arXiv subjects

Emilio Calvanese Strinati

Publications and source records attributed to Emilio Calvanese Strinati.

At least 55 records · Page 3Linked to original sources

arXiv2022

Energy-Efficient Classification at the Wireless Edge with Reliability Guarantees

Learning at the edge is a challenging task from several perspectives, since data must be collected by end devices (e.g. sensors), possibly pre-processed (e.g. data compression), and finally processed remotely to output the result of training and/or inference phases. This involves heterogeneous resources, such as radio, computing and learning related parameters. In this context, we propose an algorithm that dynamically selects data encoding scheme, local computing resources, uplink radio parameters, and remote computing resources, to perform a classification task with the minimum average end devices' energy consumption, under E2E delay and inference reliability constraints. Our method does not assume any prior knowledge of the statistics of time varying context parameters, while it only requires the solution of low complexity per-slot deterministic optimization problems, based on instantaneous observations of these parameters and that of properly defined state variables. Numerical results on convolutional neural network based image classification illustrate the effectiveness of our method in striking the best trade-off between energy, delay and inference reliability.

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arXiv2022

Effective Goal-oriented 6G Communications: the Energy-aware Edge Inferencing Case

Currently, the world experiences an unprecedentedly increasing generation of application data, from sensor measurements to video streams, thanks to the extreme connectivity capability provided by 5G networks. Going beyond 5G technology, such data aim to be ingested by Artificial Intelligence (AI) functions instantiated in the network to facilitate informed decisions, essential for the operation of applications, such as automated driving and factory automation. Nonetheless, while computing platforms hosting Machine Learning (ML) models are ever powerful, their energy footprint is a key impeding factor towards realizing a wireless network as a sustainable intelligent platform. Focusing on a beyond 5G wireless network, overlaid by a Multi-access Edge Computing (MEC) infrastructure with inferencing capabilities, our paper tackles the problem of energy-aware dependable inference by considering inference effectiveness as value of a goal that needs to be accomplished by paying the minimum price in energy consumption. Both MEC-assisted standalone and ensemble inference options are evaluated. It is shown that, for some system scenarios, goal effectiveness above 84% is achieved and sustained even by relaxing communication reliability requirements by one decimal digit, while enjoying a device radio energy consumption reduction of almost 23% at the same time. Also, ensemble inference is shown to improve system-wide energy efficiency and even achieve higher goal effectiveness, as compared to the standalone case for some system parameterizations.

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arXiv2022

Towards URLLC with Proactive HARQ Adaptation

In this work, we propose a dynamic decision maker algorithm to improve the proactive HARQ protocol for beyond 5G networks. Based on Lyapunov stochastic optimization, our adaptation control framework dynamically selects the number of proactive retransmissions for intermittent URLLC traffic scenarios under time-varying channel conditions without requiring any prior knowledge associated with this stochastic process. It then better exploits the trade-off between Radio Access Network (RAN) latency, reliability and resource efficiency, which is still limited in its realization on current HARQ designs. We then evaluate the performance of several HARQ strategies and show that our proposal further improves latency over the reactive regime without affecting the resource efficiency such as fixed proactive retransmission while maintaining target reliability.

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arXiv2022

Smart Wireless Environments Enabled by RISs: Deployment Scenarios and Two Key Challenges

Reconfigurable Intelligent Surfaces (RISs) constitute the enabler for programmable propagation of electromagnetic signals, and are lately being considered as a candidate physical-layer technology for the demanding connectivity, reliability, localization, and sustainability requirements of next generation wireless communications networks. In this paper, we present various deployment scenarios for RIS-enabled smart wireless environments that have been recently designed by the ongoing EU H2020 RISE-6G project. The scenarios are taxonomized according to performance objectives, in particular, connectivity and reliability, localization and sensing, as well as sustainability and secrecy. We identify various deployment strategies and sketch the core architectural requirements in terms of RIS control and signaling, depending on the RIS hardware architectures and their respective capabilities. Furthermore, we introduce and discuss, via preliminary simulation results and reflectarray measurements, two key novel challenges with RIS-enabled smart wireless environments, namely, the area of influence and the bandwidth of influence of RISs, which corroborate the need for careful deployment and planning of this new technology.

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arXiv2021

Dynamic Edge Computing empowered by Reconfigurable Intelligent Surfaces

In this paper, we propose a novel algorithm for energy-efficient, low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with Reconfigurable Intelligent Surfaces (RISs). In our setting, new computing requests are continuously generated by a set of devices and are handled through a dynamic queueing system. Building on stochastic optimization tools, we devise a dynamic learning algorithm that jointly optimizes the allocation of radio resources (i.e., power, transmission rates, sleep mode and duty cycle), computation resources (i.e., CPU cycles), and RIS reflectivity parameters (i.e., phase shifts), while guaranteeing a target performance in terms of average end-to-end (E2E) delay. The proposed strategy is dynamic, since it performs a low-complexity optimization on a per-slot basis while dealing with time-varying radio channels and task arrivals, whose statistics are unknown. The presence and optimization of RISs helps boosting the performance of dynamic MEC, thanks to the capability to shape and adapt the wireless propagation environment. Numerical results assess the performance in terms of service delay, learning, and adaptation capabilities of the proposed strategy for RIS empowered MEC.

