291 research outputs found

    An Efficient Uplink Multi-Connectivity Scheme for 5G mmWave Control Plane Applications

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    The millimeter wave (mmWave) frequencies offer the potential of orders of magnitude increases in capacity for next-generation cellular systems. However, links in mmWave networks are susceptible to blockage and may suffer from rapid variations in quality. Connectivity to multiple cells - at mmWave and/or traditional frequencies - is considered essential for robust communication. One of the challenges in supporting multi-connectivity in mmWaves is the requirement for the network to track the direction of each link in addition to its power and timing. To address this challenge, we implement a novel uplink measurement system that, with the joint help of a local coordinator operating in the legacy band, guarantees continuous monitoring of the channel propagation conditions and allows for the design of efficient control plane applications, including handover, beam tracking and initial access. We show that an uplink-based multi-connectivity approach enables less consuming, better performing, faster and more stable cell selection and scheduling decisions with respect to a traditional downlink-based standalone scheme. Moreover, we argue that the presented framework guarantees (i) efficient tracking of the user in the presence of the channel dynamics expected at mmWaves, and (ii) fast reaction to situations in which the primary propagation path is blocked or not available.Comment: Submitted for publication in IEEE Transactions on Wireless Communications (TWC

    ์‚ฌ์šฉ์ž ์ค‘์‹ฌ์˜ ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ํ†ต์‹  ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ์ด๋™์„ฑ ์ธ์‹ ๋ถ„์„ ํ”„๋ ˆ์ž„์›Œํฌ ๋ฐ ๋„คํŠธ์›Œํฌ ๊ด€๋ฆฌ ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2021. 2. ๋ฐ•์„ธ์›….Millimeter wave (mmWave) communication enables high rate transmission, but its network performance may be degraded significantly due to blockages between the transmitter and receiver. There have been two approaches to overcome the blockage effect and enhance link reliability: multi-connectivity and ultra-dense network (UDN). Particularly, multi-connectivity under a UDN environment facilitates user-centric communication. It requires dynamic configuration of serving base station groups so that each user experiences high quality services. This dissertation studies a mathematical framework and network manament schemes for user-centric mmWave communication systems. First, we models user mobility and mobility-aware performance in user-centric mmWave communication systems with