Mobile Virtual Network Operators Research Articles

Competitive Pricing for Resource Trading in Sliced Mobile Networks: A Multi-Agent Reinforcement Learning Approach

The emergence of network slicing as a flagship technology in 5G networks has not only enhanced network expansion and flexibility in resource management for service continuity, but also provided an avenue for establishing a viable market for resource sharing. To optimize the network's resource usage, stakeholders are encouraged to take pragmatic steps toward dynamic resource sharing. This paper designs a techno-economic model for the strategic interactions among multiple competing mobile virtual network operators (MVNOs) and their users in a trading marketplace. We formulate the dynamic pricing problem as a two-stage Stackelberg game, where the MVNOs are leaders, and the users are followers. In the first stage, the MVNOs compete to set their differentiated unit prices using a negotiation mechanism while considering system-level network load. Then, the users decide their purchasing volumes to match the prices of the MVNOs. We transform the game-based optimization problem into a stochastic Markov decision process (MDP) problem and propose a multi-agent deep Q-network (MADQN) method that obtains an optimal solution for the formulated game. Simulation results and analysis reveal that the proposed algorithm achieves convergence under the competitive pricing scheme (CPS) and independent pricing scheme (IPS) while enhancing MVNOs and users' utilities at acceptable levels.

Multi-Slice Privacy-Aware Traffic Forecasting at RAN Level: A Scalable Federated-Learning Approach

Next-generation mobile networks are expected to meet the requirements of a wide range of new vertical services. Hence, the network slicing concept has been introduced, in which Mobile Virtual Network Operators (MVNOs) are allowed to provide various types of services over the same physical infrastructure, owned by an Infrastructure Provider (InP). To cope with an ever-changing traffic demand, MVNOs seek to pre-allocate/reconfigure the resources at the base stations in an anticipatory manner, based on traffic demand predictions. Ideally, conducting per-slice traffic forecasting requires information that is likely to disclose MVNO confidential information (i.e., business strategy or private user data). To secure data ownership while conducting traffic forecasting, we propose the Federated Proximal Long Short-Term Memory (FPLSTM) framework, which allows MVNOs to train their local models with their private dataset at each base station; subsequently, an associated InP global model can be updated through the aggregation of the local models. The results obtained by training the models on a real-world dataset indicate that the forecasting performance of our proposed approach is as accurate as state-of-the-art centralized solutions, while improving data privacy. To enable scalability, we further propose the Information-based Clustering FPLSTM (IC-FPLSTM) and Random Clustering FPLSTM (RC-FPLSTM) frameworks, dealing with large-scale cellular networks. These solutions demonstrate computation and communication cost efficiency significantly above the state-of-the-art.

Consortium Blockchain-Based Spectrum Trading for Network Slicing in 5G RAN: A Multi-Agent Deep Reinforcement Learning Approach

Network slicing (NS) is envisioned as an emerging paradigm for accommodating different virtual networks on a common physical infrastructure. Considering the integration of blockchain and NS, a secure decentralized spectrum trading platform can be established for autonomous radio access network (RAN) slicing. Moreover, the realization of proper incentive mechanisms for fair spectrum trading is crucial for effective RAN slicing. This paper proposes a novel hierarchical framework for blockchain-empowered spectrum trading for NS in RAN. Specifically, we deploy a consortium blockchain platform for spectrum trading among spectrum providers and buyers for slice creation, and autonomous slice adjustment. For slice creation, the spectrum providers are infrastructure providers (InPs) and buyers are mobile virtual network operators (MVNOs). Then, underloaded MVNOs with extra spectrum to spare, trade with overloaded MVNOs, for slice spectrum adjustment. For proper incentive maximization, we propose a three-stage Stackelberg game framework among InPs, seller MVNOs, and buyer MVNOs, for joint optimal pricing and demand prediction strategies. Then, a multi-agent deep reinforcement learning (MADRL) method is designed to achieve a Stackelberg equilibrium (SE). Security assessment and extensive simulation results confirm the security and efficacy of our proposed method in terms of players’ utility maximization and fairness, compared with other baselines.

