In this paper, by jointly considering power allocation and network selection, we address the energy efficiency maximization problem in dynamic and heterogeneous wireless networks, where user equipments are typically equipped with multi-homing capability. In order to effectively deal with the dynamics of heterogeneous wireless networks, a stochastic optimization problem is formulated that optimizes the long-term energy efficiency under the constraints of system stability, peak power consumption and average transmission rate. By adopting the parametric approach and Lyapunov optimization, we derive an equivalent optimization problem out of the original problem and then investigate its optimal resource allocation. Then, to reduce the computational complexity, a suboptimal resource allocation algorithm is proposed based on relaxed optimization, which adapts to time-varying channels and stochastic traffic without requiring relevant a priori knowledge. The simulation results demonstrate the theoretical analysis and validate the adaptiveness of our proposed algorithm.

Network selection is one of the hot issues in the fusion of heterogeneous wireless networks (HWNs). However, most of previous works only consider selecting single-access network, which wastes other available network resources, rarely take account of multi-access. To make full utilization of available coexisted networks, this paper proposes a novel multi-access selection algorithm based on joint utility optimization for users with multi-mode terminals. At first, the algorithm adopts exponential smoothing method (ESM) to get smoothed values of received signal strength (RSS). Then we obtain network joint utility function under the constraints of bandwidth and number of networks, with the consideration of trade-off between network benefit and cost. At last, Lagrange multiplier and dual optimization methods are used to maximize joint utility. Users select multiple networks according to the optimal association matrix of user and network. The simulation results show that the proposed algorithm can optimize network joint utility, improve throughput, effectively reduce vertical handoff number, and ensure Quality of Service (QoS).

This paper proposes a heterogeneous wireless network with handover in the application layer. The proposed network requires no upgrade of wireless infrastructure and mobile terminals to convert the present homogeneous networks to the proposed heterogeneous network. Only installing application programs on the content server side and the mobile terminal side is required. The performance of the proposed network has been evaluated in a field trial using a mobile broadband wireless access (MBWA) air interface with wide coverage and a wireless local area network (WLAN) air interface with high throughput. The field trial results show that the maximum value of the handover outage time is only 170 ms. The proposed heterogeneous wireless network is promising since both high throughput and wide coverage area are attained by a combination of the proposed handover scheme with the present homogeneous wireless networks.

It is important to optimize aggregation schemes for heterogeneous wireless networks for maximizing communication throughput utilizing any available radio access networks. In the heterogeneous networks, differences of the quality of service (QoS), such as throughput, delay and packet loss rate, of the networks makes difficult to maximize the aggregation throughput. In this paper, we firstly analyze influences of such differences in QoS to the aggregation throughput, and show that it is possible to improve the throughput by adjusting the parameters of an aggregation system. Since manual parameter optimization is difficult and takes much time, we propose an autonomous parameter tuning scheme using a machine learning algorithm for the heterogeneous wireless network aggregation. We implement the proposed scheme on a heterogeneous cognitive radio network system. The results on our experimental network with network emulators show that the proposed scheme can improve the aggregation throughput better than the conventional schemes. We also evaluate the performance using public wireless network services, such as HSDPA, WiMAX and W-CDMA, and verify that the proposed scheme can improve the aggregation throughput by iterating the learning cycle even for the public wireless networks. Our experimental results show that the proposed scheme achieves twice better aggregation throughput than the conventional schemes.

A key technical challenge in heterogeneous wireless networks (HWNs) is the support of vertical handoff. It allows mobile users to switch connections among networks. In this paper, we propose and evaluate the application of VIKOR for vertical handoff. VIKOR is a Multiple Attribute Decision Making (MADM) method which makes decisions based on an aggregating function representing closeness to the ideal solution. We conducted simulation experiments to compare the performance of VIKOR for vertical handoff with other decision schemes such as SAW, TOPSIS, and WMC. We considered voice, data, and cost-constrained connections. Our results show that VIKOR is able to obtain satisfactory to excellent performance in the four different types of connections being considered.

We propose a neurodynamical approach to a large-scale optimization problem in Cognitive Wireless Clouds, in which a huge number of mobile terminals with multiple different air interfaces autonomously utilize the most appropriate infrastructure wireless networks, by sensing available wireless networks, selecting the most appropriate one, and reconfiguring themselves with seamless handover to the target networks. To deal with such a cognitive radio network, game theory has been applied in order to analyze the stability of the dynamical systems consisting of the mobile terminals' distributed behaviors, but it is not a tool for globally optimizing the state of the network. As a natural optimization dynamical system model suitable for large-scale complex systems, we introduce the neural network dynamics which converges to an optimal state since its property is to continually decrease its energy function. In this paper, we apply such neurodynamics to the optimization problem of radio access technology selection. We compose a neural network that solves the problem, and we show that it is possible to improve total average throughput simply by using distributed and autonomous neuron updates on the terminal side.

Multicast delivery in heterogeneous wireless networks requires careful coordination, in order to take full advantage of the resources such an interworking network environment can offer. Effective coordination, however, may require interworking signaling from coordinating network entities to receivers of a multicast service. Scalable delivery of such signaling is of great importance, since a large number of receivers may be interested in a multicast service. This paper therefore investigates the use of a multicast signaling channel (MSCH) to carry such interworking signaling in a scalable manner. Applications of interworking signaling for multicast service delivery in heterogeneous wireless networks are presented, motivating the need for an MSCH. Then a comparative study is performed analysing potential benefits of employing an MSCH for signaling message delivery compared to conventional unicast signaling. The analysis reveals that the benefits of the MSCH depend mainly on the selection of an appropriate signaling network to carry the MSCH and also on efficient addressing of a subset of receivers within the MSCH. Based on the findings, guidelines for the selection of a suitable signaling network are provided. Furthermore a novel approach is proposed that allows efficient addressing of a subset of receivers within a multicast group. The approach minimizes the required signaling load on the MSCH by reducing the size of the required addressing information. This is achieved by an aggregation of receivers with common context information. To demonstrate the concept, a prototype of the MSCH has been developed and is presented in the paper.

Future wireless/mobile system is expected to have heterogeneous wireless overlay networks for ubiquitous multimedia communication. In a such network environment, mobile users are likely to try to get attached to higher bandwidth network as bandwidth-hungry multimedia applications are increasing. However, the users have to perform vertical handoff to lower bandwidth network, as high bandwidth network become unavailable due to various reasons (such as its limited coverage, network congestion, etc.). In this paper, we discuss the problem of vertical handoff from a user's perspective. For this purpose, we formulate user satisfaction as a function of bandwidth utility and handoff latency. Then, we investigate the effect of call holding time, user movement probability, etc. on the satisfaction that a user derives from the use of network service for multimedia applications. In addition, based on the evaluation, we present an algorithm for selecting a wireless network, which maximizes the effective user satisfaction.