Quantifying Link Stability in Ad Hoc Wireless Networks Subject to Ornstein-Uhlenbeck Mobility

Cika, Arta; Badiu, Mihai-Alin; Coon, Justin P.

doi:10.1109/icc.2019.8761523

Cited by 10 publications

(3 citation statements)

References 28 publications

(37 reference statements)

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“…It is, therefore, imperative to quantify topological uncertainty when designing and implementing these systems in any real-world application. In our previous work [29], we presented a mobility metric for the evaluation of the link stability in mobile ad hoc networks in the absence of signal fading. Our next goal is to generalize this stability metric to take into account the effects of the physical characteristics of the underlying wireless channel.…”

Section: Discussionmentioning

confidence: 99%

Statistical Properties of Transmissions Subject to Rayleigh Fading and Ornstein-Uhlenbeck Mobility

Cika¹,

Badiu²,

Coon³

2019

Preprint

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In this paper, we derive closed-form expressions for significant statistical properties of the link signal-to-noise ratio (SNR) and the separation distance in mobile ad hoc networks subject to Ornstein-Uhlenbeck (OU) mobility and Rayleigh fading. In these systems, the SNR is a critical parameter as it directly influences link performance. In the absence of signal fading, the distribution of the link SNR depends exclusively on the squared distance between nodes, which is governed by the mobility model. In our analysis, nodes move randomly according to an Ornstein-Uhlenbeck process, using one tuning parameter to control the temporal dependency in the mobility pattern. We derive a complete statistical description of the squared distance and show that it forms a stationary Markov process. Then, we compute closed-form expressions for the probability density function (pdf), the cumulative distribution function (cdf), the bivariate pdf, and the bivariate cdf of the link SNR. Next, we introduce small-scale fading, modeled by a Rayleigh random variable, and evaluate the pdf of the link SNR for rational path loss exponents. The validity of our theoretical analysis is verified by extensive simulation studies. The results presented in this work can be used to quantify link uncertainty and evaluate stability in mobile ad hoc wireless systems.

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Section: Discussionmentioning

confidence: 99%

Statistical Properties of Transmissions Subject to Rayleigh Fading and Ornstein-Uhlenbeck Mobility

Cika¹,

Badiu²,

Coon³

2019

Preprint

Self Cite

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“…In the remote monitoring system, the context may be related to the distance between adjacent vehicles/mobile devices, the unexpected maneuver of the neighboring vehicles, etc. In [32], the authors prove that whether the distance between two mobile wireless devices with Ornstein-Uhlenbeck mobility is less than a certain threshold follows a firstorder Markov process. When the two devices are closer, they are more interested in each other's status information, communication and computing resources to facilitate cooperation, share resources, and avoid collisions.…”

Section: Constrained Markov Decision Process Formulationmentioning

confidence: 99%

A UoI-Optimal Policy for Timely Status Updates with Resource Constraint

Wang

Sun

et al. 2021

Entropy

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Timely status updates are critical in remote control systems such as autonomous driving and the industrial Internet of Things, where timeliness requirements are usually context dependent. Accordingly, the Urgency of Information (UoI) has been proposed beyond the well-known Age of Information (AoI) by further including context-aware weights which indicate whether the monitored process is in an emergency. However, the optimal updating and scheduling strategies in terms of UoI remain open. In this paper, we propose a UoI-optimal updating policy for timely status information with resource constraint. We first formulate the problem in a constrained Markov decision process and prove that the UoI-optimal policy has a threshold structure. When the context-aware weights are known, we propose a numerical method based on linear programming. When the weights are unknown, we further design a reinforcement learning (RL)-based scheduling policy. The simulation reveals that the threshold of the UoI-optimal policy increases as the resource constraint tightens. In addition, the UoI-optimal policy outperforms the AoI-optimal policy in terms of average squared estimation error, and the proposed RL-based updating policy achieves a near-optimal performance without the advanced knowledge of the system model.

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“…It is the only nontrivial continuous-time process that is stationary, Gaussian, and Markovian [7]. Examples of first-order systems that can be described as the OU process include interest rates, currency exchange rates, and commodity prices (with modifications) [8], control systems such as node mobility in mobile ad-hoc networks, robotic swarms, and UAV systems [9], [10], and physical processes such as the transfer of liquids or gases in and out of a tank [11].…”

Section: Introductionmentioning

confidence: 99%

Sampling and Remote Estimation for the Ornstein-Uhlenbeck Process through Queues: Age of Information and Beyond

Ornee

Sun

2019

Preprint

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Recently, a connection between the age of information and remote estimation error was found in a sampling problem of Wiener processes: If the sampler has no knowledge of the signal being sampled, the optimal sampling strategy is to minimize the age of information; however, by exploiting causal knowledge of the signal values, it is possible to achieve a smaller estimation error. In this paper, we generalize the previous study by investigating a problem of sampling a stationary Gauss-Markov process named the Ornstein-Uhlenbeck (OU) process, where we aim to find useful insights for solving the problems of sampling more general signals. The optimal sampling problem is formulated as a constrained continuous-time Markov decision process (MDP) with an uncountable state space. We provide an exact solution to this MDP: The optimal sampling policy is a threshold policy on instantaneous estimation error and the threshold is found. Further, if the sampler has no knowledge of the OU process, the optimal sampling problem reduces to an MDP for minimizing a nonlinear age of information metric. The age-optimal sampling policy is a threshold policy on expected estimation error and the threshold is found. In both problems, the optimal sampling policies can be computed by bisection search, and the curse of dimensionality is circumvented. These results hold for (i) general service time distributions of the queueing server and (ii) sampling problems both with and without a sampling rate constraint. Numerical results are provided to compare different sampling policies.

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Quantifying Link Stability in Ad Hoc Wireless Networks Subject to Ornstein-Uhlenbeck Mobility

Cited by 10 publications

References 28 publications

Statistical Properties of Transmissions Subject to Rayleigh Fading and Ornstein-Uhlenbeck Mobility

Statistical Properties of Transmissions Subject to Rayleigh Fading and Ornstein-Uhlenbeck Mobility

A UoI-Optimal Policy for Timely Status Updates with Resource Constraint

Sampling and Remote Estimation for the Ornstein-Uhlenbeck Process through Queues: Age of Information and Beyond

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