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Wolfgang Kellerer - One of the best experts on this subject based on the ideXlab platform.
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application driven cross Layer Optimization for wireless networks using mos based utility functions
International Conference on Communications, 2009Co-Authors: Srisakul Thakolsri, Wolfgang Kellerer, Eckehard SteinbachAbstract:This paper discusses a Quality of Experience (QoE) driven cross-Layer Optimization framework for efficient network resource allocation in wireless networks. The proposed scheme jointly optimizes the application Layer and the lower Layers of the wireless protocol stack with the aim of improving the user's QoE. The Mean Opinion Score (MOS) is used as a common metric for user-perceived quality in the Optimization scheme. Three different QoE-based Optimization schemes are compared to a throughput maximization scheme and a non-optimized system. We perform simulations using a software implementation of a developed HSDPA system. Results show that the MOS-based approaches lead to significant improvements of user perceived quality.
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qoe driven cross Layer Optimization for high speed downlink packet access
Journal of Communications, 2009Co-Authors: Srisakul Thakolsri, Shoaib Khan, Eckehard Steinbach, Wolfgang KellererAbstract:This paper proposes a Quality of Experience (QoE) based cross-Layer design (CLD) framework for High Speed Downlink Packet Access (HSDPA). The proposed scheme aims at maximizing the user satisfaction by taking advantage of the link adaptation mechanism of HSDPA and the rate adaptation capability of multimedia applications. The main contributions of the paper are as follows. First, we describe the multiuser rate region of HSDPA by constructing a long-term radio link Layer model. Next, we formulate multimedia QoE by constructing long-term utility functions, describe the multiuser utility space and derive its properties. We show analytically that the maximization of the sum of utility (max-MOS) can be efficiently solved by a fast greedy algorithm which searches only through the boundary of the utility space. We investigate two alternatives to the max-MOS approach, which introduce additional fairness in the system. We compare our proposed QoE-based cross Layer Optimization schemes to a system that is configured to maximize the overall throughput. For the sake of completeness, we also compare our approaches to a non-optimized HSDPA system. The performance comparison is made by simulation using a software implementation of an actually deployed HSDPA system. Results show that our QoE-based approach leads to significantly improved user perceived quality compared to the other approaches.
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MOS-Based Multiuser Multiapplication Cross-Layer Optimization for Mobile Multimedia Communication
Hindawi Limited, 2006Co-Authors: Shoaib Khan, Svetoslav Duhovnikov, Eckehard Steinbach, Wolfgang KellererAbstract:We propose a cross-Layer Optimization strategy that jointly optimizes the application Layer, the data-link Layer, and the physical Layer of a wireless protocol stack using an application-oriented objective function. The cross-Layer Optimization framework provides efficient allocation of wireless network resources across multiple types of applications run by different users to maximize network resource usage and user perceived quality of service. We define a novel Optimization scheme based on the mean opinion score (MOS) as the unifying metric over different application classes. Our experiments, applied to scenarios where users simultaneously run three types of applications, namely voice communication, streaming video and file download, confirm that MOS-based Optimization leads to significant improvement in terms of user perceived quality when compared to conventional throughput-based Optimization.
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application driven cross Layer Optimization for mobile multimedia communication using a common application Layer quality metric
International Conference on Wireless Communications and Mobile Computing, 2006Co-Authors: Shoaib Khan, Svetoslav Duhovnikov, Eckehard Steinbach, Marco Sgroi, Wolfgang KellererAbstract:This paper proposes a cross-Layer Optimization framework that provides efficient allocation of wireless network resources across multiple types of applications to maximize network capacity and user satisfaction. We define a novel Optimization scheme based on the Mean Opinion Score (MOS) as the unifying metric. Our experiments, applied to scenarios where users simultaneously run three types of applications, such as realtime voice, video conferencing and file download, confirm that MOS-based Optimization leads to significant improvement in terms of user perceived quality when compared to throughput-based Optimization.
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application driven cross Layer Optimization for video streaming over wireless networks
IEEE Communications Magazine, 2006Co-Authors: Shoaib Khan, Eckehard Steinbach, Marco Sgroi, Y Peng, Wolfgang KellererAbstract:Mobile multimedia applications require networks that optimally allocate resources and adapt to dynamically changing environments. Cross-Layer design (CLD) is a new paradigm that addresses this challenge by optimizing communication network architectures across traditional Layer boundaries. In this article we discuss the relevant technical challenges of CLD and focus on application-driven CLD for video streaming over wireless networks. We propose a cross-Layer Optimization strategy that jointly optimizes the application Layer, data link Layer, and physical Layer of the protocol stack using an application-oriented objective function in order to maximize user satisfaction. In our experiments we demonstrate the performance gain achievable with this approach. We also explore the trade-off between performance gain and additional computation and communication cost introduced by cross-Layer Optimization. Finally, we outline future research challenges in CLD.
Michele Zorzi - One of the best experts on this subject based on the ideXlab platform.
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analysis of phy application cross Layer Optimization for scalable video transmission in cellular networks
World of Wireless Mobile and Multimedia Networks, 2013Co-Authors: Iffat Ahmed, Leonardo Badia, Daniele Munaretto, Michele ZorziAbstract:We investigate the Optimization of video transmissions over cellular networks by using the H.264 Scalable Video Coding (SVC) at the application Layer and an Adaptive Modulation and Coding (AMC) scheme at the physical Layer. We analyze how the cross-Layer Optimization (XLO) of these two techniques together performs compared to a sequential and independent selection of video packets and Modulation and Coding Schemes (MCS) with no cross-Layer Optimization (NXLO), in terms of goodput and packet delivery delay. We formulate an analytical model based on a Markov chain representing the wireless channel, where each state is associated to a different channel quality corresponding to a set of possible choices of video Layer and MCS. Our numerical results show that XLO significantly outperforms NXLO for video transmissions, thereby pointing out the strong need for cross-Layer solutions in video transmission.
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fuzzy logic for cross Layer Optimization in cognitive radio networks
IEEE Communications Magazine, 2008Co-Authors: Nicola Baldo, Michele ZorziAbstract:The search for the ultimate architecture for cross-Layer Optimization in cognitive radio networks is characterized by challenges such as modularity, interpretability, imprecision, scalability, and complexity constraints. In this article we propose fuzzy logic as an effective means of meeting these challenges, as far as both knowledge representation and control implementation are concerned.
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fuzzy logic for cross Layer Optimization in cognitive radio networks
Consumer Communications and Networking Conference, 2007Co-Authors: Nicola Baldo, Michele ZorziAbstract:The search for the ultimate architecture for cross- Layer Optimization in cognitive radio networks is characterized by challenges such as modularity, scalability, complexity and interpretability constraints. In this paper we propose Fuzzy Logic as an effective means of meeting these challenges, as far as both knowledge representation and control implementation are concerned. I. INTRODUCTION Cognitive Radio devices, in their original definition, have the primary objective of providing wireless communication capabilities which are able to adapt to the needs of the user (1). One of the key challenges in this task is to be able to exploit the resources available in various scenarios, using different wireless technologies. Given the state of the art in communication systems, this resource Optimization strategy often translates into the need to break the traditional approach of protocol encapsulation, in order to allow for information exchange and interactions between the different Layers of the protocol stack. For this reason, cross-Layer Optimization is nowadays widely accepted as a fundamental component of wireless systems (2), (3), and is expected to play a major role in Cognitive Radio systems as well. In the last decade, cross-Layer Optimization strategies have been widely studied and adopted, but in most cases the aim was to achieve performance enhancements in specific scenar- ios. Most early cross-Layer work dealt with a fixed combination of applications, protocol suites and wireless technologies, and had as its main goal the Optimization of multimedia applications or transport protocols over a wireless link such as 802.11 or GPRS. In more recent years, much effort has been put by the research community in trying to synthesize all this experience on cross-Layer Optimization into a more generalized, universal cross-Layer architecture, with the aim of providing all the features and benefits of cross-Layer information exchange and interactions to arbitrary combinations of applications, proto- cols and wireless technologies (3)-(7). The ultimate Cognitive Radio is expected to be able to interact with this architecture using Optimization algorithms and Artificial Intelligence (AI) techniques in order to exploit the available resources at their maximum, and therefore to enhance the service quality per- ceived by the user. However, although the awareness of the need for a generic and universal cross-Layer framework coexisting with the tradi- tional protocol stack is fairly well established in the research community, a definitive formulation for it is still lacking. There are several challenges in the design of a cross-Layer architecture: modularity, affordable complexity and scalability of the architecture are of primary importance. In this respect the correct choice of the semantics of cross-Layer information and commands to be exchanged becomes crucial: only by rep- resenting information in an abstract, technology-independent format can the modularity and scalability constraints be met. Moreover, if all available information and tunable parameters are exported from all Layers to, e.g., a central cognitive engine, the design of the engine itself can easily become impractical because of the overwhelming complexity; as a consequence, it is often wise to have each Layer itself handling its own complexity, exporting through the cross-Layer system only a small set of highly significant pieces of information. Finally, the way information is represented should allow an easy and unambiguous interpretation, taking into account factors such as data uncertainty and incompleteness. It is our opinion that using incomplete knowledge represen- tation and qualitative reasoning can be an effective strategy in meeting the above mentioned challenges; in particular, we propose the use of Fuzzy Logic as a convenient knowledege representation scheme for cross-Layer information, and of Fuzzy Control Theory as a suitable AI technique for the im- plementation of cognitive cross-Layer Optimization strategies. The rest of this paper is organized as follows. In section II we will review some important issues in Cognitive Cross- Layer Architecture design, highlighting the key challenges and showing which ones are not met by existing proposals. In section III we will very briefly summarize the main concepts of Fuzzy Logic and Fuzzy Control Theory, and in section IV we will propose our Fuzzy Cognitive Cross-Layer Architecture. In section V we will show a possible implementation which uses fuzzy cross-Layering for the enhancement of TCP performance over 802.11. Finally, in section VI the conclusions will be drawn.
