# Autonomic Network Management and Cross-Layer Optimization in Software Defined Radio Environments
###### Abstract  
The demand for Autonomic Network Management (ANM) and optimization is as intense as ever, even though significant research has been devoted towards this direction. This paper addresses such need in Software Defined (SDR) based Cognitive Radio Networks (CRNs). We propose a new framework for ANM and network reconfiguration combining Software Defined Networks (SDN) with SDR via Network Function Virtualization (NFV) enabled Virtual Utility Functions (VUFs). This is the first approach combining ANM with SDR and SDN via NFV, demonstrating how these state-of-the-art technologies can be effectively combined to achieve reconfiguration flexibility, improved performance and efficient use of available resources. In order to show the feasibility of the proposed framework, we implemented its main functionalities in a cross-layer resource allocation mechanism for CRNs over real SDR testbeds provided by the Orchestration and Reconfiguration Control Architecture (ORCA) EU project. We demonstrate the efficacy of our framework, and based on the obtained results, we identify aspects that can be further investigated for improving the applicability and increasing performance of our broader framework. autonomic network management; software defined networks; software defined radio; network function virtualization; cross-layer resource allocation; federated testbeds 2019 11 37 In this paper, we jointly address the above considerations, by focusing on the interplay between SDN-SDR in distributed wireless networks operating in highly dynamic environments using NFV as a convergence substrate enabling the SDN-SDR interplay. The goal of QoS-aware ubiquitous connectivity and efficient use/reuse of resources requires a network architecture with advanced intelligence, capable of sensing its operational conditions and adapting its configuration accordingly [1; 2]. To accommodate those needs, we argue that a future wireless distributed network architecture should utilize principles of autonomic systems in network management dictated by a consummated implementation and inter-dependence of SDN, SDR and NFV. The combination of ANM and SDN-NFV-SDR can address the requirement of availability and resilience, especially in scenarios where the amount of devices or the network connectivity conditions are unsuitable for maintaining a frequent communication between mobile devices and centralized entities, promoting the accomplishment of novel distributed communication concepts, such as Device-to-Device communication (D2D) [3]. Although rich literature exists on SDN and NFV and their potential inter-relations, very limited work has been reported on works that combine ANM with SDN, SDR and NVF, as it is proposed in the current paper. Furthermore, we demonstrate the feasibility and convergence of ANM and SDN-SDR technologies through VUFs provided by NFV and cognitive elements, through an actual implementation proof-of-concept, over realistic testbeds provided by ORCA infrastructure [4]. Our approach for Autonomic Network Management Optimization in SDR environments, denoted as ANMO-SDR, focuses on D2D communication in SDR based CRNs, addressing the objective of QoS-aware ubiquitous connectivity and efficient use of resources with flexible network management. The management and the orchestration of all network operations is provided by the ANM components, which encapsulate autonomic control loops. The goals of ANMO-SDR are twofold: first, to implement spectrum management for Secondary Users (SUs), and second, ensure transparency to primary network operation (i.e., Primary Users (PUs)). Our proposed approach implements cooperative spectrum sensing at the Mobile Node (MN) level, achieving optimized channel assignment according to current QoS requirements through the use of virtual utility functions, while a centralized SDN-controller is responsible for global optimization tasks, enhancing the overall network performance (e.g., minimizing collisions and maximizing spectrum utilization).