Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model

• URL: http://arxiv.org/abs/2007.13299v1
• Date: Mon, 27 Jul 2020 03:52:46 GMT
• Title: Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model
• Authors: Qiyou Duan, Taejoon Kim, Hadi Ghauch
• Abstract summary: We present an enhancement to the problem of beam alignment in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. A new approach, centered on discrete monotonic optimization (DMO), is proposed, leading to significantly reduced complexity. Simulation results that demonstrate the efficacy of the proposed KM learning for mmWave beam alignment are presented.
• Score: 7.273098050146647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an enhancement to the problem of beam alignment in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, based on a modification of the machine learning-based criterion, called Kolmogorov model (KM), previously applied to the beam alignment problem. Unlike the previous KM, whose computational complexity is not scalable with the size of the problem, a new approach, centered on discrete monotonic optimization (DMO), is proposed, leading to significantly reduced complexity. We also present a Kolmogorov-Smirnov (KS) criterion for the advanced hypothesis testing, which does not require any subjective threshold setting compared to the frequency estimation (FE) method developed for the conventional KM. Simulation results that demonstrate the efficacy of the proposed KM learning for mmWave beam alignment are presented.

Related papers

• From Exponential to Quadratic: Optimal Control for a Frustrated Ising Ring Model [0.7864304771129751]
  Exponentially small spectral gaps are known to be the crucial bottleneck for traditional Quantum Annealing. We show that the model is digitally controllable with a scaling of resources that grows quadratically with the system size. We construct smooth digital schedules yielding optimal solutions with very high accuracy.
  arXiv  Detail & Related papers  (2025-02-24T09:59:05Z)
• Downlink MIMO Channel Estimation from Bits: Recoverability and Algorithm [47.7091447096969]
  A major challenge lies in acquiring the downlink channel state information (CSI) at the base station (BS) from limited feedback sent by the user equipment (UE) In this paper, a simple feedback framework is proposed, where a compression and Gaussian dithering-based quantization strategy is adopted at the UE side, and then a maximum likelihood estimator (MLE) is formulated at the BS side. The algorithm is carefully designed to integrate a sophisticated harmonic retrieval (HR) solver as subroutine, which turns out to be the key of effectively tackling this hard MLE problem.
  arXiv  Detail & Related papers  (2024-11-25T02:15:01Z)
• On the variational treatment of a class of double-well oscillators [0.0]
  We compare the well known Rayleigh-Ritz variational method (RRVM) with a recently proposed approach based on supersymmetric quantum mechanics. We show that the unproved SSQMGS upper bounds do not hold.
  arXiv  Detail & Related papers  (2023-08-17T14:59:04Z)
• An Optimization-based Deep Equilibrium Model for Hyperspectral Image Deconvolution with Convergence Guarantees [71.57324258813675]
  We propose a novel methodology for addressing the hyperspectral image deconvolution problem. A new optimization problem is formulated, leveraging a learnable regularizer in the form of a neural network. The derived iterative solver is then expressed as a fixed-point calculation problem within the Deep Equilibrium framework.
  arXiv  Detail & Related papers  (2023-06-10T08:25:16Z)
• Unbiasing time-dependent Variational Monte Carlo by projected quantum evolution [44.99833362998488]
  We analyze the accuracy and sample complexity of variational Monte Carlo approaches to simulate quantum systems classically. We prove that the most used scheme, the time-dependent Variational Monte Carlo (tVMC), is affected by a systematic statistical bias. We show that a different scheme based on the solution of an optimization problem at each time step is free from such problems.
  arXiv  Detail & Related papers  (2023-05-23T17:38:10Z)
• Effective implementation of $l_0$-Regularised Compressed Sensing with Chaotic-Amplitude-Controlled Coherent Ising Machines [1.8287426976997025]
  We propose a quantum-classical hybrid system to solve optimisation problems of compressed sensing. In the hybrid system, the CIM was an open-loop system without an amplitude control feedback loop. The results of this study demonstrate an improved degree of accuracy and a wider range of effectiveness.
  arXiv  Detail & Related papers  (2023-02-24T09:16:48Z)
• General Hamiltonian Representation of ML Detection Relying on the Quantum Approximate Optimization Algorithm [74.6114458993128]
  The quantum approximate optimization algorithm (QAOA) conceived for solving optimization problems can be run on the existing noisy intermediate-scale quantum (NISQ) devices. We solve the maximum likelihood (ML) detection problem for general constellations by appropriately adapting the QAOA. In particular, for an M-ary Gray-mapped quadrature amplitude modulation (MQAM) constellation, we show that the specific qubits encoding the in-phase components and those encoding the quadrature components are independent in the quantum system of interest.
  arXiv  Detail & Related papers  (2022-04-11T14:11:24Z)
• A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors [60.489902135153415]
  This paper introduces a variational iterative alternating scheme for hierarchical inverse problems with gamma hyperpriors. The proposed variational inference approach yields accurate reconstruction, provides meaningful uncertainty quantification, and is easy to implement.
  arXiv  Detail & Related papers  (2021-11-26T06:33:29Z)
• Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrays [54.43962058166702]
  millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems with discrete lens arrays have received great attention. In this work, we investigate the joint design of a beam precoding matrix for mmWave MU-MIMO systems with DLA.
  arXiv  Detail & Related papers  (2021-01-05T03:55:04Z)
• Pushing the Envelope of Rotation Averaging for Visual SLAM [69.7375052440794]
  We propose a novel optimization backbone for visual SLAM systems. We leverage averaging to improve the accuracy, efficiency and robustness of conventional monocular SLAM systems. Our approach can exhibit up to 10x faster with comparable accuracy against the state-art on public benchmarks.
  arXiv  Detail & Related papers  (2020-11-02T18:02:26Z)
• Metamodel Based Forward and Inverse Design for Passive Vibration Suppression [2.6774008509840996]
  Aperiodic metamaterials represent structural systems composed of different building blocks (cells) instead of a self-repeating chain of the same unit cells. This paper presents a design automation framework applied to a 1D metamaterial system, namely a drill string, where vibration suppression is of utmost importance.
  arXiv  Detail & Related papers  (2020-07-29T18:11:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.