The notion of age-of-information (AoI) is investigated in the context of large-scale wireless networks, in which transmitters need to send a sequence of information packets, which are generated as independent Bernoulli processes, to their intended receivers over a shared spectrum. Due to interference, the rate of packet depletion at any given node is entangled with both the spatial configurations, which determine the path loss, and temporal dynamics, which influence the active states, of the other transmitters, resulting in the queues to interact with each other in both space and time over the entire network. To that end, variants in the packet update frequency affect not just the inter-arrival time but also the departure process, and the impact of such phenomena on the AoI is not well understood. In this paper, we establish a theoretical framework to characterize the AoI performance in the aforementioned setting. Particularly, tractable expressions are derived for both the peak and average AoI under two different transmission protocols, namely the FCFS and the LCFS-PR. Based on the theoretical outcomes, we find that: i) networks operating under LCFS-PR are able to attain smaller values of peak and average AoI than that under FCFS, whereas the gain is more pronounced when the infrastructure is densely deployed, ii) in sparsely deployed networks, ALOHA with a universally designed channel access probability is not instrumental in reducing the AoI, thus calling for more advanced channel access approaches, and iii) when the infrastructure is densely rolled out, there exists a non-trivial ALOHA channel access probability that minimizes the peak and average AoI under both FCFS and LCFS-PR.
Due to the scarcity in the wireless spectrum and limited energy resources especially in mobile applications, efficient resource allocation strategies are critical in wireless networks. Motivated by the recent advances in deep reinforcement learning (DRL), we address multi-agent DRL-based joint dynamic channel access and power control in a wireless interference network. We first propose a multi-agent DRL algorithm with centralized training (DRL-CT) to tackle the joint resource allocation problem. In this case, the training is performed at the central unit (CU) and after training, the users make…
In this paper, we study a model relevant to semantics-aware goal-oriented communications. More specifically, observations from an external process are transmitted through status updates to a battery-powered receiver. From these updates, the receiver is informed about the status of the process and if there is sufficient energy, uses them to perform an actuation to achieve a goal. We consider a wireless power transfer model where the destination receives energy from a dedicated power transmitter and occasionally from the data transmitter. We provide the analysis for the Age of Information (AoI).…
The extreme or maximum age of information (AoI) is analytically studied for wireless communication systems. In particular, a wireless powered single-antenna source node and a receiver (connected to the power grid) equipped with multiple antennas are considered when operated under independent Rayleigh-faded channels. Via the extreme value theory and its corresponding statistical features, we demonstrate that the extreme AoI converges to the Gumbel distribution whereas its corresponding parameters are obtained in straightforward closed-form expressions. Capitalizing on this result, the risk of t…
This paper estimates the freshness of the information in a wireless relay communication system that employs simultaneous wireless information and power transfer (SWIPT) operating under ultra-reliable low-latency communication (URLLC) constraints. The Age of Information (AoI) metric calculates the time difference between the current time and the timestamp of the most recent update received by the receiver is used here to estimate the freshness of information. The short packet communication scheme is used to fulfil the reliability and latency requirements of the proposed wireless network and its…
Age of information (AoI), a metric to analyse data freshness, is of interest for time-critical applications in upcoming wireless networks. Besides, wireless power transfer (WPT) is also essential to prolong lifetime of a wireless sensor network. Thus, we study a flat-fading wireless powered cooperative network, wherein a source and a relay charge their finite-sized capacitors by harvesting energy from a remote power station. For two conventional decode-and-forward (DF) and amplify-and-forward (AF) relaying, average AoI and peak AoI (PAoI) are studied to show how the randomness of the data/powe…
Age of Information (AoI) measures the freshness of data in mission critical Internet-of-Things (IoT) applications i.e., industrial internet, intelligent transportation systems etc. In this paper, a new system model is proposed to estimate the average AoI (AAoI) in an ultra-reliable low latency communication (URLLC) enabled wireless communication system with decodeand-forward relay scheme over the quasi-static Rayleigh block fading channels. Short packet communication scheme is used to meet both reliability and latency requirements of the proposed wireless network. By resorting finite block len…
We consider a wireless sensor network in which all the nodes wish to spread their updates over the network using CSMA/CA protocol. We investigate the age of information of the spreading process from a transmitter perspective, which is referred to as the \textit{broadcast age of information (BAoI)}. To be specific, BAoI is the age of the latest update successfully broadcasted to the one-hop neighbors of a node, and thus is suitable to measure the rapidity of the update spreading process. We establish an equivalent transmission model of the network by deriving the transmission probability and th…
We consider a wireless network where N nodes compete for a shared channel over the CSMA/CA protocol to deliver observed updates to a common remote monitor. For this network, we rate the information freshness of the CSMA/CA based network using the age of information (AoI). Different from previous work, the network we consider is unsaturated. To theoretically analyze the transmission behavior of the CSMA/CA based network, we, therefore, develop an equivalent and tractable Markov transmission model. Based on this newly developed model, the transmission probability, collision probability and avera…
A Networked Multi-Agent System for Mobile Wireless Infrastructure on Demand
Universitat Pompeu Fabra（庞培法布拉大学）；Tampere University（坦佩雷大学）；Nokia（诺基亚公司）；University of Pennsylvania（宾夕法尼亚大学）；Zoox（佐克斯公司）；University of Pittsburgh（匹兹堡大学）；Universidad de la República（共和国大学）
In this paper, transmission scheduling for multiloop wireless networked control systems sharing the wireless channel is considered. A linear quadratic cost offset has been proposed to evaluate the performance gap induced by the nonideal communication. A functional relationship between linear quadratic offset metric and Age of Information has been built up. Based on the offset metric, we come up with an age-based scheduling policy and numerical simulations show that there is a significant improvement compared to the former work.