Yake Zhang - One of the best experts on this subject based on the ideXlab platform.
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Distributed Energy-Efficient Cross-Layer Optimization for Multihop MIMO Cognitive Radio Networks With Primary User Rate Protection
IEEE Transactions on Vehicular Technology, 2017Co-Authors: Weiqiang Xu, Xiaodong Wang, Wenchu Yuan, Yake ZhangAbstract:Due to the unique physical-Layer characteristics associated with multiple-input multiple-output (MIMO) and cognitive radio (CR), the network performance is tightly coupled with mechanisms at the physical, link, network, and transport Layers. In this paper, we consider an energy-efficient cross-Layer Optimization problem in multihop MIMO CR networks (CRNs) and provide a new formulation to balance the weighted network utility and the weighted power consumption of secondary users (SUs), with the minimum transmission rate constraint of primary users (PUs) and the SU power consumption constraint. However, this formulation is highly challenging due to the nonconvexity of the PU rate constraint. We propose a solution that features a linearization-based alternative Optimization method and a heuristic primal recovery method. We further develop a distributed algorithm to jointly optimize the covariance matrix of the transmitted signal vector at each SU node, bandwidth allocation at each SU link, rate control at each session source, and multihop/multipath routing. Extensive simulation results demonstrate that the performance of the proposed distributed algorithm is very close to that of the centralized algorithm, and the proposed formulation provides an efficient way to save power consumption significantly, while achieving the network utility very close to that achieved with full power consumption.
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distributed energy efficient cross Layer Optimization for multihop mimo cognitive radio networks with primary user rate protection
arXiv: Networking and Internet Architecture, 2014Co-Authors: Wenchu Yuan, Xiaodong Wang, Qingjiang Shi, Yake ZhangAbstract:Due to the unique physical-Layer characteristics associated with MIMO and cognitive radio (CR), the network performance is tightly coupled with mechanisms at the physical, link, network, and transport Layers. In this paper, we consider an energy-efficient cross-Layer Optimization problem in multihop MIMO CR networks. The objective is to balance the weighted network utility and weighted power consumption of SU sessions, with a minimum PU transmission rate constraint and SU power consumption constraints. However, this problem is highly challenging due to the nonconvex PU rate constraint. We propose a solution that features linearization-based alternative Optimization method and a heuristic primal recovery method. We further develop a distributed algorithm to jointly optimize covariance matrix at each transmitting SU node, bandwidth allocation at each SU link, rate control at each session source and multihop/multi-path routing. Extensive simulation results demonstrate that the performance of the proposed distributed algorithm is close to that of the centralized algorithm, and the proposed framework provides an efficient way to significantly save power consumption, while achieving the network utility very close to that achieved with full power consumption.
Guocong Song - One of the best experts on this subject based on the ideXlab platform.
