Market Driven Multi-domain Network Service Orchestration in 5G Networks

The advent of a new breed of enhanced multimedia services has put network operators into a position where they must support innovative services while ensuring both end-to-end Quality of Service requirements and profitability. Recently, Network Function Virtualization (NFV) has been touted as a cost-effective underlying technology in 5G networks to efficiently provision novel services. These NFV-based services have been increasingly associated with multi-domain networks. However, several orchestration issues, linked to cross-domain interactions and emphasized by the heterogeneity of underlying technologies and administrative authorities, present an important challenge. In this paper, we tackle the cross-domain interaction issue by proposing an intelligent and profitable auction-based approach to allow inter-domains resource allocation

Matching theory as enabler of efficient spectrum management in 5G networks

This is the peer reviewed version of the following article: Tsirakis, C, Lopez‐Aguilera, E, Agapiou, G, Varoutas, D. Matching theory as enabler of efficient spectrum management in 5G networks. Trans Emerging Tel Tech. 2020; 31:e3769., which has been published in final form at https://doi.org/10.1002/ett.3769. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.This paper analyzes the spectrum trading problem in virtualized fifth generation (5G) networks in order to enhance the network performance with respect to the spectrum utilization. The problem is modeled as a Many-to-Many Matching (M2MM) game with utility-based preferences and determines the matching between mobile network operators and mobile virtual network operators. The two proposed versions of utility functions for each set aim at maximizing the satisfaction of both sets with conflicting interests and improving the overall spectrum efficiency. In the simulation evaluation, the proposed scheme is compared with three different schemes in terms of the system utility, individual and pair matching satisfaction. We also investigate the scalability aspects, the strategy plan impact on the matching performance of our proposed scheme, and, at the same time, we attempt to make appropriate assumptions closer to reality. Our proposed scheme shows much better performance than the other schemes achieving a quite high level of satisfaction for the matching result on both sets.Postprint (author's final draft

Base Station Popularity-Based Dynamic Resource Allocation for VNF

Network Function Virtualization (NFV) is emerging as a promising technology in future wireless communication system to achieve resource sharing through abstracting standardized network equipment into different types of virtual network functions (VNFs) to be placed in various network slices for diverse requirements. In order to satisfy the required QoS of serving users, each network slice deploys appropriate VNFs on different Base Stations (BSs) and orchestrates them for providing uniform service like an independent virtual network. However, due to the user mobility, the VNF on the BS may not have sufficient resource to provide QoS guaranteed services for newly accessed users. It is challenging to allocate adequate resource dynamically for VNFs to guarantee the required QoS of roaming users. In this paper, we propose a dynamic resource allocation scheme for VNF based on group mobility prediction. We first employ Markov chain and online learning method together to predict group mobility of users. Then we calculate the popularities of BSs for allocating more resource to the hotspot BSs (HBSs) with aim of enabling HBS permit the enormous service requests of approaching users. We propose a complementarity mechanism to maximize resource efficiency when implementing resource allocation for VNFs. Numerical results validate the effectiveness of our proposed dynamic resource allocation scheme

Antenna allocation and pricing in virtualized massive MIMO networks via Stackelberg game

We study a resource allocation problem for the uplink of a virtualized massive multiple-input multiple-output (MIMO) system, where the antennas at the base station are priced and virtualized among the service providers (SPs). The mobile network operator (MNO) who owns the infrastructure decides the price per antenna, and a Stackelberg game is formulated for the net profit maximization of the MNO, while minimum rate requirements of SPs are satisfied. To solve the bi-level optimization problem of the MNO, we first derive the closed-form best responses of the SPs with respect to the pricing strategies of the MNO, such that the problem of the MNO can be reduced to a single-level optimization. Then, via transformations and approximations, we cast the MNO’s problem with integer constraints into a signomial geometric program (SGP), and we propose an iterative algorithm based on the successive convex approximation (SCA) to solve the SGP. Simulation results show that the proposed algorithm has performance close to the global optimum. Moreover, the interactions between the MNO and SPs in different scenarios are explored via simulations

Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization