Multiple Mobile Network Operators Research Articles

User Association for Symbiotic Spectrum and Service Sharing Among Multiple Mobile Network Operators

User Association for Symbiotic Spectrum and Service Sharing Among Multiple Mobile Network Operators

Energy-Efficient Base Station Switching-Off With Guaranteed Cooperative Profit Gain of Mobile Network Operators

This paper studies the energy-efficient cooperative base station (BS) switching-off in multi-input single-output (MISO) cellular networks, where the roaming-cost-based cooperation and the cooperative beamforming among multiple mobile network operators (MNOs) are jointly designed. To make the cooperation among MNOs practical, we introduce a new constraint to make sure the cooperative profit gain of MNOs is non-negative and also formulate an optimization problem to maximize the system energy efficiency (EE) by jointly considering multiple factors, including the operation modes of BSs, the connection states between users and BSs, the beamforming vectors of multi-antenna BSs under constraints of users’ quality of service, BSs’s power budget, and the non-negative profit gain of MNOs, etc. To solve the non-convex problem, we propose an energy-efficient beamforming-aware BS switching off method (EBBSOM), in which we first design a roaming-cost-based BS switching-off algorithm (EE-BSOA) to search the feasible inactive BSs and then propose a successive convex approximation (SCA) and Dinkelbach’s method-based EE maximization beamforming algorithm (EE-BFA) to optimize beamforming vectors. In order to keep the cooperative profit gain non-negative for MNOs and also to balance the load and power consumption of BS from the profit perspective, we present a profit-wise BS switching-off priority method. We theoretically prove that the proposed EBBSOM is able to resolve the inconsistency between BS switching-off and EE maximization. Simulations show that the proposed EBBSOM enhances the system EE without degrading the economic profit gain of MNOs. Also, the proposed profit-wise BS switching-off priority enables the EBBSOM to achieve higher EE than traditional load-wise one. Furthermore, it is also observed that there exists a feasible rate price region, which is able to clearly characterize network EE and the average profit gain of MNOs simultaneously.

A 51 Gb/s Reconfigurable mmWave Fiber-Wireless C-RAN Supporting 5G/6G MNO Network Sharing

The growing demand for ubiquitous broadband connectivity in 5G/6G Radio Access Networks (RANs) has turned Centralized RAN (C-RAN) and reconfigurable Point-to-MultiPoint (PtMP) network topologies into the mainstream approach for keeping pace with the vastly increasing traffic capacities and connection densities. Relying on an all-passive Arrayed Waveguide Grating Router (AWGR), we propose and demonstrate reconfigurable allocation of multiple parallel Fiber-Wireless (FiWi) millimeter wave (mmWave) analog (Intermediate Frequency over Fiber) IFoF interfaces. The proposed technology provides a framework for the implementation of reconfigurable multiple FiWi backhaul /midhaul/fronthaul (X-haul) connections. The use of AWGR enables wavelength slicing and the co-existence of multiple transport links over the same X-haul network infrastructure. The proposed concept is experimentally validated by two sets of experimental measurements. More specifically a 4λ- Wavelength-Division Multiplexed (WDM) signals modulated by IFoF signals are routed through the AWGR and wirelessly transmitted over the air across a 1m V-band distance by two different pairs of mmWave 60 GHz antennas. In the first experiment, a Phased Array Antenna (PAA) operating at 60 GHz is utilized achieving a 2.5 Gb/s data rate per wavelength and beam pair with an Error Vector Magnitude (EVM) of less than 12.5% and an aggregate 10 Gb/s traffic capacity. The achieved performance meets for the first time the respective 5G Key Performance Indicator (KPI) requirements for EVM quality and peak data traffic while supporting the link reconfiguration wavelength routing and mmWave beam direction. In the second experiment, the PAA is replaced by a point-to-point 60 GHz link utilizing a high-end mm-wave transmitter, thus achieving a data rate of 12.75 Gb/s per wavelength-beam with an EVM of less than 6.5% and an aggregate capacity of 51 Gb/s. To this end, the proposed solution is shown to form a promising roadmap toward flexible and reconfigurable 5G/6G mmWave C-RAN architectures where multiple Mobile Network Operators (MNOs) can share the same network infrastructure and dynamically allocate fiber, antenna beams and wavelengths.

