arXiv subjects

Federico Chiariotti

Publications and source records attributed to Federico Chiariotti.

At least 55 records · Page 3Linked to original sources

arXiv2023

Multi-Agent Reinforcement Learning for Pragmatic Communication and Control

The automation of factories and manufacturing processes has been accelerating over the past few years, boosted by the Industry 4.0 paradigm, including diverse scenarios with mobile, flexible agents. Efficient coordination between mobile robots requires reliable wireless transmission in highly dynamic environments, often with strict timing requirements. Goal-oriented communication is a possible solution for this problem: communication decisions should be optimized for the target control task, providing the information that is most relevant to decide which action to take. From the control perspective, networked control design takes the communication impairments into account in its optmization of physical actions. In this work, we propose a joint design that combines goal-oriented communication and networked control into a single optimization model, an extension of a multiagent POMDP which we call Cyber-Physical POMDP (CP-POMDP). The model is flexible enough to represent several swarm and cooperative scenarios, and we illustrate its potential with two simple reference scenarios with a single agent and a set of supporting sensors. Joint training of the communication and control systems can significantly improve the overall performance, particularly if communication is severely constrained, and can even lead to implicit coordination of communication actions.

cs.RO↗

arXiv2023

On the Role of Preemption for Timing Metrics in Coded Multipath Communication

Recent trends in communication networks have focused on Quality of Service (QoS) requirements expressed through timing metrics such as latency or Age of Information (AoI). A possible way to achieve this is coded multipath communication: redundancy is added to a block of information through a robust packet-level code, transmitting across multiple independent channels to reduce the impact of blockages or rate fluctuation. The number of these links can grow significantly over traditional two-path schemes: in these scenarios, the optimization of the timing metrics is non-trivial, and latency and AoI might require different settings. In particular, packet preemption is often the optimal solution to optimize AoI in uncoded communication, but can significantly reduce the reliability of individual blocks. In this work, we model the multipath communication as a fork-join D/M/(K,N)/L queue, where K blocks of information are encoded into N>K redundant blocks. We derive the latency and Peak AoI (PAoI) distributions for different values of the queue size L. Our results show that preemption is not always the optimal choice, as dropping a late packet on one path might affect the reliability of the whole block, and that minimizing the PAoI leads to poor latency performance.

cs.NI↗

arXiv2023

A Web of Things Architecture for Digital Twin Creation and Model-Based Reinforcement Control

Internet of Things (IoT) devices are available in a multitude of scenarios, and provide constant, contextual data which can be leveraged to automatically reconfigure and optimize smart environments. To realize this vision, Artificial Intelligence (AI) and deep learning techniques are usually employed, however they need large quantity of data which is often not feasible in IoT scenarios. Digital Twins (DTs) have recently emerged as an effective way to replicate physical entities in the digital domain, to allow for simulation and testing of models and services. In this paper, we present a novel architecture based on the emerging Web of Things (WoT) standard, which provides a DT of a smart environment and applies Deep Reinforcement Learning (DRL) techniques on real time data. We implement our system in a real deployment, and test it along with a legacy system. Our findings show that the benefits of having a digital twin, specifically for DRL models, allow for faster convergence and finer tuning.

cs.NI↗

arXiv2023

Scheduling Policy for Value-of-Information (VoI) in Trajectory Estimation for Digital Twins

This paper presents an approach to schedule observations from different sensors in an environment to ensure their timely delivery and build a digital twin (DT) model of the system dynamics. At the cloud platform, DT models estimate and predict the system's state, then compute the optimal scheduling policy and resource allocation strategy to be executed in the physical world. However, given limited network resources, partial state vector information, and measurement errors at the distributed sensing agents, the acquisition of data (i.e., observations) for efficient state estimation of system dynamics is a non-trivial problem. We propose a Value of Information (VoI)-based algorithm that provides a polynomial-time solution for selecting the most informative subset of sensing agents to improve confidence in the state estimation of DT models. Numerical results confirm that the proposed method outperforms other benchmarks, reducing the communication overhead by half while maintaining the required estimation accuracy.

eess.SP↗

arXiv2023

A Decentralized Policy for Minimization of Age of Incorrect Information in Slotted ALOHA Systems

The Age of Incorrect Information (AoII) is a metric that can combine the freshness of the information available to a gateway in an Internet of Things (IoT) network with the accuracy of that information. As such, minimizing the AoII can allow the operators of IoT systems to have a more precise and up-to-date picture of the environment in which the sensors are deployed. However, most IoT systems do not allow for centralized scheduling or explicit coordination, as sensors need to be extremely simple and consume as little power as possible. Finding a decentralized policy to minimize the AoII can be extremely challenging in this setting. This paper presents a heuristic to optimize AoII for a slotted ALOHA system, starting from a threshold-based policy and using dual methods to converge to a better solution. This method can significantly outperform state-independent policies, finding an efficient balance between frequent updates and a low number of packet collisions.

