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    ํ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

    Energy efficient design of cognitive small cells

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    Heterogeneous networks consisting of a macrocell tier and a small cell tier are considered an attractive solution to cope with the fierce increase of mobile traffic demand. Nevertheless, a massive deployment of small cell access points (SAPs) leads also to a considerable increase in energy consumption. Motivated by growing environmental awareness and the high price of energy, the design of energy efficient wireless systems for both macrocells and small cells becomes crucial. In this work, we analyze the trade-off between traffic offloading from the macrocell and the energy consumption of the small cell. Using tools from stochastic geometry, we define the user detection performance of the SAP and derive the small cell capacity accounting for the uncertainties associated with the random position of the user, the propagation channel, activity of the users, and the aggregate network interference. The proposed framework yields design guidelines for energy efficient small cells

    Energy efficient design of cognitive small cells

    Get PDF
    Heterogeneous networks consisting of a macrocell tier and a small cell tier are considered an attractive solution to cope with the fierce increase of mobile traffic demand. Nevertheless, a massive deployment of small cell access points (SAPs) leads also to a considerable increase in energy consumption. Motivated by growing environmental awareness and the high price of energy, the design of energy efficient wireless systems for both macrocells and small cells becomes crucial. In this work, we analyze the trade-off between traffic offloading from the macrocell and the energy consumption of the small cell. Using tools from stochastic geometry, we define the user detection performance of the SAP and derive the small cell capacity accounting for the uncertainties associated with the random position of the user, the propagation channel, activity of the users, and the aggregate network interference. The proposed framework yields design guidelines for energy efficient small cells
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