Fairness Guaranteed and Auction-Based x-Haul and Cloud Resource Allocation in Multi-Tenant O-RANs

The open-radio access network (O-RAN) embraces cloudification and network function virtualization for base-band function processing by dis-aggregated radio units (RUs), distributed units (DUs), and centralized units (CUs). These enable the cloud-RAN vision in full, where multiple mobile network operators (MNOs) can install their proprietary or open RUs, but lease on-demand computational resources for DU-CU functions from commonly available open-clouds via open x-haul interfaces. In this paper, we propose and compare the performances of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min-max fairness</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Vickrey-Clarke-Groves (VCG) auction</i> -based x-haul and DU-CU resource allocation mechanisms to create a multi-tenant O-RAN ecosystem that is sustainable for small, medium, and large MNOs. The min-max fair approach <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimizes the maximum OPEX of RUs</i> through cost-sharing proportional to their demands, whereas the VCG auction-based approach <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimizes the total OPEX for all resources utilized while extracting truthful demands from RUs</i> . We consider time-wavelength division multiplexed (TWDM) passive optical network (PON)-based x-haul interfaces where PON virtualization technique is used to flexibly provide optical connections among RUs and edge-clouds at macro-cell RU locations as well as open-clouds at the central office locations. Moreover, we design efficient heuristics that yield significantly better economic efficiency and network resource utilization than conventional greedy resource allocation algorithms and reinforcement learning-based algorithms.

Joint Beamforming Coordination and User Selection for CoMP-Enabled NR-U Networks

The sixth-generation (6G) era is expected to provide even higher levels of massive connectivity/Internet of Things (IoT), tremendous data rate, and low latency than the 5G communication. Therefore, the future 6G wireless networks would confront with much more severe spectrum scarcity problems, which make the new radio in unlicensed spectrum (NR-U) technology attractive. Meanwhile, the high densely deployed massive IoT devices lead to fearful intercell and intracell interference issues. To address these challenges, a coordinated multipoint (CoMP) technology can be adopted. In this work, we introduce a CoMP-based NR-U network, particularly for 6G-enabled massive IoT scenarios. Under the proposed network, we introduce a spatial listen-before-talk (LBT) scheme to control mobile network operators (MNOs) to coexist with the incumbent WiFi devices orthogonally, which can significantly improve the spectrum efficiency. To suppress the strong co-channel interference among multiple MNOs, we investigate a joint beamforming coordination and user selection problem, which is NP-hard. To deal with this problem, we first adopt a fractional programming and an integer replacement method to transform the objective problem into a tractable one. Then, we introduce a semidistributed alternating direction method of multipliers and block coordinate update (ADMM-BCU) algorithm to find a suboptimal solution, which only requires limited information exchange via a CoMP server and can help reduce energy consumption. Theoretical analysis and numerical results demonstrate the effectiveness of the proposed algorithm for large-scale 6G IoT scenarios.

Incentive mechanism for competitive edge caching in 5G-enabled Internet of things

The fifth generation (5G) of cellular networks provides the enabling environment for the Internet of Things (IoT) applications. Hence, the vast proliferation of 5G-enabled IoT devices and services led to an overwhelming growth of data traffic that could saturate the core network’s backhaul links. Nowadays, caching is an unavoidable technique to solve this issue, whereby popular contents are stored on edge nodes near to end-users. There exist several initiatives to motivate caching actors for improving the caching process, but not designed for the real-world competitive caching market. In this work, we propose an incentive caching strategy in a 5G-enabled IoT network by considering a completely competitive caching scenario with multiple 5G mobile network operators (MNOs) and multiple content providers (CPs). The MNOs manage a set of edge caches on their base stations and they are competing to fill these caching resources, while the CPs detain a set of popular contents and are in conflict to rent the MNOs’ caches. Each MNO aims to maximize its monetary profit and offload its backhaul links, as each CP aims to improve the quality of experience (QoE) of its end-users. Then, we formulate a multi-leader multi-follower Stackelberg game to model the interaction between MNOs and CPs and define the different players’ utilities. Subsequently, we propose an iterative algorithm based on the convex optimization method to investigate the Stackelberg equilibrium. Finally, the numerical results of the different experimentations demonstrate that our game-based incentive strategy can significantly alleviate the backhaul links while improving the user QoE.