cs.IT↗

arXiv2023

Semantic and Effective Communication for Remote Control Tasks with Dynamic Feature Compression

The coordination of robotic swarms and the remote wireless control of industrial systems are among the major use cases for 5G and beyond systems: in these cases, the massive amounts of sensory information that needs to be shared over the wireless medium can overload even high-capacity connections. Consequently, solving the effective communication problem by optimizing the transmission strategy to discard irrelevant information can provide a significant advantage, but is often a very complex task. In this work, we consider a prototypal system in which an observer must communicate its sensory data to an actor controlling a task (e.g., a mobile robot in a factory). We then model it as a remote Partially Observable Markov Decision Process (POMDP), considering the effect of adopting semantic and effective communication-oriented solutions on the overall system performance. We split the communication problem by considering an ensemble Vector Quantized Variational Autoencoder (VQ-VAE) encoding, and train a Deep Reinforcement Learning (DRL) agent to dynamically adapt the quantization level, considering both the current state of the environment and the memory of past messages. We tested the proposed approach on the well-known CartPole reference control problem, obtaining a significant performance increase over traditional approaches

cs.LG↗

arXiv2022

Query Timing Analysis for Content-based Wake-up Realizing Informative IoT Data Collection

Information freshness and high energy-efficiency are key requirements for sensor nodes serving Industrial Internet of Things (IIoT) applications, where a sink node must collect informative data before a deadline to control an external element. Pull-based communication is an interesting approach for optimizing information freshness and saving wasteful energy. To this end, we apply Content-based Wake-up (CoWu), in which the sink can activate a subset of nodes observing informative data at the time that wake-up signal is received. In this case, the timing of the wake-up signal plays an important role: early transmission leads to high reliability in data collection, but the received data may become obsolete by the deadline, while later transmission ensures a higher timeliness of the sensed data, but some nodes might not manage to communicate their data before the deadline. This letter investigates the timing for data collection using CoWu and characterizes the gain of CoWu. The obtained numerical results show that CoWu improves accuracy, while reducing energy consumption by about 75% with respect to round-robin scheduling.

cs.IT↗

arXiv2022

The Cost of Learning: Efficiency vs. Efficacy of Learning-Based RRM for 6G

In the past few years, Deep Reinforcement Learning (DRL) has become a valuable solution to automatically learn efficient resource management strategies in complex networks. In many scenarios, the learning task is performed in the Cloud, while experience samples are generated directly by edge nodes or users. Therefore, the learning task involves some data exchange which, in turn, subtracts a certain amount of transmission resources from the system. This creates a friction between the need to speed up convergence towards an effective strategy, which requires the allocation of resources to transmit learning samples, and the need to maximize the amount of resources used for data plane communication, maximizing users' Quality of Service (QoS), which requires the learning process to be efficient, i.e., minimize its overhead. In this paper, we investigate this trade-off and propose a dynamic balancing strategy between the learning and data planes, which allows the centralized learning agent to quickly converge to an efficient resource allocation strategy while minimizing the impact on QoS. Simulation results show that the proposed method outperforms static allocation methods, converging to the optimal policy (i.e., maximum efficacy and minimum overhead of the learning plane) in the long run.

cs.NI↗

arXiv2022

Efficient URLLC with a Reconfigurable Intelligent Surface and Imperfect Device Tracking

The use of Reconfigurable Intelligent Surfaces (RIS) technology to extend coverage and allow for better control of the wireless environment has been proposed in several use cases, including Ultra-Reliable Low-Latency Communications (URLLC), communications. However, the extremely challenging latency constraint makes explicit channel estimation difficult, so positioning information is often used to configure the RIS and illuminate the receiver device. In this work, we analyze the effect of imperfections in the positioning information on the reliability, deriving an upper bound to the outage probability. We then use this bound to perform power control, efficiently finding the minimum power that respects the URLLC constraints under positioning uncertainty. The optimization is conservative, so that all points respect the URLLC constraints, and the bound is relatively tight, with an optimality gap between 1.5 and 4.5~dB.

cs.IT↗

arXiv2022

Continent-wide Efficient and Fair Downlink Resource Allocation in LEO Satellite Constellations

