The Experts below are selected from a list of 614271 Experts worldwide ranked by ideXlab platform

Miroslav Voznak - One of the best experts on this subject based on the ideXlab platform.

  • switchable coupled relays aid massive non orthogonal multiple access networks with transmit antenna selection and energy harvesting
    Sensors, 2021
    Co-Authors: Thanhnam Tran, Miroslav Voznak
    Abstract:

    The article proposes a new switchable coupled relay model for massive MIMO-NOMA networks. The model equips a much greater number of antennas on the coupled relays to dramatically improve capacity and Energy Efficiency (EE). Each relay in a coupled relay is selected and delivered into a single transmission block to serve multiple devices. This paper also plots a new diagram of two transmission blocks which illustrates energy harvesting and signal processing. To optimize the system performance of a massive MIMO-NOMA network, i.e., Outage Probability (OP) and system throughput, this paper deploys a Transmit Antenna Selection (TAS) protocol to select the best received signals from the pre-coding channel matrices. In addition, to achieve better EE, Simultaneously Wireless Information Power Transmit (SWIPT) is implemented. Specifically, this paper derives the novel theoretical analysis in closed-form expressions, i.e., OP, system throughput and EE from a massive MIMO-NOMA network aided by switchable coupled relays. The theoretical results obtained from the closed-form expressions show that a massive MIMO-NOMA network achieves better OP and greater capacity and expends less energy than the MIMO technique. Finally, independent Monte Carlo simulations verified the theoretical results.

Thanhnam Tran - One of the best experts on this subject based on the ideXlab platform.

  • switchable coupled relays aid massive non orthogonal multiple access networks with transmit antenna selection and energy harvesting
    Sensors, 2021
    Co-Authors: Thanhnam Tran, Miroslav Voznak
    Abstract:

    The article proposes a new switchable coupled relay model for massive MIMO-NOMA networks. The model equips a much greater number of antennas on the coupled relays to dramatically improve capacity and Energy Efficiency (EE). Each relay in a coupled relay is selected and delivered into a single transmission block to serve multiple devices. This paper also plots a new diagram of two transmission blocks which illustrates energy harvesting and signal processing. To optimize the system performance of a massive MIMO-NOMA network, i.e., Outage Probability (OP) and system throughput, this paper deploys a Transmit Antenna Selection (TAS) protocol to select the best received signals from the pre-coding channel matrices. In addition, to achieve better EE, Simultaneously Wireless Information Power Transmit (SWIPT) is implemented. Specifically, this paper derives the novel theoretical analysis in closed-form expressions, i.e., OP, system throughput and EE from a massive MIMO-NOMA network aided by switchable coupled relays. The theoretical results obtained from the closed-form expressions show that a massive MIMO-NOMA network achieves better OP and greater capacity and expends less energy than the MIMO technique. Finally, independent Monte Carlo simulations verified the theoretical results.

John A Copeland - One of the best experts on this subject based on the ideXlab platform.

  • reinforcement learning for repeated Power control game in cognitive radio networks
    IEEE Journal on Selected Areas in Communications, 2012
    Co-Authors: Pan Zhou, Yusun Chang, John A Copeland
    Abstract:

    Cognitive radio (CR) users are expected to be uncoordinated users that opportunistically seek the spectrum resource from primary users (PUs) in a competitive way. In most existing works, however, CR users are required to share the interference channel Information and Power strategies to conduct the game with pricing mechanisms that incur the frequent exchange of Information. The requirement of significant communication overheads among CR users impedes fully distributed solutions for the deployment of CR networks, which is a challenging problem in the research communities. In this paper, a robust distributed Power control algorithm is designed with low implementation complexity for CR networks through reinforcement learning, which does not require the interference channel and Power strategy Information among CR users (and from CR users to PUs). To the best of our knowledge, this research provides the solution for the first time for the incomplete-Information Power control game in CR networks. During the repeated game, CR users can control their Power strategies by observing the interference from the feedback signals of PUs and transmission rates obtained in the previous step. This procedure allows achieving high spectrum efficiency while conforming to the interference constraint of PUs. This constrained repeated stochastic game with learning automaton is proved to be asymptotically equivalence to the traditional game with complete Information. The properties of existence, diagonal concavity and uniqueness for the game are studied. A Bush-Mosteller reinforcement learning procedure is designed for the Power control algorithm, and the properties of convergence and learning rate of the algorithm are analyzed. The performance of the learning-based Power control algorithm is thoroughly investigated with simulation results, which demonstrates the effectiveness of the proposed algorithm in solving variety of practical CR network problems for real-world applications.

