11 research outputs found

    Analytical Model of Proportional Fair Scheduling in Interference-limited OFDMA/LTE Networks

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    Various system tasks like interference coordination, handover decisions, admission control etc. in upcoming cellular networks require precise mid-term (spanning over a few seconds) performance models. Due to channel-dependent scheduling at the base station, these performance models are not simple to obtain. Furthermore, upcoming cellular systems will be interference-limited, hence, the way interference is modeled is crucial for the accuracy. In this paper we present an analytical model for the SINR distribution of the \textit{scheduled} subcarriers of an OFDMA system with proportional fair scheduling. The model takes the precise SINR distribution into account. We furthermore refine our model with respect to uniform modulation and coding, as applied in LTE networks. The derived models are validated by means of simulations. In additon, we show that our models are approximate estimators for the performance of rate-based proportional fair scheduling, while they outperform some simpler prediction models from related work significantly.Comment: 7 pages, 6 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

    An Improved-Water Filling Algorithm Power Allocation for DFFR Network MIMO

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    In wireless systems, interference is a major factor that limits the total network capacity. Power allocation is one of the effective techniques that has garnered interest in Network MIMO system and improved the efficiency of wireless systems. This study presents the development of a new power allocation algorithm based on water filling. This algorithm combines the Dynamic Fractional Frequency Reuse (DFFR) with a Network MIMO to maximise the performance of cell edge users. Simulation results show that the proposed algorithm provides more ergodic capacity, compared with the existing power allocation strategies. In addition, it improves the network throughput, while ensuring fairness for cell edge users in the LTE-A system. When the total transmit power is 100W, the proposed algorithm offers 50% capacity, 37.5% throughput and 38% fairness advantage over the conventional water-filling algorithm

    Adaptive transmission in heterogeneous networks

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    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166243/1/cmu2bf00018.pd

    Dynamic Fractional Frequency Reuse Based On An Improved Water-Filling For Network MIMO

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    In Long Term Evolution-Advanced (LTE-A) systems, Inter-cell Interference (ICI) is a prominent limiting factor that affects the performance of the systems, especially at the cell edges. Based on the literature, Fractional Frequency Reuse (FFR) methods are known as efficient interference management techniques. In this report, the proposed Dynamic Fractional Frequency Reuse (DFFR) technique improved the capacity and cell edge coverage performance by 70% compared to the Fractional Frequency Reuse (FFR) technique. In this study, an improved power allocation method was adopted into the DFFR technique to reach the goal of not only reducing the ICI mitigation at the cell edges, but also improving the overall capacity of the LTE-A systems. Hence, an improved water-filling algorithm was proposed, and its performance was compared with that of other methods that were considered. Through the simulation results and comparisons with other frequency reuse techniques, it was shown that the proposed method significantly improved the performance of the cell edge throughput by 42%, the capacity by 75%, and the coverage by 80%. Based on the analysis and numerical expressions, it was concluded that the proposed DFFR method provides significant performance improvements, especially for cell edge users

    The design and optimization of cooperative mobile edge

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    As the world is charging towards the Internet of Things (IoT) era, an enormous amount of sensors will be rapidly empowered with internet connectivity. Besides the fact that the end devices are getting more diverse, some of them are also becoming more powerful, such that they can function as standalone mobile computing units with multiple wireless network interfaces. At the network end, various facilities are also pushed to the mobile edge to foster internet connections. Distributed small scale cloud resources and green energy harvesters can be directly attached to the deployed heterogeneous base stations. Different from the traditional wireless access networks, where the only dynamics come from the user mobility, the evolving mobile edge will be operated in the constantly changing and volatile environment. The harvested green energy will be highly dependent on the available energy sources, and the dense deployment of a variety of wireless access networks will result in intense radio resource contention. Consequently, the wireless networks are facing great challenges in terms of capacity, latency, energy/spectrum efficiency, and security. Equivalently, balancing the dynamic network resource demand and supply is essential to the smooth network operation. Leveraging the broadcasting nature of wireless data transmission, network nodes can cooperate with each other by either allowing users to connect with multiple base stations simultaneously or offloading user workloads to neighboring base stations. Moreover, grid facilitated and radio frequency signal enabled renewable energy sharing among network nodes are introduced in this dissertation. In particular, the smart grid can transfer the green energy harvested by each individual network node from one place to another. The network node can also transmit energy from one to another using radio frequency energy transfer. This dissertation addresses the cooperative network resource management to improve the energy efficiency of the mobile edge. First, the energy efficient cooperative data transmission scheme is designed to cooperatively allocate the radio resources of the wireless networks, including spectrum and power, to the mobile users. Then, the cooperative data transmission and wireless energy sharing scheme is designed to optimize both the energy and data transmission in the network. Finally, the cooperative data transmission and wired energy sharing scheme is designed to optimize the energy flow within the smart grid and the data transmission in the network. As future work, how to motivate multiple parties to cooperate and how to guarantee the security of the cooperative mobile edge is discussed. On one hand, the incentive scheme for each individual network node with distributed storage and computing resources is designed to improve network performance in terms of latency. On the other hand, how to leverage network cooperation to balance the tradeoff between efficiency (energy efficiency and latency) and security (confidentiality and privacy) is expounded

