The Experts below are selected from a list of 6699 Experts worldwide ranked by ideXlab platform
Herwig Bruneel - One of the best experts on this subject based on the ideXlab platform.
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Efficient performance analysis of newly proposed sleep-mode mechanisms for IEEE 802.16m in case of correlated Downlink Traffic
Wireless Networks, 2013Co-Authors: Koen Turck, Stijn Vuyst, Dieter Fiems, Herwig Bruneel, Sabine WittevrongelAbstract:There is a considerable interest nowadays in improving energy efficiency of wireless telecommunications. The sleep-mode mechanism in WiMAX (IEEE 802.16) and the discontinuous reception (DRX) mechanism of LTE are prime examples of energy saving measures. Recently, Samsung proposed some modifications on the sleep-mode mechanism, scheduled to appear in the forthcoming IEEE 802.16m standard, aimed at minimizing the signaling overhead. In this work, we present a performance analysis of this proposal and clarify the differences with the standard mechanism included in IEEE 802.16e. We also propose some special algorithms aimed at reducing the computational complexity of the analysis.
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performance of the sleep mode mechanism of the new ieee 802 16m proposal for correlated Downlink Traffic
Lecture Notes in Computer Science, 2009Co-Authors: Koen Turck, Stijn Vuyst, Dieter Fiems, Sabine Wittevrongel, Herwig BruneelAbstract:There is a considerable interest nowadays in making wireless telecommunication more energy-efficient. The sleep-mode mechanism in WiMAX (IEEE 802.16e) is one of such energy saving measures. Recently, Samsung proposed some modifications on the sleep-mode mechanism, scheduled to appear in the forthcoming IEEE 802.16m standard, aimed at minimizing the signaling overhead. In this work, we present a performance analysis of this proposal and clarify the differences with the standard mechanism included in IEEE 802.16e. We also propose some special algorithms aimed at reducing the computational complexity of the analysis.
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delay versus energy consumption of the ieee 802 16e sleep mode mechanism
IEEE Transactions on Wireless Communications, 2009Co-Authors: Stijn Vuyst, Koen Turck, Dieter Fiems, Sabine Wittevrongel, Herwig BruneelAbstract:We propose a discrete-time queueing model for the evaluation of the IEEE 802.16e sleep-mode mechanism of power saving class (PSC) I in wireless access networks. Contrary to previous studies, we model the Downlink Traffic by means of a discrete batch Markov arrival process (D-BMAP) with N phases, which allows to take Traffic correlation into account. The tradeoff between energy saving and increased packet delay is discussed. In many situations, the sleep-mode performance improves for heavily correlated Traffic. Also, when compared to other strategies, the exponential sleep-period update strategy of PSC I may not always be the best.
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performance of the ieee 802 16e sleep mode mechanism in the presence of bidirectional Traffic
International Conference on Communications, 2009Co-Authors: K De Turck, Dieter Fiems, Sabine Wittevrongel, Sergey Andreev, S De Vuyst, Herwig BruneelAbstract:We refine existing performance studies of the WiMAX sleep mode operation to take into account uplink as well as Downlink Traffic. This as opposed to previous studies which neglected the influence of uplink Traffic. We obtain numerically efficient procedures to compute both delay and energy efficiency characteristics. A test scenario with an Individual Subscriber Internet Traffic model in both directions shows that even a small amount of uplink Traffic has a profound effect on the system performance.
Matti Hamalainen - One of the best experts on this subject based on the ideXlab platform.
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fair Downlink Traffic management for hybrid laa lte wi fi networks
IEEE Access, 2017Co-Authors: Ting Zhou, Yang Yang, Matti HamalainenAbstract:Due to the scarcity of the licensed spectrum allocated for mobile communication systems, licensed-assisted access long-term evolution (LAA-LTE) network is recently proposed to deploy in unlicensed spectrum, which is currently occupied by different Wi-Fi systems. It is a very challenging problem to ensure fair coexistence between LAA-LTE and Wi-Fi networks, in terms of spectrum sharing and Traffic management. To solve this problem, a fair Downlink Traffic management (FDTM) scheme is proposed in this paper for hybrid LAA-LTE/Wi-Fi networks. FDTM aims to tune the minimum contention window ( $CW_{min}$ ) values and assigns feasible weights for the LAA eNBs with different Traffic loads, thus to achieve; 1) fair spectrum sharing with the coexisting Wi-Fi networks in unlicensed spectrum and 2) fair service differentiation for Downlink LAA-LTE Traffic. Numerical results show our FDTM scheme can guarantee the throughput performance of Wi-Fi networks in shared unlicensed spectrum while supporting proportional fairness for the LAA eNBs with different Traffic loads.
