Heterogeneous Network Selection Research Articles

Investigation on MDP-based radio access technology selection in heterogeneous wireless networks

The new generation of wireless networks is characterized by heterogeneity i.e. the coexistence of two or more radio access technologies (RAT) in the same geographical area. While this coexistence of RATs offers various advantages, it also imposes many challenges for the network operator, whose aim is to maximize the generated revenue while satisfying the customers’ increasing demands. One important mechanism in heterogeneous wireless networks (HWN) is the RAT selection. It is normally triggered when a new call arrives, and provides the decision on whether the call can be admitted or not, and by which RAT it has to be served. Different approaches can be used to tackle the problem of RAT selection in HWNs. In this paper, we study Markov Decision Process (MDP)-based RAT selection in a cellular/WLAN heterogeneous network where the maximization of the revenue is considered as objective. An optimal RAT selection policy is therefore derived. The performance of the optimal scheme is evaluated in comparison with two other policies, namely Cellular-First policy and Load Balancing policy.

A Hybrid Approach for Radio Access Technology Selection in Heterogeneous Wireless Networks

In heterogeneous wireless networks, different radio access technologies are integrated and may be jointly managed. To optimize network performance and capacity, efficient common radio resource management (CRRM) mechanisms need to be defined. This paper tackles the radio access technology (RAT) selection, a key CRRM functionality, and proposes a hybrid decision framework that dynamically integrates operator objectives and user preferences. Mobile users are assisted in their decisions by the network that broadcasts cost and QoS information. Our hybrid approach involves two inter-dependent decision-making processes. The first one, on the network side, consists in deriving appropriate network information so as to guide user decisions in a way to meet operator objectives. The second one, where individual users combine their needs and preferences with the signaled network information, consists in selecting the RAT to be associated with in a way to maximize user utility. We first focus on the user side and present a satisfaction-based multi-criteria decision-making method. By avoiding inadequate decisions, our algorithm outperforms existing solutions and maximizes user utility. Further, we introduce two heuristic methods, namely the staircase and the slope tuning policies, to dynamically derive network information in a way to enhance resource utilization. The performance of each decision-making process, on the network and user sides, is evaluated separately through extensive simulations. A comparison of our hybrid approach with six different RAT selection schemes is also presented.

Radio Access Technology Selection in Heterogeneous Wireless Networks Using a Hybrid Fuzzy-Biogeography Based Optimization Technique

Radio access technology (RAT) selection in heterogeneous wireless networks is a challenging task to achieve guaranteed quality of service (QoS). This paper proposes a hybrid methodology in order to accomplish QoS through high service connectivity and reliable network transparency in a wireless heterogeneous system. The proposed hybrid methodology integrates a non-homogenous biogeography based optimization (NHBBO) with a parallel fuzzy system (PFS). The PFSs are employed to determine the probability of RAT selection, which acts as an input to the NHBBO procedure. Thus the NHBBO decide over the defined multi-point decision making algorithm to select the best RAT in the given heterogeneous network. The key role of the proposed technique is to optimize the weight coefficients of multi-point decision making algorithm and ensure maximum user satisfaction ratio to select best RAT. Several experiments are carried out using the proposed NHBBO---PFS technique to demonstrate the effectiveness and robustness in producing solutions compared to a few existing methods for RAT selection in heterogeneous wireless networks.

Game Theoretic Max-logit Learning Approaches for Joint Base Station Selection and Resource Allocation in Heterogeneous Networks

This paper investigates the problem of joint base station selection and resource allocation in an orthogonal frequency division multiple access (OFDMA) heterogeneous cellular network. The original throughput maximization problem is NP-hard and we propose solving it by using game theoretic stochastic learning approaches. To this end, we first transform the original problem into a tractable form, which has a weighted utility function. Then we prove that an exact potential game applies and it exists the best Nash equilibria which is a near optimal solution of the original problem when an efficient solution method of the weights is employed. To obtain the optimal solution, we redesign the utility function by leveraging a state space to formulate the original problem into an ordinal state based potential game, which is proved that it exists a recurrent state equilibrium point that maximizes system throughput. Furthermore, we propose two different variants of Max-logit learning algorithm based on these two games respectively: one is a simultaneous learning algorithm with less information exchange, which achieves the best Nash equilibria point of the exact potential game and the other is an efficient learning algorithm for the ordinal state based potential game, which can converge to the global optimization solution. Finally, numerical results are given to validate that theoretical findings.

