A method was proposed for generating sum and difference beam patterns simultaneously in a sparse antenna array by utilizing a common excitation vector. This approach was motivated by the challenges in accurately estim...
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ISBN:
(数字)9798350369151
ISBN:
(纸本)9798350369168
A method was proposed for generating sum and difference beam patterns simultaneously in a sparse antenna array by utilizing a common excitation vector. This approach was motivated by the challenges in accurately estimating signal arrival direction and the intricate design of feeding network structures. This study formulates a nonconvex optimization problem based on the radiation performance of dual beams, incorporating a nonconvex mainlobe constraint for the sum beam and a nonconvex null depth constraint for the difference beam. The successive convex approximation algorithm is utilized to address these formulated problems. In order to overcome infeasible solutions arising from nonconvex constraints, slack variables are introduced to aid in the pursuit of feasible points. Simulation results demonstrate that the proposed algorithm improves the radiation performance of both sum and difference beam patterns with fewer antennas compared to other algorithms, thus confirming the advantages of the proposed approach.
A distributed approach is proposed in this work to solve large-scale optimization problems, called L-DATR, under the master/worker communication model. L-DATR is a distributed limited-memory trust-region method that a...
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ISBN:
(数字)9798350376647
ISBN:
(纸本)9798350376654
A distributed approach is proposed in this work to solve large-scale optimization problems, called L-DATR, under the master/worker communication model. L-DATR is a distributed limited-memory trust-region method that allows worker nodes to perform asynchronous computations. Our method dynamically adjusts the step size and direction using trust-region strategies to improve stability and convergence. To our knowledge, this is the first implementation of a distributed trust-region limited memory quasi-Newton method with robust handling of asynchronous updates and non-uniform delays between nodes. Our method is communication-efficient because it communicates only vectors of the dimension of the decision variable. Our numerical experiments match our theoretical results and showcase significant stability improvements compared to state-of-the-art distributed algorithms.
The series for the zeta function does not converge on the critical line but the function (Equation presented) satisfies (Equation presented). So one expects that the zeros of zeta on the critical line are very near th...
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In a weighed directed graph $G=(V, E, \omega)$ with m edges and n vertices, we are interested in its basic graph parameters such as diameter, radius and eccentricities, under the nonstandard measure of min-distance wh...
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ISBN:
(数字)9781665455190
ISBN:
(纸本)9781665455206
In a weighed directed graph $G=(V, E, \omega)$ with m edges and n vertices, we are interested in its basic graph parameters such as diameter, radius and eccentricities, under the nonstandard measure of min-distance which is defined for every pair of vertices $u, v \in V$ as the minimum of the shortest path distances from u to v and from v to u. Similar to standard shortest paths distances, computing graph parameters exactly in terms of min-distances essentially requires $\tilde{\Omega}(m n)$ time under plausible hardness conjectures 1 . Hence, for faster running time complexities we have to tolerate approximations. Abboud, Vassilevska Williams and Wang [SODA 2016] were the first to study min-distance problems, and they obtained constant factor approximation algorithms in acyclic graphs, with running time $\tilde{O}(m)$ and $\tilde{O}(m \sqrt{n})$ for diameter and radius, respectively. The time complexity of radius in acyclic graphs was recently improved to $\tilde{O}(m)$ by Dalirrooyfard and Kaufmann [ICALP 2021], but at the cost of an $O(\log n)$ approximation ratio. For general graphs, the authors of [DWV+, ICALP 2019] gave the first constant factor approximation algorithm for diameter, radius and eccentricities which runs in time $\tilde{O}(m \sqrt{n})$; besides, for the diameter problem, the running time can be improved to $\tilde{O}(m)$ while blowing up the approximation ratio to $O(\log n)$. A natural question is whether constant approximation and near-linear time can be achieved simultaneously for diameter, radius and eccentricities; so far this is only possible for diameter in the restricted setting of acyclic graphs. In this paper, we answer this question in the affirmative by presenting near-linear time algorithms for all three parameters in general graphs. 1 As usual, the $\tilde{O}(\cdot)$ notation hides poly-logarithmic factors in n
We propose a two-phase systematical framework for approximation algorithm design and analysis via Lyapunov function. The first phase consists of using Lyapunov function as an input and outputs a continuous-time approx...
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Learning-based low rank approximation algorithms can significantly improve the performance of randomized low rank approximation with sketch matrix. With the learned value and fixed non-zero positions for sketch matric...