Cellular Licensed Band Sharing Technology Among Mobile Operators: A Reinforcement Learning Perspective

Next-generation wireless networks will need to support of very high data rates and low–latency communications, which will require a new wireless radio technology paradigm. The growing number of mobile users is causing spectrum scarcity; and hence, an efficient spectrum utilization method is required. Conventional scheduling-based resource allocation scheme in wireless networks under limited resources is a challenging due to the complex network situations, dynamic network environment, and diverse needs for future networks. To overcome resource scarcity in mobile networks, spectrum sharing among multiple operators may be an efficient solution. Traditional methods of dynamic spectrum sharing are model-dependent, and they are not robust to the changing wireless environments. To enable low-latency communications for complex future wireless networks, efficient machine learning algorithms can be used across the wireless network infrastructure. Integrating machine learning for resource allocation can leverage intelligent and efficient mechanisms for dynamic wireless networks. To efficiently and intelligently utilize the scarce resources of dynamic networks, this paper proposes an efficient machine learning-based spectrum sharing method among multiple mobile network operators (MNOs). A mobile network operator uses the idle slots of the another operator and transmits the information efficiently. Using the neural network model, each MNO learns the spectrum utilization of other MNOs and selects the idle slots of other MNOs. Simulation results have been computed and compared with the conventional scheme where resources are not shared. These simulation results show that the proposed neural network model can efficiently learn the network quickly, and spectrum sharing can lead to improved network performance in terms of the delay, user-perceived throughput, resource usage, packet drop, and sum throughput of the network.

Radio Resource Sharing and Edge Caching with Latency Constraint for Local 5G Operator: Geometric Programming Meets Stackelberg Game

The rapidly increasing demand in indoor small cell networks has given rise to the concept of local 5G operator (OP) for local service delivery. In this regard, we develop a novel game-theoretic framework with geometric programming to model and analyze cache-enabled small cell base stations (SBSs) with infrastructure sharing for local 5G OP networks. In such a network, the local 5G OP provides wireless network in indoor area and rent out the infrastructure which are RAN and cache storage to multiple mobile network operators (MNOs) while guarantee the quality-of-experience (QoE) at the users (UEs) of MNOs. We formulate a Stackelberg game model where the local 5G OP is the leader and the MNOs are the followers. The local 5G OP aims to maximize its profit by optimizing its infrastructure rental fee, and the MNOs aim to minimize their renting cost of infrastructure by minimizing the cache intensity subject to latency constraint at each UE. Here, the cache intensity is defined as the product of the number of SBSs per unit area and the number of popular files stored in each SBS. The optimization problems of the local 5G OP and the MNOs are transformed into geometric programming. Accordingly, the subgame perfect equilibrium of Stackelberg game is obtained through the succesive geometric programming (SGP) method. Since the MNOs share their rented infrastructure, for cost sharing, we apply the concept of Shapley value to divide the cost among the MNOs. We show that the cost sharing problem can be mapped into a simplified “airport runway cost sharing problem”, in which the Shapley value can be computed efficiently. Finally, we present an extensive performance evaluation that reveals interesting insights into designing resource sharing with edge caching in local 5G OP networks.

On the use of data from multiple mobile network operators in Europe to fight COVID-19

AbstractThe rapid spread of COVID-19 infections on a global level has highlighted the need for accurate, transparent and timely information regarding collective mobility patterns to inform de-escalation strategies as well as to provide forecasting capacity for re-escalation policies aiming at addressing further waves of the virus. Such information can be extracted using aggregate anonymized data from innovative sources such as mobile positioning data. This paper presents lessons learnt and results of a unique Business-to-Government initiative between several mobile network operators in Europe and the European Commission. Mobile positioning data have supported policy-makers and practitioners with evidence and data-driven knowledge to understand and predict the spread of the disease, the effectiveness of the containment measures, their socio-economic impacts while feeding scenarios at European Union scale and in a comparable way across countries. The challenges of these data sharing initiative are not limited to data quality, harmonization, and comparability across countries, however important they are. Equally essential aspects that need to be addressed from the onset are related to data privacy, security, fundamental rights, and commercial sensitivity.