multi-connectivity, and proposes a new analytical framework based on the stochastic geometry. To this end, we derive compact mathematical expressions for state transitions and probabilities of various events that each user experiences. Then we investigate mobility-aware performance in terms of network overhead and downlink throughput. This helps us to understand network operation in depth, and impacts of network density and multi-connection capability on the probability of handover related events. Numerical results verify the accuracy of our analysis and illustrate the correlation between mobility-aware performance and user speed. Next, we propose user-oriented configuration rules and price based association algorithms for user-centric mmWave networks with fully/partially wired backhauls. We develop a fair association algorithm by solving the optimization problem that we formulate for mmWave UDNs. The algorithm includes an access price based per-user request decision method and a price adjustment rule for load balancing. Based on insights from the algorithm, we develop path-aware access pricing policy for mmWave integrated access and backhaul networks. Numerical evaluations show that our proposed methods are superior to other comparative schemes. Our findings from analysis and optimization provide useful insights into the design of user-centric mmWave communication systems.๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ํ†ต์‹ ์€ ๊ณ ์† ์ „์†ก์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜์ง€๋งŒ ์†ก์‹ ๊ธฐ์™€ ์ˆ˜์‹ ๊ธฐ ์‚ฌ์ด์˜ ์žฅ์• ๋ฌผ๋กœ ์ธํ•ด ๋„คํŠธ์›Œํฌ ์„ฑ๋Šฅ์ด ํฌ๊ฒŒ ์ €ํ•˜๋  ์ˆ˜ ์žˆ๋‹ค. ์žฅ์• ๋ฌผ ํšจ๊ณผ๋ฅผ ๊ทน๋ณตํ•˜๊ณ  ๋งํฌ ์•ˆ์ •์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋‹ค์ค‘ ์—ฐ๊ฒฐ ๋ฐ ๋„คํŠธ์›Œํฌ ์ดˆ๊ณ ๋ฐ€ํ™” ๋‘๊ฐ€์ง€ ์ ‘๊ทผ๋ฒ•์ด ์žˆ๋‹ค. ํŠนํžˆ ๊ฐ ์‚ฌ์šฉ์ž๊ฐ€ ๊ณ ํ’ˆ์งˆ์˜ ์„œ๋น„์Šค๋ฅผ ๊ฒฝํ—˜ํ•  ์ˆ˜ ์žˆ๋„๋ก ์„œ๋น™ ๊ธฐ์ง€๊ตญ ๊ทธ๋ฃน์˜ ๋™์  ๊ตฌ์„ฑ์ด ํ•„์š”ํ•˜๋ฏ€๋กœ ์ดˆ๊ณ ๋ฐ€๋„ ๋„คํŠธ์›Œํฌ ํ™˜๊ฒฝ์—์„œ ๋‹ค์ค‘ ์—ฐ๊ฒฐ์€ ์‚ฌ์šฉ์ž ์ค‘์‹ฌ ํ†ต์‹ ์„ ์šฉ์ดํ•˜๊ฒŒ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์‚ฌ์šฉ์ž ์ค‘์‹ฌ์˜ ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ํ†ต์‹  ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ์ˆ˜ํ•™์  ํ”„๋ ˆ์ž„์›Œํฌ์™€ ๋„คํŠธ์›Œํฌ ๊ด€๋ฆฌ ์ฒด๊ณ„๋ฅผ ์—ฐ๊ตฌํ•œ๋‹ค. ๋จผ์ € ๋‹ค์ค‘ ์—ฐ๊ฒฐ์„ ์‚ฌ์šฉํ•˜์—ฌ ์‚ฌ์šฉ์ž ์ค‘์‹ฌ์˜ ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ํ†ต์‹  ์‹œ์Šคํ…œ์—์„œ ์‚ฌ์šฉ์ž ์ด๋™์„ฑ๊ณผ ์ด๋™์„ฑ ์ธ์‹ ์„ฑ๋Šฅ ์ง€ํ‘œ๋ฅผ ๋ชจ๋ธ๋งํ•˜๊ณ  ํ™•๋ฅ ๊ธฐํ•˜๋ถ„์„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ถ„์„ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๊ฐ ์‚ฌ์šฉ์ž๊ฐ€ ๊ฒฝํ—˜ํ•˜๋Š” ๋‹ค์–‘ํ•œ ์ด๋ฒคํŠธ์˜ ์ƒํƒœ ์ „์ด ํ™•๋ฅ ์— ๋Œ€ํ•œ ์ˆ˜ํ•™์  ํ‘œํ˜„์„ ๋„์ถœํ•œ๋‹ค. ๊ทธ๋Ÿฐ ๋‹ค์Œ ๋„คํŠธ์›Œํฌ ์˜ค๋ฒ„ํ—ค๋“œ ๋ฐ ๋‹ค์šด ๋งํฌ ์ˆ˜์œจ ์ธก๋ฉด์—์„œ ์ด๋™์„ฑ ์ธ์‹ ์„ฑ๋Šฅ์„ ์—ฐ๊ตฌํ•œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋„คํŠธ์›Œํฌ ์šด์˜์— ๋Œ€ํ•œ ๊นŠ์ด์žˆ๋Š” ์ดํ•ด์™€ ๋„คํŠธ์›Œํฌ ๋ฐ€๋„ ๋ฐ ๋‹ค์ค‘ ์—ฐ๊ฒฐ ๊ธฐ๋Šฅ์ด ํ•ธ๋“œ ์˜ค๋ฒ„์™€ ๊ด€๋ จ๋œ ์ด๋ฒคํŠธ์˜ ํ™•๋ฅ ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ๋Š” ๋ถ„์„์˜ ์ •ํ™•์„ฑ์„ ๊ฒ€์ฆํ•˜๊ณ  ์ด๋™์„ฑ ์ธ์‹ ์„ฑ๋Šฅ๊ณผ ์‚ฌ์šฉ์ž ์†๋„ ๊ฐ„์˜ ์ƒ๊ด€ ๊ด€๊ณ„๋ฅผ ๋ณด์—ฌ์ค€๋‹ค. ๋‹ค์Œ์œผ๋กœ ์™„์ „ ๋˜๋Š” ๋ถ€๋ถ„ ์œ ์„  ๋ฐฑํ™€์ด ์žˆ๋Š” ์‚ฌ์šฉ์ž ์ค‘์‹ฌ ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ๋„คํŠธ์›Œํฌ๋ฅผ ์œ„ํ•œ ์‚ฌ์šฉ์ž ์ค‘์‹ฌ ๊ตฌ์„ฑ ๊ทœ์น™ ๋ฐ ์ ‘์† ๊ฐ€๊ฒฉ ๊ธฐ๋ฐ˜ ์—ฐ๊ฒฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ์ดˆ๊ณ ๋ฐ€๋„ ๋„คํŠธ์›Œํฌ์— ๋Œ€ํ•œ ์ตœ์ ํ™” ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜์—ฌ ๊ณต์ •ํ•œ ์—ฐ๊ฒฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•œ๋‹ค. ์ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์—๋Š” ์ ‘์† ๊ฐ€๊ฒฉ ๊ธฐ๋ฐ˜ ์‚ฌ์šฉ์ž ๋ณ„ ์š”์ฒญ ๊ฒฐ์ • ๋ฐฉ๋ฒ•๊ณผ ๋กœ๋“œ ๋ฐธ๋Ÿฐ์‹ฑ์„ ์œ„ํ•œ ๊ฐ€๊ฒฉ ์กฐ์ • ๊ทœ์น™์ด ํฌํ•จ๋œ๋‹ค. ์œ„ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ฐœ๋ฐœ์„ ํ†ตํ•ด ์–ป์€ ํ†ต์ฐฐ๋ ฅ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ํ†ตํ•ฉ ์•ก์„ธ์Šค ๋ฐ ๋ฐฑํ™€ ๋„คํŠธ์›Œํฌ๋ฅผ ์œ„ํ•œ ๊ฒฝ๋กœ ์ธ์‹ ์ ‘์† ์š”๊ธˆ ์ •์ฑ…์„ ๊ฐœ๋ฐœํ•œ๋‹ค. ์ˆ˜์น˜ ํ‰๊ฐ€์— ๋”ฐ๋ฅด๋ฉด ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์ด ๋‹ค๋ฅธ ๋น„๊ต ๊ธฐ๋ฒ•๋ณด๋‹ค ์šฐ์ˆ˜ํ•˜๋‹ค. ๋ถ„์„ ๋ฐ ์ตœ์ ํ™” ๊ฒฐ๊ณผ๋Š” ์‚ฌ์šฉ์ž ์ค‘์‹ฌ์˜ ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ํ†ต์‹  ์‹œ์Šคํ…œ ์„ค๊ณ„์— ๋Œ€ํ•œ ์œ ์šฉํ•œ ํ†ต์ฐฐ๋ ฅ์„ ์ œ๊ณตํ•  ๊ฒƒ ์ด๋‹ค.Abstract i Contents iii List of Tables vi List of Figures vii 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Outline and Contributions . . . . . . . . . . . . . . . . . . . . . . . . 3 2 Mobility-Aware Analysis of MillimeterWave Communication Systems with Blockages 5 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.1 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.2 Connectivity Model . . . . . . . . . . . . . . . . . . . . . . 10 2.2.3 Mobility Model . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3 Mobility-Aware Analysis . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.1 Analytical Framework . . . . . . . . . . . . . . . . . . . . . 13 2.3.2 Urban Scenario with Ultra-Densely Deployed BSs . . . . . . 18 2.3.3 Handover Analysis for Macrodiversity . . . . . . . . . . . . . 22 2.3.4 Normalized Network Overhead and Mobility-Aware Downlink Throughput with Greedy User Association . . . . . . . . 24 2.4 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3 Association Control for User-Centric Millimeter Wave Communication Systems 34 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.2.1 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.2.2 Channel Model and Achievable Rate . . . . . . . . . . . . . . 