Design of Network Coding Functionality for 5G Networks

Tesi d’estudis: Tesi doctoral

Resum

Network coding (NC) has recently emerged as a new solution for improving network performance in terms of throughput and reliability. However, the multi-user nature of NC and its inherent applicability to versatile flow engineering across all layers of the protocol stack, call for novel wireless system design approaches. The goal of this thesis is to study the design of NC as a network functionality offered to the 5G wireless communication service designers. The design would facilitate the control of network throughput, reliability, and connectivity over 5G wireless networks. The contributions of this thesis are the following. We first develop a design of Network Coding Functionality as a toolbox of NC design domains and show how it can be integrated in current virtualized infrastructures. Second, we evaluate the finite-length performance of different network codes using random vs Pascal matrices. We model the encoding, re-encoding, and decoding process of different coding schemes in matrix notation and corresponding error probabilities. We then propose a binary searching algorithm to identify optimal coding rate for some specific target packet loss rates given a pre-defined coding block-length. We will focus on capacity-achieving codes and coding schemes with scheduling for representative scenarios and show the achievable rate-delay trade-off between random codes and structured codes with scheduling. In the last part of this thesis, we validate the proposed NCF design for a complete use case to enhance connectivity of Mobile Ad-hoc Network (MANET) devices over converged satellite-cloud networks in emergency applications. The key insight is that in an emergency scenario there may not be direct access to fog or cloud computing, which will then be provided via satellite and the only local computational resources available are the MANET devices. To solve this situation, we define a packet-level NCF with inputs from data service quality targets, local computation constraints and per-path statistics. Outputs are centrally-optimized coding rates balancing per-node computational resources and resulting coverage.
Data del Ajut14 de des. 2018
Idioma originalAnglès
SupervisorMaria Angeles Vazquez Castro (Director/a)

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