Agentic AI-Empowered Wireless Agent Networks With Semantic-Aware Collaboration via ILAC
Zhejiang University（浙江大学）；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；Zhejiang Provincial Key Laboratory of Info. Proc., Commun. & Netw. (IPCAN)（浙江省信息处理、通信与网络重点实验室）；King’s College London（伦敦国王学院）；Department of Engineering, King’s College London（伦敦国王学院工程系）；University of Miami（迈阿密大学）；Institute for Data Science and Computing, University of Miami（迈阿密大学数据科学与计算研究所）；The University of Hong Kong（香港大学）；Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电子电气工程系）
The freshness or timeliness of data at server is a significant key performance indicator of sensor networks, especially in tolerance critical applications such as factory automation. As an effective and intuitive measurement to data timeliness, the metric of Age of Information (AoI) has attracted an intensive recent interest of research. This paper initiates a study on the AoI of wireless sensor networks working in the finite blocklength (FBL) regime as a resource allocation problem, and proposes to minimize the long-term discounted system AoI as a Markov decision process (MDP). The proposed m…
University of Colorado（科罗拉多大学）；Virginia Tech（弗吉尼亚理工学院）
Collaborative deep reinforcement learning (CDRL) algorithms in which multiple agents can coordinate over a wireless network is a promising approach to enable future intelligent and autonomous systems that rely on real-time decision-making in complex dynamic environments. Nonetheless, in practical scenarios, CDRL faces many challenges due to the heterogeneity of agents and their learning tasks, different environments, time constraints of the learning, and resource limitations of wireless networks. To address these challenges, in this paper, a novel semantic-aware CDRL method is proposed to enab…
We analyze Age of Information (AoI) in wireless networks where nodes use a spatially adaptive random access scheme to send status updates to a central base station. We show that the set of achievable AoI in this setting is convex, and design policies to minimize weighted sum, min-max, and proportionally fair AoI by setting transmission probabilities as a function of node locations. We show that under the capture model, when the spatial topology of the network is considered, AoI can be significantly improved, and we obtain tight performance bounds on weighted sum and min-max AoI. Finally, we de…
In this work, a wireless broadcast network with a base station (BS) sending random time-sensitive information updates to multiple users with interference constraints is considered. The Age of Synchronization (AoS), namely the amount of time elapsed since the information stored at the network user becomes desynchronized, is adopted to measure data freshness from the perspective of network users. Compared with the more widely used metric---the Age of Information (AoI), AoS accounts for the freshness of the randomly changing content. The AoS minimization scheduling problem is formulated into a di…
The growing demand on high-quality and low-latency multimedia services has led to much interest in edge caching techniques. Motivated by this, we in this paper consider edge caching at the base stations with unknown content popularity distributions. To solve the dynamic control problem of making caching decisions, we propose a deep actor-critic reinforcement learning based multi-agent framework with the aim to minimize the overall average transmission delay. To evaluate the proposed framework, we compare the learning-based performance with three other caching policies, namely least recently us…
We consider distributed caching of content across several small base stations (SBSs) in a wireless network, where the content is encoded using a maximum distance separable code. Specifically, we apply soft time-to-live (STTL) cache management policies, where coded packets may be evicted from the caches at periodic times. We propose a reinforcement learning (RL) approach to find coded STTL policies minimizing the overall network load. We demonstrate that such caching policies achieve almost the same network load as policies obtained through optimization, where the latter assumes perfect knowled…
We propose a mechanism for distributed resource management and interference mitigation in wireless networks using multi-agent deep reinforcement learning (RL). We equip each transmitter in the network with a deep RL agent that receives delayed observations from its associated users, while also exchanging observations with its neighboring agents, and decides on which user to serve and what transmit power to use at each scheduling interval. Our proposed framework enables agents to make decisions simultaneously and in a distributed manner, unaware of the concurrent decisions of other agents. More…
We consider the problem of minimizing the time average cost of sampling and transmitting status updates by users over a wireless channel subject to average Age of Information constraints (AoI). Errors in the transmission may occur and the scheduling algorithm has to decide if the users sample a new packet or attempt for retransmission of the packet sampled previously. The cost consists of both sampling and transmission costs. The sampling of a new packet after a failure imposes an additional cost in the system. We formulate a stochastic optimization problem with time average cost in the object…
