The Experts below are selected from a list of 163875 Experts worldwide ranked by ideXlab platform

Ying-chang Liang - One of the best experts on this subject based on the ideXlab platform.

  • joint active and passive beamforming for reconfigurable intelligent surface enhanced symbiotic radio system
    IEEE Wireless Communications Letters, 2021
    Co-Authors: Hao Chen, Gang Yang, Ying-chang Liang
    Abstract:

    This paper considers a symbiotic radio (SR) system enhanced by a reconfigurable intelligent surface (RIS). The active transmit beamforming of the base station (BS) and the passive beamforming of the RIS are jointly designed to minimize the BS’s transmission power, subject to the signal-to-interference-plus-noise ratio constraints for decoding backscatter signal, and the rate constraint for primary communication. To solve the non-Convex Problem, we propose an efficient iterative algorithm based on alternating optimization and semi-definite relaxation. The algorithm’s convergence and complexity are analyzed. Numerical results show that the RIS-enhanced SR achieves lower transmission power than SR without RIS.

  • Reconfigurable Intelligent Surface Enhanced NOMA Assisted Backscatter Communication System.
    arXiv: Information Theory, 2020
    Co-Authors: Jiakuo Zuo, Yuanwei Liu, Liang Yang, Lingyang Song, Ying-chang Liang
    Abstract:

    A reconfigurable intelligent surface (RIS) enhanced non-orthogonal multiple access assisted backscatter communication (RIS-NOMABC) system is considered. A joint optimization Problem over power reflection coefficients and phase shifts is formulated. To solve this non-Convex Problem, a low complexity algorithm is proposed by invoking the alternative optimization, successive Convex approximation and manifold optimization algorithms. Numerical results corroborate that the proposed RIS-NOMABC system outperforms the conventional non-orthogonal multiple access assisted backscatter communication (NOMABC) system without RIS, and demonstrate the feasibility and effectiveness of the proposed algorithm.

Martin Kruczenski - One of the best experts on this subject based on the ideXlab platform.

  • s matrix bootstrap in 3 1 dimensions regularization and dual Convex Problem
    Journal of High Energy Physics, 2021
    Co-Authors: Martin Kruczenski
    Abstract:

    The S-matrix bootstrap maps out the space of S-matrices allowed by analyticity, crossing, unitarity, and other constraints. For the 2 → 2 scattering matrix S2→2 such space is an infinite dimensional Convex space whose boundary can be determined by maximizing linear functionals. On the boundary interesting theories can be found, many times at vertices of the space. Here we consider 3 + 1 dimensional theories and focus on the equivalent dual Convex minimization Problem that provides strict upper bounds for the regularized primal Problem and has interesting practical and physical advantages over the primal Problem. Its variables are dual partial waves kl(s) that are free variables, namely they do not have to obey any crossing, unitarity or other constraints. Nevertheless they are directly related to the partial waves fl(s), for which all crossing, unitarity and symmetry properties result from the minimization. Numerically, it requires only a few dual partial waves, much as one wants to possibly match experimental results. We consider the case of scalar fields which is related to pion physics.

  • S-matrix bootstrap in 3+1 dimensions: regularization and dual Convex Problem
    2021
    Co-Authors: Martin Kruczenski
    Abstract:

    The S-matrix bootstrap maps out the space of S-matrices allowed by analyticity, crossing, unitarity, and other constraints. For the $2\rightarrow 2$ scattering matrix $S_{2\rightarrow 2}$ such space is an infinite dimensional Convex space whose boundary can be determined by maximizing linear functionals. On the boundary interesting theories can be found, many times at vertices of the space. Here we consider $3+1$ dimensional theories and focus on the equivalent dual Convex minimization Problem that provides strict upper bounds for the regularized primal Problem and has interesting practical and physical advantages over the primal Problem. Its variables are dual partial waves $k_\ell(s)$ that are free variables, namely they do not have to obey any crossing, unitarity or other constraints. Nevertheless they are directly related to the partial waves $f_\ell(s)$, for which all crossing, unitarity and symmetry properties result from the minimization. Numerically, it requires only a few dual partial waves, much as one wants to possibly match experimental results. We consider the case of scalar fields which is related to pion physics.

Tomaso Erseghe - One of the best experts on this subject based on the ideXlab platform.

  • cooperative localization in wsns a hybrid Convex nonConvex solution
    IEEE Transactions on Signal and Information Processing over Networks, 2018
    Co-Authors: Nicola Piovesan, Tomaso Erseghe
    Abstract:

    We propose an efficient solution to peer-to-peer localization in a wireless sensor network that works in two stages. At the first stage the optimization Problem is relaxed into a Convex Problem, given in the form recently proposed by Soares et al. The Convex Problem is efficiently solved in a distributed way by an alternating direction method of multipliers approach, which provides a significant improvement in speed with respect to the original solution. In the second stage, a soft transition to the original, nonConvex, nonrelaxed formulation is applied in such a way to force the solution toward a local minimum. The algorithm is built in such a way to be fully distributed, and it is tested in meaningful situations, showing its effectiveness in localization accuracy and speed of convergence, as well as its inner robustness.

  • distributed optimal power flow using admm
    IEEE Transactions on Power Systems, 2014
    Co-Authors: Tomaso Erseghe
    Abstract:

    Distributed optimal power flow (OPF) is a challenging non-linear, non-Convex Problem of central importance to the future power grid. Although many approaches are currently available in the literature, these require some form of central coordination to properly work. In this paper a fully distributed and robust algorithm for OPF is proposed which does not require any form of central coordination. The algorithm is based upon the alternating direction multiplier method (ADMM) in a form recently proposed by the author, which, in turn, builds upon the work of Schizas The approach is customized as a region-based optimization procedure, and it is tested in meaningful scenarios.

