24 research outputs found

    Single-Frequency Network Terrestrial Broadcasting with 5GNR Numerology

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    L'abstract รจ presente nell'allegato / the abstract is in the attachmen

    Improved autocorrelation method for time synchronization in filtered orthogonal frequency division multiplexing

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    Time synchronization is essential in multicarrier systems such as filtered orthogonal frequency division multiplexing (F-OFDM) because it determines the whole systemโ€™s performance. Differ with OFDM, where subcarrier allocation is not flexible. In F-OFDM, the subcarrier allocation is more flexible, and the whole subcarrier in one symbol can be grouped into several subbands. The use of subcarriers that are limited to only one subband can reduce the performance of time synchronization based on autocorrelation (AC) methods. In this study, we first compare the performance of the AC-based time synchronization algorithms used in F-OFDM when training symbols are limited to one subband. Secondly, we made improvements to the AC-based time synchronization with the averaging technique of its timing metric, thus increasing the accuracy of time estimates in the F-OFDM system. The averaging technique of the timing metric improved the performance of the AC method in cases where the training symbol is limited to one subband, as shown in the simulation results

    Towards Massive Connectivity Support for Scalable mMTC Communications in 5G networks

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    The fifth generation of cellular communication systems is foreseen to enable a multitude of new applications and use cases with very different requirements. A new 5G multiservice air interface needs to enhance broadband performance as well as provide new levels of reliability, latency and supported number of users. In this paper we focus on the massive Machine Type Communications (mMTC) service within a multi-service air interface. Specifically, we present an overview of different physical and medium access techniques to address the problem of a massive number of access attempts in mMTC and discuss the protocol performance of these solutions in a common evaluation framework