The impact of mobile virtual network operators on the mobile telecommunications services market: The case of the Czech Republic

Mobile virtual network operators (MVNOs) offer mobile telecommunications services by purchasing capacity from mobile network operators (MNOs) and competing on the retail market. This study examines national data to identify possible impacts of MVNOs on the mobile market and on mobile service users. While open wholesale markets for MVNOs are relatively widespread in European countries, evidence of their impact on end users is limited due to a lack of previous research. This study uses data from the market entry of MVNOs in the Czech Republic in 2013 to estimate the impact on end users. Based on synthetic control methods, the results clearly show that the market entry of MVNOs led to a decrease in the price of calls and to a decrease in the average monthly revenue per user (ARPU) of operators. However, no significant effect on the price of data or on consumption was found. These results have some implications for policy and for the mobile industry.

Blockchain-Based Computing Resource Trading in Autonomous Multi-Access Edge Network Slicing: A Dueling Double Deep Q-Learning Approach

We investigate the computing resource allocation in multi-access edge network slicing (NS) in the context of revenue and multi-access edge computing (MEC) resource management. The significant variety of slice resource utilization levels across slice tenants (i. e., Mobile Virtual Network Operators (MVNOs)) challenges MEC resource management in NS with MEC, leading to virtual machine resource (VMR) (i. e., computing resource) wastage or scarcity. As a result, for efficient MEC resource management, the infrastructure provider (InP) encourages dynamic resource sharing and trading (DRST) of unutilized slice VMR quotas. Nevertheless, cellular network security and privacy issues deter MVNOs from collaborating on effective DRST. The security characteristics inherent in blockchain have recently gained much interest for secure resource trading. Thus, this paper proposes a unique hierarchical blockchain-based inter-slice computing resource trading (ISCRT) scheme for peer-to-peer (P2P) MVNOs in an autonomous multi-sliced MEC-based 5G network. For secure ISCRT transactions, a consortium blockchain network with hyperledger smart contracts (SC) is designed. We model the demand and pricing problems of buyer and seller MVNOs for the unutilized VMRs using a two-stage Stackelberg game. Then, to obtain the Stackelberg equilibrium (SE), an enhanced dueling double deep Q-network (D3QN) algorithm is proposed, which intelligently determines the optimal demand and pricing policies of MVNOs for the unutilized VMRs during ISCRT transactions at negotiation intervals. Simulation analysis shows that the proposed enhanced D3QN algorithm outperforms benchmark schemes in terms of the MVNO slice-level satisfaction and VMR utilization while reducing double-spending attacks in ISCRT settings by 16% and increasing both players’ utility.

MVNOCoreSim: A Digital Twin for Virtualized IoT-Centric Mobile Core Networks

The rapid growth of connected devices has led to the implementation of IoT-centric platforms designed to provide connectivity for machine-to-machine type communication. Specifically, mobile virtual network operators (MVNOs) provide global service for IoT use cases by leveraging roaming in readily deployed physical networks. This global deployment of connected devices poses a significant challenge regarding the operation of centralized core networks with respect to dimensioning, scaling as well as system survivability in case of signaling incidents. Overload control mechanisms for IoT mobile networks offer a proactive solution for mitigating excessive signaling traffic from IoT devices in mobile core networks. However, global scaling and a heterogeneous composition of devices present network operators with additional challenges. To address these challenges, this work presents a detailed, protocol level simulation framework of a real-world IoT MVNO core network. We present both models for the expected IoT signaling load and the processing of signaling messages based on measurements in a live, production environment as well as a dedicated testbed. Finally, we present a case study on various overload mechanisms and identify critical performance characteristics to compare their performance. The results of this study categorize overload control mechanisms and shed light on the necessity for MVNOs to deal with the upcoming IoT traffic by defining appropriate overload control mechanisms in mobile core networks to ensure network survivability under unforeseen conditions.