Heterogeneous Computational Resource Allocation for C-RAN: A Contract-Theoretic Approach

In this work, we develop a contract theory framework to tackle the allocations of heterogeneous baseband processing units (BBUs) in cloud radio access network. We first model a monopoly market by viewing the BBUs as a kind of resource. The infrastructure provider (InP), as the monopolist, owns all the heterogeneous BBUs of different processing abilities and maintaining costs, and leases them to multiple mobile network operators (MNOs) to gain profit. The MNOs intend to rent reasonable amount of BBUs to provide services to their mobile clients. Then we propose a contract theory framework, in which contract items are optimized to maximize the InP's utility. We design the optimal contracts with complete and asymmetric information on the MNOs. Our contract design achieves the near optimum solution to heterogeneous computational resource allocation even under the information asymmetric case. Our derivations indicate that the optimal contracts with asymmetric information achieve a lower utility for the InP than the ones with complete information and the utility reduction is higher when the BBUs are heterogeneous rather than homogeneous. Numerical results demonstrate the benefit of having heterogeneous BBUs relative to having homogeneous BBUs in terms of achieving a higher utility for the InP.

Indoor Localization Using Multi-operator Public Land Mobile Networks and Support Vector Machine Learning Algorithms

Given Public Land Mobile Networks (PLMN) ubiquitous infrastructure and excellent coverage, they can be used for providing location information for various location-based services, especially in indoor environments. This paper investigates indoor positioning solutions which utilize received signal strength measurements obtained by mobile device from PLMN cells belonging to multiple mobile network operators. Two indoor positioning methods, based on Support Vector Machine learning algorithms and space-partitioning principle, are proposed. The first method utilizes Support Vector Regression (SVR) algorithm, whilst the second one introduces space-partitioning principle and the combined use of Support Vector Classification and SVR algorithms. The proposed techniques are thoroughly investigated in a real indoor environment. In addition, several criteria for choosing relevant cells for the positioning purposes have been explored. Positioning with SVR has demonstrated good results, while utilizing space-partitioning principle has further reduced the average positioning error by 27%. Moreover, the proposed solution has outperformed positioning methods based on k Nearest Neighbours and Artificial Neural Networks, when implemented in the same verification test bed.

Distributed Resource Allocation for Network Slicing Over Licensed and Unlicensed Bands

Network slicing is considered one of the key enabling technologies for 5G due to its ability to customize and "slice" a common resource to support diverse services and verticals.This paper introduces a novel inter-operator network slicing framework in which multiple mobile network operators (MNOs) can coordinate and jointly slice their accessible spectrum resources in both licensed and unlicensed bands. For licensed band slicing, we propose an inter-operator spectrum aggregation method that allows two or more MNOs to cooperate and share their licensed bands to support a common set of service types. We then consider the sharing of unlicensed bands. Because all MNOs enjoy equal rights to accessing these bands, we introduce the concept of right sharing for MNOs to share and trade their spectrum access rights. We develop a {\em modified back-of-the-envelope (mBoE) method} for MNOs to evaluate their {\em Value-of-Rights (VoR)} when coexisting with other wireless technologies. A {\em network slicing game} based on the overlapping coalition formation game is formulated to investigate cooperation between MNOs. We prove that our proposed game always has at least one stable slicing structure that maximizes the social welfare. To implement our proposed framework without requiring MNOs to reveal private information to other MNOs, we develop a distributed algorithm called D-ADMM-PVS. Performance evaluation of our proposed framework is provided using a discrete-event simulator that is driven by real MNO deployment scenarios based on over 400 base station locations deployed by two primary cellular operators in the city of Dublin.Numerical results show that our proposed framework can almost double the capacity for all supported services for each MNO in an urban setting.

Matching Theory for Over-the-Top Service Provision in 5G Networks

Modern over-the-top (OTT) applications can be accessed via Internet connections over cellular networks, possibly shared and managed by multiple mobile network operators (MNOs). The OTT service providers (OSPs) need to interact with MNOs, requesting resources for serving users of different categories and with different quality-of-service requirements. For this purpose, OSPs need OTT application flow prioritization in resource allocation, while the network resource scheduling should respect network neutrality that forbids OSP prioritization. OSPs also need to request resources periodically, according to their performance goals, i.e., grade-of-service (GoS) level (blocking probability), causing delay in flows’ accommodation due to: 1) the time required for information exchange between OSPs and MNOs, affected by network congestion, and 2) the time required for flows to receive resources, affected by the number of concurrently active flows. Acknowledging the lack of OSP-oriented resource management approaches, we: 1) introduce a novel matching theoretic flow prioritization (MTFP) algorithm that respects network neutrality and 2) design analytical models that enable the thorough investigation of the GoS and delay performance in various scenarios. Our results (analytical and simulation) show that MTFP improves both metrics compared to the best effort approach, whereas its performance is affected by the number of flows and the resource allocation frequency.