We consider a wireless network with a base station serving multiple traffic streams to different destinations. Packets from each stream arrive to the base station according to a stochastic process and are enqueued in a separate (per stream) queue. The queueing discipline controls which packet within each queue is available for transmission. The base station decides, at every time t, which stream to serve to the corresponding destination. The goal of scheduling decisions is to keep the information at the destinations fresh. Information freshness is captured by the Age of Information (AoI) metri…
Spatio-Temporal Attention Enhanced Multi-Agent DRL for UAV-Assisted Wireless Networks with Limited Communications
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University（中山大学智能系统工程学院深圳校区）；Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)（广东海洋工程科学实验室（珠海））；Department of Equipment Management and Unmanned Aerial Vehicle Engineering, Air Force Engineering University（空军工程大学装备管理与无人机工程系）；School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics（江西财经大学计算机与人工智能学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）
Age of information (AoI) is a recently proposed metric for measuring information freshness. AoI measures the time that elapsed since the last received update was generated. We consider the problem of minimizing average and peak AoI in a wireless networks, consisting of a set of source-destination links, under general interference constraints. When fresh information is always available for transmission, we show that a stationary scheduling policy is peak age optimal. We also prove that this policy achieves average age that is within a factor of two of the optimal average age. In the case where …
Deep reinforcement learning offers a model-free alternative to supervised deep learning and classical optimization for solving the transmit power control problem in wireless networks. The multi-agent deep reinforcement learning approach considers each transmitter as an individual learning agent that determines its transmit power level by observing the local wireless environment. Following a certain policy, these agents learn to collaboratively maximize a global objective, e.g., a sum-rate utility function. This multi-agent scheme is easily scalable and practically applicable to large-scale cel…
This work demonstrates the potential of deep reinforcement learning techniques for transmit power control in wireless networks. Existing techniques typically find near-optimal power allocations by solving a challenging optimization problem. Most of these algorithms are not scalable to large networks in real-world scenarios because of their computational complexity and instantaneous cross-cell channel state information (CSI) requirement. In this paper, a distributively executed dynamic power allocation scheme is developed based on model-free deep reinforcement learning. Each transmitter collect…
We consider a wireless uplink network consisting of multiple end devices and an access point (AP). Each device monitors a physical process with stochastic arrival of status updates and sends these updates to the AP over a shared channel. The AP aims to schedule the transmissions of these devices to optimize the network-wide information freshness, quantified by the Age of Information (AoI) metric. Due to the stochastic arrival of the status updates at the devices, the AP only has partial observations of system times of the latest status updates at the devices when making scheduling decisions. W…
Age-of-Information (AoI) is a recently proposed metric for quantifying the freshness of information from the UE's perspective in a communication network. Recently, Kadota et al. [1] have proposed an index-type approximately optimal scheduling policy for minimizing the average-AoI metric for a downlink transmission problem. For delay-sensitive applications, including real-time control of a cyber-physical system, or scheduling URLLC traffic in 5G, it is essential to have a more stringent uniform control on AoI across all users. In this paper, we derive an exactly optimal scheduling policy for th…
Device-to-device (D2D) links scheduling for avoiding excessive interference is critical to the success of wireless D2D communications. Most of the traditional scheduling schemes only consider the maximum throughput or fairness of the system and do not consider the freshness of information. In this paper, we propose a novel D2D links scheduling scheme to optimize an age of information (AoI) and throughput jointly scheduling problem when D2D links transmit packets under the last-come-first-serve policy with packet-replacement (LCFS-PR). It is motivated by the fact that the maximum throughput sch…
Age-of-information (AoI) is a metric quantifying information freshness at the receiver. Since AoI combines packet generation frequency, packet loss, and delay into a single metric, it has received a lot of research attention as an interface between communication network and application. In this work, we apply AoI to the problem of wireless scheduling for multi-loop networked control systems (NCS), i.e., feedback control loops closed over a shared wireless network. We model the scheduling problem as a Markov decision process (MDP) with AoI as its observable states and derive a relation of contr…
This paper considers a large-scale wireless network consisting of source-destination (SD) pairs, where the sources send time-sensitive information, termed status updates, to their corresponding destinations in a time-slotted fashion. We employ Age of information (AoI) for quantifying the freshness of the status updates measured at the destination nodes for two different queuing disciplines, namely Type I and II queues. Type I queue is assumed to transmit the status updates in a first-come-first-served (FCFS) fashion with no storage facility. However, Type I queue may not necessarily minimize A…