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arXiv2021

Discontinuous Computation Offloading for Energy-Efficient Mobile Edge Computing

We propose a novel strategy for energy-efficient dynamic computation offloading, in the context of edge-computing-aided beyond 5G networks. The goal is to minimize the energy consumption of the overall system, comprising multiple User Equipment (UE), an access point (AP), and an edge server (ES), under constraints on the end-to-end service delay and the packet error rate performance over the wireless interface. To reduce the energy consumption, we exploit low-power sleep operation modes for the users, the AP and the ES, shifting the edge computing paradigm from an always on to an always available architecture, capable of guaranteeing an on-demand target service quality with the minimum energy consumption. To this aim, we propose an online algorithm for dynamic and optimal orchestration of radio and computational resources called Discontinuous Computation Offloading (DisCO). In such a framework, end-to-end delay constraints translate into constraints on overall queueing delays, including both the communication and the computation phases of the offloading service. DisCO hinges on Lyapunov stochastic optimization, does not require any prior knowledge on the statistics of the offloading traffic or the radio channels, and satisfies the long-term performance constraints imposed by the users. Several numerical results illustrate the advantages of the proposed method.

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arXiv2021

Reconfigurable, Intelligent, and SustainableWireless Environments for 6G Smart Connectivity

Various visions on the forthcoming sixth Generation (6G) networks point towards flexible connect-and-compute technologies to support future innovative services and the corresponding use cases. 6G should be capable to accommodate ever-evolving and heterogeneous applications, future regulations, and diverse user-, service-, and location-based requirements. A key element towards building smart and energy sustainable wireless systems beyond 5G is the Reconfigurable Intelligent Surface (RIS), which offers programmable control and shaping of the wireless propagation environment. Capitalizing on this technology potential, in this article we introduce two new concepts: i) wireless environment as a service, which leverages a novel RIS-empowered networking paradigm to trade off diverse, and usually conflicting, connectivity objectives; and ii) performance-boosted areas enabled by RIS-based connectivity, representing competing service provisioning areas that are highly spatially and temporally focused. We discuss the key technological enablers and research challenges with the proposed networking paradigm, and highlight the potential profound role of RISs in the recent Open Radio Access Network (O-RAN) architecture.

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arXiv2021

Learning Semantics: An Opportunity for Effective 6G Communications

Recently, semantic communications are envisioned as a key enabler of future 6G networks. Back to Shannon's information theory, the goal of communication has long been to guarantee the correct reception of transmitted messages irrespective of their meaning. However, in general, whenever communication occurs to convey a meaning, what matters is the receiver's understanding of the transmitted message and not necessarily its correct reconstruction. Hence, semantic communications introduce a new paradigm: transmitting only relevant information sufficient for the receiver to capture the meaning intended can save significant communication bandwidth. Thus, this work explores the opportunity offered by semantic communications for beyond 5G networks. In particular, we focus on the benefit of semantic compression. We refer to semantic message as a sequence of well-formed symbols learned from the "meaning" underlying data, which have to be interpreted at the receiver. This requires a reasoning unit, here artificial, on a knowledge base: a symbolic knowledge representation of the specific application. Therefore, we present and detail a novel architecture that enables representation learning of semantic symbols for effective semantic communications. We first discuss theoretical aspects and successfully design objective functions, which help learn effective semantic encoders and decoders. Eventually, we show promising numerical results for the scenario of text transmission, especially when the sender and receiver speak different languages.

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arXiv2021

Toward 6G: From New Hardware Design to Wireless Semantic and Goal-Oriented Communication Paradigms

Several speculative visions are conjecturing on what 6G services will be able to offer at the horizon of 2030. Nevertheless, the 6G design process is at its preliminary stages. The reality today is that hardware, technologies and new materials required to effectively meet the unprecedented performance targets required for future 6G services and network operation, have not been designed, tested or even do not exist yet. Today, a solid vision on the cost-benefit trade-offs of machine learning and artificial intelligence support for 6G network and services operation optimization is missing. This includes the possible support from hardware efficiency, operation effectiveness and, the immeasurable cost due to data acquisition-transfer-processing. The contribution of this paper is three-fold. This is the first paper deriving crucial 6G key performance indicators on hardware and technology design. Second, we present a new hardware technologies design methodology conceived to enable the effective software-hardware components integration required to meet the challenging performance envisioned for future 6G networks. Third, we suggest a paradigm shift towards goal-oriented and semantic communications, in which a totally new opportunity of joint design of hardware, artificial intelligence and effective communication is offered. The proposed vision is consolidated by our recent results on hardware, technology and machine learning performance.