New Era in shared cellular networks: Moving into open and virtualized platform

SummarySoftware‐defined networking (SDN) concept provides many features including hardware abstraction, programmable networking and centralized policy control. This allows benefiting from recently developed open source frameworks to build service platforms from a dynamic softwarized ecosystem of networking. There are two major technologies for building the next generation mobile network: Radio Access Network (RAN) sharing and Cloud RAN (C‐RAN). RAN sharing ensures efficient usage of network equipment among multiple mobile network operators (MNOs) and C‐RAN benefits installation, evolution, management, and performance improvements. One of the main benefits that can be used along with these features is dynamic virtualization of RAN and core/backhaul networks to ensure network sharing among multiple MNOs for efficient usage of their network infrastructures. In this work, we propose an SDN‐based C‐RAN architecture managed by C‐RAN controllers, which are integrated with a virtualization controller that is crucial for core/backhaul network sharing of the proposed next generation cellular network architecture. We further provide a use case study of shared mobile architecture for channel‐aware remote radio head (RRH) assignment to multiple MNOs. Two C‐RAN–aided cooperative algorithms, named ANCESTOR and MORGAN, that ensure load balancing among RRHs and better received signal strength levels are provided. Moreover, by selecting MNO assignments judiciously, MORGAN is shown to converge to a pure strategy Nash Equilibrium with high probability in the shared network architecture. The results reveal the proposed methods' performance advantages over static distribution and convergence properties.

Inter-Operator Resource Management for Millimeter Wave Multi-Hop Backhaul Networks

In this paper, a novel framework is proposed for optimizing the operation and performance of a large-scale multi-hop millimeter wave (mmW) backhaul within a wireless small cell network having multiple mobile network operators (MNOs). The proposed framework enables the small base stations to jointly decide on forming the multi-hop, mmW links over backhaul infrastructure that belongs to multiple, independent MNOs, while properly allocating resources across those links. In this regard, the problem is addressed using a novel framework based on matching theory composed of two, highly inter-related stages: a multi-hop network formation stage and a resource management stage. One unique feature of this framework is that it jointly accounts for both wireless channel characteristics and economic factors during both network formation and resource management. The multi-hop network formation stage is formulated as a one-to-many matching game, which is solved using a novel algorithm, that builds on the so-called deferred acceptance algorithm and is shown to yield a stable and Pareto optimal multi-hop mmW backhaul network. Then, a one-to-many matching game is formulated to enable proper resource allocation across the formed multi-hop network. This game is then shown to exhibit peer effects and, as such, a novel algorithm is developed to find a stable and optimal resource management solution that can properly cope with these peer effects. Simulation results show that, with manageable complexity, the proposed framework yields substantial gains, in terms of the average sum rate, reaching up to 27% and 54%, respectively, compared with a non-cooperative scheme in which inter-operator sharing is not allowed and a random allocation approach. The results also show that our framework improves the statistics of the backhaul sum rate and provides insights on how to manage pricing and the cost of the cooperative mmW backhaul network for the MNOs.

Shared Spectrum Access Communications: A Neutral Host Micro Operator Approach

In this paper, we conceive an advanced neutral host micro operator (NH-{\mu}O) network approach providing venues with services tailored to their specialized/specific requirements and/or local context related services that the mobile network operators (MNOs) are poorly-suited to providing it, as well as mobile broadband experience to the users from MNOs in a venue where only a single infrastructure is mandated under shared spectrum access framework. A radio access network slicing concept is conceived to support and optimize both the slice instance (SI) use cases independently and efficiently by running all network implementations in parallel, simultaneously on a common physical network infrastructure. We devise a common shared architecture for the NH-{\mu}O small cell base stations and dynamic spectrum assignment control unit, and their required functionalities supporting coexistence of different SIs as well as multiple MNOs in shared spectrum access communications. We devise both inter-SI and intra- SI dynamic spectrum allocation policies considering time-varying requirements of different SIs. The policies are capable of taking care of application level priority, -i.e., mixture of guaranteed quality of service and best-effort service users served by each SI while ensuring a healthy competition. Our proposed framework serves two-fold advantages, such as it gives the venue owner its own managed wireless networks tailored to its very specific requirements, and it also brings out cost savings and coverage extension for MNOs and efficiency of resources that arise from sharing wireless networks, and delivering the network capacity into high density venues.