The Role of DLT for Beyond 5G Systems and Services: A Vision

Security, privacy, and trust are the key factors to unlock the full potential of future communication as beyond 5G and 6G systems enable new and more disruptive business models involved with multiple stakeholders, including mobile network operators, mobile virtual network operators, infrastructure providers, third party service providers, policy makers, end-users, and etc. The huge set of data, interaction process, and service management across these stakeholders require new technologies that can help the telecom industry to manage various data related to users, interaction process, and services in a way that is immutable, transparent, and secure with reduced OPEX to stay ahead of the market and to meet the evolving horizontal and vertical service and security requirements. This article presents an overview of the more promising key enablers, such as blockchain, permissioned distributed ledger technologies, and smart contract that can tackle the challenges of data security, privacy, and trust management in various potential beyond 5G and 6G application scenarios.

Stackelberg game-based dynamic resource trading for network slicing in 5G networks

Network slicing (NS) has been envisioned as the main technology in 5G to accommodate different virtualized operators with versatile service requirements, on a common substrate infrastructure. NS has come to radically reform the interaction among different entities in the resource sharing market, where resource is exchanged for monetary gains as a way of ensuring efficient resource utilization. However, resource exchange is only attractive to entities if they are able to achieve their respective benefits in a win–win situation. In this paper, we design an economic model that analyzes the pricing and purchasing interaction between multiple mobile virtual network operators (MVNOs) and their respective users for resource trading in NS. We formulate the pricing and purchasing problem as a two-stage multi-leader multi-follower (MLMF) Stackelberg game where the MVNOs as leaders set their unit price first, and then users as followers respond by determining their purchasing volume. Then, we theoretically prove the existence and uniqueness of a Nash equilibrium (NE). To obtain an optimal dynamic pricing solution for the MVNOs, we transform the game-based optimization problem into a stochastic Markov decision process (MDP) problem and propose a method based on multi-agent dueling deep Q-Network (DQN) algorithm. Simulation results show that the proposed algorithm achieves convergence under competitive pricing scheme (CPS) and independent pricing scheme (IPS), while ensuring high MVNOs’ profit and users’ utility at acceptable levels. In terms of cumulative profits, the proposed dueling DQN-based pricing algorithm achieves percentage gains of 15.3%, 30.4%, and 71.4% against Q-learning-based pricing, premium pricing, and undercut pricing algorithms as benchmarks.

Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks

Radio access network (RAN) slicing is a key element in enabling current 5G networks and next-generation networks to meet the requirements of different services in various verticals. However, the heterogeneous nature of these services' requirements, along with the limited RAN resources, makes RAN slicing very complex. Indeed, the challenge that mobile virtual network operators (MVNOs) face is to rapidly adapt their RAN slicing strategies to the frequent changes of the environment constraints and service requirements. Machine learning techniques, such as deep reinforcement learning (DRL), are increasingly considered a key enabler for automating the management and orchestration of RAN slicing operations. Nerveless, the ability to generalize DRL models to multiple RAN slicing environments may be limited due to their strong dependence on the environment data on which they are trained. Federated learning enables MVNOs to leverage more diverse training inputs for DRL without the high cost of collecting this data from different RANs. In this article, we propose a federated deep reinforcement learning approach for Open RAN Slicing. In this approach, MVNOs collaborate to improve the performance of their DRL-based RAN slicing models. Each MVNO trains a DRL model and sends it for aggregation. The aggregated model is then sent back to each MVNO for immediate use and further training. The simulation results show the effectiveness of the proposed DRL approach.

Slicing-based resource optimization in multi-access edge network using ensemble learning aided DDPG algorithm

Recently, the technological development in edge computing and content caching can provide high-quality services for users in the wireless communication networks. As a promising technology, multi-access edge computing (MEC) can offload tasks to the nearby edge servers, which alleviates the pressure of users. However, various services and dynamic wireless channel conditions make effective resource allocation challenging. In addition, network slicing can create a logical virtual network and allocate resources flexibly among multiple tenants. In this paper, we construct an integrated architecture of communication, computing and caching to solve the joint optimization problem of task scheduling and resource allocation. In order to coordinate network functions and dynamically allocate limited resources, this paper adopts an improved deep reinforcement learning (DRL) method, which fully jointly considers the diversity of user request services and the dynamic wireless channel conditions to obtain the mobile virtual network operator (MVNO) maximal profit function. Considering the slow convergence speed of the DRL algorithm, this paper combines DRL and ensemble learning. The simulation result shows that the resource allocation scheme inspired by DRL is significantly better than the other compared strategies. The output of the result of DRL algorithm combined with ensemble learning is faster and more cost-effective.

