Cell association with user behaviour awareness in heterogeneous cellular networks

In heterogeneous cellular networks (HetNets) with macro base station (BS) and multiple small BSs (SBSs), cell association of user equipment (UE) affects UE transmission rate and network throughput. Conventional cell association rules are usually based on UE received signal-to-interference-and-noise-ratio (SINR) without being aware of other UE statistical characteristics, such as user movement and distribution. User behaviors can indeed be exploited for improving long-term network performance. In this paper, we investigate UE cell association in HetNets by exploiting both individual and clustering user behaviors with the aim to maximize long-term system throughput. We model the problem as a stochastic optimization problem, and prove that it is PSPACE-hard. For mathematical tractability, we solve the problem in two steps. In the first step, we investigate UE association for a specific SBS. We use a restless multiarmed bandit model to derive an association priority index for the SBS. In the second step, we develop an index enabled association (IDEA) policy for making the cell association decisions in general HetNets based on the indices derived in the first step. IDEA determines a set of admissible BSs for a UE based on SINR, and then associates the UE with the BS that has the smallest index in the set. We conduct simulation experiments to compare IDEA with other three cell association policies. Numerical results demonstrate the significant advantages of IDEA in typical scenarios

Stackelberg Game for Access Permission in Femtocell Network with Multiple Network Operators

Femtocells are widely recognized as a promising technology to meet the requirements of indoor coverage in forthcoming fifth generation cellular networks (5G). As femtocell holders (FHs) can be users themselves or mobile network operators, it makes challenges to holistic network resource utilization. In particular, due to the selfishness nature, FHs are usually unwilling to accommodate extra users without compensation. This inspires us to develop an effective refunding mechanism, with aim to allow competitive network operators to employ truthful refunding policy, and to encourage FHs to make appropriate access permission. In this paper, we first define a refunding strategy function and price-coefficient for the refunding policy. We then formulate the access permission as a Stackelberg game and theoretically prove the existence of unique Nash Equilibrium. Numerical results validate the effectiveness of our proposed mechanism and overall network efficiency is improved significantly as well

Auction-Stackelberg game framework for access permission in femtocell networks with multiple network operators

With the explosive growth of indoor data traffic in forthcoming fifth generation cellular networks, it is imperative for mobile network operators to improve network coverage and capacity. Femtocells are widely recognized as a promising technology to address these demands. As femtocells are sold or loaned by a mobile network operator (MNO) to its residential or enterprise customers, MNOs usually employ refunding scheme to compensate the femtocell holders (FHs) providing indoor access to other subscribers by configuring the femtocell to operate in open or hybrid access mode. Due to the selfishness nature, competition between network operators as well as femtocell holders makes it challenging for operators to select appropriate FHs for trading access resources. This inspires us to develop an effective refunding framework, with aim to improve overall network resource utilization, through promoting FHs to make reasonable access permission for well-matched macro users. In this paper, we develop a two-stage auction–Stackelberg game (ASGF) framework for access permission in femtocell networks, where MNO and mobile virtual network operator lease access resources from multiple FHs. We first design an auction mechanism to determine the winner femtocell that fulfils the access request of macro users. We next formulate the access permission problem between the winner femtocell and operators as a Stackelberg game, and theoretically prove the existence of unique equilibrium. As a higher system payoff can be gained by improving individual players’ payoff in the game, each player can choose the best response to others’ action by implementing access permission, while avoiding solving a complicated optimization problem. Numerical results validate the effectiveness of our proposed ASGF based refunding framework and the overall network efficiency can be improved significantly

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

User Behavior Aware Cell Association in Heterogeneous Cellular Networks

In heterogeneous cellular networks (HetNets), cell association of User Equipment (UE) affects UE transmit rate and network throughput. Conventional cell association rules are usually based on UE received Signal-to-Interference-and-Noise-Ratio (SINR) without taking into account user behaviors, which can indeed be exploited for improving network performance. In this paper, we investigate UE cell association in HetNets based on individual user behavior characteristics with aim to maximize long- term expected system throughput. We model the problem as a stochastic optimization model Restless Multi-Armed Bandit (RMAB). As it is a PSPACE-hard problem, we develop a primal-dual heuristic index algorithm and the solution specifies the rule that determines which arms in the RMAB model to be selected at each decision time. According to the solution of RMAB, we propose a new cell association strategy called Index Enabled Association (IDEA). We also conduct simulation experiments to compare IDEA with conventional max-SINR cell association strategy and an existing game-based RAT selection scheme. Numerical results demonstrate the advantages of IDEA in typical scenarios

Cooperative Caching with Content Popularity Prediction for Mobile Edge Caching

Mobile Edge Caching (MEC) can be exploited for reducing redundant data transmissions and improving content delivery performance in mobile networks. However, under the MEC architecture, dynamic user preference is challenging the delivery efficiency due to the imperfect match between users\u27 demands and cached content. In this paper, we propose a learning-based cooperative content caching policy to predict the content popularity and cache the desired content proactively. We formulate the optimal cooperative content caching problem as a 0-1 integer programming for minimizing the average downloading latency. After using an artificial neural network to learn content popularity, we use a greedy algorithm for its approximate solution. Numerical results validate that the proposed policy can significantly increase content cache hit rate and reduce content delivery latency when compared with popular caching strategies

The pathological and therapeutic roles of mesenchymal stem cells in preeclampsia

Mesenchymal stem cells (MSCs) have made progress in the treatment of ischemic and inflammatory diseases. Preeclampsia (PE) is characterized by placenta ischemic and inflammatory injury. Our paper summarized the new role of MSCs in PE pathology and its potency in PE therapy and analyzed its current limitations. Intravenously administered MSCs dominantly distributed in perinatal tissues. There may be additional advantages to using MSCs-based therapies for reproductive disorders. It will provide new ideas for future research in this field

A hybrid authentication protocol for LTE/LTE-A network

The wireless technology has revolutionized and had a significant impact on every aspect of people's life. Confidential information, financial transactions, and sensitive conversations are frequent via the wireless network and securing all these data are of the utmost importance. In this paper, we discuss the major weaknesses of the long-term evolution (LTE) authentication process and propose a new approach-the hybrid evolved packet system (HEPS) protocol to address the vulnerabilities. The proposed protocol has been verified logically, using Burrows-Abadi-Needham logic, and systematically, using the automated validation of internet security protocol and application tool. The HEPS protocol will optimize the performance of the LTE authentication process and fundamentally solve the security issue of the process.Published versio

Paired Bid-Based Double Auction Mechanism for RAN Slicing in 5G-and-Beyond System

Network slicing has been widely deemed as a promising technology that enables the sharing of infrastructure resources for 5G-and-beyond mobile networks. Infrastructure Providers (InPs) abstract physical network into multiple isolated network slices, each of which can be operated as a virtual network by different Mobile Virtual Network Operators (MVNOs). However, the asymmetric information between resource supply of InP and usage requirement of MVNO challenges the resource allocation when enforcing slicing in the radio access network (RAN). In this paper, we propose a paired bid-based double-auction mechanism for a slicing-based RAN to improve resource allocation efficiency. We construct a market model in which the MVNOs and the InPs submit respective bids and ask Network Slice Broker (NSB) for slice transactions, and the NSB determines the winner pairs and corresponding payments to clear the slice market by maximizing social welfare. Numerical results validate the effectiveness of our proposed mechanism on improving the overall network resource allocation efficiency without collecting full information on the competitive strategies and utility functions of the MVNOs and InPs