The Experts below are selected from a list of 4452 Experts worldwide ranked by ideXlab platform

Thomas L. Marzetta - One of the best experts on this subject based on the ideXlab platform.

  • joint unicast and multi group multicast transmission in massive mimo systems
    arXiv: Information Theory, 2019
    Co-Authors: Meysam Sadeghi, Emil Bjornson, Erik G. Larsson, Chau Yuen, Thomas L. Marzetta
    Abstract:

    We study the joint unicast and multi-group multicast transmission in massive multiple-input-multiple-output (MIMO) systems. We consider a system model that accounts for channel estimation and pilot contamination, and derive achievable spectral efficiencies (SEs) for unicast and multicast user terminals (UTs), under maximum ratio transmission and zero-forcing precoding. For unicast transmission, our objective is to maximize the weighted sum SE of the unicast UTs, and for the multicast transmission, our objective is to maximize the minimum SE of the multicast UTs. These two objectives are coupled in a conflicting manner, due to their shared power resource. Therefore, we formulate a multiobjective optimization problem (MOOP) for the two conflicting objectives. We derive the Pareto boundary of the MOOP analytically. As each Pareto Optimal Point describes a particular efficient trade-off between the two objectives of the system, we determine the values of the system parameters (uplink training powers, downlink transmission powers, etc.) to achieve any desired Pareto Optimal Point. Moreover, we prove that the Pareto region is convex, hence the system should serve the unicast and multicast UTs at the same time-frequency resource. Finally, we validate our results using numerical simulations.

  • Joint Unicast and Multi-group Multicast Transmission in Massive MIMO Systems.
    IEEE Transactions on Wireless Communications, 2018
    Co-Authors: Meysam Sadeghi, Emil Bjornson, Erik G. Larsson, Chau Yuen, Thomas L. Marzetta
    Abstract:

    We study the joint unicast and multi-group multicast transmission in massive multiple-input multiple-output systems. We consider a system model that accounts for channel estimation and pilot contamination and derive achievable spectral efficiencies (SEs) for unicast and multicast user terminals (UTs) under maximum ratio transmission and zero-forcing precoding. For unicast transmission, our objective is to maximize the weighted sum SE of the unicast UTs, and for the multicast transmission, our objective is to maximize the minimum SE of the multicast UTs. These two objectives are coupled in a conflicting manner, due to their shared power resource. Therefore, we formulate a multiobjective optimization problem (MOOP) for the two conflicting objectives. We derive the Pareto boundary of the MOOP analytically. As each Pareto Optimal Point describes a particular efficient tradeoff between the two objectives of the system, we determine the values of the system parameters (uplink training powers, downlink transmission powers, and so on) to achieve any desired Pareto Optimal Point. Moreover, we prove that the Pareto region is convex, and hence, the system should serve the unicast and multicast UTs at the same time–frequency resource. Finally, we validate our results using numerical simulations.

Erik G. Larsson - One of the best experts on this subject based on the ideXlab platform.

  • joint unicast and multi group multicast transmission in massive mimo systems
    arXiv: Information Theory, 2019
    Co-Authors: Meysam Sadeghi, Emil Bjornson, Erik G. Larsson, Chau Yuen, Thomas L. Marzetta
    Abstract:

    We study the joint unicast and multi-group multicast transmission in massive multiple-input-multiple-output (MIMO) systems. We consider a system model that accounts for channel estimation and pilot contamination, and derive achievable spectral efficiencies (SEs) for unicast and multicast user terminals (UTs), under maximum ratio transmission and zero-forcing precoding. For unicast transmission, our objective is to maximize the weighted sum SE of the unicast UTs, and for the multicast transmission, our objective is to maximize the minimum SE of the multicast UTs. These two objectives are coupled in a conflicting manner, due to their shared power resource. Therefore, we formulate a multiobjective optimization problem (MOOP) for the two conflicting objectives. We derive the Pareto boundary of the MOOP analytically. As each Pareto Optimal Point describes a particular efficient trade-off between the two objectives of the system, we determine the values of the system parameters (uplink training powers, downlink transmission powers, etc.) to achieve any desired Pareto Optimal Point. Moreover, we prove that the Pareto region is convex, hence the system should serve the unicast and multicast UTs at the same time-frequency resource. Finally, we validate our results using numerical simulations.