Arumugam Nallanathan - One of the best experts on this subject based on the ideXlab platform.

  • Energy Efficient Multicast Precoding for Multiuser Multibeam Satellite Communications
    2020
    Co-Authors: Qi Chenhao, Chen Huajian, Deng Yansha, Arumugam Nallanathan
    Abstract:

    Aiming at maximizing the energy efficiency (EE) of multicast multibeam satellite communications, we consider the precoding design under the total power and quality of service (QoS) constraints. Since the original EE maximization Problem is nonConvex, it is sequentially converted into a concave-Convex fractional programming Problem by introducing some variables and using the first-order Taylor low bound approximation. Based on the Charnes-Cooper transformation, it is further converted into a Convex Problem. Then an iterative algorithm is presented to design the energy efficient precoding. To find feasible initialization points for the algorithm and ensure its convergence, another Convex optimization Problem with some nonnegative slack variables and a positive penalty parameter is iteratively solved. In particular, the algorithm is verified by the measured channel data of multibeam satellite communications

  • joint task assignment and wireless resource allocation for cooperative mobile edge computing
    International Conference on Communications, 2018
    Co-Authors: Hong Xing, Liang Liu, Arumugam Nallanathan
    Abstract:

    This paper studies a multi-user cooperative mobile- edge computing (MEC) system, in which a local mobile user can offload intensive computation tasks to multiple nearby edge devices serving as helpers for remote execution. We focus on the scenario where the local user has a number of independent tasks that can be executed in parallel but cannot be further partitioned. We consider a time division multiple access (TDMA) communication protocol, in which the local user can offload computation tasks to the helpers and download results from them over pre- scheduled time slots. Under this setup, we minimize the local user's computation latency by optimizing the task assignment jointly with the time and power allocations, subject to individual energy constraints at the local user and the helpers. However, the joint task assignment and wireless resource allocation Problem is a mixed-integer non-linear program (MINLP) that is hard to solve optimally. To tackle this challenge, we first relax it into a Convex Problem, and then propose an efficient suboptimal solution based on the optimal solution to the relaxed Convex Problem. Finally, numerical results show that our proposed joint design significantly reduces the local user's computation latency, as compared against other benchmark schemes that design the task assignment separately from the offloading/downloading resource allocations and local execution.

  • Spectrum Allocation and Power Control for Non-Orthogonal Multiple Access in HetNets
    IEEE Transactions on Wireless Communications, 2017
    Co-Authors: Jingjing Zhao, Arumugam Nallanathan, Kok Keong Chai, Yue Chen
    Abstract:

    In this paper, a novel resource allocation design is investigated for non-orthogonal multiple access (NOMA) enhanced heterogeneous networks (HetNets), where small cell base stations (SBSs) are capable of communicating with multiple small cell users (SCUs) via the NOMA protocol. With the aim of maximizing the sum rate of SCUs while taking the fairness issue into consideration, a joint Problem of spectrum allocation and power control is formulated. In particular, the spectrum allocation Problem is modeled as a many-to-one matching game with peer effects. We propose a novel algorithm where the SBSs and resource blocks interact to decide their desired allocation. The proposed algorithm is proved to converge to a two-sided exchange-stable matching. Furthermore, we introduce the concept of `exploration' into the matching game for further improving the SCUs' sum rate. The power control of each SBS is formulated as a non-Convex Problem, where the sequential Convex programming is adopted to iteratively update the power allocation result by solving the approximate Convex Problem. The obtained solution is proved to satisfy the Karush-Kuhn-Tucker conditions. We unveil that: 1) the proposed algorithm closely approaches the optimal solution within a limited number of iterations; 2) the `exploration' action is capable of further enhancing the performance of the matching algorithm; and 3) the developed NOMA-enhanced HetNets achieve a higher SCUs' sum rate compared with the conventional OMA-based HetNets.

Yue Chen - One of the best experts on this subject based on the ideXlab platform.

  • Spectrum Allocation and Power Control for Non-Orthogonal Multiple Access in HetNets
    IEEE Transactions on Wireless Communications, 2017
    Co-Authors: Jingjing Zhao, Arumugam Nallanathan, Kok Keong Chai, Yue Chen
    Abstract:

    In this paper, a novel resource allocation design is investigated for non-orthogonal multiple access (NOMA) enhanced heterogeneous networks (HetNets), where small cell base stations (SBSs) are capable of communicating with multiple small cell users (SCUs) via the NOMA protocol. With the aim of maximizing the sum rate of SCUs while taking the fairness issue into consideration, a joint Problem of spectrum allocation and power control is formulated. In particular, the spectrum allocation Problem is modeled as a many-to-one matching game with peer effects. We propose a novel algorithm where the SBSs and resource blocks interact to decide their desired allocation. The proposed algorithm is proved to converge to a two-sided exchange-stable matching. Furthermore, we introduce the concept of `exploration' into the matching game for further improving the SCUs' sum rate. The power control of each SBS is formulated as a non-Convex Problem, where the sequential Convex programming is adopted to iteratively update the power allocation result by solving the approximate Convex Problem. The obtained solution is proved to satisfy the Karush-Kuhn-Tucker conditions. We unveil that: 1) the proposed algorithm closely approaches the optimal solution within a limited number of iterations; 2) the `exploration' action is capable of further enhancing the performance of the matching algorithm; and 3) the developed NOMA-enhanced HetNets achieve a higher SCUs' sum rate compared with the conventional OMA-based HetNets.