Periodic Reporting for period 4 - FOGHORN (FOG-aided wireless networks for communication, cacHing and cOmputing: theoRetical and algorithmic fouNdations)
Okres sprawozdawczy: 2021-12-01 do 2023-07-31
The overall goal of FOGHORN was to develop fundamental theoretical insights and algorithmic principles for the operation of cloud- and edge-aided wireless networks, encompassing communication, caching, and computing resources. The results from the project offer insights and formal frameworks for engineering the transformation of wireless networks towards a fog-aided architecture, away from base station-centric systems that have dominated telecom deployments for decades.
Fog-aided network disaggregate control and data planes across edge and cloud. Designing mechanisms and protocols for the operation of such systems results in a complex engineering problem. In it, as a function of key system parameters such as traffic type, number of antennas, density of deployment, and fronthaul architecture, the network operator needs to decide where to locate and implement communication, computing, and caching functionalities at different layers of the protocol stack. FOGHORN has provided general information-theoretic and communication-theoretic frameworks in which to formulate such design problems, making it possible to determine principled paradigms for optimized functional splits, while accounting for existing technological limitations and for potential future advances.
The ideas and results produced by FOGHORN have become part of the current and next-generation design of wireless systems, which are increasingly based on the concept of edge-cloud disaggregation. Furthermore, the project has pioneered the integration of computing and wireless systems, including the use of artificial intelligence and the introduction of novel technologies such as neuromorphic computing.
Fog-aided network disaggregate control and data planes across edge and cloud. Designing mechanisms and protocols for the operation of such systems results in a complex engineering problem. In it, as a function of key system parameters such as traffic type, number of antennas, density of deployment, and fronthaul architecture, the network operator needs to decide where to locate and implement communication, computing, and caching functionalities at different layers of the protocol stack. FOGHORN has provided general information-theoretic and communication-theoretic frameworks in which to formulate such design problems, making it possible to determine principled paradigms for optimized functional splits, while accounting for existing technological limitations and for potential future advances.
The ideas and results produced by FOGHORN have become part of the current and next-generation design of wireless systems, which are increasingly based on the concept of edge-cloud disaggregation. Furthermore, the project has pioneered the integration of computing and wireless systems, including the use of artificial intelligence and the introduction of novel technologies such as neuromorphic computing.
The main results of FOGHORN address fog-aided wireless systems with a focus on communications, caching, and computing. These topics are reviewed in turn in the following.
Communications
FOGHORN developed fog-aided communication protocols that introduce novel functionality splits across cloud and edge across both data and control planes. These include rate selection and scheduling at the control plane and encoding/ decoding at the data plane. Particular attention was paid to common settings with heterogeneous traffic requirements, introducing the novel concept of heterogeneous non-orthogonal multiple access (H-NOMA), as well as to multi-tenant architectures. Fundamental theoretical limits were derived for the capacity benefits of reflecting intelligence surfaces (RISs) at the edge. Furthermore, applications of the proposed frameworks were studied for exemplifying scenarios such as virtual reality social networks and satellite networks.
Moving beyond the transmission of bits, the project pioneered the framework of semantics-based multiple access protocols that leverage processing across cloud and edge via type-based modulation. Type-based multiple access protocols were designed leveraging information-theoretic principles, demonstrating significant advantages over standard bit-based methods.
Finally, the project introduced novel methods for the design of data-efficient and reliable artificial intelligence (AI)-based solutions for communications in fog-aided wireless networks. Data-efficient methods can design effective protocols and algorithms by leveraging few data samples. FOGHORN pioneered the use of meta-learning as a general framework for the optimization of data-efficient protocols, inspiring many follow-up projects.
Reliable AI methods can provide trustworthy measures of uncertainty, supporting the use of AI within larger engineered systems encompassing both classical and AI-based algorithms. FOGHORN proposed for the first time principled frameworks based on Bayesian meta-learning and conformal prediction. The project showcased their benefits for a variety of functionalities such as beamforming, decoding, channel prediction, channel generation, and localization.
Caching
FOGHORN addressed the fundamental problem of understanding optimal functional splits and operation for caching content in fog-aided wireless systems, deriving optimal placement and delivery strategies across both fronthaul and wireless, or edge, segments. The introduced information-theoretic framework allows for fractional caching, as well as for general forms of fronthaul compression and data transmission. Extensions of the theoretical framework and of the optimality results were presented to address heterogeneous content popularity, partial wireless connectivity, device-to-device transmissions, linear precoding, imperfect channel state information, secrecy constraints, and online caching. The project also pioneered the idea of soft-time-to-live caching. Furthermore, optimal placement algorithms were derived for semantic-aware caching and routing strategies that adaptively place content and select optimal routes based on content similarity.
Computing
FOGHORN contributed to the definition of optimized computing and communication strategies in fog-aided wireless networks in terms of architectures, of fundamental design methodologies and protocols, and of technological insights.
At the level of network architectures, the project introduced the concept of a drone-based mobile cloud computing system in which a moving drone is endowed with computing capabilities to offer computation offloading opportunities.
On the front of fundamental design and protocols, FOGHORN proposed novel distributed protocols based on over-the-air computing that leverage the new idea of channel-driven sampling. Through this novel approach, distributed communication systems can ensure differential privacy “for free” by leveraging channel noise as privacy mechanism, while also benefiting from the presence of channel noise as seed randomness for distributed Bayesian learning. The project also introduced new coded distributed matrix multiplication in fog networks, as well as the first distributed unlearning mechanisms in device-to-device edge networks.