  • Joint Unicast and Multi-group Multicast Transmission in Massive MIMO Systems.
    IEEE Transactions on Wireless Communications, 2018
    Co-Authors: Meysam Sadeghi, Emil Bjornson, Erik G. Larsson, Chau Yuen, Thomas L. Marzetta
    Abstract:

    We study the joint unicast and multi-group multicast transmission in massive multiple-input multiple-output systems. We consider a system model that accounts for channel estimation and pilot contamination and derive achievable spectral efficiencies (SEs) for unicast and multicast user terminals (UTs) under maximum ratio transmission and zero-forcing precoding. For unicast transmission, our objective is to maximize the weighted sum SE of the unicast UTs, and for the multicast transmission, our objective is to maximize the minimum SE of the multicast UTs. These two objectives are coupled in a conflicting manner, due to their shared power resource. Therefore, we formulate a multiobjective optimization problem (MOOP) for the two conflicting objectives. We derive the Pareto boundary of the MOOP analytically. As each Pareto Optimal Point describes a particular efficient tradeoff between the two objectives of the system, we determine the values of the system parameters (uplink training powers, downlink transmission powers, and so on) to achieve any desired Pareto Optimal Point. Moreover, we prove that the Pareto region is convex, and hence, the system should serve the unicast and multicast UTs at the same time–frequency resource. Finally, we validate our results using numerical simulations.

  • efficient computation of the Pareto boundary for the miso interference channel with perfect csi
    Modeling and Optimization in Mobile Ad-Hoc and Wireless Networks, 2010
    Co-Authors: Eleftherios Karipidis, Erik G. Larsson
    Abstract:

    We consider the two-user multiple-input singleoutput (MISO) interference channel and the rate region which is achieved when the receivers treat the interference as additive Gaussian noise and the transmitters have perfect channel state information (CSI). We propose a computationally efficient method for calculating the Pareto boundary of the rate region. We show that the problem of finding an arbitrary Pareto-Optimal rate pair, along with its enabling beamforming vector pair, can be cast as a sequence of second-order cone programming (SOCP) feasibility problems. The SOCP problems are convex and they are solved very efficiently using standard off-the-shelf (namely, interior-Point) algorithms. The number of SOCP problems that must be solved, for the computation of a Pareto-Optimal Point, grows only logarithmically with the desired accuracy of the solution.

  • efficient computation of the Pareto boundary for the miso interference channel with perfect csi invited paper
    2010
    Co-Authors: Eleftherios Karipidis, Erik G. Larsson
    Abstract:

    We consider the two-user multiple-input single- output (MISO) interference channel and the rate region which is achieved when the receivers treat the interference as additive Gaussian noise and the transmitters have perfect channel state in- formation (CSI). We propose a computationally efficient method for calculating the Pareto boundary of the rate region. We show that the problem of finding an arbitrary Pareto-Optimal rate pair, along with its enabling beamforming vector pair, can be cast as a sequence of second-order cone programming (SOCP) feasibility problems. The SOCP problems are convex and they are solved very efficiently using standard off-the-shelf (namely, interior- Point) algorithms. The number of SOCP problems that must be solved, for the computation of a Pareto-Optimal Point, grows only logarithmically with the desired accuracy of the solution.

Meysam Sadeghi - One of the best experts on this subject based on the ideXlab platform.

  • joint unicast and multi group multicast transmission in massive mimo systems
    arXiv: Information Theory, 2019
    Co-Authors: Meysam Sadeghi, Emil Bjornson, Erik G. Larsson, Chau Yuen, Thomas L. Marzetta
    Abstract:

    We study the joint unicast and multi-group multicast transmission in massive multiple-input-multiple-output (MIMO) systems. We consider a system model that accounts for channel estimation and pilot contamination, and derive achievable spectral efficiencies (SEs) for unicast and multicast user terminals (UTs), under maximum ratio transmission and zero-forcing precoding. For unicast transmission, our objective is to maximize the weighted sum SE of the unicast UTs, and for the multicast transmission, our objective is to maximize the minimum SE of the multicast UTs. These two objectives are coupled in a conflicting manner, due to their shared power resource. Therefore, we formulate a multiobjective optimization problem (MOOP) for the two conflicting objectives. We derive the Pareto boundary of the MOOP analytically. As each Pareto Optimal Point describes a particular efficient trade-off between the two objectives of the system, we determine the values of the system parameters (uplink training powers, downlink transmission powers, etc.) to achieve any desired Pareto Optimal Point. Moreover, we prove that the Pareto region is convex, hence the system should serve the unicast and multicast UTs at the same time-frequency resource. Finally, we validate our results using numerical simulations.