39 3.2.3 User Centric mmWave Communication Framework . . . . . . 39 3.3 Traffic Load Management . . . . . . . . . . . . . . . . . . . . . . . . 44 3.3.1 Optimal Association and Admission Control . . . . . . . . . 45 3.3.2 Outage Analysis . . . . . . . . . . . . . . . . . . . . . . . . 51 3.4 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.4.1 Evaluation Environments . . . . . . . . . . . . . . . . . . . . 53 3.4.2 Performance Comparison . . . . . . . . . . . . . . . . . . . . 55 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4 Path Selection and Path-Aware Access Pricing Policy in Millimeter Wave IAB Networks 60 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.2.1 Geographic and Pathloss Models . . . . . . . . . . . . . . . . 62 4.2.2 IAB Network Model . . . . . . . . . . . . . . . . . . . . . . 63 4.3 Path Selection Strategies . . . . . . . . . . . . . . . . . . . . . . . . 66 4.4 Path-Aware Access Pricing Policy . . . . . . . . . . . . . . . . . . . 69 4.5 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 5 Conclusion 80 5.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 5.2 Limitations and Future Work . . . . . . . . . . . . . . . . . . . . . . 82 Abstract (In Korean) 90Docto

    B5G ์ดˆ๊ณ ๋ฐ€๋„ ๋„คํŠธ์›Œํฌ์—์„œ ์œ ์—ฐํ•œ ์ด๋™์„ฑ ๊ด€๋ฆฌ ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2022. 8. ๋ฐ•์„ธ์›….์ฐจ์„ธ๋Œ€ ๋ชจ๋ฐ”์ผ ์ด๋™ํ†ต์‹ ์„ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์„œ๋น„์Šค์™€ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์ด ๋“ฑ์žฅํ•จ์— ๋”ฐ๋ผ, ์‚ฌ์šฉ์ž๋“ค์€ ๊ธฐ์กด ์‹œ์Šคํ…œ ๋Œ€๋น„ ๋” ๋†’์€ ์ „์†ก ์†๋„๋ฅผ ์š”๊ตฌํ•œ๋‹ค. ๋”๋ถˆ์–ด ์‚ฌ์šฉ์ž๋“ค์€ ๋†’์€ ๋ฐ์ดํ„ฐ ์ „์†ก ์†๋„๋ฅผ ์•ˆ์ •์ ์œผ๋กœ ๋ณด์žฅ ๋ฐ›๊ธฐ๋ฅผ ์›ํ•œ๋‹ค. ๋ชจ๋ฐ”์ผ ๋„คํŠธ์›Œํฌ ์‚ฌ์šฉ์ž์˜ ๋ฐ์ดํ„ฐ ํŠธ๋ž˜ํ”ฝ์ด ์ฆ๊ฐ€ํ• ์ˆ˜๋ก ๋„คํŠธ์›Œํฌ์˜ ๋ฐ€์ง‘๋„๋ฅผ ์ฆ๊ฐ€์‹œํ‚ค๋Š” ์ดˆ๊ณ ๋ฐ€๋„ ๋„คํŠธ์›Œํฌ (ultra-dense network)์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ๋†’์•„์ง€๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ์ดˆ๊ณ ๋ฐ€๋„ ๋„คํŠธ์›Œํฌ ํ™˜๊ฒฝ์—์„œ๋Š” ๊ธฐ์กด ๋„คํŠธ์›Œํฌ ๋Œ€๋น„ ํ•ธ๋“œ์˜ค๋ฒ„๊ฐ€ ์ž์ฃผ ๋ฐœ์ƒํ•˜๊ฒŒ ๋˜๊ณ , ์ด๋กœ ์ธํ•ด ๋„คํŠธ์›Œํฌ ์„ฑ๋Šฅ์ด ์ œํ•œ๋˜๋Š” ๋ฌธ์ œ๊ฐ€ ๋“œ๋Ÿฌ๋‚˜๊ณ  ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ดˆ๊ณ ๋ฐ€๋„ ๋„คํŠธ์›Œํฌ์˜ ์„ฑ๋Šฅ์„ ๊ทน๋Œ€ํ™”ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ํšจ์œจ์ ์ธ ์ด๋™์„ฑ ๊ด€๋ฆฌ์˜ ์ค‘์š”์„ฑ์ด ์–ด๋Š๋•Œ๋ณด๋‹ค ๋ถ€๊ฐ๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” ์ดˆ๊ณ ๋ฐ€๋„ ๋„คํŠธ์›Œํฌ ํ™˜๊ฒฝ์—์„œ ํšจ์œจ์ ์ธ ์ด๋™์„ฑ ๊ด€๋ฆฌ๋ฅผ ์œ„ํ•˜์—ฌ ๋‹ค์Œ์˜ ์„ธ ๊ฐ€์ง€ ์ „๋žต์„ ๊ณ ๋ คํ•œ๋‹ค. 1) MIAB (mobile integrated access and backhaul) ๋„คํŠธ์›Œํฌ์—์„œ์˜ ์ด๋™์„ฑ ๊ด€๋ฆฌ, 2) ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ ์žฅ์• ๋ฌผ ์˜ˆ์ธก์„ ํ†ตํ•œ ์‚ฌ์ „์ ์ธ ํ•ธ๋“œ์˜ค๋ฒ„, 3) ๋‹ค์ค‘ ์—ฐ๊ฒฐ ํ™˜๊ฒฝ์—์„œ์˜ ๊ฐ•์ธํ•œ ์ด๋™์„ฑ ๊ด€๋ฆฌ. ์ฒซ์งธ๋กœ, MIAB ๋„คํŠธ์›Œํฌ์—์„œ ์‚ฌ์šฉ์ž์˜ QoS (quality-of-service)์— ์‹ฌ๊ฐํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ํ•ธ๋“œ์˜ค๋ฒ„ ์ง€์—ฐ ์‹œ๊ฐ„์„ ๊ฐ์†Œ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ํ•ธ๋“œ์˜ค๋ฒ„ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. MIAB ๋„คํŠธ์›Œํฌ ํ™˜๊ฒฝ์—์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” intra-gNB ํ•ธ๋“œ์˜ค๋ฒ„, inter-gNB ํ•ธ๋“œ์˜ค๋ฒ„, ๋ถ€๋ชจ MIAB ๋…ธ๋“œ ํ•ธ๋“œ์˜ค๋ฒ„์˜ ์„ธ ๊ฐ€์ง€ ํ•ธ๋“œ์˜ค๋ฒ„ ์ผ€์ด์Šค๋ฅผ ๋ถ„๋ฅ˜ํ•˜๊ณ , ๋ถ€๋ชจ MIAB ๋…ธ๋“œ์™€ ์ž๋…€ MIAB ๋…ธ๋“œ์˜ ์†๋„์— ๋”ฐ๋ฅธ ๊ฐ ํ•ธ๋“œ์˜ค๋ฒ„ ์ผ€์ด์Šค ๋ฐœ์ƒ ํ™•๋ฅ  ๋ชจ๋ธ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ƒํ–ฅ๋งํฌ ์ปจํŠธ๋กค ํ”Œ๋ ˆ์ธ ๋ฐ์ดํ„ฐ ์ „์†ก ์ง€์—ฐ ์‹œ๊ฐ„์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ œ์•ˆํ•˜๋Š” ํ•ธ๋“œ์˜ค๋ฒ„ ๊ธฐ๋ฒ•์€ ์ €์ง€์—ฐ ์ƒํ–ฅ๋งํฌ ์ปจํŠธ๋กค ํ”Œ๋ ˆ์ธ ๋ฐ์ดํ„ฐ ์ „์†ก ๊ธฐ๋ฒ•๊ณผ ์ž๋…€ MIAB ๋…ธ๋“œ์˜ RACH-less ํ•ธ๋“œ์˜ค๋ฒ„ ๊ธฐ๋ฒ•์„ ํฌํ•จํ•œ๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ์ œ์•ˆํ•˜๋Š” MIAB ํ•ธ๋“œ์˜ค๋ฒ„ ๊ธฐ๋ฒ•์ด ๊ธฐ์กด ํ•ธ๋“œ์˜ค๋ฒ„ ๊ธฐ๋ฒ• ๋Œ€๋น„ ํ•ธ๋“œ์˜ค๋ฒ„ ์ง€์—ฐ ์‹œ๊ฐ„ ๋ฐ ์˜ค๋ฒ„ํ—ค๋“œ ์„ฑ๋Šฅ๋ณด๋‹ค ๋งค์šฐ ๋›ฐ์–ด๋‚œ ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ ์•ˆ์ •์ ์ธ ์ด๋™์„ฑ ๊ด€๋ฆฌ๋ฅผ ์œ„ํ•œ ์žฅ์• ๋ฌผ ์˜ˆ์ธก ๊ธฐ๋ฐ˜์˜ ์‚ฌ์ „ ํ•ธ๋“œ์˜ค๋ฒ„ (BAPH) ๊ธฐ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. BAPH๋Š” ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ์žฅ์• ๋ฌผ๊ณผ ์ฐจ๋Ÿ‰์˜ ์ด๋™์„ฑ์„ ์˜ˆ์ธกํ•˜๊ณ , ํŠน์ • ์ฐจ๋Ÿ‰์ด ๊ธฐ์ง€๊ตญ๊ณผ LoS (line-of-sight)์— ์žˆ๋Š”์ง€๋ฅผ ์˜ˆ์ธกํ•œ๋‹ค. ์˜ˆ์ธก๋œ ์ฐจ๋Ÿ‰์˜ ๋ฏธ๋ž˜ ์œ„์น˜์™€ ์žฅ์• ๋ฌผ์˜ ๋ฏธ๋ž˜ ์œ„์น˜ ์ •๋ณด๋ฅผ ์ด์šฉํ•˜์—ฌ gNB์™€ RLF (radio link failure)๋ฅผ ๊ฒช๊ธฐ ์ด์ „ ์‹œ์ ์— ๋‹ค๋ฅธ gNB๋กœ ํ•ธ๋“œ์˜ค๋ฒ„๋ฅผ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ์‚ฌ์ „ ํ•ธ๋“œ์˜ค๋ฒ„ ๊ธฐ๋ฒ•์˜ ์„ฑ๋Šฅ์„ ๋‹ค์–‘ํ•œ ๋„๋กœ ํ™˜๊ฒฝ ๋ฐ ์ฐจ๋Ÿ‰ ์†๋„๋ฅผ ๋ฐ˜์˜ํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋‹ค์ค‘ ์—ฐ๊ฒฐ ๋„คํŠธ์›Œํฌ์˜ ์žฅ์ ์„ ์ตœ๋Œ€๋กœ ๋Œ์–ด๋‚ด๊ธฐ ์œ„ํ•œ ์•ˆ์ •์ ์ธ ํ•ธ๋“œ์˜ค๋ฒ„ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์•ต์ปค BS์™€ ์•กํ‹ฐ๋ธŒ BS ์ง‘ํ•ฉ์ด ๊ฐ ๋ชจ๋ฐ”์ผ ๊ธฐ๊ธฐ๋งˆ๋‹ค ์„ค์ •๋˜๋Š” ์‚ฌ์šฉ์ž ์ค‘์‹ฌ ์ดˆ๊ณ ๋ฐ€๋„ ๋„คํŠธ์›Œํฌ (UUDN)์„ ๊ณ ๋ คํ•œ๋‹ค. ์•ต์ปค BS๋Š” ์ฃผ๋ณ€ BS๋“ค์˜ RACH ์˜ค์ผ€์ŠคํŠธ๋ ˆ์ด์…˜์„ ํ†ตํ•˜์—ฌ ๋ชจ๋ฐ”์ผ ๊ธฐ๊ธฐ๊ฐ€ ํ•ธ๋“œ์˜ค๋ฒ„๋ฅผ ์œ„ํ•ด ์ „์†กํ•˜๋Š” RACH ํ”„๋ฆฌ์•ฐ๋ธ”์„ ๋‹ค์ˆ˜์˜ BS๊ฐ€ ๋ฐ›์„ ์ˆ˜ ์žˆ๋„๋ก ํ•œ๋‹ค. ๋˜ํ•œ ํ•ธ๋“œ์˜ค๋ฒ„ ๊ณผ์ •์—์„œ ํ•˜๋‚˜์˜ ์•กํ‹ฐ๋ธŒ BS์— RLF๊ฐ€ ๋ฐœ์ƒํ•˜๋Š” ๊ฒฝ์šฐ ํ•ธ๋“œ์˜ค๋ฒ„ ๊ณผ์ •์„ ์ง€์†ํ•˜์—ฌ ๋น ๋ฅด๊ฒŒ RLF๋ฅผ ๋ณต๊ตฌํ•˜๋Š” ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ์ œ์•ˆํ•˜๋Š” ํ•ธ๋“œ์˜ค๋ฒ„ ๊ธฐ๋ฒ•์ด RLF ์ง€์† ์‹œ๊ฐ„์„ ํ˜„์žฌ ํ•ธ๋“œ์˜ค๋ฒ„ ๊ธฐ๋ฒ• ๋Œ€๋น„ ์ค„์ด๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ์—ฐ๊ฒฐ ์ˆ˜๊ฐ€ ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒฝ์šฐ์— RLF ์ง€์† ๊ธฐ๊ฐ„์„ ๋” ๊ฐ์†Œ์‹œํ‚ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์š”์•ฝํ•˜๋ฉด, ์ฐจ์„ธ๋Œ€ ๋ชจ๋ฐ”์ผ ๋„คํŠธ์›Œํฌ์—์„œ ์ด๋™์„ฑ ๊ด€๋ฆฌ์™€ ๊ด€๋ จ๋œ ์ƒˆ๋กœ์šด ๋„คํŠธ์›Œํฌ ๊ตฌ์กฐ ๋ฐ ํ”„๋กœํ† ์ฝœ์— ๋Œ€ํ•œ ๋ฌธ์ œ๋ฅผ ์ œ๊ธฐํ•œ๋‹ค. ํ˜„์žฌ ์ด๋™์„ฑ ๊ด€๋ฆฌ ๊ธฐ๋ฒ•์ด ๊ณ ์ด๋™์„ฑ ํ™˜๊ฒฝ์ด๋‚˜ ์ดˆ๊ณ ๋ฐ€๋„ ๋„คํŠธ์›Œํฌ ํ™˜๊ฒฝ์—์„œ ๋„คํŠธ์›Œํฌ์˜ ์„ฑ๋Šฅ์„ ์ €ํ•˜์‹œํ‚ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ์ฐจ์„ธ๋Œ€ ๋ชจ๋ฐ”์ผ ๋„คํŠธ์›Œํฌ ์‹œ์Šคํ…œ์—์„œ์˜ ์ƒˆ๋กœ์šด ์ด๋™์„ฑ ๊ด€๋ฆฌ ๊ธฐ๋ฒ• ๋ฐ ์ „๋žต์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๋ชจ๋“  ์ด๋™์„ฑ ๊ด€๋ฆฌ ๊ธฐ๋ฒ•์€ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์„ฑ๋Šฅ์„ ํ‰๊ฐ€๋˜์—ˆ๋‹ค.As new services and applications for next-generation mobile communication emerge, users of mobile communication systems require a higher data rate than the existing system. In addition, mobile communication users want a high data rate to be reliably guaranteed anytime, anywhere. As data traffic of mobile network users increases, ultra-dense networks (UDN) that increase network density are in the limelight. However, in such a UDN environment, handovers (HOs) occur more frequently than in the existing mobile network system, which limits network performance. Therefore, to maximize the performance of the UDN, the importance of efficient mobility management is being emphasized more than ever. In this dissertation, the following three strategies are considered for efficient mobility management in the UDN environment: 1) Mobility management in mobile integrated access and backhaul (MIAB) networks, 2) Proactive HO through blockage prediction based on deep learning technology, 3) Reliable HO using the anchor node in the multi-connectivity environment. First, we propose a novel handover (HO) scheme for the MIAB network to reduce handover interruption time (HIT) and radio link failure (RLF) that have a significant impact on users' quality of service (QoS). We investigate HO cases that cover intra-gNB HO, inter-gNB HO, and parent MIAB node HO and develop their probabilistic models according to the velocities of the parent MIAB node and the child MIAB node. In addition, we investigate the latency in uplink (UL) control plane (CP) data transmission and each HO case for the baseline MIAB network. Our proposed HO scheme consists of low-latency UL CP data transmission with semi-persistent resource pre-allocation and RACH-less HO procedure for child MIAB nodes. Through simulation, we verify our proposed