The integration of Low Earth Orbit (LEO) satellite constellations into 5G and Beyond is essential to achieve efficient global connectivity. As LEO satellites are a global infrastructure with predictable dynamics, a pre-planned fair and load-balanced allocation of the radio resources to provide efficient downlink connectivity over large areas is an achievable goal. In this paper, we propose a distributed and a global optimal algorithm for satellite-to-cell resource allocation with multiple beams. These algorithms aim to achieve a fair allocation of time-frequency resources and beams to the cells based on the number of users in connected mode (i.e., registered). Our analyses focus on evaluating the trade-offs between average per-user throughput, fairness, number of cell handovers, and computational complexity in a downlink scenario with fixed cells, where the number of users is extracted from a population map. Our results show that both algorithms achieve a similar average per-user throughput. However, the global optimal algorithm achieves a fairness index over 0.9 in all cases, which is more than twice that of the distributed algorithm. Furthermore, by correctly setting the handover cost parameter, the number of handovers can be effectively reduced by more than 70% with respect to the case where the handover cost is not considered.

cs.NI↗

arXiv2022

Joint Scheduling and Coding for Reliable, Latency-bounded Transmission over Parallel Wireless Links

Several novel industrial applications involve human control of vehicles, cranes, or mobile robots through various high-throughput feedback systems, such as Virtual Reality (VR) and tactile/haptic signals. The near real-time interaction between the system and the operator requires strict latency constraints in packet exchange, which is difficult to guarantee over wireless communication links. In this work, we advocate that packet-level coding and packet scheduling over multiple parallel (unreliable) links have the potential to provide reliable, latency-bounded communication for applications with periodic data generation patterns. However, this goal can be reached only through a careful joint design of such mechanisms, whose interactions can be subtle and difficult to predict. In this paper we first discuss these aspects in general terms, and then present a Markov Decision Process (MDP) model that can be used to find a scheme that optimally exploits the multichannel wireless access in order to maximize the fraction of data blocks delivered within deadline. Our illustrative example is then used to show the optimal coding/scheduling strategies under different combinations of wireless links, also showing that the common solution of backing up a high bitrate unreliable mmWave link with a low bitrate more stable sub-6 GHz link can actually be ineffective in the considered scenario

cs.NI↗

arXiv2022

Optimal Latency-Oriented Scheduling in Parallel Queuing Systems

The evolution of 5G and Beyond networks has enabled new applications with stringent end-to-end latency requirements, but providing reliable low-latency service with high throughput over public wireless networks is still a significant challenge. One of the possible ways to solve this is to exploit path diversity, encoding the information flow over multiple streams across parallel links. The challenge presented by this approach is the design of joint coding and scheduling algorithms that adapt to the state of links to take full advantage of path diversity. In this paper, we address this problem for a synchronous traffic source that generates data blocks at regular time intervals (e.g., a video with constant frame rate) and needs to deliver each block within a predetermined deadline. We first develop a closed-form performance analysis in the simple case of two parallel servers without any buffering and single-packet blocks, and propose a model for the general problem based on a Markov Decision Process (MDP). We apply policy iteration to obtain the coding and scheduling policy that maximizes the fraction of source blocks delivered within the deadline: our simulations show the drawbacks of different commonly applied heuristic solutions, drawing general design insights on the optimal policy.

cs.NI↗

arXiv2022

Temporal Characterization of VR Traffic for Network Slicing Requirement Definition

Over the past few years, the concept of VR has attracted increasing interest thanks to its extensive industrial and commercial applications. Currently, the 3D models of the virtual scenes are generally stored in the VR visor itself, which operates as a standalone device. However, applications that entail multi-party interactions will likely require the scene to be processed by an external server and then streamed to the visors. However, the stringent Quality of Service (QoS) constraints imposed by VR's interactive nature require Network Slicing (NS) solutions, for which profiling the traffic generated by the VR application is crucial. To this end, we collected more than 4 hours of traces in a real setup and analyzed their temporal correlation. More specifically, we focused on the CBR encoding mode, which should generate more predictable traffic streams. From the collected data, we then distilled two prediction models for future frame size, which can be instrumental in the design of dynamic resource allocation algorithms. Our results show that even the state-of-the-art H.264 CBR mode can have significant fluctuations, which can impact the NS optimization. We then exploited the proposed models to dynamically determine the Service Level Agreement (SLA) parameters in an NS scenario, providing service with the required QoS while minimizing resource usage.

cs.NI↗

arXiv2022

Beam Aware Stochastic Multihop Routing for Flying Ad-hoc Networks

Routing is a crucial component in the design of Flying Ad-Hoc Networks (FANETs). State of the art routing solutions exploit the position of Unmanned Aerial Vehicles (UAVs) and their mobility information to determine the existence of links between them, but this information is often unreliable, as the topology of FANETs can change quickly and unpredictably. In order to improve the tracking performance, the uncertainty introduced by imperfect measurements and tracking algorithms needs to be accounted for in the routing. Another important element to consider is beamforming, which can reduce interference, but requires accurate channel and position information to work. In this work, we present the Beam Aware Stochastic Multihop Routing for FANETs (BA-SMURF), a Software-Defined Networking (SDN) routing scheme that takes into account the positioning uncertainty and beamforming design to find the most reliable routes in a FANET. Our simulation results show that joint consideration of the beamforming and routing can provide a 5% throughput improvement with respect to the state of the art.