Combinatorial Auction-Based Two-Stage Matching Mechanism for Mobile Data Offloading

In this paper, we study the problem of mobile data offloading for a network that contains multiple mobile network operators (MNOs), multiple WiFi or femtocell access points (APs) and multiple mobile users (MUs). MNOs offload their subscribed MUs` data traffic by leasing the unused Internet connection bandwidth of third party APs. We propose a combinatorial auction-based two-stage matching mechanism comprised of MU-AP matching and AP-MNO matching. The MU-AP matching is designed to match the MUs to APs in order to maximize the total offloading data traffic and achieve better MU satisfaction. Conversely, for AP-MNO matching, MNOs compete for APs` service using the Nash bargaining solution (NBS) and the Vickrey auction theories and, in turn, APs will receive monetary compensation. We demonstrated that the proposed mechanism converges to a distributed stable matching result. Numerical results demonstrate that the proposed algorithm well capture the tradeoff among the total data traffic, social welfare and the QoS of MUs compared to other schemes. Moreover, the proposed mechanism can considerably offload the total data traffic and improve the network social welfare with less computation complexity and communication overhead.

On Optimal Infrastructure Sharing Strategies in Mobile Radio Networks

The rapid evolution of mobile radio network technologies poses severe technical and economical challenges to mobile network operators (MNOs); on the economical side, the continuous roll-out of technology updates is highly expensive, which may lead to the extreme, where offering advanced mobile services becomes no longer affordable for MNOs which thus, are not incentivized to innovate. Mobile infrastructure sharing among MNOs becomes then an important building block to lower the required per-MNO investment cost involved in the technology roll-out and management phases. We focus on a radio access network (RAN) sharing situation where multiple MNOs with a consolidated network infrastructure coexist in a given set of geographical areas; the MNOs have then to decide if it is profitable to upgrade their RAN technology by deploying additional small-cell base stations and whether to share the investment (and the deployed infrastructure) of the new small-cells with other operators. We address such strategic problems by giving a mathematical framework for the RAN infrastructure sharing problem which returns the “best” infrastructure sharing strategies for operators (coalitions and network configuration) when varying techno-economic parameters such as the achievable throughput in different sharing configurations and the pricing models for the service offered to the users. The proposed formulation is then leveraged to analyze the impact of the aforementioned parameters/input in a realistic mobile network environment based on LTE technology.

Wireless resource virtualization: opportunities, challenges, and solutions

AbstractWireless resource virtualization (WRV) is currently emerging as a key technology to overcome the major challenges facing the mobile network operators (MNOs) such as reducing the capital, minimizing the operating expenses, improving the quality of service, and satisfying the growing demand for mobile services. Achieving such conflicting objectives simultaneously requires a highly efficient utilization of the available resources including the network infrastructure and the reserved spectrum. In this paper, the most dominant WRV frameworks are discussed where different levels of network infrastructure and spectrum resources are shared between multiple MNOs. Moreover, we summarize the major benefits and most pressing business challenges of deploying WRV. We further highlight the technical challenges and requirements for abstraction and sharing of spectrum resources in next generation networks. In addition, we provide guidelines for implementing comprehensive solutions that are able to abstract and share the spectrum resources in next generation network. The paper also presents an efficient algorithm for base station virtualization in long‐term evolution (LTE) networks to share the wireless resources between MNOs who apply different scheduling polices. The proposed algorithm maintains a high‐level of isolation and offers throughput performance gain. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Efficient Low-Complexity Scheduler for Wireless Resource Virtualization

This letter proposes an efficient low-complexity scheduler to virtualize the wireless resource blocks (RBs) and share them between users of multiple mobile network operators (MNOs). The scheduler aims at maximizing the throughput while maintaining access proportional fairness (APF) among users as well as MNOs. Normalized signal-to-noise ratio (SNR) selection criterion is used to impose APF between the users of each MNO, while APF between MNOs is maintained by assigning different weights to the normalized SNRs. The optimal weights for all MNOs are computed offline using iterative search method. Analytical and simulation results show that the proposed scheme can maintain APF at the user and MNO levels. Moreover, the throughput of the proposed scheduler is consistently higher than in the static sharing scenario.