Mobile Carrier Change Intention Analysis Based on IT Service Management Platform

The purpose of this study is to understand the user intention to continue or change mobile carriers, considering the current mobile phone market in Japan. The market was mainly shared by three Major Mobile-Carriers (3MMC): docomo, au, and SoftBank. However, the number of Mobile Virtual Network Operators (MVNOs) customers has been increasing recently, and Rakuten Mobile has entered the mobile phone market as a Mobile Network Operator (MNO). Currently, the mobile phone market is highly competitive and there is little difference in mobile phone services offered by various carriers. Under such competitive conditions, mobile carriers need an appropriate method to develop service strategies to maintain or increase their market share. To understand users’ mobile carrier change intention, we classified each carrier’s users into two user segments based on continuous or change intention and analyzed the differences between the segments. We showed that the user segmentation model can extract information about the differences in characteristics of two user segments from their decision-making factors and user attributes with respect to mobile services. Understanding these differences is important as carriers consider when and what strategies to implement in order to maintain or increase market share.

Pricing‐based power allocation in cloud radio access network with multiple access technology selection

AbstractIn this article, we consider an uplink economy‐efficient resource allocation framework in a multicellular cloud radio access network (C‐RAN) architecture with network virtualization, where a mobile network operator (MNO) interacts with a number of mobile virtual network operators (MVNOs) with a predetermined business model. OFDMA and Massive MIMO multiple access technologies have been assumed to be available for each MVNO at two different prices. In this setup, we propose a multi‐access technology selection approach (MATSA) with the objective of maximizing delivered rate to end users, reducing operation costs and maximizing MVNOs' profit subject to a set of constraints, which leads to a nonconvex resource allocation problem with very high computational complexity. The utility function is defined as the difference between the total throughput and the utilization cost for each technology. To tackle this problem, we apply complementary geometric programming (CGP) and successive convex approximation (SCA), that results in a two‐step iterative solution. Simulation results demonstrate superiority of the proposed approach compared to a similar scenario with predetermined multi‐access technology, especially for large numbers of users.

A Secure Authentication Scheme for Teleservices Using Multi-Server Architecture

The telecommunications industry covers various sectors and services such as broadband, telecom equipment, telecom infrastructure, telephone service providers, mobile virtual network operators, 5G, and the white space spectrum. Smart Cards may be chosen as one of the best mechanisms for authorized access to these services in the telecom sector. Recently, Jin Kwak proposed a scheme based on dynamic identity for authentication purposes, mentioning that the scheme does not suffer from security breaches and attacks. This paper illustrates Jin Kwak’s technique and finds that it violates the purpose contrary to his claim. Due to a design issue in his scheme, an adversary may guess the password in a polynomial time and impersonate a legal user. Furthermore, other attacks, including replay attack, are also possible, as the time stamp was not protected in this scheme. We propose an improved version of this scheme, and it is free from various attacks, including password guessing by hiding the identity of the user and replay attacks by using the time stamp securely. The results mentioned in performance and efficiency comparison show a faster scheme than many existing schemes.