    ํŽจํ† ์…€ ๋„คํŠธ์›Œํฌ์—์„œ ์ž์› ๊ด€๋ฆฌ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2014. 8. ์ „ํ™”์ˆ™.๋ชจ๋ฐ”์ผ ํŠธ๋ž˜ํ”ฝ ์ˆ˜์š”๊ฐ€ ํญ๋ฐœ์ ์œผ๋กœ ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ์‹ค๋‚ด ์‚ฌ์šฉ์ž๋“ค์—๊ฒŒ ๋‚ฎ์€ ๋น„์šฉ์œผ๋กœ ๊ณ ํ’ˆ์งˆ์˜ ๋ฐ์ดํ„ฐ ์„œ๋น„์Šค๋ฅผ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ๋Š” ํŽจํ† ์…€์ด ์ฃผ๋ชฉ์„ ๋ฐ›๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํŽจํ† ์…€์ด ๊ธฐ์กด์˜ ๋งคํฌ๋กœ์…€ ์œ„์— ๊ตฌ์ถ•๋œ two-tier ํŽจํ† ์…€ ๋„คํŠธ์›Œํฌ์—์„œ ์ฃผํŒŒ์ˆ˜ ํšจ์œจ๊ณผ ์—๋„ˆ์ง€ ํšจ์œจ ํ–ฅ์ƒ์„ ์œ„ํ•œ ๋‘ ๊ฐ€์ง€ ์ž์› ๊ด€๋ฆฌ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋จผ์ €, ์ฃผํŒŒ์ˆ˜ ํšจ์œจ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ํŽจํ† ์…€๋“ค๊ณผ ์ค‘์ฒฉ ๋งคํฌ๋กœ์…€ ์‚ฌ์ด์˜ ํ•˜ํ–ฅ ๋งํฌ ๋ฌด์„  ์ž์› ๋ถ„ํ• (radio resource partitioning) ๊ธฐ๋ฒ•์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ œ์•ˆํ•˜๋Š” ๋ฌด์„  ์ž์› ๋ถ„ํ•  ๊ธฐ๋ฒ•์—์„œ๋Š” ๋ชจ๋ฐ”์ผ ๋ฐ์ดํ„ฐ ํญ์ฆ ๋ฌธ์ œ์— ๋Œ€ํ•œ ๋˜ ๋‹ค๋ฅธ ํ•ด๊ฒฐ ๋ฐฉ์•ˆ์ธ ๋ถ„ํ•  ์ฃผํŒŒ์ˆ˜ ์žฌ์‚ฌ์šฉ(fractional frequency reuse, FFR) ๊ธฐ์ˆ ์ด ์ ์šฉ๋œ ๋งคํฌ๋กœ์…€ ๋„คํŠธ์›Œํฌ๋ฅผ ๊ณ ๋ คํ•˜์˜€๋‹ค. FFR ๊ตฌ์กฐ์—์„œ ๋งคํฌ๋กœ์…€์˜ ์ฃผํŒŒ์ˆ˜ ๋Œ€์—ญ์€ ๋‹ค์ˆ˜์˜ ์ฃผํŒŒ์ˆ˜ ๋ถ„ํ• ๋“ค(frequency partitions, FPs)๋กœ ๋‚˜๋ˆ„์–ด์ง€๊ณ , FP๋งˆ๋‹ค ๋‹ค๋ฅธ ์ „์†ก ์ „๋ ฅ์ด ํ• ๋‹น๋œ๋‹ค. ์ œ์•ˆํ•œ ๊ธฐ๋ฒ•์—์„œ ๊ฐ FP๋Š” ๋‹ค์‹œ ๋งคํฌ๋กœ ์ „์šฉ ๋ถ€๋ถ„(macro-dedicated portion), ๊ณต์šฉ ๋ถ€๋ถ„(shared portion), ๊ทธ๋ฆฌ๊ณ  ํŽจํ†  ์ „์šฉ ๋ถ€๋ถ„(femto-dedicated portion)์œผ๋กœ ๊ตฌ์„ฑ๋˜๊ณ , ์ด ์„ธ ๋ถ€๋ถ„์˜ ๋น„์œจ์€ FP๋งˆ๋‹ค ๋‹ค๋ฅด๊ฒŒ ์„ค์ •๋œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๊ธฐ๋ฒ•์€ ์ตœ์ ํ™” ๋ฐฉ์‹์„ ์ด์šฉํ•˜์—ฌ ์ฃผํŒŒ์ˆ˜ ํšจ์œจ์„ ์ตœ๋Œ€ํ™”ํ•˜๋„๋ก ๊ฐ FP ๋‚ด ์ž์› ๋ถ„ํ•  ๋น„์œจ์„ ๊ฒฐ์ •ํ•œ๋‹ค. ๋‹ค์Œ์œผ๋กœ, ๊ณตํ•ญ ๋ฐ ์‡ผํ•‘๋ชฐ๊ณผ ๊ฐ™์ด ์‚ฌ์šฉ์ž๋“ค์ด ๋ฐ€์ง‘๋œ ๊ณต๊ณต์žฅ์†Œ์— ๋งŽ์€ ์ˆ˜์˜ ํŽจํ†  ๊ธฐ์ง€๊ตญ๋“ค์ด ์„ค์น˜๋œ ๊ฐœ๋ฐฉํ˜• ํŽจํ† ์…€ ๋„คํŠธ์›Œํฌ์—์„œ ์—๋„ˆ์ง€ ํšจ์œจ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ์ž์› ๊ด€๋ฆฌ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๊ณ ๋ คํ•˜๋Š” ํŽจํ† ์…€ ๋„คํŠธ์›Œํฌ์—์„œ๋Š” ํŽจํ†  ๊ธฐ์ง€๊ตญ๋“ค์ด ์ตœ๋Œ€ ํŠธ๋ž˜ํ”ฝ ๋ถ€ํ•˜๋ฅผ ์ง€์›ํ•˜๊ธฐ ์œ„ํ•ด ๋†’์€ ๋ฐ€๋„๋กœ ์„ค์น˜๋˜๊ธฐ ๋•Œ๋ฌธ์— ๋Œ€๋ถ€๋ถ„์˜ ๋™์ž‘ ์‹œ๊ฐ„ ๋™์•ˆ ํŽจํ† ์…€๋“ค์€ ๋ฌด์„  ์ž์›์„ ์ถฉ๋ถ„ํžˆ ํ™œ์šฉํ•˜์ง€ ์•Š๋Š”๋‹ค. ๋”ฐ๋ผ์„œ ์‚ฌ์šฉ์ž๋“ค์˜ ์…€ ์ ‘์†์„ ์ ์ ˆํžˆ ์กฐ์ •ํ•˜์—ฌ ๊ฐ€๋Šฅํ•œ ์ ์€ ํŽจํ†  ๊ธฐ์ง€๊ตญ๋“ค์„ ํ™œ์„ฑํ™”์‹œํ‚ค๊ณ  ๊ทธ ์ด์™ธ์˜ ํŽจํ†  ๊ธฐ์ง€๊ตญ๋“ค์„ ์ˆ˜๋ฉด ๋ชจ๋“œ(sleep mode)๋กœ ๋™์ž‘์‹œํ‚จ๋‹ค๋ฉด ํ•ด๋‹น ํŽจํ† ์…€ ์„ค์น˜ ์ง€์—ญ์—์„œ์˜ ๋„คํŠธ์›Œํฌ ์—๋„ˆ์ง€ ํšจ์œจ์„ ํฌ๊ฒŒ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์—๋„ˆ์ง€ ํšจ์œจ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ํŽจํ†  ๊ธฐ์ง€๊ตญ์˜ ๋™์ž‘ ๋ชจ๋“œ(active ๋˜๋Š” sleep)์™€ ์‚ฌ์šฉ์ž๋“ค์˜ ์…€ ์ ‘์†์„ ๋™์‹œ์— ๊ฒฐ์ •ํ•˜๋Š” ํŽจํ†  ๊ธฐ์ง€๊ตญ ๋™์ž‘ ๋ชจ๋“œ ๊ฒฐ์ • ๋ฐ ์‚ฌ์šฉ์ž ์ ‘์† (femto BS sleep decision and user association, SDUA) ๊ธฐ๋ฒ•์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ œ์•ˆํ•˜๋Š” ๊ธฐ๋ฒ•์—์„œ SDUA ๋ฌธ์ œ๋Š” ์‚ฌ์šฉ์ž๋“ค์—๊ฒŒ ๋งŒ์กฑํ•  ๋งŒํ•œ ์„œ๋น„์Šค๋ฅผ ์ œ๊ณตํ•˜๋ฉด์„œ ์ „์ฒด ์—๋„ˆ์ง€ ์†Œ๋ชจ๋ฅผ ์ตœ์†Œ๋กœ ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ์ตœ์ ํ™” ๋ฌธ์ œ๋กœ ์ •์‹ํ™”๋˜์—ˆ๋‹ค. SDUA ๋ฌธ์ œ๋Š” ๊ธฐ์ง€๊ตญ์˜ ๋™์ž‘ ๋ชจ๋“œ์™€ ์‚ฌ์šฉ์ž์˜ ์…€ ์ ‘์†์ด ์ƒํ˜ธ ์˜ํ–ฅ์„ ์ฃผ์–ด์„œ ๊ณ„์‚ฐ ๋ณต์žก๋„๊ฐ€ ๋†’์œผ๋ฏ€๋กœ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋จผ์ € ํ™œ์„ฑํ™” ํŽจํ†  ๊ธฐ์ง€๊ตญ๋“ค์˜ ์ง‘ํ•ฉ์ด ์ฃผ์–ด์ง„ ์ƒํƒœ์—์„œ ์ตœ์ ์˜ ์‚ฌ์šฉ์ž ์ ‘์†(user association, UA) ๋ฌธ์ œ๋ฅผ ํ’€๊ณ , ๊ฐ๊ธฐ ๋‹ค๋ฅธ ์ง‘ํ•ฉ๋“ค์— ๋Œ€ํ•ด์„œ ์ตœ์ ํ™” UA๋ฅผ ๋ฐ˜๋ณต์ ์œผ๋กœ ์ˆ˜ํ–‰ํ•จ์œผ๋กœ์จ ์ตœ์„ ์˜ ํ™œ์„ฑํ™” ํŽจํ†  ๊ธฐ์ง€๊ตญ ์ง‘ํ•ฉ์„ ์ฐพ๋Š” ํœด๋ฆฌ์Šคํ‹ฑ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ œ์•ˆํ•˜๋Š” ๋‘ ์ž์› ๊ด€๋ฆฌ ๊ธฐ๋ฒ•๋“ค์ด ๊ฐ๊ฐ ์ฃผํŒŒ์ˆ˜ ํšจ์œจ๊ณผ ์—๋„ˆ์ง€ ํšจ์œจ์— ๋Œ€ํ•ด์„œ ๊ธฐ์กด์˜ ๊ธฐ๋ฒ•๋“ค๋ณด๋‹ค ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋ณด์ž„์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ํ™•์ธํ•˜์˜€๋‹ค.Femtocell has received wide attention as a promising solution to meet explosively increasing traffic demand in cellular networks, since it can provide high quality data services to indoor users at low cost. In this thesis, we study resource management in two-tier femtocell networks where the femtocells are underlaid by macrocells, from two different aspects: spectral effciency and energy eciency. First, we design a downlink radio resource partitioning scheme between femtocells and their overlaid macrocell to enhance the spectral eciency. We consider that the overlaid macrocell network adopts the fractional frequency reuse (FFR) techniques, which is also one of solutions to the mobile data surge problem. With FFR, the frequency band of a macrocell is divided into several frequency partitions (FPs) and the transmission power levels assigned to FPs differ from each other. With the proposed scheme, every FP is divided into the macro-dedicated, the shared, and the femto-dedicated portions. The ratio of these three portions is different for each FP. We suggest a method to determine a proper ratio of portions in each FP, by using optimization approach. Next, we propose a scheme to enhance the energy efficiency in open access femtocell networks where many femto base stations (BSs) are deployed in a large public area such as office building, shopping mall, etc. In