Merouane Debbah - One of the best experts on this subject based on the ideXlab platform.
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predictive deployment of uav base stations in wireless networks machine learning meets contract theory
IEEE Transactions on Wireless Communications, 2021Co-Authors: Qianqian Zhang, Walid Saad, Mehdi Bennis, Merouane Debbah, Wangda ZuoAbstract:In this paper, a novel framework is proposed to enable a predictive deployment of unmanned aerial vehicles (UAVs) as temporary base stations (BSs) to complement ground cellular systems in face of Downlink Traffic overload. First, a novel learning approach, based on the weighted expectation maximization (WEM) algorithm, is proposed to estimate the user distribution and the Downlink Traffic demand. Next, to guarantee a truthful information exchange between the BS and UAVs, using the framework of contract theory, an offload contract is developed, and the sufficient and necessary conditions for having a feasible contract are analytically derived. Subsequently, an optimization problem is formulated to deploy an optimal UAV onto the hotspot area in a way that the utility of the overloaded BS is maximized. Simulation results show that the proposed WEM approach yields a prediction error of around 10%. Compared with the expectation maximization and k-mean approaches, the WEM method shows a significant advantage on the prediction accuracy, as the Traffic load in the cellular system becomes spatially uneven. Furthermore, compared with two event-driven deployment schemes based on the closest-distance and maximal-energy metrics, the proposed predictive approach enables UAV operators to provide efficient communication service for hotspot users in terms of the Downlink capacity, energy consumption and service delay. Simulation results also show that the proposed method significantly improves the revenues of both the BS and UAV networks, compared with two baseline schemes.
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data correlation aware resource management in wireless virtual reality vr an echo state transfer learning approach
IEEE Transactions on Communications, 2019Co-Authors: Mingzhe Chen, Walid Saad, Changchuan Yin, Merouane DebbahAbstract:Providing seamless connectivity for wireless virtual reality (VR) users has emerged as a key challenge for future cloud-enabled cellular networks. In this paper, the problem of wireless VR resource management is investigated for a wireless VR network in which VR contents are sent by a cloud to cellular small base stations (SBSs). The SBSs will collect tracking data from the VR users, over the uplink, in order to generate the VR content and transmit it to the end-users using Downlink cellular links. For this model, the data requested or transmitted by the users can exhibit correlation, since the VR users may engage in the same immersive virtual environment with different locations and orientations. As such, the proposed resource management framework can factor in such spatial data correlation, so as to better manage uplink and Downlink Traffic. This potential spatial data correlation can be factored into the resource allocation problem to reduce the Traffic load in both the uplink and Downlink. In the Downlink, the cloud can transmit 360° contents or specific visible contents (e.g., user field of view) that are extracted from the original 360° contents to the users according to the users’ data correlation so as to reduce the backhaul Traffic load. In the uplink, each SBS can associate with the users that have similar tracking information so as to reduce the tracking data size. This data correlation-aware resource management problem is formulated as an optimization problem whose goal is to maximize the users’ successful transmission probability, defined as the probability that the content transmission delay of each user satisfies an instantaneous VR delay target. To solve this problem, a machine learning algorithm that uses echo state networks (ESNs) with transfer learning is introduced. By smartly transferring information on the SBS’s utility, the proposed transfer-based ESN algorithm can quickly cope with changes in the wireless networking environment due to users’ content requests and content request distributions. Simulation results demonstrate that the developed algorithm achieves up to 15.8% and 29.4% gains in terms of successful transmission probability compared to Q-learning with data correlation and Q-learning without data correlation, respectively.