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arXiv2021

Transferable and Distributed User Association Policies for 5G and Beyond Networks

We study the problem of user association, namely finding the optimal assignment of user equipment to base stations to achieve a targeted network performance. In this paper, we focus on the knowledge transferability of association policies. Indeed, traditional non-trivial user association schemes are often scenario-specific or deployment-specific and require a policy re-design or re-learning when the number or the position of the users change. In contrast, transferability allows to apply a single user association policy, devised for a specific scenario, to other distinct user deployments, without needing a substantial re-learning or re-design phase and considerably reducing its computational and management complexity. To achieve transferability, we first cast user association as a multi-agent reinforcement learning problem. Then, based on a neural attention mechanism that we specifically conceived for this context, we propose a novel distributed policy network architecture, which is transferable among users with zero-shot generalization capability i.e., without requiring additional training.Numerical results show the effectiveness of our solution in terms of overall network communication rate, outperforming centralized benchmarks even when the number of users doubles with respect to the initial training point.

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arXiv2021

Wireless Environment as a Service Enabled by Reconfigurable Intelligent Surfaces: The RISE-6G Perspective

The design of 6th Generation (6G) wireless networks points towards flexible connect-and-compute technologies capable to support innovative services and use cases. Targeting the 2030 horizon, 6G networks are poised to pave the way for sustainable human-centered smart societies and vertical industries, such that wireless networks will be transformed into a distributed smart connectivity infrastructure, where new terminal types are embedded in the daily environment. In this context, the RISE-6G project aims at investigating innovative solutions that capitalize on the latest advances in the emerging technology of Reconfigurable Intelligent Surfaces (RISs), which offers dynamic and goal-oriented radio wave propagation control, enabling the concept of the wireless environment as a service. The project will focus on: i) the realistic modeling of RIS-assisted signal propagation, ii) the investigation of the fundamental limits of RIS-empowered wireless communications and sensing, and iii) the design of efficient algorithms for orchestrating networking RISs, in order to implement intelligent, sustainable, and dynamically programmable wireless environments enabling diverse services that go well beyond the 5G capabilities. RISE-6G will offer two unprecedented proof-of-concepts for realizing controlled wireless environments in near-future use cases.

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arXiv2021

Energy Efficient Edge Computing: When Lyapunov Meets Distributed Reinforcement Learning

In this work, we study the problem of energy-efficient computation offloading enabled by edge computing. In the considered scenario, multiple users simultaneously compete for limited radio and edge computing resources to get offloaded tasks processed under a delay constraint, with the possibility of exploiting low power sleep modes at all network nodes. The radio resource allocation takes into account inter- and intra-cell interference, and the duty cycles of the radio and computing equipment have to be jointly optimized to minimize the overall energy consumption. To address this issue, we formulate the underlying problem as a dynamic long-term optimization. Then, based on Lyapunov stochastic optimization tools, we decouple the formulated problem into a CPU scheduling problem and a radio resource allocation problem to be solved in a per-slot basis. Whereas the first one can be optimally and efficiently solved using a fast iterative algorithm, the second one is solved using distributed multi-agent reinforcement learning due to its non-convexity and NP-hardness. The resulting framework achieves up to 96.5% performance of the optimal strategy based on exhaustive search, while drastically reducing complexity. The proposed solution also allows to increase the network's energy efficiency compared to a benchmark heuristic approach.

cs.LG↗

arXiv2021

6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications

The goal of this paper is to promote the idea that including semantic and goal-oriented aspects in future 6G networks can produce a significant leap forward in terms of system effectiveness and sustainability. Semantic communication goes beyond the common Shannon paradigm of guaranteeing the correct reception of each single transmitted packet, irrespective of the meaning conveyed by the packet. The idea is that, whenever communication occurs to convey meaning or to accomplish a goal, what really matters is the impact that the correct reception/interpretation of a packet is going to have on the goal accomplishment. Focusing on semantic and goal-oriented aspects, and possibly combining them, helps to identify the relevant information, i.e. the information strictly necessary to recover the meaning intended by the transmitter or to accomplish a goal. Combining knowledge representation and reasoning tools with machine learning algorithms paves the way to build semantic learning strategies enabling current machine learning algorithms to achieve better interpretation capabilities and contrast adversarial attacks. 6G semantic networks can bring semantic learning mechanisms at the edge of the network and, at the same time, semantic learning can help 6G networks to improve their efficiency and sustainability.

