Vasilis Friderikos

Chien-Cheng Wu, Vasilis Friderikos, Cedomir Stefanovic

Network Slicing (NS) is crucial for efficiently enabling divergent network applications in next generation networks. Nonetheless, the complex Quality of Service (QoS) requirements and diverse heterogeneity in network services entails high computational time for Network Slice Provisioning (NSP) optimization. The legacy optimization methods are challenging to meet the low latency and high reliability of network applications. To this end, we model the real-time NSP as an Online Network Slice Provisioning (ONSP) problem. Specifically, we formulate the ONSP problem as an online Multi-Objective Integer Programming Optimization (MOIPO) problem. Then, we approximate the solution of the MOIPO problem by applying the Proximal Policy Optimization (PPO) method to the traffic demand prediction. Our simulation results show the effectiveness of the proposed method compared to the state-of-the-art MOIPO solvers with a lower SLA violation rate and network operation cost.

Jongyul Lee, Vasilis Friderikos

Deployment of small cells in dense urban areas dedicated to the heterogeneous network (HetNet) and associated relay nodes for improving backhauling is expected to be an important structural element in the design of beyond 5G (B5G) and 6G wireless access networks. A key operational aspect in HetNets is how to optimally implement the wireless backhaul links to efficiently support the traffic demand. In this work, we utilize the recently proposed Robotic Aerial Small Cells (RASCs) that are able to grasp at different tall urban landforms as wireless relay nodes for backhauling. This can be considered as an alternative to fixed small cells (FSCs) which lack flexibility since once installed their position cannot be altered. More specifically, on-demand deployment of RASCs is considered for constructing a millimeter-wave (mmWave) backhaul network to optimize available network capacity using a network flow-based mixed integer linear programming (MILP) formulation. Numerical investigations reveal that for the same required achievable throughput, the number of RASCs required are 25\% to 65\% less than the number of required FSCs. This result can have significant implications in reducing required wireless network equipment (capex) to provide a given network capacity and allows for an efficient and flexible network densification.

Yantong Wang, Vasilis Friderikos, Sebastian Andraos

Emerging 5G and beyond wireless industrial virtualized networks are expected to support a significant number of robotic manipulators. Depending on the processes involved, these industrial robots might result in significant volume of multi-modal traffic that will need to traverse the network all the way to the (public/private) edge cloud, where advanced processing, control and service orchestration will be taking place. In this paper, we perform the traffic engineering by capitalizing on the underlying pseudo-deterministic nature of the repetitive processes of robotic manipulators in an industrial environment and propose an integer linear programming (ILP) model to minimize the maximum aggregate traffic in the network. The task sequence and time gap requirements are also considered in the proposed model. To tackle the curse of dimensionality in ILP, we provide a random search algorithm with quadratic time complexity. Numerical investigations reveal that the proposed scheme can reduce the peak data rate up to 53.4% compared with the nominal case where robotic manipulators operate in an uncoordinated fashion, resulting in significant improvement in the utilization of the underlying network resources.

Yantong Wang, Ye Hu, Zhaohui Yang et al.

With the continuous trend of data explosion, delivering packets from data servers to end users causes increased stress on both the fronthaul and backhaul traffic of mobile networks. To mitigate this problem, caching popular content closer to the end-users has emerged as an effective method for reducing network congestion and improving user experience. To find the optimal locations for content caching, many conventional approaches construct various mixed integer linear programming (MILP) models. However, such methods may fail to support online decision making due to the inherent curse of dimensionality. In this paper, a novel framework for proactive caching is proposed. This framework merges model-based optimization with data-driven techniques by transforming an optimization problem into a grayscale image. For parallel training and simple design purposes, the proposed MILP model is first decomposed into a number of sub-problems and, then, convolutional neural networks (CNNs) are trained to predict content caching locations of these sub-problems. Furthermore, since the MILP model decomposition neglects the internal effects among sub-problems, the CNNs' outputs have the risk to be infeasible solutions. Therefore, two algorithms are provided: the first uses predictions from CNNs as an extra constraint to reduce the number of decision variables; the second employs CNNs' outputs to accelerate local search. Numerical results show that the proposed scheme can reduce 71.6% computation time with only 0.8% additional performance cost compared to the MILP solution, which provides high quality decision making in real-time.

Yantong Wang, Vasilis Friderikos

The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network as well as reducing latency to access popular content. In that respect end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e, at close proximity to the users. In addition to model based caching schemes learning-based edge caching optimizations has recently attracted significant attention and the aim hereafter is to capture these recent advances for both model based and data driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, a number of key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for caching

Yantong Wang, Vasilis Friderikos

Caching of popular content closer to the mobile user can significantly increase overall user experience as well as network efficiency by decongesting backbone network segments in the case of congestion episodes. In order to find the optimal caching locations, many conventional approaches rely on solving a complex optimization problem that suffers from the curse of dimensionality, which may fail to support online decision making. In this paper we propose a framework to amalgamate model based optimization with data driven techniques by transforming an optimization problem to a grayscale image and train a convolutional neural network (CNN) to predict optimal caching location policies. The rationale for the proposed modelling comes from CNN's superiority to capture features in grayscale images reaching human level performance in image recognition problems. The CNN is trained with optimal solutions and numerical investigations reveal that the performance can increase by more than 400% compared to powerful randomized greedy algorithms. To this end, the proposed technique seems as a promising way forward to the holy grail aspect in resource orchestration which is providing high quality decision making in real time.