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This paper investigates federated learning (FL) in a multi-hop communication setup, such as in constellations with inter-satellite links. In this setup, part of the FL clients are responsible for forwarding other client's results to the parameter server. Instead of using conventional routing, the communication efficiency can be improved significantly by using in-network model aggregation at each intermediate hop, known as incremental aggregation (IA). Prior works [1] have indicated diminishing gains for IA under gradient sparsification. Here we study this issue and propose several novel correlated sparsification methods for IA. Numerical results show that, for some of these algorithms, the full potential of IA is still available under sparsification without impairing convergence. We demonstrate a 15x improvement in communication efficiency over conventional routing and a 11x improvement over state-of-the-art (SoA) sparse IA., Comment: This paper is accepted for the 25th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) conference

This paper studies decision-making for goal-oriented effective communication. We consider an end-to-end status update system where a sensing agent (SA) observes a source, generates and transmits updates to an actuation agent (AA), while the AA takes actions to accomplish a goal at the endpoint. We integrate the push- and pull-based update communication models to obtain a push-and-pull model, which allows the transmission controller at the SA to decide to push an update to the AA and the query controller at the AA to pull updates by raising queries at specific time instances. To gauge effectiveness, we utilize a grade of effectiveness (GoE) metric incorporating updates' freshness, usefulness, and timeliness of actions as qualitative attributes. We then derive effect-aware policies to maximize the expected discounted sum of updates' effectiveness subject to induced costs. The effect-aware policy at the SA considers the potential effectiveness of communicated updates at the endpoint, while at the AA, it accounts for the probabilistic evolution of the source and importance of generated updates. Our results show the proposed push-and-pull model outperforms models solely based on push- or pull-based updates both in terms of efficiency and effectiveness. Additionally, using effect-aware policies at both agents enhances effectiveness compared to periodic and/or probabilistic effect-agnostic policies at either or both agents., Comment: Submitted for possible publication

There is a broad consensus that artificial intelligence (AI) will be a defining component of the sixth-generation (6G) networks. As a specific instance, AI-empowered sensing will gather and process environmental perception data at the network edge, giving rise to integrated sensing and edge AI (ISEA). Many applications, such as autonomous driving and industrial manufacturing, are latency-sensitive and require end-to-end (E2E) performance guarantees under stringent deadlines. However, the 5G-style ultra-reliable and low-latency communication (URLLC) techniques designed with communication reliability and agnostic to the data may fall short in achieving the optimal E2E performance of perceptive wireless systems. In this work, we introduce an ultra-low-latency (ultra-LoLa) inference framework for perceptive networks that facilitates the analysis of the E2E sensing accuracy in distributed sensing by jointly considering communication reliability and inference accuracy. By characterizing the tradeoff between packet length and the number of sensing observations, we derive an efficient optimization procedure that closely approximates the optimal tradeoff. We validate the accuracy of the proposed method through experimental results, and show that the proposed ultra-Lola inference framework outperforms conventional reliability-oriented protocols with respect to sensing performance under a latency constraint.

This paper analyzes monostatic sensing by a user equipment (UE) for a setting in which the UE is unable to resolve multiple targets due to their interference within a single resolution bin. It is shown how sensing accuracy, in terms of both detection rate and localization accuracy, can be boosted by a reconfigurable intelligent surface (RIS), which can be advantageously used to provide signal diversity and aid in resolving the targets. Specifically, assuming prior information on the presence of a cluster of targets, a RIS beam sweep procedure is used to facilitate the high resolution sensing. We derive the Cram\'er-Rao lower bounds (CRLBs) for channel parameter estimation and sensing and an upper bound on the detection probability. The concept of coherence is defined and analyzed theoretically. Then, we propose an orthogonal matching pursuit (OMP) channel estimation algorithm combined with data association to fuse the information of the non-RIS signal and the RIS signal and perform sensing. Finally, we provide numerical results to verify the potential of RIS for improving sensor resolution, and to demonstrate that the proposed methods can realize this potential for RIS-assisted high resolution sensing.

The integration of Non-Terrestrial Networks (NTNs) with Low Earth Orbit (LEO) satellite constellations into 5G and Beyond is essential to achieve truly global connectivity. A distinctive characteristic of LEO mega-constellations is that they constitute a global infrastructure with predictable dynamics, which enables the pre-planned allocation of the radio resources. However, the different bands that can be used for ground-to-satellite communication are affected differently by atmospheric conditions such as precipitation, which introduces uncertainty on the attenuation of the communication links at high frequencies. Based on this, we present a compelling case for applying integrated sensing and communications (ISAC) in heterogeneous and multi-layer LEO satellite constellations over wide areas. Specifically, we present an ISAC framework and frame structure to accurately estimate the attenuation in the communication links due to precipitation, with the aim of finding the optimal serving satellites and resource allocation for downlink communication with users on ground. The results show that, by dedicating an adequate amount of resources for sensing and solving the association and resource allocation problems jointly, it is feasible to increase the average throughput by 59% and the fairness by 600% when compared to solving these problems separately., Comment: Submitted for publication to IEEE Transactions on Wireless Communications

A reconfigurable intelligent surface (RIS) is commonly made of low-cost passive and reflective meta-materials with excellent beam steering capabilities. It is applied to enhance wireless communication systems as a customizable signal reflector. However, RIS can also be adversely employed to disrupt the existing communication systems by introducing new types of vulnerability to the physical layer. We consider the \emph{RIS-In-The-Middle (RITM) attack}, in which an adversary uses RIS to jeopardize the direct channel between two transceivers by providing an alternative one with higher signal quality. This adversary can eavesdrop on all exchanged data by the legitimate users, but also perform a false data injection to the receiver. This work devises anti-attack techniques based on a non-reciprocal channel produced by a defensive RIS (D-RIS). The proposed precoding and combining methods and the channel estimation procedure for a non-reciprocal link are effective against potential adversaries while keeping the existing advantages of the RIS. We analyse the robustness of the system against attacks in terms of achievable secrecy rate and probability of detecting fake data. We believe that this defensive role of RIS can be a basis for new protocols and algorithms in the area., Comment: 14 pages, journal paper