MIAB HO scheme outperforms the baseline HO scheme in terms of HO delay and HO overhead. Second, we propose the blockage-aware proactive HO (BAPH) scheme to support reliable mobility management. BAPH leverages a deep neural network (DNN) to predict the mobility of blockages and which BSs will be in the line-of-sight (LoS) with the vehicle device. With the predicted future blockage locations, the network supports proactive HO to the target gNB before radio link failure (RLF) occurs with the current serving gNB. We evaluate the performance of the proposed proactive HO scheme through simulations in various road environments. Finally, a reliable HO scheme that fully leverages the advantage of the multi-connectivity network is proposed. We consider a user-centric UDN (UUDN) architecture composed of an anchor BS and active BSs set for each UE. The anchor BS orchestrates the RACH of neighbor BSs that are not included in the active BS set for the target UE so that multiple target BSs can receive the RACH preamble transmitted by the UE. In addition, we propose a fast RLF recovery scheme that allows the existing HO process to continue when RLF occurs in the serving BS included in the active BS set. Through simulation, the performance of the proposed HO scheme is verified that the RLF duration of the UE is reduced compared to the current HO scheme even when the number of connections increases in a multi-connectivity environment. In summary, we claim issues in the new network architecture and protocols for the next-generation mobile networks related to mobility management. We demonstrate that the existing mobility management schemes limit the network performance in high-mobility environments and UDN environments. Therefore, we propose new mobility management schemes and strategies for the future mobile network system. All the proposed mobility management schemes are evaluated with simulation results.1 Introduction 1 1.1 Vision of B5G and Challenges 1 1.1.1 Vision of B5G Networks and Services 1 1.1.2 Research Trends and Challenges 2 1.2 Motivation 3 1.3 Main Contributions 5 1.3.1 Mobile Integrated Access and Backhaul Handover 5 1.3.2 Blockage Prediction-based Proactive Handover 6 1.3.3 Mobility Management in Distributed User-centric Ultra-dense Network 7 1.4 Organization of the Dissertation 7 2 Mobility Management of Multi-hop Mobile Integrated Access and Backhaul Network 9 2.1 Introduction 9 2.1.1 Contributions 11 2.1.2 Organization 12 2.2 Preliminary Study 12 2.2.1 IAB Network and Moving Cells 12 2.2.2 5G NR Handover 13 2.2.3 Motivation 14 2.3 System Model 15 2.3.1 Network Model 15 2.3.2 Communication Model 15 2.3.3 Directional Beamforming Model 16 2.3.4 Handover Model 