  • learning through reinforcement for repeated Power control game in cognitive radio networks
    Global Communications Conference, 2010
    Co-Authors: Pan Zhou, Yusun Chang, John A Copeland
    Abstract:

    This paper studies the repeated Power control game in cognitive radio (CR) networks through reinforcement learning without channel and Power strategy Information exchange among CR users. Unlike traditional game-theoretical approaches on CR Power control, this research solves the incomplete Information Power control problems for selfish and autonomous CR users for the first time. Each CR user in the problem only knows its own channel and Power strategy while the Information of primary users (PUs) and other different types of CR users are unknown. The formulated Power control problem is a constrained repeated stochastic game with learning automaton. The objective of this repeated game is to maximize the average utility of each CR user under the interference Power constraints of PUs. At each time step, the CR user only knows its own utility and the interference functions after the play but no further Information. This Power control game is proved to be asymptotically equivalent to the traditional game theory approaches. The properties of existence, diagonal concavity and uniqueness for this game are illustrated in detail. A Bush-Mosteller reinforcement learning procedure is designed for the Power control algorithm. Finally, the learning based Power control algorithm is implemented, and the simulation results with detailed analysis are shown to enforce the effectiveness of the proposed algorithms.

Rui Zhang - One of the best experts on this subject based on the ideXlab platform.

  • joint active and passive beamforming optimization for intelligent reflecting surface assisted swipt under qos constraints
    IEEE Journal on Selected Areas in Communications, 2020
    Co-Authors: Rui Zhang
    Abstract:

    Intelligent reflecting surface (IRS) is a new and revolutionizing technology for achieving spectrum and energy efficient wireless networks. By leveraging massive low-cost passive elements that are able to reflect radio-frequency (RF) signals with adjustable phase shifts, IRS can achieve high passive beamforming gains, which are particularly appealing for improving the efficiency of RF-based wireless Power transfer. Motivated by the above, we study in this paper an IRS-assisted simultaneous wireless Information and Power transfer (SWIPT) system. Specifically, a set of IRSs are deployed to assist in the Information/Power transfer from a multi-antenna access point (AP) to multiple single-antenna Information users (IUs) and energy users (EUs), respectively. We aim to minimize the transmit Power at the AP via jointly optimizing its transmit precoders and the reflect phase shifts at all IRSs, subject to the quality-of-service (QoS) constraints at all users, namely, the individual signal-to-interference-plus-noise ratio (SINR) constraints at IUs and the energy harvesting constraints at EUs. However, this optimization problem is non-convex with intricately coupled variables, for which the existing alternating optimization approach is shown to be inefficient as the number of QoS constraints increases. To tackle this challenge, we first apply proper transformations on the QoS constraints and then propose an efficient iterative algorithm by applying the penalty-based optimization method. Moreover, by exploiting the short-range coverage of IRS, we further propose a more computationally efficient algorithm by optimizing the phase shifts at all IRSs in parallel. Simulation results demonstrate the effectiveness of employing multiple IRSs for enhancing the performance of SWIPT systems as well as the significant performance gains achieved by our proposed algorithms over benchmark schemes. The impact of IRS on the transmitter/receiver design for SWIPT is also unveiled.

