Nadine Abbas

Energy-throughput tradeoffs in cellular/WiFi heterogeneous networks with traffic splitting

Heterogeneous networks are expected to play a major role towards meeting the exploding traffic demand over cellular systems. Particularly, existing WiFi hotspots will be dynamically utilized to offload the traffic of cellular mobile subscribers. This will be further facilitated by forthcoming advances in mobile device capabilities that will include the ability to operate multiple wireless interfaces simultaneously. To this end, we focus in this work on cellular/WiFi heterogeneous networks with traffic splitting where a mobile device can utilize existing cellular and WiFi links simultaneously to achieve various performance gains. We propose a multi-objective approach for traffic splitting that captures the tradeoffs between throughput maximization on one hand and battery energy minimization on the other hand. We evaluate the proposed approach using parameters determined via experimental measurements using Samsung Galaxy SIII mobile devices. Results are presented for various scenarios in order to quantify and analyze the throughput-energy tradeoffs of traffic splitting in cellular/WiFi heterogeneous networks.

Exploiting multiple wireless interfaces in smartphones for traffic offloading

Smartphones are evolving at a fast rate in terms of their computational, storage, and communications capabilities. A high-end smartphone is equipped with multiple wireless interfaces with varying bit rates, energy consumption requirements, and coverage ranges. The joint utilization of the existing wireless interfaces facilitates the development of advanced techniques to boost the performance of wireless networks and enhance the experience of mobile users. Among these techniques is device-to-device cooperation where a smartphone receives content from a base station on a given wireless interface and distributes it to other devices in its vicinity via another wireless interface. Another technique is traffic offloading in heterogeneous network scenarios where a smartphone downloads content using multiple wireless interfaces. In this paper, we study the readiness of high-end smartphones to utilize multiple wireless interfaces simultaneously focusing on capabilities and challenges. We adopt an experimental approach using a mobile cooperative video distribution testbed to obtain and evaluate performance results with focus on energy consumption. We consider various scenarios involving a combination of wireless technologies that include Bluetooth, WiFi, WiFi-Direct, and 3G.

An optimized approach to video traffic splitting in heterogeneous wireless networks with energy and QoE considerations

Abstract Due to the exploding traffic demands with the ubiquitous anticipated spread of 5G and Internet of Things, research has been active to devise mechanisms for meeting these demands while maintaining high quality user experience. In support of this direction, 3GPP is working towards cellular/WiFi interworking in heterogeneous networks to boost throughput, capacity, coverage and quality of experience. However, the continuous use of multiple wireless interfaces will increase the system performance but at the expense of more energy. As a result, there is a need for a dynamic use of multiple interfaces to provide a balance between energy consumption, throughput and user experience. Previous work in this field has considered improving throughput and reducing energy consumption, but did not consider simultaneously quality of experience as perceived by the end user. In this work, we aim at devising real-time traffic splitting strategies between WiFi and cellular networks to maximize user experience, reduce delay, and balance the needed energy consumption. We develop solutions for cellular/WiFi network resource management using Lyapunov drift-plus-penalty optimization approach. We evaluate the proposed approach using parameters determined via experimental measurements from mobile devices, and using our own test bed implementation to provide an evaluation under realistic operation conditions. Results show the performance effectiveness of the proposed traffic splitting approach in terms of throughput, delay, queue stability, energy consumption and quality of user experience by monitoring the frequency and lengths of video stalls.

Optimal WiMAX frame packing for minimum energy consumption

Minimizing energy consumption is an urgent and challenging problem. As in any communication system, high energy efficiency in WiMAX systems should be maintained by increasing resource efficiency. Thus, WiMAX resources should be properly utilized by optimizing the construction of downlink (DL) bursts. This paper proposes an energy-efficient scheme that maximizes the use of resources at the base station (BS) by reducing the energy wasted caused by sending padding bits instead of useful data. The problem was formulated as nonlinear integer programming model. Due to the complexity of the problem, this paper presents first the formulation of the base model for optimal DL bursts construction problem assuming the packet is represented by one burst. Then, the formulation is expanded to allow the representation of packets by several bursts. The results show an improvement in data packing that maximizes the utilization of frames, and minimizes energy wastage.

An energy-efficient scheme for WiMAX downlink bursts construction

One of the challenges in WiMAX networks is to increase resource and power efficiency by optimizing the downlink (DL) burst construction. This paper proposes an energy-efficient scheme that maximizes the use of resources at the base station (BS) while reducing the energy wasted caused by sending padding bits instead of useful data. This paper presents the derivation of the general optimization problem that was formulated as a nonlinear integer programming model. A simple case formulation is used to illustrate the general application. The results show an improvement in data packing that maximizes the utilization of frames, and minimizes energy wastage.

A comprehensive WiMAX simulator

The most challenging issue in WiMAX network planning is to measure and enhance the Quality of Service (QoS) of WiMAX networks. In this paper, a comprehensive WiMAX simulator is proposed to evaluate the performance of the system. The key parts of the simulator are described including end-to-end communication path, traffic generation, Medium Access Control (MAC) and Physical (PHY) layers, resource allocation, frame construction and configuration options such as Adaptive Modulation and Coding (AMC). Several experiments are conducted to assess different scenarios while varying one or more of the following: input traffic size, traffic load, presence of fragmentation and AMC. The results show the scalability of the system as it can support a large number of users while showing the real-life representation of the traffic models. The simulations also show the flexibility of implementing AMC schemes according to desired distributions. The high accuracy of the simulator is shown by comparing the simulator results to theoretical expected values.

Traffic offloading with maximum user capacity in dense D2D cooperative networks

Ultra dense networks and device-to-device communications are expected to play a major role in 5G networks to meet tremendous traffic requirements. In our work, we address traffic offloading in dense device-to-device cooperative heterogeneous networks with focus on use cases where a very large number of users request simultaneously common streaming content from a remote server with quality of service guarantees. We formulate an optimization problem to maximize the number of users served and reduce the number of access points deployed while satisfying a set of system constraints. The solution determines the best strategy for downloading the content either over long range connectivity from the access points or short range connectivity from peer mobile devices. Results are presented for various scenarios in a stadium setting to demonstrate the significant gains of optimized traffic offloading in ultra dense wireless networks.