A Network-Assisted Approach for RAT Selection in Heterogeneous Cellular Networks

When several radio access technologies (e.g., HSPA, LTE, WiFi, and WiMAX) cover the same region, deciding to which one mobiles connect is known as the Radio Access Technology (RAT) selection problem. To reduce network signaling and processing load, decisions are generally delegated to mobile users. Mobile users aim to selfishly maximize their utility. However, as they do not cooperate, their decisions may lead to performance inefficiency. In this paper, to overcome this limitation, we propose a network-assisted approach. The network provides information for the mobiles to make more accurate decisions. By appropriately tuning network information, user decisions are globally expected to meet operator objectives, avoiding undesirable network states. Deriving network information is formulated as a semi-Markov decision process (SMDP), and optimal policies are computed using the Policy Iteration algorithm. Also, and since network parameters may not be easily obtained, a reinforcement learning approach is introduced to derive what to signal to mobiles. The performances of optimal, learning-based, and heuristic policies, such as blocking probability and average throughput, are analyzed. When tuning thresholds are pertinently set, our heuristic achieves performance very close to the optimal solution. Moreover, although it provides lower performance, our learning-based algorithm has the crucial advantage of requiring no prior parameterization.

EMF-Aware Cell Selection in Heterogeneous Cellular Networks

The growing concern on the exposure of users to the electromagnetic field (EMF) has recently brought new challenges to the mobile research community. In this letter, we propose a novel cell association framework for heterogeneous cellular networks (HetNets), which aims to balance the load amongst heterogeneous cells so as to improve the resource usage and to increase the user satisfaction in terms of both data rate and EMF exposure. We model the cell selection problem as a General Assignment Problem (GAP) and we present two heuristic algorithms, which solve it with limited complexity. Our analysis shows that the proposed solutions lead to notable improvements with respect to legacy association schemes.

Learning with handoff cost constraint for network selection in heterogeneous wireless networks

AbstractIn heterogeneous wireless networks, network selection algorithms provide the user with the optimum network access choice. The optimal network is evaluated according to network parameters. Considering that the network parameters are dynamic and unavailable for the user in realistic heterogeneous wireless network environments, most existing network selection algorithms cannot work effectively. Learning‐based algorithms can address the problem of uncertain network parameters, while they commonly need considerable network handoff, resulting in unbearable handoff cost. In order to tackle the uncertainty of network parameters, we formulate the network selection problem as a multi‐armed bandit problem. Moreover, two online learning‐based network selection algorithms with a special consideration on reducing network handoff cost are proposed. By updating in a block manner, both algorithms achieve optimal logarithmic‐order regret and limited network handoff cost. The simulation indicates that the two algorithms can significantly reduce the network handoff cost and improve the transmission performance compared with existing algorithms, simultaneously. Copyright © 2014 John Wiley & Sons, Ltd.

An overview of intelligent selection and prediction method in heterogeneous wireless networks

Heterogeneous wireless access technologies will coexist in next generation wireless networks. These technologies form integrated networks, and these networks support multiple services with high quality level. Various access technologies allow users to select the best available access network to meet the requirements of each type of communication service. Being always best connected anytime and anywhere is a major concern in a heterogeneous wireless networks environment. Always best connected enables network selection mechanisms to keep mobile users always connected to the best network. We present an overview of the network selection and prediction problems and challenges. In addition, we discuss a comprehensive classification of related theoretic approaches, and also study the integration between these methods, finding the best solution of network selection and prediction problems. The optimal solution can fulfill the requirements of the next generation wireless networks.

Energy Efficient Heterogeneous Network Selection Method Based on Fuzzy Neural Network

Heterogeneous network selection algorithm can be expected to play an increasingly important role in the future wireless communication network. Due to the difference in energy consumption of different networks, selecting appropriate access network according to energy consumption characteristics and applications for Mobile Terminal (MT) can effectively extend battery life. In this paper, a fuzzy neural network based energy efficient network selection method is proposed. To select the optimal network and achieve the best balance between performance and energy consumption, the proposed method takes network bandwidth, signal strength, Quality of Service (QoS) and energy consumption into account. A novel energy efficiency utility function is proposed to calculate the energy utility value of different applications based on real-time and non-real-time service. And the calculated energy utility value together with other QoS parameters is input into Fuzzy Neural Network (FNN) for subsequent processing. Finally, the most appropriate network can be selected by comparing the inference results of fuzzy neural network. Computer simulations show that the proposed algorithms can reduce the energy consumption for MT effectively, and has superior performance in terms of access blocking probability and packet dropping probability.

Heterogeneous wireless network selection algorithm based on group decision

In the environment of heterogeneous wireless networks, it is vital to select a currently optimal network for applications and subscribers. The use of multiple attribute decision making (MADM) for heterogeneous network selection can provide subscribers with satisfactory service quality. Converting heterogeneous network selection into a MADM problem, the authors present an improved algorithm for MADM based on group decision theory. The algorithm combines weight vectors of multiple attribute decision making to obtain a combinational weight vector. Then the results’ compatibility will be assessed. If they do not meet the requirements of compatibility, the judgment matrix will be modified until a comprehensive vector that satisfies compatibility requirements is produced. The vector is combined with simple weighting method (SAW) for network selection. Simulation shows that the algorithm can provide users with satisfactory quality of service (QoS).