cs.NI↗

arXiv2022

A Perspective on Time towards Wireless 6G

With the advent of 5G technology, the notion of latency got a prominent role in wireless connectivity, serving as a proxy term for addressing the requirements for real-time communication. As wireless systems evolve towards 6G, the ambition to immerse the digital into the physical reality will increase. Besides making the real-time requirements more stringent, this immersion will bring the notions of time, simultaneity, presence, and causality to a new level of complexity. A growing body of research points out that latency is insufficient to parameterize all real-time requirements. Notably, one such requirement that received a significant attention is information freshness, defined through the Age of Information (AoI) and its derivatives. The objective of this article is to investigate the general notion of timing in wireless communication systems and networks and its relation to effective information generation, processing, transmission, and reconstruction at the senders and receivers. We establish a general statistical framework of timing requirements in wireless communication systems, which subsumes both latency and AoI. The framework is made by associating a timing component with the two basic statistical operations, decision and estimation. We first use the framework to present a representative sample of the existing works that deal with timing in wireless communication. Next, it is shown how the framework can be used with different communication models of increasing complexity, starting from the basic Shannon one-way communication model and arriving to communication models for consensus, distributed learning, and inference. Overall, this paper fills an important gap in the literature by providing a systematic treatment of various timing measures in wireless communication and sets the basis for design and optimization for the next-generation real-time systems.

cs.IT↗

arXiv2022

Statistical Characterization of Closed-Loop Latency at the Mobile Edge

The stringent timing and reliability requirements in mission-critical applications require a detailed statistical characterization of the latency. Teleoperation is a representative use case, in which a human operator (HO) remotely controls a robot by exchanging command and feedback signals. We present a framework to analyze the latency of a closed-loop teleoperation system consisting of three entities: HO, robot located in remote environment, and a Base Station (BS) with Mobile edge Computing (MEC) capabilities. A model of each component of the system is used to analyze the closed-loop latency and decide upon the optimal compression strategy. The closed-form expression of the distribution of the closed-loop latency is difficult to estimate, such that suitable upper and lower bounds are obtained. We formulate a non-convex optimization problem to minimize the closed-loop latency. Using the obtained upper and lower bound on the closed-loop latency, a computationally efficient procedure to optimize the closed-loop latency is presented. The simulation results reveal that compression of sensing data is not always beneficial, while system design based on average performance leads to under-provisioning and may cause performance degradation. The applicability of the proposed analysis is much wider than teleoperation, for systems whose latency budget consists of many components.

cs.NI↗

arXiv2022

Scheduling of Sensor Transmissions Based on Value of Information for Summary Statistics

The optimization of Value of Information (VoI) in sensor networks integrates awareness of the measured process in the communication system. However, most existing scheduling algorithms do not consider the specific needs of monitoring applications, but define VoI as a generic Mean Square Error (MSE) of the whole system state regardless of the relevance of individual components. In this work, we consider different summary statistics, i.e., different functions of the state, which can represent the useful information for a monitoring process, particularly in safety and industrial applications. We propose policies that minimize the estimation error for different summary statistics, showing significant gains by simulation.

cs.NI↗

arXiv2022

Temporal Characterization of XR Traffic with Application to Predictive Network Slicing

Over the past few years, eXtended Reality (XR) has attracted increasing interest thanks to its extensive industrial and commercial applications, and its popularity is expected to rise exponentially over the next decade. However, the stringent Quality of Service (QoS) constraints imposed by XR's interactive nature require Network Slicing (NS) solutions to support its use over wireless connections: in this context, quasi-Constant Bit Rate (CBR) encoding is a promising solution, as it can increase the predictability of the stream, making the network resource allocation easier. However, traffic characterization of XR streams is still a largely unexplored subject, particularly with this encoding. In this work, we characterize XR streams from more than 4 hours of traces captured in a real setup, analyzing their temporal correlation and proposing two prediction models for future frame size. Our results show that even the state-of-the-art H.264 CBR mode can have significant frame size fluctuations, which can impact the NS optimization. Our proposed prediction models can be applied to different traces, and even to different contents, achieving very similar performance. We also show the trade-off between network resource efficiency and XR QoS in a simple NS use case.

cs.NI↗