Age of Information Analysis for Dynamic Spectrum Sharing

Zhao, Yao; Zhou, Bo; Saad, Walid; Luo, Xiliang

doi:10.1109/globalsip45357.2019.8969400

Cited by 20 publications

(4 citation statements)

References 18 publications

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“…Each deterministic policy θ λ * , λ * ∈ {λ * 1 , λ * 2 } is the optimal solution to the unconstrained problem (24) for a given multiplier λ * . The optimal values of the multipliers λ * 1 and λ * 2 can be solved by iterative algorithms such as the Robbins-Monro algorithm [24], [35], [36]. The policy θ CMDP selects θ λ * 1 with a probability α, and chooses θ λ * 2 with a probability 1 − α.…”

Section: A Proof Of Theoremmentioning

confidence: 99%

Optimizing Information Freshness in Two-Hop Status Update Systems under a Resource Constraint

Wang

Chen

et al. 2020

Preprint

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This paper considers a two-hop status update system, in which an information source aims for the timely delivery of status updates to the destination with the aid of a relay. The timeliness of status updates is quantized by a recently proposed metric, termed the Age of Information (AoI). We study a slotted communication scenario with error-prone communication channels. The relay is assumed to be an energy-constraint device and our goal is to devise scheduling policies that adaptively switch between the information decoding and information forwarding to minimize the long-term average AoI at the destination, under a resource constraint on the average number of forwarding operations at the relay.We first identify an optimal scheduling policy by modelling the considered scheduling problem as a constrained Markov decision process (CMDP) problem. We resolve the CMDP problem by transforming it into an unconstrained Markov decision process (MDP) using a Lagrangian method. The structural properties of the optimal scheduling policy is analyzed, which is shown to have a multiple threshold structure. For implementation simplicity, based on the structural properties of the CMDP-based policy, we then propose a low-complexity double threshold relaying (DTR) policy with only two thresholds, one for relay's age and the other one for the age gain between destination and relay. We manage to derive approximate closed-form expressions of the average AoI at the destination, and the average number of forwarding operations at the relay for the DTR policy, by modelling the tangled evolution of age at the relay and destination as a Markov chain (MC). Numerical results are provided to verify all the theoretical analysis, and show that the low-complexity DTR policy can achieve near optimal performance compared with the optimal scheduling policy derived from the CMDP problem. The simulation results also unveil

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Section: A Proof Of Theoremmentioning

confidence: 99%

Optimizing Information Freshness in Two-Hop Status Update Systems under a Resource Constraint

Wang

Chen

et al. 2020

Preprint

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“…It is defined as the time elapsed since the generation time of the latest successfully received status-update at the destination. Some innovative efforts have been devoted to the AoI of CRN [24][25][26][27][28]. In [24], the authors considered a cognitive wireless sensor network with a cluster of SUs, where the authors proposed a joint and scheduling strategy that optimized energy efficiency of a communication system subject to the expected AoI.…”

Section: Introductionmentioning

confidence: 99%

“…In [24], the authors considered a cognitive wireless sensor network with a cluster of SUs, where the authors proposed a joint and scheduling strategy that optimized energy efficiency of a communication system subject to the expected AoI. The authors in [25] considered an overlay CRN where the SU acted as a relay. The SU forwarded the PU's packets or transmitted its own packets.…”

Section: Introductionmentioning

confidence: 99%

Age of Information Minimization for Radio Frequency Energy-Harvesting Cognitive Radio Networks

Sun

Zhang

Yang

et al. 2022

Entropy

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The Age of Information (AoI) measures the freshness of information and is a critic performance metric for time-sensitive applications. In this paper, we consider a radio frequency energy-harvesting cognitive radio network, where the secondary user harvests energy from the primary users’ transmissions and opportunistically accesses the primary users’ licensed spectrum to deliver the status-update data pack. We aim to minimize the AoI subject to the energy causality and spectrum constraints by optimizing the sensing and update decisions. We formulate the AoI minimization problem as a partially observable Markov decision process and solve it via dynamic programming. Simulation results verify that our proposed policy is significantly superior to the myopic policy under different parameter settings.

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“…Typically, the AoI is defined as the time elapsed since the most recently received status packet at the destination was collected by the IoT devices, and hence it naturally captures how fresh the information is from the destinations perspective. Due to the importance of information freshness in the IoT, the concept of AoI has received significant attention in a variety of scenarios that include multi-user networks [6]- [9], multi-hop networks [10], [11], IoT monitoring systems [12]- [14], energy harvesting systems [15]- [17], cognitive networks [18], [19], and remote estimation systems [20].…”

Section: Introductionmentioning

confidence: 99%

On the Age of Information in Internet of Things Systems with Correlated Devices

Zhou

Saad

2020

Preprint

Self Cite

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In this paper, a real-time Internet of Things (IoT) monitoring system is considered in which multiple IoT devices must transmit timely updates on the status information of a common underlying physical process to a common destination. In particular, a real-world IoT scenario is considered in which multiple (partially) observed status information by different IoT devices are required at the destination, so that the real-time status of the physical process can be properly re-constructed. By taking into account such correlated status information at the IoT devices, the problem of IoT device scheduling is studied in order to jointly minimize the average age of information (AoI) at the destination and the average energy cost at the IoT devices. Particularly, two types of IoT devices are considered: Type-I devices whose status updates randomly arrive and type-II devices whose status updates can be generated-at-will with an associated sampling cost. This stochastic problem is formulated as an infinite horizon average cost Markov decision process (MDP). The optimal scheduling policy is shown to be thresholdbased with respect to the AoI at the destination, and the threshold is non-increasing with the channel condition of each device. For a special case in which all devices are type-II, the original MDP can be reduced to an MDP with much smaller state and action spaces. The optimal policy is further shown to have a similar thresholdbased structure and the threshold is non-decreasing with an energy cost function of the devices. Simulation results illustrate the structure of the optimal policy and show the effectiveness of the optimal policy compared with a myopic baseline policy.

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Age of Information Analysis for Dynamic Spectrum Sharing

Cited by 20 publications

References 18 publications

Optimizing Information Freshness in Two-Hop Status Update Systems under a Resource Constraint

Optimizing Information Freshness in Two-Hop Status Update Systems under a Resource Constraint

Age of Information Minimization for Radio Frequency Energy-Harvesting Cognitive Radio Networks

On the Age of Information in Internet of Things Systems with Correlated Devices

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