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arXiv2020

6G in the Sky: On-Demand Intelligence at the Edge of 3D Networks

6G will exploit satellite, aerial and terrestrial platforms jointly to improve radio access capability and to unlock the support of on-demand edge cloud services in the three dimensional space (3D) by incorporating Mobile Edge Computing (MEC) functionalities on aerial platforms and low orbit satellites. This will extend the MEC support to devices and network elements in the sky and will forge a space borne MEC enabling intelligent personalized and distributed on demand services. 3D end users will experience the impression of being surrounded by a distributed computer fulfilling their requests in apparently zero latency. In this paper, we consider an architecture providing communication, computation, and caching (C3) services on demand, anytime and everywhere in 3D space, building on the integration of conventional ground (terrestrial) base stations and flying (non-terrestrial) nodes. Given the complexity of the overall network, the C3 resources and the management of the aerial devices need to be jointly orchestrated via AI-based algorithms, exploiting virtualized networks functions dynamically deployed in a distributed manner across terrestrial and non-terrestrial nodes.

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arXiv2020

Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks

Network densification and millimeter-wave technologies are key enablers to fulfill the capacity and data rate requirements of the fifth generation (5G) of mobile networks. In this context, designing low-complexity policies with local observations, yet able to adapt the user association with respect to the global network state and to the network dynamics is a challenge. In fact, the frameworks proposed in literature require continuous access to global network information and to recompute the association when the radio environment changes. With the complexity associated to such an approach, these solutions are not well suited to dense 5G networks. In this paper, we address this issue by designing a scalable and flexible algorithm for user association based on multi-agent reinforcement learning. In this approach, users act as independent agents that, based on their local observations only, learn to autonomously coordinate their actions in order to optimize the network sum-rate. Since there is no direct information exchange among the agents, we also limit the signaling overhead. Simulation results show that the proposed algorithm is able to adapt to (fast) changes of radio environment, thus providing large sum-rate gain in comparison to state-of-the-art solutions.

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arXiv2019

6G: The Next Frontier

The current development of 5G networks represents a breakthrough in the design of communication networks, for its ability to provide a single platform enabling a variety of different services, from enhanced mobile broadband communications, automated driving, Internet-of-Things, with its huge number of connected devices, etc. Nevertheless, looking at the current development of technologies and new services, it is already possible to envision the need to move beyond 5G with a new architecture incorporating new services and technologies. The goal of this paper is to motivate the need to move to a sixth generation (6G) of mobile communication networks, starting from a gap analysis of 5G, and predicting a new synthesis of near future services, like hologram interfaces, ambient sensing intelligence, a pervasive introduction of artificial intelligence and the incorporation of technologies, like TeraHertz (THz) or Visible Light Communications (VLC), 3-dimensional coverage.

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arXiv2019

Resilient Design of 5G Mobile-Edge Computing Over Intermittent mmWave Links

Two enablers of the 5th Generation (5G) of mobile communication systems are the high data rates achievable with millimeter-wave radio signals and the cloudification of the network's mobile edge, made possible also by Multi-access Edge Computing (MEC). In 5G networks, user devices may exploit the high capacity of their mobile connection and the computing capabilities of the edge cloud to offload computational tasks to MEC servers, which run applications on devices' behalf. This paper investigates new methods to perform power- and latency-constrained offloading. First, aiming to minimize user devices' transmit power, the opportunity to exploit concurrent communication links between the device and the edge cloud is studied. The optimal number of channels for simultaneous transmission is characterized in a deterministic and a probabilistic scenario. Subsequently, blocking events that obstruct millimeter-wave channels making them `intermittent' are considered. Resource overprovisioning and error-correcting codes against asymmetric block erasures are proposed to jointly contrast blocking and exploit multi-link communications' diversity. The asymmetric block-erasure channel is characterized by a study of its outage probability. The analysis is performed in a framework that yields closed-form expressions. These, together with corroborating numerical results, are intended to provide reference points and bounds to optimal performance in practical applications.

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arXiv2017

Optimal Cross Slice Orchestration for 5G Mobile Services

5G mobile networks encompass the capabilities of hosting a variety of services such as mobile social networks, multimedia delivery, healthcare, transportation, and public safety. Therefore, the major challenge in designing the 5G networks is how to support different types of users and applications with different quality-of-service requirements under a single physical network infrastructure. Recently, network slicing has been introduced as a promising solution to address this challenge. Network slicing allows programmable network instances which match the service requirements by using network virtualization technologies. However, how to efficiently allocate resources across network slices has not been well studied in the literature. Therefore, in this paper, we first introduce a model for orchestrating network slices based on the service requirements and available resources. Then, we propose a Markov decision process framework to formulate and determine the optimal policy that manages cross-slice admission control and resource allocation for the 5G networks. Through simulation results, we show that the proposed framework and solution are efficient not only in providing slice-as-a-service based on the service requirements, but also in maximizing the provider's revenue.

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