Estimating the probability of rare channel conditions is a central challenge in ultra-reliable wireless communication, where random events, such as deep fades, can cause sudden variations in the channel quality. This paper proposes a sample-efficient framework for predicting the statistics of such events by utilizing spatial dependency between channel measurements acquired from various locations. The proposed framework combines radio maps with non-parametric models and extreme value theory (EVT) to estimate rare-event channel statistics under a Bayesian formulation. The framework can be applied to a wide range of problems in wireless communication and is exemplified by rate selection in ultra-reliable communications. Notably, besides simulated data, the proposed framework is also validated with experimental measurements. The results in both cases show that the Bayesian formulation provides significantly better results in terms of throughput compared to baselines that do not leverage measurements from surrounding locations. It is also observed that the models based on EVT are generally more accurate in predicting rare-event statistics than non-parametric models, especially when only a limited number of channel samples are available. Overall, the proposed methods can significantly reduce the number of measurements required to predict rare channel conditions and guarantee reliability., Comment: Submitted for IEEE Transaction on Communications

Goal-oriented communication has become one of the focal concepts in sixth-generation communication systems owing to its potential to provide intelligent, immersive, and real-time mobile services. The emerging paradigms of goal-oriented communication constitute closed loops integrating communication, computation, and sensing. However, challenges arise for closed-loop timing analysis due to multiple random factors that affect the communication/computation latency, as well as the heterogeneity of feedback mechanisms across multi-modal sensing data. To tackle these problems, we aim to provide a unified timing analysis framework for closed-loop goal-oriented communication (CGC) systems over fading channels. The proposed framework is unified as it considers computation, compression, and communication latency in the loop with different configurations. To capture the heterogeneity across multi-modal feedback, we categorize the sensory data into the periodic-feedback and event-triggered, respectively. We formulate timing constraints based on average and tail performance, covering timeliness, jitter, and reliability of CGC systems. A method based on saddlepoint approximation is proposed to obtain the distribution of closed-loop latency. The results show that the modified saddlepoint approximation is capable of accurately characterizing the latency distribution of the loop with analytically tractable expressions. This sets the basis for low-complexity co-design of communication and computation., Comment: submitted to IEEE Trans. Wireless Commun

This paper introduces a full solution for decentralized routing in Low Earth Orbit satellite constellations based on continual Deep Reinforcement Learning (DRL). This requires addressing multiple challenges, including the partial knowledge at the satellites and their continuous movement, and the time-varying sources of uncertainty in the system, such as traffic, communication links, or communication buffers. We follow a multi-agent approach, where each satellite acts as an independent decision-making agent, while acquiring a limited knowledge of the environment based on the feedback received from the nearby agents. The solution is divided into two phases. First, an offline learning phase relies on decentralized decisions and a global Deep Neural Network (DNN) trained with global experiences. Then, the online phase with local, on-board, and pre-trained DNNs requires continual learning to evolve with the environment, which can be done in two different ways: (1) Model anticipation, where the predictable conditions of the constellation are exploited by each satellite sharing local model with the next satellite; and (2) Federated Learning (FL), where each agent's model is merged first at the cluster level and then aggregated in a global Parameter Server. The results show that, without high congestion, the proposed Multi-Agent DRL framework achieves the same E2E performance as a shortest-path solution, but the latter assumes intensive communication overhead for real-time network-wise knowledge of the system at a centralized node, whereas ours only requires limited feedback exchange among first neighbour satellites. Importantly, our solution adapts well to congestion conditions and exploits less loaded paths. Moreover, the divergence of models over time is easily tackled by the synergy between anticipation, applied in short-term alignment, and FL, utilized for long-term alignment., Comment: 30 pages, 11 figures

In this paper, to tackle the blockage issue in massive multiple-input-multiple-output (mMIMO) systems, a reconfigurable intelligent surface (RIS) is seamlessly deployed to support devices with ultra-reliable and low-latency communications (URLLC). The transmission power of the base station and the phase shifts of the RIS are jointly devised to maximize the weighted sum rate while considering the spatially correlation and channel estimation errors. Firstly, \textcolor{black}{the relationship between the channel estimation error and spatially correlated RIS's elements is revealed by using the linear minimum mean square error}. Secondly, based on the maximum-ratio transmission precoding, a tight lower bound of the rate under short packet transmission is derived. Finally, the NP-hard problem is decomposed into two optimization problems, where the transmission power is obtained by geometric programming and phase shifts are designed by using gradient ascent method. Besides, we have rigorously proved that the proposed algorithm can rapidly converge to a sub-optimal solution with low complexity. Simulation results confirm the tightness between the analytic results and Monte Carlo simulations. Furthermore, the two-timescale scheme provides a practical solution for the short packet transmission., Comment: This paper has already been accepted by IEEE Transactions on Wireless Communications

This paper presents an approach for energy-neutral Internet of Things (IoT) scenarios where the IoT devices (IoTDs) rely entirely on their energy harvesting capabilities to sustain operation. We use a Markov chain to represent the operation and transmission states of the IoTDs, a modulated Poisson process to model their energy harvesting process, and a discrete-time Markov chain to model their battery state. The aim is to efficiently manage the duty cycling of the IoTDs, so as to prolong their battery life and reduce instances of low-energy availability. We propose a duty-cycling management based on K- nearest neighbors, aiming to strike a trade-off between energy efficiency and detection accuracy. This is done by incorporating spatial and temporal correlations among IoTDs' activity, as well as their energy harvesting capabilities. We also allow the base station to wake up specific IoTDs if more information about an event is needed upon initial detection. Our proposed scheme shows significant improvements in energy savings and performance, with up to 11 times lower misdetection probability and 50\% lower energy consumption for high-density scenarios compared to a random duty cycling benchmark.