18 2.4 Analysis of Mobility Management in MIAB Networks 18 2.4.1 UL CP Data Transmission Latency 19 2.4.2 Handover Latency 21 2.4.3 Handover Probability 23 2.5 Proposed Mobile IAB Handover Scheme 27 2.5.1 Low-Latency UL CP Data Transmission Scheme 27 2.5.2 Inter-gNB Handover Scheme 29 2.6 Performance Evaluation 31 2.6.1 HO Probability 33 2.6.2 Handover Latency 34 2.6.3 Effective Spectral Efficiency 37 2.7 Summary 41 3 Blockage-aware Proactive Handover for mmWave V2I Communications 42 3.1 Introduction 42 3.2 Preliminaries and Motivation 44 3.2.1 Effect of Blockages in mmWave Systems 44 3.2.2 DNN based Network Prediction 46 3.2.3 Motivation 46 3.3 Analysis on Blockage Effect 47 3.4 Proposed BAPH System 51 3.4.1 System Architecture 51 3.4.2 Communication Model 51 3.4.3 Deep Learning-based Location Prediction 52 3.4.4 Unobserved Blockage Detection and Estimation 54 3.4.5 Proactive Handover Process 57 3.6 Performance Evaluation 58 3.7 Summary 62 4 Anchor Node Based Reliable Handover in User-centric Ultra-dense Network 64 4.1 Introduction 64 4.2 Motivation 65 4.2.1 HO Process in 5G NR 65 4.2.2 HO Failure in the Baseline HO Scheme 66 4.3 Proposed Distributed User-centric Ultra-Dense Network Architecture 69 4.4 Anchor Node-based Mobility Management 71 4.5 Performance Evaluation 73 4.6 Summary 75 5 Concluding Remarks 76 5.1 Research Contributions 76 5.2 Future Research Directions 77 Abstract (In Korean) 85๋ฐ•

    Boosting 5G mm-Wave IAB Reliability with Reconfigurable Intelligent Surfaces

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    The introduction of the mm-Wave spectrum into 5G NR promises to bring about unprecedented data throughput to future mobile wireless networks but comes with several challenges. Network densification has been proposed as a viable solution to increase RAN resilience, and the newly introduced IAB is considered a key enabling technology with compelling cost-reducing opportunities for such dense deployments. Reconfigurable Intelligent Surfaces (RIS) have recently gained extreme popularity as they can create Smart Radio Environments by EM wave manipulation and behave as inexpensive passive relays. However, it is not yet clear what role this technology can play in a large RAN deployment. With the scope of filling this gap, we study the blockage resilience of realistic mm-Wave RAN deployments that use IAB and RIS. The RAN layouts have been optimised by means of a novel mm-Wave planning tool based on MILP formulation. Numerical results show how adding RISs to IAB deployments can provide high blockage resistance levels while significantly reducing the overall network planning cost
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