  • Joint Unicast and Multi-group Multicast Transmission in Massive MIMO Systems.
    IEEE Transactions on Wireless Communications, 2018
    Co-Authors: Meysam Sadeghi, Emil Bjornson, Erik G. Larsson, Chau Yuen, Thomas L. Marzetta
    Abstract:

    We study the joint unicast and multi-group multicast transmission in massive multiple-input multiple-output systems. We consider a system model that accounts for channel estimation and pilot contamination and derive achievable spectral efficiencies (SEs) for unicast and multicast user terminals (UTs) under maximum ratio transmission and zero-forcing precoding. For unicast transmission, our objective is to maximize the weighted sum SE of the unicast UTs, and for the multicast transmission, our objective is to maximize the minimum SE of the multicast UTs. These two objectives are coupled in a conflicting manner, due to their shared power resource. Therefore, we formulate a multiobjective optimization problem (MOOP) for the two conflicting objectives. We derive the Pareto boundary of the MOOP analytically. As each Pareto Optimal Point describes a particular efficient tradeoff between the two objectives of the system, we determine the values of the system parameters (uplink training powers, downlink transmission powers, and so on) to achieve any desired Pareto Optimal Point. Moreover, we prove that the Pareto region is convex, and hence, the system should serve the unicast and multicast UTs at the same time–frequency resource. Finally, we validate our results using numerical simulations.

Abbasi, Qammer H. - One of the best experts on this subject based on the ideXlab platform.

  • Modulation mode detection and classification for in-vivo nano-scale communication systems operating in terahertz band
    IEEE, 2019
    Co-Authors: Ozair Iqbal Muhammad, Mahboob Ur Rahman Muhammad, Imran, Muhammad Ali, Alomainy Akram, Abbasi, Qammer H.
    Abstract:

    This paper initiates the efforts to design an intelligent/cognitive nano receiver operating in terahertz band. Specifically, we investigate two essential ingredients of an intelligent nano receiver—modulation mode detection (to differentiate between pulse-based modulation and carrier-based modulation) and modulation classification (to identify the exact modulation scheme in use). To implement modulation mode detection, we construct a binary hypothesis test in nano-receiver’s passband and provide closed-form expressions for the two error probabilities. As for modulation classification, we aim to represent the received signal of interest by a Gaussian mixture model (GMM). This necessitates the explicit estimation of the THz channel impulse response and its subsequent compensation (via deconvolution). We then learn the GMM parameters via expectation–maximization algorithm. We then do Gaussian approximation of each mixture density to compute symmetric Kullback–Leibler divergence in order to differentiate between various modulation schemes (i.e., ${M}$ -ary phase shift keying and ${M}$ -ary quadrature amplitude modulation). The simulation results on mode detection indicate that there exists a unique Pareto-Optimal Point (for both SNR and the decision threshold), where both error probabilities are minimized. The main takeaway message by the simulation results on modulation classification is that for a pre-specified probability of correct classification, higher SNR is required to correctly identify a higher order modulation scheme. On a broader note, this paper should trigger the interest of the community in the design of intelligent/cognitive nano receivers (capable of performing various intelligent tasks, e.g., modulation prediction, and so on)

Qammer H Abbasi - One of the best experts on this subject based on the ideXlab platform.

  • modulation mode detection and classification for in vivo nano scale communication systems operating in terahertz band
    IEEE Transactions on Nanobioscience, 2019
    Co-Authors: Muhammad Ozair Iqbal, Muhammad Mahboob Ur Rahman, Muhammad Imran, Akram Alomainy, Qammer H Abbasi
    Abstract:

    This paper initiates the efforts to design an intelligent/cognitive nano receiver operating in terahertz band. Specifically, we investigate two essential ingredients of an intelligent nano receiver—modulation mode detection (to differentiate between pulse-based modulation and carrier-based modulation) and modulation classification (to identify the exact modulation scheme in use). To implement modulation mode detection, we construct a binary hypothesis test in nano-receiver’s passband and provide closed-form expressions for the two error probabilities. As for modulation classification, we aim to represent the received signal of interest by a Gaussian mixture model (GMM). This necessitates the explicit estimation of the THz channel impulse response and its subsequent compensation (via deconvolution). We then learn the GMM parameters via expectation–maximization algorithm. We then do Gaussian approximation of each mixture density to compute symmetric Kullback–Leibler divergence in order to differentiate between various modulation schemes (i.e., ${M}$ -ary phase shift keying and ${M}$ -ary quadrature amplitude modulation). The simulation results on mode detection indicate that there exists a unique Pareto-Optimal Point (for both SNR and the decision threshold), where both error probabilities are minimized. The main takeaway message by the simulation results on modulation classification is that for a pre-specified probability of correct classification, higher SNR is required to correctly identify a higher order modulation scheme. On a broader note, this paper should trigger the interest of the community in the design of intelligent/cognitive nano receivers (capable of performing various intelligent tasks, e.g., modulation prediction, and so on).