Radio frequency (RF) wireless power transfer (WPT) is a promising technology for sustainable support of massive Internet of Things (IoT). However, RF-WPT systems are characterized by low efficiency due to channel attenuation, which can be mitigated by precoders that adjust the transmission directivity. This work considers a multi-antenna RF-WPT system with multiple non-linear energy harvesting (EH) nodes with energy demands changing over discrete time slots. This leads to the charging scheduling problem, which involves choosing the precoders at each slot to minimize the total energy consumption and meet the EH requirements. We model the problem as a Markov decision process and propose a solution relying on a low-complexity beamforming and deep deterministic policy gradient (DDPG). The results show that the proposed beamforming achieves near-optimal performance with low computational complexity, and the DDPG-based approach converges with the number of episodes and reduces the system's power consumption, while the outage probability and the power consumption increase with the number of devices., Comment: 6 pages, 5 figures

The paper examines a scenario wherein sensors are deployed within an Industrial Networked Control System, aiming to construct a digital twin (DT) model for a remotely operated Autonomous Guided Vehicle (AGV). The DT model, situated on a cloud platform, estimates and predicts the system's state, subsequently formulating the optimal scheduling strategy for execution in the physical world. However, acquiring data crucial for efficient state estimation and control computation poses a significant challenge, primarily due to constraints such as limited network resources, partial observation, and the necessity to maintain a certain confidence level for DT estimation. We propose an algorithm based on Value of Information (VoI), seamlessly integrated with the Extended Kalman Filter to deliver a polynomial-time solution, selecting the most informative subset of sensing agents for data. Additionally, we put forth an alternative solution leveraging a Graph Neural Network to precisely ascertain the AGV's position with a remarkable accuracy of up to 5 cm. Our experimental validation in an industrial robotic laboratory environment yields promising results, underscoring the potential of high-accuracy DT models in practice.

This paper introduces EcoPull, a sustainable Internet of Things (IoT) framework empowered by tiny machine learning (TinyML) models for fetching images from wireless visual sensor networks. Two types of learnable TinyML models are installed in the IoT devices: i) a behavior model and ii) an image compressor model. The first filters out irrelevant images for the current task, reducing unnecessary transmission and resource competition among the devices. The second allows IoT devices to communicate with the receiver via latent representations of images, reducing communication bandwidth usage. However, integrating learnable modules into IoT devices comes at the cost of increased energy consumption due to inference. The numerical results show that the proposed framework can save > 70% energy compared to the baseline while maintaining the quality of the retrieved images at the ES., Comment: Paper submitted to IEEE GLOBECOM 2024. Copyright may be transferred without further notice

This paper considers energy-efficient connectivity for Internet of Things (IoT) devices in a coexistence scenario between two distinctive communication models: pull- and push-based. In pull-based, the base station (BS) decides when to retrieve a specific type of data from the IoT devices, while in push-based, the IoT device decides when and which data to transmit. To this end, this paper advocates introducing the content-based wake-up (CoWu), which enables the BS to remotely activate only a subset of pull-based nodes equipped with wake-up receivers, observing the relevant data. In this setup, a BS pulls data with CoWu at a specific time instance to fulfill its tasks while collecting data from the nodes operating with a push-based communication model. The resource allocation plays an important role: longer data collection duration for pull-based nodes can lead to high retrieval accuracy while decreasing the probability of data transmission success for push-based nodes, and vice versa. Numerical results show that CoWu can manage communication requirements for both pull-based and push-based nodes while realizing the high energy efficiency (up to 38%) of IoT devices, compared to the baseline scheduling method., Comment: Paper submitted to Globecom 2024. Copyright may be transferred without further notice

Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiber-optical domain. This paper introduces the Decentralized Multi-Party, Multi-Network AI (DMMAI) framework for integrating AI into 6G networks deployed at scale. DMMAI harmonizes AI-driven controls across diverse network platforms and thus facilitates networks that autonomously configure, monitor, and repair themselves. This is particularly crucial at the network edge, where advanced applications meet heightened functionality and security demands. The radio/optical integration is vital due to the current compartmentalization of AI research within these domains, which lacks a comprehensive understanding of their interaction. Our approach explores multi-network orchestration and AI control integration, filling a critical gap in standardized frameworks for AI-driven coordination in 6G networks. The DMMAI framework is a step towards a global standard for AI in 6G, aiming to establish reference use cases, data and model management methods, and benchmarking platforms for future AI/ML solutions.

We consider a setup with Internet of Things (IoT), where a base station (BS) collects data from nodes that use two different communication modes. The first is pull-based, where the BS retrieves the data from specific nodes through queries. In addition, the nodes that apply pull-based communication contain a wake-up receiver: upon a query, the BS sends wake-up signal (WuS) to activate the corresponding devices equipped with wake-up receiver (WuDs). The second one is push-based communication, in which the nodes decide when to send to the BS. Consider a time-slotted model, where the time slots in each frame are shared for both pull-based and push-based communications. Therein, this coexistence scenario gives rise to a new type of problem with fundamental trade-offs in sharing communication resources: the objective to serve a maximum number of queries, within a specified deadline, limits the transmission opportunities for push sensors, and vice versa. This work develops a mathematical model that characterizes these trade-offs, validates them through simulations, and optimizes the frame design to meet the objectives of both the pull- and push-based communications., Comment: Paper submitted to the 25th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2024). Copyright may be transferred without further notice

Neuromorphic computing leverages the sparsity of temporal data to reduce processing energy by activating a small subset of neurons and synapses at each time step. When deployed for split computing in edge-based systems, remote neuromorphic processing units (NPUs) can reduce the communication power budget by communicating asynchronously using sparse impulse radio (IR) waveforms. This way, the input signal sparsity translates directly into energy savings both in terms of computation and communication. However, with IR transmission, the main contributor to the overall energy consumption remains the power required to maintain the main radio on. This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs. A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making. To address this problem, as a second contribution, this work proposes a novel methodology that leverages the use of a digital twin (DT), i.e., a simulator, of the physical system, coupled with a sequential statistical testing approach known as Learn Then Test (LTT) to provide theoretical reliability guarantees. The proposed DT-LTT methodology is broadly applicable to other design problems, and is showcased here for neuromorphic communications. Experimental results validate the design and the analysis, confirming the theoretical reliability guarantees and illustrating trade-offs among reliability, energy consumption, and informativeness of the decisions., Comment: Under review

