Showing papers by "Yin Sun published in 2017"

Update or Wait: How to Keep Your Data Fresh

Abstract: In this paper, we study how to optimally manage the freshness of information updates sent from a source node to a destination via a channel. A proper metric for data freshness at the destination is the age-of-information , or simply age , which is defined as how old the freshest received update is, since the moment that this update was generated at the source node (e.g., a sensor). A reasonable update policy is the zero-wait policy, i.e., the source node submits a fresh update once the previous update is delivered, which achieves the maximum throughput and the minimum delay. Surprisingly, this zero-wait policy does not always minimize the age. This counter-intuitive phenomenon motivates us to study how to optimally control information updates to keep the data fresh and to understand when the zero-wait policy is optimal. We introduce a general age penalty function to characterize the level of dissatisfaction on data staleness and formulate the average age penalty minimization problem as a constrained semi-Markov decision problem with an uncountable state space. We develop efficient algorithms to find the optimal update policy among all causal policies and establish sufficient and necessary conditions for the optimality of the zero-wait policy. Our investigation shows that the zero-wait policy is far from the optimum if: 1) the age penalty function grows quickly with respect to the age; 2) the packet transmission times over the channel are positively correlated over time; or 3) the packet transmission times are highly random (e.g., following a heavy-tail distribution).

Age-optimal information updates in multihop networks

Abstract: The problem of reducing the age-of-information has been extensively studied in single-hop networks. In this paper, we minimize the age-of-information in general multihop networks. If the packet transmission times over the network links are exponentially distributed, we prove that a preemptive Last Generated First Served (LGFS) policy results in smaller age processes at all nodes of the network (in a stochastic ordering sense) than any other causal policy. In addition, for arbitrary distributions of packet transmission times, the non-preemptive LGFS policy is shown to minimize the age processes at all nodes among all non-preemptive work-conserving policies (again in a stochastic ordering sense). It is surprising that such simple policies can achieve optimality of the joint distribution of the age processes at all nodes even under arbitrary network topologies, as well as arbitrary packet generation and arrival times. These optimality results not only hold for the age processes, but also for any non-decreasing functional of the age processes.

Remote estimation of the Wiener process over a channel with random delay

Abstract: In this paper, we consider a problem of sampling a Wiener process, with samples forwarded to a remote estimator via a channel that consists of a queue with random delay. The estimator reconstructs a real-time estimate of the signal from causally received samples. Motivated by recent research on age-of-information, we study the optimal sampling strategy that minimizes the mean square estimation error subject to a sampling frequency constraint. We prove that the optimal sampling strategy is a threshold policy, and find the optimal threshold. This threshold is determined by the sampling frequency constraint and how much the Wiener process varies during the channel delay. An interesting consequence is that even in the absence of the sampling frequency constraint, the optimal strategy is not zero-wait sampling in which a new sample is taken once the previous sample is delivered; rather, it is optimal to wait for a non-zero amount of time after the previous sample is delivered, and then take the next sample. Further, if the sampling times are independent of the observed Wiener process, the optimal sampling problem reduces to an age-of-information optimization problem that has been recently solved. Our comparisons show that the estimation error of the optimal sampling policy is much smaller than those of age-optimal sampling, zero-wait sampling, and classic uniform sampling.

Optimal Sampling and Remote Estimation of the Wiener Process over a Channel with Random Delay

Abstract: In this paper, we consider a sampling and remote estimation problem, where samples of a Wiener process are forwarded to a remote estimator via a channel with queueing and random delay. The estimator reconstructs an estimate of the real-time signal value from causally received samples. We obtain the jointly optimal sampling and estimation strategy that minimizes the mean-square estimation error subject to a maximum sampling rate constraint. We prove that a threshold-based sampler and a minimum mean-square error (MMSE) estimator are jointly optimal, and the optimal threshold is found exactly. Our jointly optimal solution exhibits an interesting coupling between the source and channel, which is different from the source-channel separation in many previous information theoretical studies. If the sampling times are independent of the observed Wiener process, the joint sampling and estimation optimization problem reduces to an age-of-information optimization problem that has been recently solved. Our theoretical and numerical comparisons show that the estimation error of the optimal sampling policy can be much smaller than those of age-optimal sampling, zero-wait sampling, and classic periodic sampling.

Remote Estimation of the Wiener Process over a Channel with Random Delay

Abstract: In this paper, we consider a problem of sampling a Wiener process, with samples forwarded to a remote estimator via a channel that consists of a queue with random delay. The estimator reconstructs a real-time estimate of the signal from causally received samples. Motivated by recent research on age-of-information, we study the optimal sampling strategy that minimizes the mean square estimation error subject to a sampling frequency constraint. We prove that the optimal sampling strategy is a threshold policy, and find the optimal threshold. This threshold is determined by the sampling frequency constraint and how much the Wiener process varies during the channel delay. An interesting consequence is that even in the absence of the sampling frequency constraint, the optimal strategy is not zero-wait sampling in which a new sample is taken once the previous sample is delivered; rather, it is optimal to wait for a non-zero amount of time after the previous sample is delivered, and then take the next sample. Further, if the sampling times are independent of the observed Wiener process, the optimal sampling problem reduces to an age-of-information optimization problem that has been recently solved. Our comparisons show that the estimation error of the optimal sampling policy is much smaller than those of age-optimal sampling, zero-wait sampling, and classic uniform sampling.