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cross Layer Optimization for ofdm wireless networks part i theoretical framework
IEEE Transactions on Wireless Communications, 2005Co-Authors: Guocong SongAbstract:In this paper, we provide a theoretical framework for cross-Layer Optimization for orthogonal frequency division multiplexing (OFDM) wireless networks. The utility is used in our study to build a bridge between the physical Layer and the media access control (MAC) Layer and to balance the efficiency and fairness of wireless resource allocation. We formulate the cross-Layer Optimization problem as one that maximizes the average utility of all active users subject to certain conditions, which are determined by adaptive resource allocation schemes. We present necessary and sufficient conditions for utility-based optimal subcarrier assignment and power allocation and discuss the convergence properties of Optimization. Numerical results demonstrate a significant performance gain for the cross-Layer Optimization and the gain increases with the number of active users in the networks.
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cross Layer Optimization for ofdm wireless networks part ii algorithm development
IEEE Transactions on Wireless Communications, 2005Co-Authors: Guocong SongAbstract:We have established a theoretical framework for cross-Layer Optimization in orthogonal frequency division multiplexing (OFDM) wireless networks. In this paper, we focus on effective and practical algorithms for efficient and fair resource allocation in OFDM wireless networks. We have taken various conditions into account and developed a variety of efficient algorithms, including sorting-search dynamic subcarrier assignment, greedy bit loading, and power allocation, as well as objective aggregation algorithms. We have also modified those algorithms for a certain type of nonconcave utility functions. To further improve performance by exploiting time diversity, a low-pass time filter can be easily incorporated into all of the algorithms. Simulation results have confirmed that the utility-based cross-Layer Optimization can significantly enhance the system performance and guarantee fairness. The gains come from multiuser diversity, frequency diversity, as well as time diversity. The fairness is automatically achieved by the behavior of marginal utility functions.
Weiqiang Xu - One of the best experts on this subject based on the ideXlab platform.
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Distributed Energy-Efficient Cross-Layer Optimization for Multihop MIMO Cognitive Radio Networks With Primary User Rate Protection
IEEE Transactions on Vehicular Technology, 2017Co-Authors: Weiqiang Xu, Xiaodong Wang, Wenchu Yuan, Yake ZhangAbstract:Due to the unique physical-Layer characteristics associated with multiple-input multiple-output (MIMO) and cognitive radio (CR), the network performance is tightly coupled with mechanisms at the physical, link, network, and transport Layers. In this paper, we consider an energy-efficient cross-Layer Optimization problem in multihop MIMO CR networks (CRNs) and provide a new formulation to balance the weighted network utility and the weighted power consumption of secondary users (SUs), with the minimum transmission rate constraint of primary users (PUs) and the SU power consumption constraint. However, this formulation is highly challenging due to the nonconvexity of the PU rate constraint. We propose a solution that features a linearization-based alternative Optimization method and a heuristic primal recovery method. We further develop a distributed algorithm to jointly optimize the covariance matrix of the transmitted signal vector at each SU node, bandwidth allocation at each SU link, rate control at each session source, and multihop/multipath routing. Extensive simulation results demonstrate that the performance of the proposed distributed algorithm is very close to that of the centralized algorithm, and the proposed formulation provides an efficient way to save power consumption significantly, while achieving the network utility very close to that achieved with full power consumption.
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energy management and cross Layer Optimization for wireless sensor network powered by heterogeneous energy sources
IEEE Transactions on Wireless Communications, 2015Co-Authors: Weiqiang Xu, Yushu Zhang, Xiaodong WangAbstract:Recently, utilizing renewable energy for wireless system has attracted extensive attention. However, due to the instable energy supply and the limited battery capacity, renewable energy cannot guarantee to provide the perpetual operation for wireless sensor networks (WSN). The coexistence of renewable energy and electricity grid is expected as a promising energy supply manner to remain function of WSN for a potentially infinite lifetime. In this paper, we propose a new system model suitable for WSN, taking into account multiple energy consumptions due to sensing, transmission and reception, heterogeneous energy supplies from renewable energy, electricity grid and mixed energy, and multi-dimension stochastic natures due to energy harvesting profile, electricity price and channel condition. A discrete-time stochastic cross-Layer Optimization problem is formulated to achieve the optimal trade-off between the time-average rate utility and electricity cost subject to the data and energy queuing stability constraints. The Lyapunov drift-plus-penalty with perturbation technique and block coordinate descent method is applied to obtain a fully distributed and low-complexity cross-Layer algorithm only requiring knowledge of the instantaneous system state. The explicit trade-off between the Optimization objective and queue backlog is theoretically proven. Finally, through extensive simulations, the theoretic claims are verified, and the impacts of a variety of system parameters on overall objective, rate utility and electricity cost are investigated.