A wireless communication system is studied that operates in the presence of multiple reconfigurable intelligent surfaces (RISs). In particular, a multi-operator environment is considered where each operator utilizes an RIS to enhance its communication quality. Although out-of-band interference does not exist (since each operator uses isolated spectrum resources), RISs controlled by different operators do affect the system performance of one another due to the inherently rapid phase shift adjustments that occur on an independent basis. The system performance of such a communication scenario is analytically studied for the practical case where discrete-only phase shifts occur at RIS. The proposed framework is quite general since it is valid under arbitrary channel fading conditions as well as the presence (or not) of the transceiver's direct link. Finally, the derived analytical results are verified via numerical and simulation trial as well as some novel and useful engineering outcomes are manifested.

This paper presents an experimental validation for prediction of rare fading events using channel distribution information (CDI) maps that predict channel statistics from measurements acquired at surrounding locations using spatial interpolation. Using experimental channel measurements from 127 locations, we demonstrate the use case of providing statistical guarantees for rate selection in ultra-reliable low-latency communication (URLLC) using CDI maps. By using only the user location and the estimated map, we are able to meet the desired outage probability with a probability between 93.6-95.6% targeting 95%. On the other hand, a model-based baseline scheme that assumes Rayleigh fading meets the target outage requirement with a probability of 77.2%. The results demonstrate the practical relevance of CDI maps for resource allocation in URLLC., Comment: Accepted for IEEE International Conference on Communications (ICC) in june 2024

Mega-constellations of small satellites have evolved into a source of massive amount of valuable data. To manage this data efficiently, on-board federated learning (FL) enables satellites to train a machine learning (ML) model collaboratively without having to share the raw data. This paper introduces a scheme for scheduling on-board FL for constellations connected with intra-orbit inter-satellite links. The proposed scheme utilizes the predictable visibility pattern between satellites and ground station (GS), both at the individual satellite level and cumulatively within the entire orbit, to mitigate intermittent connectivity and best use of available time. To this end, two distinct schedulers are employed: one for coordinating the FL procedures among orbits, and the other for controlling those within each orbit. These two schedulers cooperatively determine the appropriate time to perform global updates in GS and then allocate suitable duration to satellites within each orbit for local training, proportional to usable time until next global update. This scheme leads to improved test accuracy within a shorter time., Comment: 2023 IEEE GLOBECOM

Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents. These advancements impose new requirements on future 6G mobile network architectures. To meet these demands, it is essential to transcend classical boundaries and integrate communication, computation, control, and intelligence. This paper presents the 6G-GOALS approach to goal-oriented and semantic communications for AI-Native 6G Networks. The proposed approach incorporates semantic, pragmatic, and goal-oriented communication into AI-native technologies, aiming to facilitate information exchange between intelligent agents in a more relevant, effective, and timely manner, thereby optimizing bandwidth, latency, energy, and electromagnetic field (EMF) radiation. The focus is on distilling data to its most relevant form and terse representation, aligning with the source's intent or the destination's objectives and context, or serving a specific goal. 6G-GOALS builds on three fundamental pillars: i) AI-enhanced semantic data representation, sensing, compression, and communication, ii) foundational AI reasoning and causal semantic data representation, contextual relevance, and value for goal-oriented effectiveness, and iii) sustainability enabled by more efficient wireless services. Finally, we illustrate two proof-of-concepts implementing semantic, goal-oriented, and pragmatic communication principles in near-future use cases. Our study covers the project's vision, methodologies, and potential impact.

Wireless systems beyond 5G evolve towards embracing both sensing and communication, resulting in increased convergence of the digital and the physical world. The existence of fused digital-physical realms raises critical questions regarding temporal ordering, causality, and the synchronization of events. This paper addresses the temporal challenges arising from the fact that the wireless infrastructure becomes an entity with multisensory perception. With the growing reliance on real-time interactions and applications such as digital twins, extended reality, and the metaverse, the need for accurate timestamping and temporal forensics becomes crucial. The paper introduces a model that incorporates Temporal Windows of Integration (TWI) to emulate human multisensory perception and discusses the implications for setting timing constraints in real-time applications and enabling temporal forensics. The analysis explores trade-offs, probabilities, and bounds for simultaneity and causality violation in the context of wireless systems evolving towards perceptive networks. This work underscores the significance of timestamping in the evolving wireless landscape, provide insights into system-level implications, and points out new research avenues for systems that combine sensing and communications., Comment: Submitted for possible publication

Intelligent metasurfaces are one of the favorite technologies for integrating sixth-generation (6G) networks, especially the reconfigurable intelligent surface (RIS) that has been extensively researched in various applications. In this context, a feature that deserves further exploration is the frequency scattering that occurs when the elements are periodically switched, referred to as Space-Time-Coding metasurface (STCM) topology. This type of topology causes impairments to the established communication methods by generating undesirable interference both in frequency and space, which is worsened when using wideband signals. Nevertheless, it has the potential to bring forward useful features for sensing and localization. This work exploits STCM sensing capabilities in target detection, localization, and classification using narrowband downlink pilot signals at the base station (BS). The results of this novel approach reveal the ability to retrieve a scattering point (SP) localization within the sub-centimeter and sub-decimeter accuracy depending on the SP position in space. We also analyze the associated detection and classification probabilities, which show reliable detection performance in the whole analyzed environment. In contrast, the classification is bounded by physical constraints, and we conclude that this method presents a promising approach for future integrated sensing and communications (ISAC) protocols by providing a tool to perform sensing and localization services using legacy communication signals., Comment: 13 pages, 9 figures, 1 table. Manuscript submitted to IEEE-TWC on January 6th, 2024