Fast efficient spectrum allocation and heterogeneous network selection based on modified dynamic evolutionary game

Dynamic evolutionary game has attracted a lot of attention in cognitive network because it can adaptively learn during the strategy under changing conditions adopting replicator dynamics equation. But the information required by the original replicator dynamics equation is large. In this paper, we provide a modified replicator dynamics equation, which can adaptively converge to the desired stable state with faster speed. Noted that, the necessary information transmission in the evolutionary process is much less than that of the original replicator dynamics equation. Moreover, we apply the modified dynamics equation to (i) opportunistic spectrum access with multiple primary users selling free spectrum opportunities to multiple secondary users; (ii) heterogeneous network selection. Simulation results show that the evolving time is cut down greatly and equal maximal payoff is obtained. Besides, the proposed method is robust even if there is large time delay in the process of information transmission.

A MIH-based approach for best network selection in heterogeneous wireless networks

In the next generation wireless networks, different technologies belonging to one or more operators should be integrated to form a heterogeneous environment based on an IP core network infrastructure. This ensures user mobility and service continuity by maintaining connections when switching between various technologies and it introduces new resources and possibilities for applications. In this context, an automatic interface selection based on instantaneous and practical constraints and user preferences (Quality of Service (QoS) parameters, available resources, security, power consumption, etc.) is therefore required. The different network selection and handover schemes proposed in the literature can be classified into three approaches according to who is responsible for making the handover decision: the terminal, the network or by a cooperation between both of them. However, these approaches keep presenting some drawbacks; namely the problem of resources management and network load balancing whenever the selection is controlled by the mobile terminal (MT) and the problem of scalability and unknown operator's management policy whenever the selection is rather controlled by the network.In this article, first we propose a MIH based approach for handover initiation and preparation for heterogeneous wireless network. The proposed framework is based on the principals of IEEE 802.21 for context information gathering and optimized handover decision making. Second, we propose a new architecture and new network selection scheme that explicitly take into account the current resource usage and the user preferences. Furthermore, our solution ensures the selection of the most suitable network for each flow while taking into consideration its expectations in terms of QoS. A feasibility study of implementing a new architecture on a single MT is evaluated by using typical scenarios and using various algorithms. Thanks to the introduced function entities and modules in the proposed architecture, network utilization balancing and user and application expectations, which are successfully assured without operator intervention. Performance analysis shows that the proposed algorithm best meets the common quality requirements.

User Preference Heterogeneous Network Selection in Less Subjective Ways

Heterogeneous network selection is a popular topic with the rapid development of telecommunication technologies. Literatures employed different ways for solving the issue with drawbacks more or less especially neglecting the user preference and using much subjective configurations. This paper proposes a user preference scheme including realtime or non-realtime pursuit, money and battery consumption concern and non-specific requirement. For front four modes, a technique for order preference by similarity to ideal solution based method with vote which reduces the subjectiveness is introduced. Then, a function-less and totally unsubjective method called directional distance function based data envelopment analysis method faces the non-specific requirement. Several examples test these two methods and the results support them.

A novel noncooperative game competing model using generalized simple additive weighting method to perform network selection in heterogeneous wireless networks

SummaryNetwork selection mechanisms have a significant role in guaranteeing the QoS for users in a heterogeneous wireless networks environment. These mechanisms allow the selection of an optimal wireless network to satisfy the needs of users. Users are provided with the opportunity to select from multiple connectivity opportunities available all over various wireless networks. Furthermore, the network operators themselves can execute active selection strategies that facilitate proper decision making, in which user preferences are considered. This study proposes a new noncooperative competing game‐theoretic model and strategy space based on user preference. This model can solve network selection problems and capture the inter‐linkages of decisions taken by various networks. A generalized simple additive weighting method is incorporated into the framework of noncooperative game theory. In addition, the utility function is employed to assess the usefulness of the system. Simulation results and analysis illustrate the efficacy of the suggested model in attaining optimum network utility for heterogeneous wireless networks while optimizing user satisfaction. Copyright © 2014 John Wiley & Sons, Ltd.

Distributed Kalman filtering via node selection in heterogeneous sensor networks

In this paper, we propose a strategy for distributed Kalman filtering over sensor networks, based on node selection, rather than on sensor fusion. The presented approach is particularly suitable when sensors with limited sensing capability are considered. In this case, strategies based on sensor fusion may exhibit poor results, as several unreliable measurements may be included in the fusion process. On the other hand, our approach implements a distributed strategy able to select only the node with the most accurate estimate and to propagate it through the whole network in finite time. The algorithm is based on the definition of a metric of the estimate accuracy, and on the application of an agreement protocol based on max-consensus. We prove the convergence, in finite time, of all the local estimates to the most accurate one at each discrete iteration, as well as the equivalence with a centralised Kalman filter with multiple measurements, evolving according to a state-dependent switching dynamics. An application of the algorithm to the problem of distributed target tracking over a network of heterogeneous range-bearing sensors is shown. Simulation results and a comparison with two distributed Kalman filtering strategies based on sensor fusion confirm the suitability of the approach.