those areas, the femtocells are overlapped and underutilized during most of the operation time because femto BSs are densely deployed to support the peak traffic load. So, if we properly coordinate the user association with cells and put the femto BSs having no associated users to sleep, the network energy efficiency in the femtocell deployment area can be greatly enhanced. Therefore, we propose a femto BS sleep decision and user association (SDUA) scheme that jointly determines the operation modes (i.e., active or sleep) of femto BSs and the association between users and the active BSs. The SDUA problem is formulated as an optimization problem that aims at minimizing the total energy consumption while providing the satisfied service to users. Since the SDUA problem is too complicated to be solved, we first solve the optimal user association (UA) problem for given set of active femto BSs and then design a heuristic algorithm that finds the best set of active femto BSs by iteratively performing the optimal UA with each different set. By simulation, it is shown that the proposed schemes achieve their design goals properly and outperform existing schemes.1 Introduction 1.1 Background and Motivation 1.2 Proposed Resource Management Schemes 1.2.1 Radio Resource Partitioning Scheme for Spectral Efficiency Enhancement 1.2.2 Base Station Sleep Management Scheme for Energy Efficiency Enhancement 1.3 Organization 2 Radio Resource Partitioning Scheme for Spectral Efficiency Enhancement 2.1 System Model 2.1.1 Heterogeneous Network 2.1.2 Capacity Model 2.2 Proposed Downlink Radio Resource Partitioning Scheme 2.2.1 Macrocell Protection Mechanism 2.2.2 Determination of Dedicated Portion for Macrocell/Femtocell Users 2.3 Capacity Estimation 2.3.1 Achievable Macrosector Capacity 2.3.2 Achievable Femtocell Capacities 2.3.3 SHG Availability of Femtocell 3 Base Station Sleep Management Scheme for Energy Efficiency Enhancement 3.1 System Model 3.1.1 Open Access Femtocell Network 3.1.2 Operation Modes and Power Consumption of a BS 3.1.3 Energy Efficiency 3.2 Analysis on Energy Efficiency 3.2.1 Mathematical Model 3.2.2 Derivation of Energy Efficiency 3.2.3 Numerical Results and Discussion 3.3 Proposed Femto BS Sleep Decision and User Association (SDUA)Scheme 3.3.1 Problem Formulation 3.3.2 Solution Approach 3.3.3 Implementation Example of SIR Estimation 4 Performance Evaluation 4.1 Radio Resource Partitioning Scheme 4.1.1 Simulation Model 4.1.2 Simulation Results 4.2 Base Station Sleep Management Scheme 4.2.1 Simulation Model 4.2.2 Simulation Results 5 Conclusion Bibliography AbstractDocto