In mobile networks, Open Radio Access Network (ORAN) provides a framework for implementing network slicing that interacts with the resources at the lower layers. Both monitoring and Radio Access Network (RAN) control is feasible for both 4G and 5G systems. In this work, we consider how data-driven resource allocation in a 4G context can enable adaptive slice allocation to steer the experienced latency of Virtual Reality (VR) traffic towards a requested latency. We develop an xApp for the near real-time RAN Intelligent Controller (RIC) that embeds a heuristic algorithm for latency control, aiming to: (1) maintain latency of a VR stream around a requested value; and (2) improve the available RAN allocation to offer higher bit rate to another user. We have experimentally demonstrated the proposed approach in an ORAN testbed. Our results show that the data-driven approach can dynamically follow the variation of the traffic load while satisfying the required latency. This results in 15.8% more resources to secondary users than a latency-equivalent static allocation., Comment: IEEE GLOBECOM 2023

Compared to the generations up to 4G, whose main focus was on broadband and coverage aspects, 5G has expanded the scope of wireless cellular systems towards embracing two new types of connectivity: massive machine-type communication (mMTC) and ultra-reliable low-latency communications (URLLC). This paper will discuss the possible evolution of these two types of connectivity within the umbrella of 6G wireless systems. The paper consists of three parts. The first part deals with the connectivity for a massive number of devices. While mMTC research in 5G was predominantly focused on the problem of uncoordinated access in the uplink for a large number of devices, the traffic patterns in 6G may become more symmetric, leading to closed-loop massive connectivity. One of the drivers for this is distributed learning/inference. The second part of the paper will discuss the evolution of wireless connectivity for critical services. While latency and reliability are tightly coupled in 5G, 6G will support a variety of safety critical control applications with different types of timing requirements, as evidenced by the emergence of metrics related to information freshness and information value. Additionally, ensuring ultra-high reliability for safety critical control applications requires modeling and estimation of the tail statistics of the wireless channel, queue length, and delay. The fulfillment of these stringent requirements calls for the development of novel AI-based techniques, incorporating optimization theory, explainable AI, generative AI and digital twins. The third part will analyze the coexistence of massive connectivity and critical services. We will consider scenarios in which a massive number of devices need to support traffic patterns of mixed criticality. This will be followed by a discussion about the management of wireless resources shared by services with different criticality., Comment: 19 pages, 8 figures

Driven by the development goal of network paradigm and demand for various functions in the sixth-generation (6G) mission-critical Internet-of-Things (MC-IoT), we foresee a goal-oriented integration of sensing, communication, computing, and control (GIS3C) in this paper. We first provide an overview of the tasks, requirements, and challenges of MC-IoT. Then we introduce an end-to-end GIS3C architecture, in which goal-oriented communication is leveraged to bridge and empower sensing, communication, control, and computing functionalities. By revealing the interplay among multiple subsystems in terms of key performance indicators and parameters, this paper introduces unified metrics, i.e., task completion effectiveness and cost, to facilitate S3C co-design in MC-IoT. The preliminary results demonstrate the benefits of GIS3C in improving task completion effectiveness while reducing costs. We also identify and highlight the gaps and challenges in applying GIS3C in the future 6G networks.

We consider a wireless networked control system (WNCS) with bidirectional imperfect links for real-time applications such as smart grids. To maintain the stability of WNCS, captured by the probability that plant state violates preset values, at minimal cost, heterogeneous physical processes are monitored by multiple sensors. This status information, such as dynamic plant state and Markov Process-based context information, is then received/estimated by the controller for remote control. However, scheduling multiple sensors and designing the controller with limited resources is challenging due to their coupling, delay, and transmission loss. We formulate a Constrained Markov Decision Problem (CMDP) to minimize violation probability with cost constraints. We reveal the relationship between the goal and different updating actions by analyzing the significance of information that incorporates goal-related usefulness and contextual importance. Subsequently, a goal-oriented deterministic scheduling policy is proposed. Two sensing-assisted control strategies and a control-aware estimation policy are proposed to improve the violation probability-cost tradeoff, integrated with the scheduling policy to form a goal-oriented co-design framework. Additionally, we explore retransmission in downlink transmission and qualitatively analyze its preference scenario. Simulation results demonstrate that the proposed goal-oriented co-design policy outperforms previous work in simultaneously reducing violation probability and cost

This paper investigates the role and the impact of control operations for dynamic mobile edge computing (MEC) empowered by Reconfigurable Intelligent Surfaces (RISs), in which multiple devices offload their computation tasks to an access point (AP) equipped with an edge server (ES), with the help of the RIS. While usually ignored, the control aspects related to channel estimation (CE), resource allocation (RA), and control signaling play a fundamental role in the user-perceived delay and energy consumption. In general, the higher the resources involved in the control operations, the higher their reliability; however, this introduces an overhead, which reduces the number of resources available for computation offloading, possibly increasing the overall latency experienced. Conversely, a lower control overhead translates to more resources available for computation offloading but impacts the CE accuracy and RA flexibility. This paper establishes a basic framework for integrating the impact of control operations in the performance evaluation of the RIS-aided MEC paradigm, clarifying their trade-offs through theoretical analysis and numerical simulations., Comment: Paper submitted to Asilomar Conference on Signals, Systems, and Computers 2023. Copyright may be transferred without further notice

The standard client selection algorithms for Federated Learning (FL) are often unbiased and involve uniform random sampling of clients. This has been proven sub-optimal for fast convergence under practical settings characterized by significant heterogeneity in data distribution, computing, and communication resources across clients. For applications having timing constraints due to limited communication opportunities with the parameter server (PS), the client selection strategy is critical to complete model training within the fixed budget of communication rounds. To address this, we develop a biased client selection strategy, GreedyFed, that identifies and greedily selects the most contributing clients in each communication round. This method builds on a fast approximation algorithm for the Shapley Value at the PS, making the computation tractable for real-world applications with many clients. Compared to various client selection strategies on several real-world datasets, GreedyFed demonstrates fast and stable convergence with high accuracy under timing constraints and when imposing a higher degree of heterogeneity in data distribution, systems constraints, and privacy requirements., Comment: Accepted for publication in IEEE Networking Letters