Utility Optimization for Resource Allocation in Multi-Access Edge Network Slicing: A Twin-Actor Deep Deterministic Policy Gradient Approach

To achieve the service-oriented features of the 5G, network slicing aims to create logical virtual networks where multiple services are provided on a common physical infrastructure. The performance of network slicing depends on the intelligent management of multi-dimensional resources, which are exactly what multi-access edge computing (MEC) provides. This paper proposes joint optimization of communication, computing and caching (3C) resources in multi-access edge network slicing. The optimization objective of the two-level resource allocation problem is to maximize the utility obtained by mobile virtual network operators while ensuring the quality of service (QoS). The deep reinforcement learning (DRL) approach is employed which enables the resource allocation scheme to intelligently adapt to the dynamic environment. Specifically, we propose a novel DRL approach named twin-actor deep deterministic policy gradient (twin-actor DDPG). Since the action space is continuous, the DDPG is adopted where the actor generates the deterministic policy while the critic evaluates the policy and guides the actor to obtain the optimal policy. A novel twin-actor structure is put forward to replace the actor of the DDPG, thus the slice-level action and user-level action can be generated respectively. The convergence and effectiveness of the proposed DRL based algorithm is are verified by numerical simulation.

RANSlicing: Towards Multi-Tenancy in 5G Radio Access Networks

A significant purpose of 5G networks is allowing sharing resources among different network tenants such as service providers and Mobile Virtual network Operators. Numerous domains are taken in account regarding resource sharing containing different infrastructure (storage, compute and networking), Radio Access Network (RAN) and Radio Frequency (RF) spectrum. RAN and spectrum, transport. Spectrum sharing and RAN are anticipated as the fundamental part in multi-tenant 5G network. Nevertheless, there is a shortage of evaluation platforms to determine the number of benefits that can be acquired from multilevel spectrum sharing rather than single-level spectrum sharing. The work presented in this paper intend to address this issue by presenting a modified SimuLTE model is used for evaluating active RAN based on multi-tenant 5G networks. The result shows an understanding into the actual advantages of RAN slicing for multi-tenants in 5G networks.

Two Tier Slicing Resource Allocation Algorithm Based on Deep Reinforcement Learning and Joint Bidding in Wireless Access Networks.

Network slicing (NS) is an emerging technology in recent years, which enables network operators to slice network resources (e.g., bandwidth, power, spectrum, etc.) in different types of slices, so that it can adapt to different application scenarios of 5 g network: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable and low-latency communications (URLLC). In order to allocate these sliced network resources more effectively to users with different needs, it is important that manage the allocation of network resources. Actually, in the practical network resource allocation problem, the resources of the base station (BS) are limited and the demand of each user for mobile services is different. To better deal with the resource allocation problem, more effective methods and algorithms have emerged in recent years, such as the bidding method, deep learning (DL) algorithm, ant colony algorithm (AG), and wolf colony algorithm (WPA). This paper proposes a two tier slicing resource allocation algorithm based on Deep Reinforcement Learning (DRL) and joint bidding in wireless access networks. The wireless virtual technology divides mobile operators into infrastructure providers (InPs) and mobile virtual network operators (MVNOs). This paper considers a single base station, multi-user shared aggregated bandwidth radio access network scenario and joins the MVNOs to fully utilize base station resources, and divides the resource allocation process into two tiers. The algorithm proposed in this paper takes into account both the utilization of base station (BS) resources and the service demand of mobile users (MUs). In the upper tier, each MVNO is treated as an agent and uses a combination of bidding and Deep Q network (DQN) allows the MVNO to get more resources from the base station. In the lower tier allocation process, each MVNO distributes the received resources to the users who are connected to it, which also uses the Dueling DQN method for iterative learning to find the optimal solution to the problem. The results show that in the upper tier, the total system utility function and revenue obtained by the proposed algorithm are about 5.4% higher than double DQN and about 2.6% higher than Dueling DQN; In the lower tier, the user service quality obtained by using the proposed algorithm is more stable, the system utility function and Se are about 0.5–2.7% higher than DQN and Double DQN, but the convergence is faster.