    Planificaciรณn y Optimizaciรณn Automรกtica de Redes Mรณviles LTE

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    Osa Ginรฉs, V. (2013). Planificaciรณn y Optimizaciรณn Automรกtica de Redes Mรณviles LTE [Tesis doctoral no publicada]. Universitat Politรจcnica de Valรจncia. https://doi.org/10.4995/Thesis/10251/29755TESI

    Gestiรณn de Recursos Radio en Redes Mรณviles Celulares Basadas en Tecnologรญa OFDMA para la Provisiรณn de QoS y Control de la Interferencia

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    El trabajo realizado en esta tesis, enmarcado en el contexto de la provisiรณn de QoS en redes mรณviles de banda ancha, se ha centrado en la propuesta y evaluaciรณn de algoritmos de asignaciรณn de recursos radio en el enlace descendente para la gestiรณn de la interferencia en redes basadas en tecnologรญa OFDMA. En un contexto de redes mรณviles de banda ancha en las que los usuarios demandan cada vez servicios mรกs diversos y con requisitos de QoS mรกs heterogรฉneos, resulta indispensable obtener un aprovechamiento mรกximo de los recursos radio disponibles en el sistema. Con este fin, la mayor parte de las redes contemplan un despliegue con reรบso unidad de modo que los mismos recursos son utilizados en todas las celdas del sistema. En este contexto, interferencia intercelular (ICI) es uno de los factores que mรกs impacto tienen en las prestaciones finales ofrecidas por los sistemas, especialmente para los usuarios situados en la zona exterior de la celda. El problema, lejos de estar resuelto, continรบa siendo objeto de estudio pues no existe una soluciรณn รณptima al mismo y existen un gran nรบmero de factores a implicados. El objetivo de esta tesis ha sido definir mecanismos de control de las interferencias intercelulares (en el caso de considerar sistema de reรบso frecuencial total a nivel de celda) e intersector (en el caso de considerar reรบso unidad en cada sector) que mitigan el efecto de las mismas y mejoran la calidad de la seรฑal recibida por estos usuarios exteriores. Bajo las restricciones definidas por el mecanismo de control de interferencias, se han diseรฑado algoritmos eficientes para la asignaciรณn dinรกmica de recursos radio dependientes del canal, que aseguren a su vez el cumplimiento de los requisitos de QoS de los distintos flujos de datos
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