The traditional role of the network layer is the transfer of packet replicas from source to destination through intermediate network nodes. We present a generative network layer that uses Generative AI (GenAI) at intermediate or edge network nodes and analyze its impact on the required data rates in the network. We conduct a case study where the GenAI-aided nodes generate images from prompts that consist of substantially compressed latent representations. The results from network flow analyses under image quality constraints show that the generative network layer can achieve an improvement of more than 100% in terms of the required data rate., Comment: Accepted for publication in IEEE Networking Letters

Since the advent of ultra-reliable and low-latency communications (URLLC), the requirements of low-latency applications tend to be completely characterized by a single pre-defined latency-reliability target. That is, operation is optimal whenever the pre-defined latency threshold is met but the system is assumed to be in error when the latency threshold is violated. This vision is severely limited and does not capture the real requirements of most applications, where multiple latency thresholds can be defined, together with incentives or rewards associated with meeting each of them. Such formulation is a generalization of the single-threshold case popularized by URLLC and, in the asymptotic case, approximates to defining a cost for each point in the support of the latency distribution. In this paper, we explore the implications of defining multiple latency targets on the design of access protocols and on the optimization of repetition-based access strategies in orthogonal and non-orthogonal multiple access scenarios with users that present heterogeneous traffic characteristics and requirements. We observe that the access strategies of the users can be effectively adapted to the requirements of the application by carefully defining the latency targets and the associated rewards., Comment: To be published in Proceedings of Asilomar conference 2023

Data markets facilitate decentralized data exchange for applications such as prediction, learning, or inference. The design of these markets is challenged by varying privacy preferences as well as data similarity among data owners. Related works have often overlooked how data similarity impacts pricing and data value through statistical information leakage. We demonstrate that data similarity and privacy preferences are integral to market design and propose a query-response protocol using local differential privacy for a two-party data acquisition mechanism. In our regression data market model, we analyze strategic interactions between privacy-aware owners and the learner as a Stackelberg game over the asked price and privacy factor. Finally, we numerically evaluate how data similarity affects market participation and traded data value., Comment: Submitted to IEEE Transactions on Neural Networks and Learning Systems (submission version)

Spatially correlated device activation is a typical feature of the Internet of Things (IoT). This motivates the development of channel scheduling (CS) methods that mitigate device collisions efficiently in such scenarios, which constitutes the scope of this work. Specifically, we present a quadratic program (QP) formulation for the CS problem considering the joint activation probabilities among devices. This formulation allows the devices to stochastically select the transmit channels, thus, leading to a soft-clustering approach. We prove that the optimal QP solution can only be attained when it is transformed into a hard-clustering problem, leading to a pure integer QP, which we transform into a pure integer linear program (PILP). We leverage the branch-and-cut (B&C) algorithm to solve PILP optimally. Due to the high computational cost of B&C, we resort to some sub-optimal clustering methods with low computational costs to tackle the clustering problem in CS. Our findings demonstrate that the CS strategy, sourced from B&C, significantly outperforms those derived from sub-optimal clustering methods, even amidst increased device correlation.

Conventional retransmission (ARQ) protocols are designed with the goal of ensuring the correct reception of all the individual transmitter's packets at the receiver. When the transmitter is a learner communicating with a teacher, this goal is at odds with the actual aim of the learner, which is that of eliciting the most relevant label information from the teacher. Taking an active learning perspective, this paper addresses the following key protocol design questions: (i) Active batch selection: Which batch of inputs should be sent to the teacher to acquire the most useful information and thus reduce the number of required communication rounds? (ii) Batch encoding: Can batches of data points be combined to reduce the communication resources required at each communication round? Specifically, this work introduces Communication-Constrained Bayesian Active Knowledge Distillation (CC-BAKD), a novel protocol that integrates Bayesian active learning with compression via a linear mix-up mechanism. Comparisons with existing active learning protocols demonstrate the advantages of the proposed approach., Comment: 6 pages, 4 figures, conference version, accepted in IEEE ICC 2024, Workshop on Task-Oriented and Generative Communications For 6G

We study a pull-based communication system where a sensing agent updates an actuation agent using a query control policy, which is adjusted in the evolution of an observed information source and the usefulness of each update for achieving a specific goal. For that, a controller decides whether to pull an update at each slot, predicting what is probably occurring at the source and how much effective impact that update could have at the endpoint. Thus, temporal changes in the source evolution could modify the query arrivals so as to capture important updates. The amount of impact is determined by a grade of effectiveness (GoE) metric, which incorporates both freshness and usefulness attributes of the communicated updates. Applying an iterative algorithm, we derive query decisions that maximize the long-term average GoE for the communicated packets, subject to cost constraints. Our analytical and numerical results show that the proposed query policy exhibits higher effectiveness than existing periodic and probabilistic query policies for a wide range of query arrival rates., Comment: Accepted to be presented in the IEEE ICC 2024

This letter introduces an energy-efficient pull-based data collection framework for Internet of Things (IoT) devices that use Tiny Machine Learning (TinyML) to interpret data queries. A TinyML model is transmitted from the edge server to the IoT devices. The devices employ the model to facilitate the subsequent semantic queries. This reduces the transmission of irrelevant data, but receiving the ML model and its processing at the IoT devices consume additional energy. We consider the specific instance of image retrieval in a single device scenario and investigate the gain brought by the proposed scheme in terms of energy efficiency and retrieval accuracy, while considering the cost of computation and communication, as well as memory constraints. Numerical evaluation shows that, compared to a baseline scheme, the proposed scheme reaches up to 67% energy reduction under the accuracy constraint when many images are stored. Although focused on image retrieval, our analysis is indicative of a broader set of communication scenarios in which the preemptive transmission of an ML model can increase communication efficiency., Comment: 5 pages, 3 figures, Submitted for possible publication