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The integration of reconfigurable intelligent surfaces (RIS) and artificial noise (AN) significantly enhances physical layer security (PLS) in wireless networks, provided that RIS’s phase shifts are precisely optimiz...
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ISBN:
(数字)9798350351255
ISBN:
(纸本)9798350351262
The integration of reconfigurable intelligent surfaces (RIS) and artificial noise (AN) significantly enhances physical layer security (PLS) in wireless networks, provided that RIS’s phase shifts are precisely optimized to prevent security vulnerabilities. This paper introduces a reinforcement learning (RL)based algorithm designed to optimize the phase shifts in RIS-partitioning-aided PLS systems operating in the millimeter wave (mm-Wave), without requiring channel state information (CSI) for any users. The RL algorithm optimizes the phase shifts by efficiently selecting the best beam from a predefined codebook for different partitions, which simultaneously enhances the intended signal for legitimate users and increases the effectiveness of AN on eavesdroppers, thereby maximizing the system’s secrecy capacity (SC) and addressing the inherent non-convex challenges. Additionally, the paper details the development of an experimental testbed that provides essential data to refine the algorithm. The numerical results from the testbed highlight the significant impact of RIS partitioning in PLS, which can enhance the SC by an average of 55% over the full RIS scenario, and confirm the effectiveness of the RL-based algorithm in reducing computational complexity by approximately 80% compared to the exhaustive search algorithm.
In this work, we investigate the energy effi-ciency (EE) metric in reconfigurable intelligent surface (RIS)-assisted multi-user (MU) massive multiple-input multiple-output (mMIMO) systems. Our analysis involves the op...
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ISBN:
(数字)9798350362510
ISBN:
(纸本)9798350362527
In this work, we investigate the energy effi-ciency (EE) metric in reconfigurable intelligent surface (RIS)-assisted multi-user (MU) massive multiple-input multiple-output (mMIMO) systems. Our analysis involves the optimization of the number of antennas M and reflective elements (REs) N in the base station (BS) and RIS arrays, respectively, along with the optimization of passive beamforming and power allocation. To address this problem, we introduce a novel joint reinforcement-analytical methodology (JRAM) algorithm, utilizing reinforcement learning (RL) for the elements selection in both arrays and subsequently conventional analytical techniques to optimize passive beamforming and power allocation for the selected elements at the BS and RIS. The numerical results underscore the significance of jointly optimizing the number of operative antennas and REs, leading to a remarkable gain of approximately 40.3% compared to optimizing just the operative elements in the BS and passive beamforming optimization.
The traditional approach to POMDPs is to convert them into fully observed MDPs by considering a belief state as an information state. However, a belief-state based approach requires perfect knowledge of the system dyn...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
The traditional approach to POMDPs is to convert them into fully observed MDPs by considering a belief state as an information state. However, a belief-state based approach requires perfect knowledge of the system dynamics and is therefore not applicable in the learning setting where the system model is unknown. Various approaches to circumvent this limitation have been proposed in the literature. We present a unified treatment of some of these approaches by viewing them as models where the agent maintains a local recursively updateable “agent state” and chooses actions based on the agent state. We highlight the different classes of agent-state based policies and the various approaches that have been proposed in the literature to find good policies within each class. These include the designer’s approach to find optimal non-stationary agent-state based policies, policy search approaches to find a locally optimal stationary agent-state based policies, and the approximate information state to find approximately optimal stationary agent-state based policies. We then present how ideas from the approximate information state approach have been used to improve Q-learning and actor-critic algorithms for learning in POMDPs.
This paper proposes a two-stage wall parameter estimation method based on multi-channel to retrieve the unknown position, thickness, and relative permittivity of wall. In the first stage, it aims to estimate the slope...
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ISBN:
(数字)9798350367331
ISBN:
(纸本)9798350367348
This paper proposes a two-stage wall parameter estimation method based on multi-channel to retrieve the unknown position, thickness, and relative permittivity of wall. In the first stage, it aims to estimate the slope and intercept of the front surface of the wall. This is achieved by formulating a cost-minimization problem, where the position of the front wall is modeled using a linear analytical expression. Subsequently, in the second stage, it focuses on calculating the positions of equivalent arrays that are aligned parallel to the wall for each transmitting antenna, so as to enable the estimation of the wall’s thickness and its relative permittivity. The effectiveness of the algorithm is validated through electromagnetic simulations.
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