  • joint active and passive beamforming optimization for intelligent reflecting surface assisted swipt under qos constraints
    arXiv: Information Theory, 2019
    Co-Authors: Qingqing Wu, Rui Zhang
    Abstract:

    Intelligent reflecting surface (IRS) is a new and revolutionizing technology for achieving spectrum and energy efficient wireless networks. By leveraging massive low-cost passive elements that are able to reflect radio-frequency (RF) signals with adjustable phase shifts, IRS can achieve high passive beamforming gains, which are particularly appealing for improving the efficiency of RF-based wireless Power transfer. Motivated by the above, we study in the paper an IRS-assisted simultaneous wireless Information and Power transfer (SWIPT) system. Specifically, a set of IRSs are deployed to assist in the Information/Power transfer from a multi-antenna access point (AP) to multiple single-antenna Information users (IUs) and energy users (EUs), respectively. We aim to minimize the transmit Power at the AP via jointly optimizing its transmit precoders and the reflect phase shifts at all IRSs, subject to the quality-of-service (QoS) constraints at all users, namely, the individual signal-to-interference-plus-noise ratio (SINR) constraints at IUs and energy harvesting constraints at EUs. However, this optimization problem is non-convex with intricately coupled variables, for which the existing alternating optimization approach is shown to be inefficient as the number of QoS constraints increases. To tackle this challenge, we first apply proper transformations on the QoS constraints and then propose an efficient iterative algorithm by applying the penalty-based method. Moreover, by exploiting the short-range coverage of IRSs, we further propose a low-complexity algorithm by optimizing the phase shifts of all IRSs in parallel.

Pan Zhou - One of the best experts on this subject based on the ideXlab platform.

  • reinforcement learning for repeated Power control game in cognitive radio networks
    IEEE Journal on Selected Areas in Communications, 2012
    Co-Authors: Pan Zhou, Yusun Chang, John A Copeland
    Abstract:

    Cognitive radio (CR) users are expected to be uncoordinated users that opportunistically seek the spectrum resource from primary users (PUs) in a competitive way. In most existing works, however, CR users are required to share the interference channel Information and Power strategies to conduct the game with pricing mechanisms that incur the frequent exchange of Information. The requirement of significant communication overheads among CR users impedes fully distributed solutions for the deployment of CR networks, which is a challenging problem in the research communities. In this paper, a robust distributed Power control algorithm is designed with low implementation complexity for CR networks through reinforcement learning, which does not require the interference channel and Power strategy Information among CR users (and from CR users to PUs). To the best of our knowledge, this research provides the solution for the first time for the incomplete-Information Power control game in CR networks. During the repeated game, CR users can control their Power strategies by observing the interference from the feedback signals of PUs and transmission rates obtained in the previous step. This procedure allows achieving high spectrum efficiency while conforming to the interference constraint of PUs. This constrained repeated stochastic game with learning automaton is proved to be asymptotically equivalence to the traditional game with complete Information. The properties of existence, diagonal concavity and uniqueness for the game are studied. A Bush-Mosteller reinforcement learning procedure is designed for the Power control algorithm, and the properties of convergence and learning rate of the algorithm are analyzed. The performance of the learning-based Power control algorithm is thoroughly investigated with simulation results, which demonstrates the effectiveness of the proposed algorithm in solving variety of practical CR network problems for real-world applications.

  • learning through reinforcement for repeated Power control game in cognitive radio networks
    Global Communications Conference, 2010
    Co-Authors: Pan Zhou, Yusun Chang, John A Copeland
    Abstract:

    This paper studies the repeated Power control game in cognitive radio (CR) networks through reinforcement learning without channel and Power strategy Information exchange among CR users. Unlike traditional game-theoretical approaches on CR Power control, this research solves the incomplete Information Power control problems for selfish and autonomous CR users for the first time. Each CR user in the problem only knows its own channel and Power strategy while the Information of primary users (PUs) and other different types of CR users are unknown. The formulated Power control problem is a constrained repeated stochastic game with learning automaton. The objective of this repeated game is to maximize the average utility of each CR user under the interference Power constraints of PUs. At each time step, the CR user only knows its own utility and the interference functions after the play but no further Information. This Power control game is proved to be asymptotically equivalent to the traditional game theory approaches. The properties of existence, diagonal concavity and uniqueness for this game are illustrated in detail. A Bush-Mosteller reinforcement learning procedure is designed for the Power control algorithm. Finally, the learning based Power control algorithm is implemented, and the simulation results with detailed analysis are shown to enforce the effectiveness of the proposed algorithms.