One of the primary goals of future wireless systems is to foster sustainability, for which, radio frequency (RF) wireless power transfer (WPT) is considered a key technology enabler. The key challenge of RF-WPT systems is the extremely low end-to-end efficiency, mainly due to the losses introduced by the wireless channel. Distributed antenna systems are undoubtedly appealing as they can significantly shorten the charging distances, thus, reducing channel losses. Interestingly, radio stripe systems provide a cost-efficient and scalable way to deploy a distributed multi-antenna system, and thus have received a lot of attention recently. Herein, we consider an RF-WPT system with a transmit radio stripe network to charge multiple indoor energy hotspots, i.e., spatial regions where the energy harvesting devices are expected to be located, including near-field locations. We formulate the optimal radio stripe deployment problem aimed to maximize the minimum power received by the users and explore two specific predefined shapes, namely the straight line and polygon-shaped configurations. Then, we provide efficient solutions relying on geometric programming to optimize the location of the radio stripe elements. The results demonstrate that the proposed radio stripe deployments outperform a central fully-digital square array with the same number of elements and utilizing larger radio stripe lengths can enhance the performance, while increasing the system frequency may degrade it., Comment: 6 pages, 4 figures

We propose an efficient solution to the state estimation problem in multi-scan multi-sensor multiple extended target sensing scenarios. We first model the measurement process by a doubly inhomogeneous-generalized shot noise Cox process and then estimate the parameters using a jump Markov chain Monte Carlo sampling technique. The proposed approach scales linearly in the number of measurements and can take spatial properties of the sensors into account, herein, sensor noise covariance, detection probability, and resolution. Numerical experiments using radar measurement data suggest that the algorithm offers improvements in high clutter scenarios with closely spaced targets over state-of-the-art clustering techniques used in existing multiple extended target tracking algorithms.

Rate-splitting multiple access (RSMA) has attracted a lot of attention as a general and powerful multiple access scheme. In the uplink, instead of encoding the whole message into one stream, a user can split its message into two parts and encode them into two streams before transmitting a superposition of these two streams. The base station (BS) uses successive interference cancellation (SIC) to decode the streams and reconstruct the original messages. Focusing on the packet transmission reliability, we investigate the features of RSMA in the context of hybrid automatic repeat request (HARQ), a well-established mechanism for enhancing reliability. This work proposes a HARQ scheme for uplink RSMA with different retransmission times for a two-user scenario and introduces a power allocation strategy for the two split streams. The results show that compared with non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA), RSMA outperforms them in terms of error probability and power consumption. The results show that RSMA with HARQ has the potential to improve the reliability and efficiency of wireless communication systems.

The wired charging and the need for battery replacements are critical barriers to unlimited, scalable, and sustainable mobile connectivity, motivating the interest in radio frequency (RF) wireless power transfer (WPT) technology. However, the inherently low end-to-end power transfer efficiency (PTE) and health/safety-related apprehensions about the technology are critical obstacles. Indeed, RF-WPT implementation and operation require efficient and cautious strategies and protocols, especially when targeting high-power charging, which constitutes the scope of this work. Herein, we overview the main factors affecting the end-to-end PTE of RF-WPT systems and their multiplicative effect and interdependencies. Moreover, we discuss key electromagnetic field (EMF) exposure metrics, safety limits, and approaches for efficient and EMF-aware deployment and operation. Quantitatively, we show that near-field RF charging may significantly reduce EMF exposure, and thus must be promoted. We also present our vision of a cyber-physical system for efficient and safe wireless charging, specify key components and their interrelation, and illustrate numerically the PTE attained by two modern low-power multi-antenna architectures in a simple setup. Throughout the paper, we highlight the need for high end-to-end PTE architectures and charging protocols transparently complying with EMF exposure regulations and outline relevant challenges and research directions. This work expands the vision and understanding of modern RF-WPT technology and constitutes a step towards making the technology attractive for worldwide commercial exploitation., Comment: 8 pages, 5 figures, 1 table

Low-latency communication plays an increasingly important role in delay-sensitive applications by ensuring the real-time information exchange. However, due to the constraint on the maximum instantaneous power, guaranteeing bounded latency is challenging. In this paper, we investigate the reliability-latency-rate tradeoff in low-latency communication systems with finite-blocklength coding (FBC). Specifically, we are interested in the fundamental tradeoff between error probability, delay-violation probability (DVP), and service rate. Based on the effective capacity (EC), we present the gain-conservation equations to characterize the reliability-latency-rate tradeoffs in low-latency communication systems. In particular, we investigate the low-latency transmissions over an additive white Gaussian noise (AWGN) channel and a Nakagami-$m$ fading channel. By defining the service rate gain, reliability gain, and real-time gain, we conduct an asymptotic analysis to reveal the fundamental reliability-latency-rate tradeoff of ultra-reliable and low-latency communications in the high signal-to-noise-ratio (SNR) regime. To analytically evaluate and optimize the quality-of-service-constrained throughput of low-latency communication systems adopting FBC, an EC-approximation method is conceived to derive the closed-form expression of that throughput. Our results may offer some insights into the efficient scheduling of low-latency wireless communications, in which statistical latency and reliability metrics are crucial.

We introduce a generalization of one-way superdense coding to two-way communication protocols for transmitting classical bits by using entangled quantum pairs. The proposed protocol caters for provision of entangled pairs, which is assumed to be given in one-way superdense coding. The proposed protocol gives a 50% increase in both data rate and energy efficiency compared to the classical protocol. Even when decoherence is taken into consideration, the quantum protocol performs better as long as the decoherence time is not extremely short., Comment: 3 pages

Location information is often used as a proxy to guarantee the performance of a wireless communication link. However, localization errors can result in a significant mismatch with the guarantees, particularly detrimental to users operating the ultra-reliable low-latency communication (URLLC) regime. This paper unveils the fundamental statistical relations between location estimation uncertainty and wireless link reliability, specifically in the context of rate selection for ultra-reliable communication. We start with a simple one-dimensional narrowband Rayleigh fading scenario and build towards a two-dimensional scenario in a rich scattering environment. The wireless link reliability is characterized by the meta-probability, the probability with respect to localization error of exceeding the outage capacity, and by removing other sources of errors in the system, we show that reliability is sensitive to localization errors. The $\epsilon$-outage coherence radius is defined and shown to provide valuable insight into the problem of location-based rate selection. However, it is generally challenging to guarantee reliability without accurate knowledge of the propagation environment. Finally, several rate-selection schemes are proposed, showcasing the problem's dynamics and revealing that properly accounting for the localization error is critical to ensure good performance in terms of reliability and achievable throughput., Comment: Submitted for IEEE Transactions of Wireless Communications on July 16th 2023