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data correlation aware resource management in wireless virtual reality vr an echo state transfer learning approach
arXiv: Information Theory, 2019Co-Authors: Mingzhe Chen, Walid Saad, Changchuan Yin, Merouane DebbahAbstract:In this paper, the problem of wireless virtual reality (VR) resource management is investigated for a wireless VR network in which VR contents are sent by a cloud to cellular small base stations (SBSs). The SBSs will collect tracking data from the VR users, over the uplink, in order to generate the VR content and transmit it to the end-users using Downlink cellular links. For this model, the data requested or transmitted by the users can exhibit correlation, since the VR users may engage in the same immersive virtual environment with different locations and orientations. As such, the proposed resource management framework can factor in such spatial data correlation, to better manage uplink and Downlink Traffic. This potential spatial data correlation can be factored into the resource allocation problem to reduce the Traffic load in both uplink and Downlink. In the Downlink, the cloud can transmit 360 contents or specific visible contents that are extracted from the original 360 contents to the users according to the users' data correlation to reduce the backhaul Traffic load. For uplink, each SBS can associate with the users that have similar tracking information so as to reduce the tracking data size. This data correlation-aware resource management problem is formulated as an optimization problem whose goal is to maximize the users' successful transmission probability, defined as the probability that the content transmission delay of each user satisfies an instantaneous VR delay target. To solve this problem, an echo state networks (ESNs) based transfer learning is introduced. By smartly transferring information on the SBS's utility, the proposed transfer-based ESN algorithm can quickly cope with changes in the wireless networking environment.
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data correlation aware resource management in wireless virtual reality vr an echo state transfer learning approach
Post-Print, 2019Co-Authors: Mingzhe Chen, Walid Saad, Changchuan Yin, Merouane DebbahAbstract:Providing seamless connectivity for wireless virtual reality (VR) users has emerged as a key challenge for future cloud-enabled cellular networks. In this paper, the problem of wireless VR resource management is investigated for a wireless VR network in which VR contents are sent by a cloud to cellular small base stations (SBSs). The SBSs will collect tracking data from the VR users, over the uplink, in order to generate the VR content and transmit it to the end-users using Downlink cellular links. For this model, the data requested or transmitted by the users can exhibit correlation, since the VR users may engage in the same immersive virtual environment with different locations and orientations. As such, the proposed resource management framework can factor in such spatial data correlation, so as to better manage uplink and Downlink Traffic. This potential spatial data correlation can be factored into the resource allocation problem to reduce the Traffic load in both uplink and Downlink. In the Downlink, the cloud can transmit 360 • contents or specific visible contents (e.g., user field of view) that are extracted from the original 360 • contents to the users according to the users' data correlation so as to reduce the backhaul Traffic load. In the uplink, each SBS can associate with the users that have similar tracking information so as to reduce the tracking data size. This data correlation-aware resource management problem is formulated as an optimization problem whose goal is to maximize the users' successful transmission probability, defined as the probability that the content transmission delay of each user satisfies an instantaneous VR delay target. To solve this problem, a machine learning algorithm that uses echo state networks (ESNs) with transfer learning is introduced. By smartly transferring information on the SBS's utility, the proposed transfer-based ESN algorithm can quickly cope with changes in the wireless networking environment due to users' content requests and content request distributions. Simulation results demonstrate that the developed algorithm achieves up to 15.8% and 29.4% gains in terms of successful transmission probability compared to Q-learning with data correlation and Q-learning without data correlation.
Ting Zhou - One of the best experts on this subject based on the ideXlab platform.
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fair Downlink Traffic management for hybrid laa lte wi fi networks
IEEE Access, 2017Co-Authors: Ting Zhou, Yang Yang, Matti HamalainenAbstract:Due to the scarcity of the licensed spectrum allocated for mobile communication systems, licensed-assisted access long-term evolution (LAA-LTE) network is recently proposed to deploy in unlicensed spectrum, which is currently occupied by different Wi-Fi systems. It is a very challenging problem to ensure fair coexistence between LAA-LTE and Wi-Fi networks, in terms of spectrum sharing and Traffic management. To solve this problem, a fair Downlink Traffic management (FDTM) scheme is proposed in this paper for hybrid LAA-LTE/Wi-Fi networks. FDTM aims to tune the minimum contention window ( $CW_{min}$ ) values and assigns feasible weights for the LAA eNBs with different Traffic loads, thus to achieve; 1) fair spectrum sharing with the coexisting Wi-Fi networks in unlicensed spectrum and 2) fair service differentiation for Downlink LAA-LTE Traffic. Numerical results show our FDTM scheme can guarantee the throughput performance of Wi-Fi networks in shared unlicensed spectrum while supporting proportional fairness for the LAA eNBs with different Traffic loads.
Sabine Wittevrongel - One of the best experts on this subject based on the ideXlab platform.
