data. In densely deployed M2M networks, controlling and aggregating the generated data is critical. We propose an energy efficient data aggregation scheme for a hierarchical M2M network. We develop a coverage probability-based optimal data aggregation scheme for M2M devices to minimize the average total energy expenditure per unit area per unit time or simply the {\em energy density} of an M2M Communication network. Our analysis exposes the key tradeoffs between the energy density of the M2M network and the coverage characteristics for successive and parallel transmission schemes that can be either half-duplex or full-duplex. Comparing the rate and energy performances of the transmission models, we observe that successive mode and half-duplex parallel mode have better coverage characteristics compared to full-duplex parallel scheme. Simulation results show that the uplink coverage characteristics dominate the trend of the energy consumption for both successive and parallel schemes.

Roberto Bisiani - One of the best experts on this subject based on the ideXlab platform.

Derya Malak - One of the best experts on this subject based on the ideXlab platform.

  • optimizing data aggregation for uplink Machine to Machine Communication networks
    IEEE Transactions on Communications, 2016
    Co-Authors: Derya Malak, Harpreet S Dhillon, Jeffrey G Andrews
    Abstract:

    Machine-to-Machine (M2M) Communication’s severe power limitations challenge the interconnectivity, access management, and reliable Communication of data. In densely deployed M2M networks, controlling and aggregating the generated data is critical. We propose an energy-efficient data aggregation scheme for a hierarchical M2M network. We develop a coverage probability-based optimal data aggregation scheme for M2M devices to minimize the average total energy expenditure per unit area per unit time or simply the energy density of an M2M Communication network. Our analysis exposes the key tradeoffs between the energy density of the M2M network and the coverage characteristics for successive and parallel transmission schemes that can be either half-duplex or full-duplex. Comparing the rate and energy performances of the transmission models, we observe that successive mode and half-duplex parallel mode have better coverage characteristics compared to full-duplex parallel scheme. Simulation results show that the uplink coverage characteristics dominate the trend of the energy consumption for both successive and parallel schemes.

  • optimizing data aggregation for uplink Machine to Machine Communication networks
    arXiv: Information Theory, 2015
    Co-Authors: Derya Malak, Harpreet S Dhillon, Jeffrey G Andrews
    Abstract:

    Machine-to-Machine (M2M) Communication's severe power limitations challenge the interconnectivity, access management, and reliable Communication of data. In densely deployed M2M networks, controlling and aggregating the generated data is critical. We propose an energy efficient data aggregation scheme for a hierarchical M2M network. We develop a coverage probability-based optimal data aggregation scheme for M2M devices to minimize the average total energy expenditure per unit area per unit time or simply the {\em energy density} of an M2M Communication network. Our analysis exposes the key tradeoffs between the energy density of the M2M network and the coverage characteristics for successive and parallel transmission schemes that can be either half-duplex or full-duplex. Comparing the rate and energy performances of the transmission models, we observe that successive mode and half-duplex parallel mode have better coverage characteristics compared to full-duplex parallel scheme. Simulation results show that the uplink coverage characteristics dominate the trend of the energy consumption for both successive and parallel schemes.

Harpreet S Dhillon - One of the best experts on this subject based on the ideXlab platform.

  • optimizing data aggregation for uplink Machine to Machine Communication networks
    IEEE Transactions on Communications, 2016
    Co-Authors: Derya Malak, Harpreet S Dhillon, Jeffrey G Andrews
    Abstract:

    Machine-to-Machine (M2M) Communication’s severe power limitations challenge the interconnectivity, access management, and reliable Communication of data. In densely deployed M2M networks, controlling and aggregating the generated data is critical. We propose an energy-efficient data aggregation scheme for a hierarchical M2M network. We develop a coverage probability-based optimal data aggregation scheme for M2M devices to minimize the average total energy expenditure per unit area per unit time or simply the energy density of an M2M Communication network. Our analysis exposes the key tradeoffs between the energy density of the M2M network and the coverage characteristics for successive and parallel transmission schemes that can be either half-duplex or full-duplex. Comparing the rate and energy performances of the transmission models, we observe that successive mode and half-duplex parallel mode have better coverage characteristics compared to full-duplex parallel scheme. Simulation results show that the uplink coverage characteristics dominate the trend of the energy consumption for both successive and parallel schemes.

  • optimizing data aggregation for uplink Machine to Machine Communication networks
    arXiv: Information Theory, 2015
    Co-Authors: Derya Malak, Harpreet S Dhillon, Jeffrey G Andrews
    Abstract:

    Machine-to-Machine (M2M) Communication's severe power limitations challenge the interconnectivity, access management, and reliable Communication of data. In densely deployed M2M networks, controlling and aggregating the generated data is critical. We propose an energy efficient data aggregation scheme for a hierarchical M2M network. We develop a coverage probability-based optimal data aggregation scheme for M2M devices to minimize the average total energy expenditure per unit area per unit time or simply the {\em energy density} of an M2M Communication network. Our analysis exposes the key tradeoffs between the energy density of the M2M network and the coverage characteristics for successive and parallel transmission schemes that can be either half-duplex or full-duplex. Comparing the rate and energy performances of the transmission models, we observe that successive mode and half-duplex parallel mode have better coverage characteristics compared to full-duplex parallel scheme. Simulation results show that the uplink coverage characteristics dominate the trend of the energy consumption for both successive and parallel schemes.

Shulan Feng - One of the best experts on this subject based on the ideXlab platform.

  • Probabilistic Rateless Multiple Access for Machine-to-Machine Communication
    IEEE Transactions on Wireless Communications, 2015
    Co-Authors: Mahyar Shirvanimoghaddam, Mischa Dohler, Branka Vucetic, Shulan Feng
    Abstract:

    Future Machine-to-Machine (M2M) Communications need to support a massive number of devices communicating with each other with little or no human intervention. Random access techniques were originally proposed to enable M2M multiple access, but suffer from severe congestion and access delay in an M2M system with a large number of devices. In this paper, we propose a novel multiple access scheme for M2M Communications based on the capacity-approaching analog fountain code to efficiently minimize the access delay and satisfy the delay requirement for each device. This is achieved by allowing M2M devices to transmit at the same time on the same channel in an optimal probabilistic manner based on their individual delay requirements. Simulation results show that the proposed scheme achieves a near optimal rate performance and at the same time guarantees the delay requirements of the devices. We further propose a simple random access strategy and characterize the required overhead. Simulation results show that the proposed approach significantly outperforms the existing random access schemes currently used in long term evolution advanced (LTE-A) standard in terms of the access delay.