Finally, FOGHORN pioneered the use of neuromorphic signal processing and learning machines at the edge for semantic-aware transmission protocols, targeting applications such as sensing and remote inference.
Communications
FOGHORN developed fog-aided communication protocols that introduce novel functionality splits across cloud and edge across both data and control planes. These include rate selection and scheduling at the control plane and encoding/ decoding at the data plane. Particular attention was paid to common settings with heterogeneous traffic requirements, introducing the novel concept of heterogeneous non-orthogonal multiple access (H-NOMA), as well as to multi-tenant architectures. Fundamental theoretical limits were derived for the capacity benefits of reflecting intelligence surfaces (RISs) at the edge. Furthermore, applications of the proposed frameworks were studied for exemplifying scenarios such as virtual reality social networks and satellite networks.
Moving beyond the transmission of bits, the project pioneered the framework of semantics-based multiple access protocols that leverage processing across cloud and edge via type-based modulation. Type-based multiple access protocols were designed leveraging information-theoretic principles, demonstrating significant advantages over standard bit-based methods.
Finally, the project introduced novel methods for the design of data-efficient and reliable artificial intelligence (AI)-based solutions for communications in fog-aided wireless networks. Data-efficient methods can design effective protocols and algorithms by leveraging few data samples. FOGHORN pioneered the use of meta-learning as a general framework for the optimization of data-efficient protocols, inspiring many follow-up projects.
Reliable AI methods can provide trustworthy measures of uncertainty, supporting the use of AI within larger engineered systems encompassing both classical and AI-based algorithms. FOGHORN proposed for the first time principled frameworks based on Bayesian meta-learning and conformal prediction. The project showcased their benefits for a variety of functionalities such as beamforming, decoding, channel prediction, channel generation, and localization.
Caching
FOGHORN addressed the fundamental problem of understanding optimal functional splits and operation for caching content in fog-aided wireless systems, deriving optimal placement and delivery strategies across both fronthaul and wireless, or edge, segments. The introduced information-theoretic framework allows for fractional caching, as well as for general forms of fronthaul compression and data transmission. Extensions of the theoretical framework and of the optimality results were presented to address heterogeneous content popularity, partial wireless connectivity, device-to-device transmissions, linear precoding, imperfect channel state information, secrecy constraints, and online caching. The project also pioneered the idea of soft-time-to-live caching. Furthermore, optimal placement algorithms were derived for semantic-aware caching and routing strategies that adaptively place content and select optimal routes based on content similarity.
Computing
FOGHORN contributed to the definition of optimized computing and communication strategies in fog-aided wireless networks in terms of architectures, of fundamental design methodologies and protocols, and of technological insights.
At the level of network architectures, the project introduced the concept of a drone-based mobile cloud computing system in which a moving drone is endowed with computing capabilities to offer computation offloading opportunities.
On the front of fundamental design and protocols, FOGHORN proposed novel distributed protocols based on over-the-air computing that leverage the new idea of channel-driven sampling. Through this novel approach, distributed communication systems can ensure differential privacy “for free” by leveraging channel noise as privacy mechanism, while also benefiting from the presence of channel noise as seed randomness for distributed Bayesian learning. The project also introduced new coded distributed matrix multiplication in fog networks, as well as the first distributed unlearning mechanisms in device-to-device edge networks.
Finally, FOGHORN pioneered the use of neuromorphic signal processing and learning machines at the edge for semantic-aware transmission protocols, targeting applications such as sensing and remote inference.
FOGHORN generated novel frameworks and results to guide the engineering of fog-aided wireless networks as it pertains to communications, caching, and computing.
The vision and scope of FOGHORN has only grown in importance throughout the work on the project. The fog architecture described in FOGHORN has become a central component of the 5G standard network architecture, in which base stations are distributed across centralized and edge network elements. Furthermore, in the closing stage of the project, 6G systems were envisioned to embrace even further the concept of edge-cloud disaggregation, as well as the integration of computing, storage, and communication.
Novel technologies, most notably artificial intelligence and neuromorphic computing, have made significant strides during the course of the project, and their application to wireless system was pioneered within FOGHORN. The project also introduced the notion of semantic communications in the context of next-generation wireless systems, which was deemed to be a central paradigm for the design of 6G systems by the end of the project. The final phase of FOGHORN also considered the potential benefits of quantum computing for co-processing in tasks such as reliable decision making.
The vision and scope of FOGHORN has only grown in importance throughout the work on the project. The fog architecture described in FOGHORN has become a central component of the 5G standard network architecture, in which base stations are distributed across centralized and edge network elements. Furthermore, in the closing stage of the project, 6G systems were envisioned to embrace even further the concept of edge-cloud disaggregation, as well as the integration of computing, storage, and communication.
Novel technologies, most notably artificial intelligence and neuromorphic computing, have made significant strides during the course of the project, and their application to wireless system was pioneered within FOGHORN. The project also introduced the notion of semantic communications in the context of next-generation wireless systems, which was deemed to be a central paradigm for the design of 6G systems by the end of the project. The final phase of FOGHORN also considered the potential benefits of quantum computing for co-processing in tasks such as reliable decision making.