The emergence of mega-constellations of interconnected satellites has a major impact on the integration of cellular wireless and non-terrestrial networks, while simultaneously offering previously inconceivable data gathering capabilities. This paper studies the problem of running a federated learning (FL) algorithm within low Earth orbit satellite constellations connected with intra-orbit inter-satellite links (ISL), aiming to efficiently process collected data in situ. Satellites apply on-board machine learning and transmit local parameters to the parameter server (PS). The main contribution is a novel approach to enhance FL in satellite constellations using intra-orbit ISLs. The key idea is to rely on predictability of satellite visits to create a system design in which ISLs mitigate the impact of intermittent connectivity and transmit aggregated parameters to the PS. We first devise a synchronous FL, which is extended towards an asynchronous FL for the case of sparse satellite visits to the PS. An efficient use of the satellite resources is attained by sparsification-based compression the aggregated parameters within each orbit. Performance is evaluated in terms of accuracy and required data transmission size. We observe a sevenfold increase in convergence speed over the state-of-the-art using ISLs, and $10\times$ reduction in communication load through the proposed in-network aggregation strategy.

5G has expanded the traditional focus of wireless systems to embrace two new connectivity types: ultra-reliable low latency and massive communication. The technology context at the dawn of 6G is different from the past one for 5G, primarily due to the growing intelligence at the communicating nodes. This has driven the set of relevant communication problems beyond reliable transmission towards semantic and pragmatic communication. This paper puts the evolution of low-latency and massive communication towards 6G in the perspective of these new developments. At first, semantic/pragmatic communication problems are presented by drawing parallels to linguistics. We elaborate upon the relation of semantic communication to the information-theoretic problems of source/channel coding, while generalized real-time communication is put in the context of cyber-physical systems and real-time inference. The evolution of massive access towards massive closed-loop communication is elaborated upon, enabling interactive communication, learning, and cooperation among wireless sensors and actuators., Comment: Submitted for publication to IEEE BITS (revised version preprint)

In wireless communication systems, mmWave beam tracking is a critical task that affects both sensing and communications, as it is related to the knowledge of the wireless channel. We consider a setup in which a Base Station (BS) needs to dynamically choose whether the resource will be allocated for one of the three operations: sensing (beam tracking), downlink, or uplink transmission. We devise an approach based on the Proximal Policy Optimization (PPO) algorithm for choosing the resource allocation and beam tracking at a given time slot. The proposed framework takes into account the variable quality of the wireless channel and optimizes the decisions in a coordinated manner. Simulation results demonstrate that the proposed method achieves significant performance improvements in terms of average packet error rate (PER) compared to the baseline methods while providing a significant reduction in beam tracking overhead. We also show that our proposed PPO-based framework provides an effective solution to the resource allocation problem in beam tracking and communication, exhibiting a great generalization performance regardless of the stochastic behavior of the system.

Taking inspiration from linguistics, the communications theoretical community has recently shown a significant recent interest in pragmatic , or goal-oriented, communication. In this paper, we tackle the problem of pragmatic communication with multiple clients with different, and potentially conflicting, objectives. We capture the goal-oriented aspect through the metric of Value of Information (VoI), which considers the estimation of the remote process as well as the timing constraints. However, the most common definition of VoI is simply the Mean Square Error (MSE) of the whole system state, regardless of the relevance for a specific client. Our work aims to overcome this limitation by including different summary statistics, i.e., value functions of the state, for separate clients, and a diversified query process on the client side, expressed through the fact that different applications may request different functions of the process state at different times. A query-aware Deep Reinforcement Learning (DRL) solution based on statically defined VoI can outperform naive approaches by 15-20%.

The trade-off between reliability, latency, and energy-efficiency is a central problem in communication systems. Advanced hybrid automated repeat request (HARQ) techniques can reduce the number of retransmissions required for reliable communication, but they have a significant computational cost. On the other hand, strict energy constraints apply mainly to devices, while the access point receiving their packets is usually connected to the electrical grid. Therefore, moving the computational complexity required for HARQ schemes from the transmitter to the receiver may provide a way to overcome this trade-off. To achieve this, we propose the Reinforcement-based Adaptive Feedback (RAF) scheme, in which the receiver adaptively learns how much additional redundancy it requires to decode a packet and sends rich feedback (i.e., more than a single bit), requesting the coded retransmission of specific symbols. Simulation results show that the RAF scheme achieves a better trade-off between energy-efficiency, reliability, and latency, compared to existing HARQ solutions and a fixed threshold-based policy. Our RAF scheme can easily adapt to different modulation schemes, and since it relies on the posterior probabilities of the codeword symbols at the decoder, it can generalize to different channel statistics.

We treat the problem of client selection in a Federated Learning (FL) setup, where the learning objective and the local incentives of the participants are used to formulate a goal-oriented communication problem. Specifically, we incorporate the risk-averse nature of participants and obtain a communication-efficient on-device performance, while relying on feedback from the Parameter Server (\texttt{PS}). A client has to decide its transmission plan on when not to participate in FL. This is based on its intrinsic incentive, which is the value of the trained global model upon participation by this client. Poor updates not only plunge the performance of the global model with added communication cost but also propagate the loss in performance on other participating devices. We cast the relevance of local updates as \emph{semantic information} for developing local transmission strategies, i.e., making a decision on when to ``not transmit". The devices use feedback about the state of the PS and evaluate their contributions in training the learning model in each aggregation period, which eventually lowers the number of occupied connections. Simulation results validate the efficacy of our proposed approach, with up to $1.4\times$ gain in communication links utilization as compared with the baselines.