The Age of Information in Multihop Networks

Abstract: Information updates in multihop networks such as Internet of Things (IoT) and intelligent transportation systems have received significant recent attention. In this paper, we minimize the age of a single information flow in interference-free multihop networks. When preemption is allowed and the packet transmission times are exponentially distributed, we prove that a preemptive Last-Generated, First-Served (LGFS) policy results in smaller age processes across all nodes in the network than any other causal policy (in a stochastic ordering sense). In addition, for the class of New-Better-than-Used (NBU) distributions, we show that the non-preemptive LGFS policy is within a constant age gap from the optimum average age. In contrast, our numerical result shows that the preemptive LGFS policy can be very far from the optimum for some NBU transmission time distributions. Finally, when preemption is prohibited and the packet transmission times are arbitrarily distributed, the non-preemptive LGFS policy is shown to minimize the age processes across all nodes in the network among all work-conserving policies (again in a stochastic ordering sense). Interestingly, these results hold under quite general conditions, including (i) arbitrary packet generation and arrival times, and (ii) for minimizing both the age processes in stochastic ordering and any non-decreasing functional of the age processes.

Analog beam tracking in linear antenna arrays: Convergence, optimality, and performance

Abstract: The directionality of millimeter-wave (mmWave) communications creates a significant challenge in serving fast-moving mobile terminals on, e.g., high-speed vehicles, trains, and UAVs. This challenge is exacerbated in mmWave systems using analog antenna arrays, because of the inherent non-convexity in the control of the phase shifters. In this paper, we develop a recursive beam tracking algorithm which can simultaneously achieve fast tracking speed, high tracking accuracy, low complexity, and low pilot overhead. In static scenarios, this algorithm converges to the minimum Cramer-Rao lower bound (CRLB) of beam tracking with high probability. In dynamic scenarios, even at SNRs as low as 0dB, our algorithm is capable of tracking a mobile moving randomly at an absolute angular velocity of 1020 degrees per second, using only 5 pilot symbols per second. If combining with a simple TDMA pilot pattern, this algorithm can track hundreds of high-speed mobiles in 5G configurations. Our simulations show that the tracking performance of this algorithm is much better than several state-of-the-art algorithms.

Age-Optimal Information Updates in Multihop Networks

Abstract: The problem of reducing the age-of-information has been extensively studied in the single-hop networks. In this paper, we minimize the age-of-information in general multihop networks. If the packet transmission times over the network links are exponentially distributed, we prove that a preemptive Last Generated First Served (LGFS) policy results in smaller age processes at all nodes of the network (in a stochastic ordering sense) than any other causal policy. In addition, for arbitrary general distributions of packet transmission times, the non-preemptive LGFS policy is shown to minimize the age processes at all nodes of the network among all non-preemptive work-conserving policies (again in a stochastic ordering sense). It is surprising that such simple policies can achieve optimality of the joint distribution of the age processes at all nodes even under arbitrary network topologies, as well as arbitrary packet generation and arrival times. These optimality results not only hold for the age processes, but also for any non-decreasing functional of the age processes.

Fast Analog Beam Tracking in Phased Antenna Arrays: Theory and Performance

Abstract: The directionality of millimeter-wave (mmWave) communications introduces a significant challenge in serving fast-rotating/moving terminals, e.g., mobile AR/VR, high-speed vehicles, trains, UAVs.This challenge is exacerbated in mmWave systems using analog beamforming, because of the inherent non-convexity in the analog beam tracking problem. In this paper, we obtain the Cramer-Rao lower bound (CRLB) of beam tracking and optimize the analog beamforming vectors to get the minimum CRLB. Then, we develop a low complexity analog beam tracking algorithm that simultaneously optimizes the analog beamforming vector and the estimate of beam direction. Finally, by establishing a new basic theory, we provide the theoretical convergence analysis of the proposed analog beam tracking algorithm, which proves that the minimum CRLB of the MSE is achievable with high probability. Our simulations show that this algorithm can achieve faster tracking speed, higher tracking accuracy and higher data rate than several state-of-the-art algorithms. The key analytical tools used in our algorithm design are stochastic approximation and recursive estimation with a control parameter.