Access point selection in heterogeneous wireless networks using belief propagation

The next generation wireless network will be composed by various heterogenous wireless access networks, such as cellular network, worldwide interoperability for microwave access (WiMAX), wireless local area network (WLAN), etc. Different access networks cooperatively provide high-bandwidth connectivity with bandwidth guarantees. This paper proposes a utility-based access point selection scheme, which selects an accessible point for each user, such that the bandwidth requirement of each user is satisfied, and also the defined utility function is maximized. Due to the NP-complete nature of the problem, the existing proposals apply the greedy method to find a solution. We find that belief propagation is an efficient tool to solve this problem, and thus, we derive the same optimization objective in a new way, and then draw a factor graph representation which describes our combinatorial optimization problem. Afterwards, we develop the belief propagation algorithm, and show that our algorithm converges. Finally, we conduct numerical experiments to evaluate the convergency and accuracy of the belief propagation in load balancing problem.

Network Selection in Cognitive Heterogeneous Networks Using Stochastic Learning

Coexistence of multiple radio access technologies (RATs) is a promising paradigm to improve spectral efficiency. This letter presents a game-theoretic network selection scheme in a cognitive heterogeneous networking environment with time-varying channel availability. We formulate the network selection problem as a noncooperative game with secondary users (SUs) as the players, and show that the game is an ordinal potential game (OPG). A decentralized, stochastic learning-based algorithm is proposed where each SU's strategy progressively evolves toward the Nash equilibrium (NE) based on its own action-reward history, without the need to know actions in other SUs. The convergence properties of the proposed algorithm toward an NE point are theoretically and numerically verified. The proposed algorithm demonstrates good throughput and fairness performances in various network scenarios.

A Novel Differentiate Weight Algorithm on Heterogeneous Network Selection

How to select a target network meeting the needs of Quality of Service (QoS) guarantee in heterogeneous network environment has become a research hotspot. In this paper, a novel differentiate weight algorithm on heterogeneous network selection is proposed. As regard of the different needs of QoS for each class of traffic in heterogeneous network, the QoS parameter weight for each class of traffic is calculated by G1 method, and then the target network is selected by technique for order preference by similarity to ideal solution (TOPSIS). At last, simulation results show that the algorithm proposed in this paper is better than average weight algorithm on selecting target network reasonably and balancing network loads.

Heterogeneous Relay Selection

Relay selection, a simple and efficient method of operating multi-relay networks, has in the past been considered only when all relays follow the same protocol. This work studies relay selection in heterogeneous relay networks where relays with different protocols can co-exist. The new techniques for the analysis of such networks are demonstrated on a mixture of decode-forward relays and amplify-forward relays, as well as a mixture of non-orthogonal amplify-forward (NAF) and dynamic decode-forward (DDF) relays, and also a mixture of NAF and compress-forward relays. In each case the diversity-multiplexing tradeoff is calculated. In the process, a noteworthy limitation of previous relay selection schemes is also relaxed and removed: In the past in multi-relay networks the direct source-destination link has been ignored whenever it introduces dependencies among selection decision variables, as it often does. This has been motivated chiefly by the lack of suitable analytical techniques for order statistics of dependent random variables. In this work, relay selection analysis is extended to allow the presence of a direct source-destination link. As a side benefit, the relay selection results in homogeneous networks (where relays are all of the same type) are also extended to include the presence of a source-destination link.

GRA Based Network Selection in Heterogeneous Wireless Networks

4G wireless networks will integrate heterogeneous technologies such as Wireless LAN and third generation (3G) cellular networks and have the capability to offer various services at any time as per user requirements, anywhere with seamless interoperability at affordable cost. One important challenge in such a heterogeneous wireless environment is to enable network selection mechanisms in order to keep the mobile users always best connected anywhere and at any time. In this paper, a multi-criteria access network selection algorithm is proposed in Worldwide Interoperability for Microwave Access---Wireless Fidelity environment, in order to facilitate the provision of high quality services and at the same time to satisfy different types of user service level agreements. Analytical hierarchy process (AHP) and grey relational analysis (GRA) methods are applying for optimal access network selection. The proposed methodology combines the AHP to decide the relative weights of criteria set according to network's performance, as well as the GRA to rank the network alternatives. The advantages of the GRA method are that the results are based on the original data, the calculations are simple and straightforward, and finally it is one of the best methods to make decision under heterogeneous wireless network environment.