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Efficient performance analysis of newly proposed sleep-mode mechanisms for IEEE 802.16m in case of correlated Downlink Traffic
Wireless Networks, 2013Co-Authors: Koen Turck, Stijn Vuyst, Dieter Fiems, Herwig Bruneel, Sabine WittevrongelAbstract:There is a considerable interest nowadays in improving energy efficiency of wireless telecommunications. The sleep-mode mechanism in WiMAX (IEEE 802.16) and the discontinuous reception (DRX) mechanism of LTE are prime examples of energy saving measures. Recently, Samsung proposed some modifications on the sleep-mode mechanism, scheduled to appear in the forthcoming IEEE 802.16m standard, aimed at minimizing the signaling overhead. In this work, we present a performance analysis of this proposal and clarify the differences with the standard mechanism included in IEEE 802.16e. We also propose some special algorithms aimed at reducing the computational complexity of the analysis.
-
performance of the sleep mode mechanism of the new ieee 802 16m proposal for correlated Downlink Traffic
Lecture Notes in Computer Science, 2009Co-Authors: Koen Turck, Stijn Vuyst, Dieter Fiems, Sabine Wittevrongel, Herwig BruneelAbstract:There is a considerable interest nowadays in making wireless telecommunication more energy-efficient. The sleep-mode mechanism in WiMAX (IEEE 802.16e) is one of such energy saving measures. Recently, Samsung proposed some modifications on the sleep-mode mechanism, scheduled to appear in the forthcoming IEEE 802.16m standard, aimed at minimizing the signaling overhead. In this work, we present a performance analysis of this proposal and clarify the differences with the standard mechanism included in IEEE 802.16e. We also propose some special algorithms aimed at reducing the computational complexity of the analysis.
-
delay versus energy consumption of the ieee 802 16e sleep mode mechanism
IEEE Transactions on Wireless Communications, 2009Co-Authors: Stijn Vuyst, Koen Turck, Dieter Fiems, Sabine Wittevrongel, Herwig BruneelAbstract:We propose a discrete-time queueing model for the evaluation of the IEEE 802.16e sleep-mode mechanism of power saving class (PSC) I in wireless access networks. Contrary to previous studies, we model the Downlink Traffic by means of a discrete batch Markov arrival process (D-BMAP) with N phases, which allows to take Traffic correlation into account. The tradeoff between energy saving and increased packet delay is discussed. In many situations, the sleep-mode performance improves for heavily correlated Traffic. Also, when compared to other strategies, the exponential sleep-period update strategy of PSC I may not always be the best.
-
performance of the ieee 802 16e sleep mode mechanism in the presence of bidirectional Traffic
International Conference on Communications, 2009Co-Authors: K De Turck, Dieter Fiems, Sabine Wittevrongel, Sergey Andreev, S De Vuyst, Herwig BruneelAbstract:We refine existing performance studies of the WiMAX sleep mode operation to take into account uplink as well as Downlink Traffic. This as opposed to previous studies which neglected the influence of uplink Traffic. We obtain numerically efficient procedures to compute both delay and energy efficiency characteristics. A test scenario with an Individual Subscriber Internet Traffic model in both directions shows that even a small amount of uplink Traffic has a profound effect on the system performance.
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Performance analysis of the IEEE 802.16e sleep mode for correlated Downlink Traffic
Telecommunication Systems, 2008Co-Authors: Koen Turck, Stijn Vuyst, Dieter Fiems, Sabine WittevrongelAbstract:In this paper, we evaluate the performance of the IEEE 802.16e sleep mode mechanism in wireless access networks. This mechanism reduces the energy consumption of a mobile station (MS) by allowing it to turn off its radio interface (sleep mode) when there is no Traffic present at its serving base station (BS). After a sleep period expires, the MS briefly checks the BS for data packets and switches off for the duration of another sleep period if none are available. Specifically for IEEE 802.16e, each additional sleep period doubles in length, up to a certain maximum. Clearly, the sleep mode mechanism can extend the battery life of the MS considerably, but also increases the delay at the BS buffer. For the performance analysis, we use a discrete-time queueing model with general service times and multiple server vacations. The vacations represent the sleep periods and have a length depending on the number of preceding vacations. Unlike previous studies, we take the (short-range) Traffic correlation into account by assuming a D-BMAP arrival process, i.e. the distribution of the number of packet arrivals per slot is modulated by the transitions in a Markov chain with N background states. As results, we obtain the distribution of the number of packets in the queue at various sets of time epochs, the distribution of the packet delay and the antenna activity rate. We apply these results to the IEEE 802.16e sleep mode mechanism with correlated Downlink Traffic. By means of some examples, we show the influence of both the configuration parameters and the Traffic correlation on the delay and the energy consumption.