Sampling of the Wiener Process for Remote Estimation over a Channel with Random Delay

Abstract: In this paper, we consider a problem of sampling a Wiener process, with samples forwarded to a remote estimator over a channel that is modeled as a queue. The estimator reconstructs an estimate of the real-time signal value from causally received samples. We study the optimal online sampling strategy that minimizes the mean square estimation error subject to a sampling rate constraint. We prove that the optimal sampling strategy is a threshold policy, and find the optimal threshold. This threshold is determined by how much the Wiener process varies during the random service time and the maximum allowed sampling rate. Further, if the sampling times are independent of the observed Wiener process, the above sampling problem for minimizing the estimation error is equivalent to a sampling problem for minimizing the age of information. This reveals an interesting connection between the age of information and remote estimation error. Our comparisons show that the estimation error achieved by the optimal sampling policy can be much smaller than those of age-optimal sampling, zero-wait sampling, and periodic sampling.

Super Fast Beam Tracking in Phased Antenna Arrays.

Abstract: The directionality of millimeter-wave (mmWave) communications introduces a significant challenge in serving fast-rotating/moving terminals, e.g., mobile AR/VR, high-speed vehicles, trains, UAVs.This challenge is exacerbated in mmWave systems using analog beamforming, because of the inherent non-convexity in the analog beam tracking problem. In this paper, we obtain the Cramer-Rao lower bound (CRLB) of beam tracking and optimize the analog beamforming vectors to get the minimum CRLB. Then, we develop a low complexity analog beam tracking algorithm that simultaneously optimizes the analog beamforming vector and the estimate of beam direction. Finally, by establishing a new basic theory, we provide the theoretical convergence analysis of the proposed analog beam tracking algorithm, which proves that the minimum CRLB of the MSE is achievable with high probability. Our simulations show that this algorithm can achieve faster tracking speed, higher tracking accuracy and higher data rate than several state-of-the-art algorithms. The key analytical tools used in our algorithm design are stochastic approximation and recursive estimation with a control parameter.

Minimizing the Age of the Information through Queues

Abstract: In this paper, we investigate scheduling policies that minimize the age of information in single-hop queueing systems. We propose a Last-Generated, First-Serve (LGFS) scheduling policy, in which the packet with the earliest generation time is processed with the highest priority. If the service times are i.i.d. exponentially distributed, the preemptive LGFS policy is proven to be age-optimal in a stochastic ordering sense. If the service times are i.i.d. and satisfy a New-Better-than-Used (NBU) distributional property, the non-preemptive LGFS policy is shown to be within a constant gap from the optimum age performance. These age-optimality results are quite general: (i) They hold for arbitrary packet generation times and arrival times (including out-of-order packet arrivals), (ii) They hold for multi-server packet scheduling with the possibility of replicating a packet over multiple servers, (iii) They hold for minimizing not only the time-average age and mean peak age, but also for minimizing the age stochastic process and any non-decreasing functional of the age stochastic process. If the packet generation time is equal to packet arrival time, the LGFS policies reduce to the Last-Come, First-Serve (LCFS) policies. Hence, the age optimality results of LCFS-type policies are also established.

Designing Low-Complexity Heavy-Traffic Delay-Optimal Load Balancing Schemes: Theory to Algorithms

Abstract: We establish a unified analytical framework for load balancing systems, which allows us to construct a general class $\Pi$ of policies that are both throughput optimal and heavy-traffic delay optimal. This general class $\Pi$ includes as special cases popular policies such as join-shortest-queue and power-of-$d$, but not the join-idle-queue (JIQ) policy. In fact, we show that JIQ, which is not in $\Pi$, is actually not heavy-traffic delay optimal. Owing to the significant flexibility offered by class $\Pi$, we are able to design a new policy called join-below-threshold (JBT-d), which maintains the simplicity of pull-based policies such as JIQ, but updates its threshold dynamically. We prove that JBT-$d$ belongs to the class $\Pi$ when the threshold is picked appropriately and thus it is heavy-traffic delay optimal. Extensive simulations show that the new policy not only has a low complexity in message rates, but also achieves excellent delay performance, comparable to the optimal join-shortest-queue in various system settings.

Analog Beam Tracking in Linear Antenna Arrays: Convergence, Optimality, and Performance

Abstract: The directionality of millimeter-wave (mmWave) communications creates a significant challenge in serving fast-moving mobile terminals on, e.g., high-speed vehicles, trains, and UAVs. This challenge is exacerbated in mmWave systems using analog antenna arrays, because of the inherent non-convexity in the control of the phase shifters. In this paper, we develop a recursive beam tracking algorithm which can simultaneously achieve fast tracking speed, high tracking accuracy, low complexity, and low pilot overhead. In static scenarios, this algorithm converges to the minimum Cramer-Rao lower bound (CRLB) of beam tracking with high probability. In dynamic scenarios, even at SNRs as low as 0dB, our algorithm is capable of tracking a mobile moving randomly at an absolute angular velocity of 10-20 degrees per second, using only 5 pilot symbols per second. If combining with a simple TDMA pilot pattern, this algorithm can track hundreds of high-speed mobiles in 5G configurations. Our simulations show that the tracking performance of this algorithm is much better than several state-of-the-art algorithms.