Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - FOGHORN (FOG-aided wireless networks for communication, cacHing and cOmputing: theoRetical and algorithmic fouNdations)

Teaser

The overall goal of FOGHORN is to develop fundamental theoretical insights and algorithmic principles for the operation of fog-aided, that is, cloud- and edge-aided, wireless networks, encompassing communication, caching and computing resources. The main results from this...

Summary

The overall goal of FOGHORN is to develop fundamental theoretical insights and algorithmic principles for the operation of fog-aided, that is, cloud- and edge-aided, wireless networks, encompassing communication, caching and computing resources. The main results from this project have the main objective of guiding engineering choices that will enable the transformative shift of wireless networks towards the fog-aided architecture, after decades of dominance of base station-centric systems, unlocking new academic opportunities and technologies.

The subject matter of the project has only grown in importance from the time of submission of this proposal. The fog architecture described in the project has been included by 3GPP in the 5G standard network architecture, and the integration of computing and communication has become the subject of a novel line of research in the information-theoretic literature.

The FOGHORN project is organized into three main WPs.

WP1: Fog-aided wireless networks for communication. A key feature of a fog-aided network is that control and data planes can be split across edge and cloud. Accounting for the complementarity and synergy of cloud and edge processing, the main question addressed by WP1 is: As a function of key system parameters such as traffic type, number of antennas, ranging from regular to massive antenna arrays, density of deployment, fronthaul architecture, including single- and multi-hop, what is the optimal functional split at control and data planes? What are corresponding optimal algorithmic solutions and performance trade-offs? A key aspect of this WP is the modelling of heterogeneous traffic requirements from the three generic services of 5G, namely enhanced Mobile BroadBand (eMBB), massive Machine-Type Communication (mMTC).

WP2: Fog-aided wireless networks with edge caching. The second work package targets the optimal operation of a fog-aided wireless network with edge caching capabilities. A key tenet of the proposed approach is that edge caching, fronthaul, and wireless transmission should be jointly designed so as to leverage the synergistic and complementary features of cloud and edge processing. The WP is based on a novel network information-theoretic asymptotic framework that enables a quantitative, tractable and insightful analysis of latency in a fog-aided wireless network accounting for fronthaul and wireless transmissions in the presence of caching.

WP3: Fog-aided wireless networks for computing. Edge and mobile cloud computing enables the offloading energy-consuming tasks from resource-constrained mobile devices, which are typically mobile broadband users or machine-type devices, to nearby servers at the edge or at the cloud. The goal of this project is the optimization of network resources for the execution of computational tasks at edge and cloud.

Work performed

Following the original schedule, the activity in the first reporting period has mostly focused on WP1 and WP2. However, due to technological developments that were not foreseen at the time of writing the proposal, activity around WP3 has also been carried out.

WP1: Fog-aided wireless networks for communication

The main contributors to WP1, beside the PI, have been Dr Jingjing Zhang (Post-Doc) and Rahif Kassib (PhD student), both working at KCL and supported through this project. There have also been a number of external collaborators, namely Dr Jinkyu Kang and Prof Joonhyuk Kang (KAIST); Dr Jihong Park (Oulu); Prof Petar Popovski and Dr Kasper Trillingsgaard (Aalborg Unviersity); Malihe Aliasgari and Prof Joerg Kliewer (NJIT); Jaein Kim and Prof Inkyu Lee (Korea University); Prof S.-H. Park (Chonbuk University); Prof Shlomo Shamai and Prof Yonina Eldar (Technion); Ghizi Mountaser and Prof Toktam Mahmoodi (KCL); Morteza Varasteh, Boozoo Rassouli, and Prof Deniz Gunduz (Imperial College); Seongah Jeong (Samsung). The main achievements are briefly described as follows.

• Reference [1] tackles a problem of central importance for Task 1.1 (Data link control) by focusing on uplink communications and investigating the cloud-edge allocation of two important network functions: the control functionality of rate selection and the data-plane function of decoding. Three functional splits are considered: 1) distributed radio access network, in which both functions are implemented in a decentralized way at the RRSs; 2) cloud RAN, in which instead both functions are carried out centrally at the RCC; and 3) a new functional split, referred to as Fog-RAN (F-RAN), with separate decentralized edge control and centralized cloud data processing. Using the adaptive sum-rate as the performance criterion, this work is the first to prove that the considered F-RAN functional split can provide significant gains in the presence of user mobility.
• When transmission is strongly constrained by energy and delay, as in IoT applications, analog transmission to edge processors in fog architectures may be preferable over conventional digital transmission. In the context of Task 1.1, this is investigated in references [11][12] from an information-theoretic viewpoint. These works study the zero-delay transmission of a Gaussian source over an additive white Gaussian noise with a one-bit analog-to-digital converter (ADC) front end and a possibly correlated side information at the receiver. The design of the optimal encoder and decoder is derived analytically, providing useful design insights into optimal
• The problem of scheduling and radio resource allocation in F-RAN models, which is the subject of Task 1.3 is addressed in references [4][7][15][8][9]. In particular, in reference [4] a Virtual Reality (VR) mobile social network is considered, whereby the states of all interacting users should be updated synchronously and with low latency via two-way communications with edge computing servers. The resulting end-to-end latency depends on the relationship between the virtual and physical locations of the wireless VR users and of the edge servers. In this work, the problem of analyzing and optimizing the end-to-end latency is investigated for a simple network topology, yielding important insights into the interplay between physical and virtual geometries.
• In references [7] and [15] fronthaul and wireless transmission resources are optimized for an F-RAN architecture assuming massive MIMO. Specifically in [7] hybrid downlink beamforming techniques are optimized in the presence of centralized cloud processing, while [15] considers uplink channel estimation and tackles the problem of jointly optimizing the pilot sequences and the pre-RF chains analog combiners.
• Multi-tenant for cellular architectures are instead considered in [8][9], where the spectrum available for downlink transmission is partitioned into private and shared subbands, and the partic

Final results

Please refer to the box above for a discussion of main results and advances with respect to the state of the art. For the next reporting period, we expect to proceed according to the project plan and to continue also the investigation of neuromorphic computing in WP3 as a key novel technology for the implementation of edge computing.