# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 8 Final Discussion and Conclusions
The new intelligent network knowledge plane based on accelerated GNNs will orchestrate and deliver service-aware end-to-end network services across different NSPs with the capability to self-evolve upon changes in the underlying programmable domains. In turn, NSPs also incorporate AI within their domains, allowing automation of specific network functions (such as self-orchestration) and the provisioning of intra-NSP services and resources as desired. Regarding the SDN/NFV domain, we will investigate distributed AI functions at network nodes and MEC elements to enforce self-optimisation and self-configuration capabilities, as well as the new concept of AI-supported generic transceiver to adapt and control the working conditions towards an optimal performance under diverse application scenarios. Regarding the RINA domain, in future works we will focus on further improving its capability of providing QoS guarantees by modelling the characteristics of the DIF layers and feed GNNs with this information to obtain accurate performance metric predictions. Section 1, D.C.; Section 2, all; Section 3, A.C., J.S.-V., P.A. ; Section 4, P.B.-R., J.S.-V., J.S.-P.; Section 5, S.S., J.A.L., J.M.G., F.A.B., A.P. ; Section 6, D.C., J.P. ([PERSON]); Section 7, A.C., J.P. ([PERSON]), J.D.-P.; Section 8, all. All authors have read and agreed to the published version of the manuscript. This work has been partially funded by the Spanish Ministry of Economy and Competitiveness under contract FEDER TEC2017-90034-C2 (ALLIANCE project) and by the Generalitat de Catalunya under contract 2017 SGR-1037 and 2017 SGR-605. Not applicable. Data Availability Statement: The access to the different data and/or datasets publicly available from the ALLIANCE project to the research community is indicated in each of the previous sections dedicated to the results. We would like to thank [PERSON] for his invaluable support and all of our students for their contributions to the ALLIANCE project. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
AI Artificial Intelligence  
ALLIANCE  
Acchitecting a knowledge-defined 5G-enabled network infrastructure  
toward the upcoming digital society  
AN Autonomous System Number  
B5G  
Beyond 5G  
BGP  
Border Gateway Protocol  
C-RAN  
Cloud RAN  
CA Certification Authority  
CPRI  
Common Public Radio Interface  
DL  
Deep Learning  
DPI  
Deep Packet Inspection  
DRL  
Deep Reinforcement Learning  
DSP  
digital signal processing  
e-CPRI  
Ethernet-based CPRI  
GNN  
Graph Neural Network  
HD  
High Definition  
KDN  
Knowledge-Defined Networking  
MCF  
Multicore Fibre  
MEC  
Multi-access Edge Computing  
ML  
Machine Learning  
MNN  
Metro Provider Network  
NFV  
Network Function Virtualisation  
NOMA  
Non-orthogonal multiple access  
NOMA-CAP  
NOMA with multiband Carrierless Amplitude and Phase  
OBSAI  
Open Base Station Standard Initiative  
PCS  
Probabilistic Constellation Shaping  
RPKI  
Resource Public Key Infrastructure  
PON  
Passive Optical Network  
PoS  
Proof of Stake  
PPP  
Public-Private Partnership  
QoE  
Quality of Experience  
QoS  
Quality of Service  
RAN  
Radio Access Networks  
RINA  
Recursive Inter-Network Architecture  
RoF  
Radio-over-Fibre  
RPKI  
Resource Public Key Infrastructure  
RRH  
Remote Radio Head  
SDN  
Software Defined Networking  
SPN  
Service Provider Network