Cognitive RAN slicing resource allocation based on Stackelberg game

The cognitive network has become a promising method to solve the spectrum resources shortage problem. Especially for the optimization of network slicing resources in the cognitive radio access network (RAN), we are interested in the profit of the mobile virtual network operator (MVNO) and the utility of secondary users (SUs). In cognitive RAN, we aim to find the optimal scheme for the MVNO to efficiently allocate slice resources to SUs. Since the MVNO and SUs are selfish and the game between the MVNO and SUs is difficult to reach equilibrium, we consider modeling this scheme as a Stackelberg game. Leveraging mathematical programming with equilibrium constraints (MPEC) and Karush-Kuhn-Tucker (KKT) conditions, we can obtain a single-level optimization problem, and then prove that the problem is a convex optimization problem. The simulation results show that the proposed method is superior to the noncooperative game. While guaranteeing the Quality of Service (QoS) of primary users (PUs) and SUs, the proposed method can balance the profit of the MVNO and the utility of SUs.

RANSlicing: Towards Multi-Tenancy in 5G Radio Access Networks

A significant purpose of 5G networks is allowing sharing resources among different network tenants such as service providers and Mobile Virtual network Operators. Numerous domains are taken in account regarding resource sharing containing different infrastructure (storage, compute and networking), Radio Access Network (RAN) and Radio Frequency (RF) spectrum. RAN and spectrum, transport. Spectrum sharing and RAN are anticipated as the fundamental part in multi-tenant 5G network. Nevertheless, there is a shortage of evaluation platforms to determine the number of benefits that can be acquired from multilevel spectrum sharing rather than single-level spectrum sharing. The work presented in this paper intend to address this issue by presenting a modified SimuLTE model is used for evaluating active RAN based on multi-tenant 5G networks. The result shows an understanding into the actual advantages of RAN slicing for multi-tenants in 5G networks.

Blockchain-Enabled Resource Trading and Deep Reinforcement Learning-Based Autonomous RAN Slicing in 5G

The advent of radio access network (RAN) slicing is envisioned as a new paradigm for accommodating different virtualized networks on a single infrastructure in 5G and beyond. Consequently, infrastructure providers (InPs) desire virtualized networks to share their subleased resources for effective resource management. Nonetheless, security and privacy challenges in the wireless network deter operators from collaborating with one another for resource trading. Lately, blockchain technology has received overwhelming attention for secure resource trading thanks to its security features. This paper proposes a novel hierarchical framework for blockchain-based resource trading among peer-to-peer (P2P) mobile virtual network operators (MVNOs), for autonomous resource slicing in 5G RAN. Specifically, a consortium blockchain network that supports hyperledger smart contract (SC) is deployed to set up secure resource trading among seller and buyer MVNOs. With the aim of designing a fair incentive mechanism, we model the pricing and demand problem of the seller and buyers as a two-stage Stackelberg game, where the seller MVNO is the leader and buyer MVNOs are followers. To achieve a Stackelberg equilibrium (SE) for the formulated game, a dueling deep Q-network (Dueling DQN) scheme is designed to achieve optimal pricing and demand policies for autonomous resource allocation at negotiation interval. Comprehensive simulation results analysis prove that the proposed scheme reduces double spending attacks by 12% in resource trading settings, and maximizes the utilities of players. The proposed scheme also outperforms deep Q-Network (DQN), Q-learning (QL) and greedy algorithm (GA), in terms of slice and system level satisfaction and resource utilization.

Virtual Resource Allocation for Wireless Virtualized Heterogeneous Network With Hybrid Energy Supply

In this work, two novel virtual user association and resource allocation algorithms are introduced for a wireless virtualized heterogeneous network with hybrid energy supply. In the considered system, macro base stations (MBSs) are supplied by the grid power and small base stations (SBSs) have the energy harvesting capability in addition to the grid power supplement. Multiple infrastructure providers (InPs) own the physical resources, i.e., BSs and radio resources. The Mobile Virtual Network Operators (MVNOs) are able to recent these resources from the InPs and operate the virtualized resources for providing services to different users. In particular, aiming to maximize the overall utility for the MVNOs, a joint resource (spectrum and power) allocation and user association problem is presented. First, we present an alternating direction method of multipliers (ADMM)-based algorithm solution to find the near-optimal solution in a static manner. Moreover, we also utilize deep reinforcement learning to design the optimal policy without knowing a priori knowledge of the dynamic nature of networks. We have conducted extensive simulation and the performance evaluation demonstrate the advantages and effectiveness of the proposed schemes.