    5G ์ดํ›„ ๋ฌด์„  ๋„คํŠธ์›Œํฌ๋ฅผ ์œ„ํ•œ ๋ฌด์„  ์ ‘์† ๊ธฐ์ˆ  ํ–ฅ์ƒ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2020. 8. ๋ฐ•์„ธ์›….Recently, operators are creating services using 5G systems in various fields, e.g., manufacturing, automotive, health care, etc. 5G use cases include transmission of small packets using IoT devices to high data rate transmission such as high-definition video streaming. When a large-scale IoT device transmits a small packet, power saving is important, so it is necessary to disconnect from the base station and then establish a connection through random access to transmit data. However, existing random access procedures are difficult to satisfy various latency requirements. It is attractive to use a wide bandwidth of the millimeter wave spectrum for high data rate transmission. In order to overcome the channel characteristics, beamforming technology is applied. However, when determining a beam pair between a transmitter and a receiver, interference is not considered. In this dissertation, we consider the following three enhancements to enable 5G and beyond use cases: (i) Two-step random access procedure for delay-sensitive devices, (ii) self-uplink synchronization framework for solving preamble collision problem, and (iii) interference-aware beam adjustment for interference coordination. First, RAPID, two-step random access for delay-sensitive devices, is proposed to reduce latency requirement value for satisfying specific reliability. When devices, performing RAPID and contention-based random access, coexist, it is important to determine a value that is the number of preambles for RAPID to reduce random access load. Simulation results show that RAPID achieves 99.999% reliability with 80.8% shorter uplink latency, and also decreases random access load by 30.5% compared with state-of-the-art techniques. Second, in order to solve preamble collision problem, we develop self-uplink synchronization framework called EsTA. Preamble collision occurs when multiple devices transmit the same preamble. Specifically, we propose a framework that helps the UE to estimate the timing advance command using a deep neural network model and to determine the TA value. Estimation accuracy can achieve 98โ€“99% when subcarrier spacing is 30 and 60 kHz. Finally, we propose IBA, which is interference-aware beam adjustment method to reduce interference in millimeter wave networks. Unlike existing methods of reducing interference by scheduling time and frequency resources differently, interference is controlled through beam adjustment. In IBA, it is important to reduce search space of finding new beam pair to reduce interference. In practical, it is impossible to search beam pair of all combinations. Therefore, through Monte Carlo method, we can reduce search space to achieve local optimum. IBA achieve enhancement of lower 50%throughput up to 50% compared with only applying beam adjustment. In summary, we propose a two-step random access, a self-uplink synchronization framework, and interference-aware beam adjustment for 5G and beyond use cases. Through these researches, we achieve enhancements of network performance such as latency and throughput compared with state-of-the-art techniques.์ตœ๊ทผ ์‚ฌ์—…์ž๋Š” ์ œ์กฐ, ์ž๋™์ฐจ, ํ—ฌ์Šค ์ผ€์–ด ๋“ฑ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ 5G ์‹œ์Šคํ…œ์„ ์‚ฌ์šฉํ•˜์—ฌ ์„œ๋น„์Šค๋ฅผ ๋งŒ๋“ค๊ณ  ์žˆ๋‹ค. 5G ์‚ฌ์šฉ ์‚ฌ๋ก€์—๋Š” IoT ์žฅ์น˜๋ฅผ ์ด์šฉํ•œ ์ž‘์€ ํŒจํ‚ท ์ „์†ก์—์„œ๊ณ ํ™”์งˆ ๋น„๋””์˜ค ์ŠคํŠธ๋ฆฌ๋ฐ๊ณผ ๊ฐ™์€ ๊ณ ์† ๋ฐ์ดํ„ฐ ์ „์†ก๊นŒ์ง€ ํฌํ•จ๋œ๋‹ค. ๋Œ€๊ทœ๋ชจ IoT ์žฅ์น˜๊ฐ€์ž‘์€ ํŒจํ‚ท์„ ์ „์†กํ•˜๋Š” ๊ฒฝ์šฐ ์ „๋ ฅ ์†Œ๋ชจ ์ ˆ์•ฝ์ด ์ค‘์š”ํ•˜๋ฏ€๋กœ ๊ธฐ์ง€๊ตญ๊ณผ์˜ ์—ฐ๊ฒฐ์„ ๋Š์€๋‹ค์Œ ๋žœ๋ค ์•ก์„ธ์Šค๋ฅผ ํ†ตํ•ด ๋‹ค์‹œ ๊ธฐ์ง€๊ตญ๊ณผ ์—ฐ๊ฒฐํ•˜์—ฌ ๋ฐ์ดํ„ฐ๋ฅผ ์ „์†กํ•ด์•ผํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜๊ธฐ์กด์˜ ๋žœ๋ค ์•ก์„ธ์Šค ์ ˆ์ฐจ๋Š” ๋‹ค์–‘ํ•œ ์ง€์—ฐ์‹œ๊ฐ„ ์š”๊ฑด์„ ๋งŒ์กฑ์‹œํ‚ค๊ธฐ ์–ด๋ ต๋‹ค. ํ•œํŽธ, ๋†’์€๋ฐ์ดํ„ฐ ์ „์†ก ์†๋„๋ฅผ ์œ„ํ•ด ๋„“์€ ๋Œ€์—ญํญ์˜ ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ๋Œ€์—ญ์„ ์‚ฌ์šฉํ•œ๋‹ค. ์ด๋•Œ, ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ๋Œ€์—ญ ์ฑ„๋„ ํŠน์„ฑ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ๋น”ํฌ๋ฐ ๊ธฐ์ˆ ์ด ์ ์šฉ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ˜„์žฌ 5Gํ‘œ์ค€์—์„œ ์†ก์‹ ๊ธฐ์™€ ์ˆ˜์‹ ๊ธฐ ์‚ฌ์ด์˜ ๋น” ์Œ์„ ๊ฒฐ์ •ํ•  ๋•Œ, ๊ฐ„์„ญ์€ ๊ณ ๋ ค๋˜์ง€ ์•Š๋Š”๋‹ค. ์ด๋…ผ๋ฌธ์—์„œ๋Š” 5G ๋ฐ ๊ทธ ์ดํ›„์˜ ๋„คํŠธ์›Œํฌ์—์„œ ๋‹ค์–‘ํ•œ ์‚ฌ์šฉ ์‚ฌ๋ก€๋ฅผ ์ง€์›ํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์Œ์„ธ ๊ฐ€์ง€ ๊ฐœ์„  ์‚ฌํ•ญ์„ ๊ณ ๋ คํ•œ๋‹ค. (i) ์ง€์—ฐ์— ๋ฏผ๊ฐํ•œ ์žฅ์น˜๋ฅผ ์œ„ํ•œ 2 ๋‹จ๊ณ„ ๋žœ๋ค ์•ก์„ธ์Šค์ ˆ์ฐจ, (ii) ํ”„๋ฆฌ์•ฐ๋ธ” ์ถฉ๋Œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ์ž์ฒด ์ƒํ–ฅ๋งํฌ ๋™๊ธฐํ™” ํ”„๋ ˆ์ž„ ์›Œํฌ,๊ทธ๋ฆฌ๊ณ  (iii) ๊ฐ„์„ญ์„ ์ค„์ด๊ธฐ ์œ„ํ•œ ๊ฐ„์„ญ ์ธ์‹ ๋น” ์กฐ์ •์ด๋‹ค. ์ฒซ์งธ, ์ง€์—ฐ์— ๋ฏผ๊ฐํ•œ ์žฅ์น˜๋ฅผ ์œ„ํ•œ 2 ๋‹จ๊ณ„ ๋žœ๋ค ์•ก์„ธ์Šค์ธ RAPID๋Š” ํŠน์ • ์‹ ๋ขฐ๋„๋ฅผ ๋งŒ์กฑ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ์ง€์—ฐ์‹œ๊ฐ„์„ ์ค„์ด๊ธฐ ์œ„ํ•ด ์ œ์•ˆ๋˜์—ˆ๋‹ค. RAPID์™€ ๊ฒฝํ•ฉ ๊ธฐ๋ฐ˜ ๋žœ๋ค ์•ก์„ธ์Šค๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ์žฅ์น˜๊ฐ€ ๊ณต์กดํ•  ๊ฒฝ์šฐ RAPID๊ฐ€ ๋žœ๋ค ์•ก์„ธ์Šค ๋ถ€ํ•˜๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•ด RAPID๋ฅผ ์œ„ํ•ด ํ• ๋‹น๋˜๋Š” ํ”„๋ฆฌ์•ฐ๋ธ” ์ˆ˜๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅด๋ฉด RAPID๋Š” 99.999%์˜์‹ ๋ขฐ๋„๋ฅผ ๋งŒ์กฑ์‹œํ‚ค๋Š” ์ง€์—ฐ์‹œ๊ฐ„์„ ์ตœ์‹  ๊ธฐ์ˆ ์— ๋น„ํ•ด 80.8% ์ค„์ด๋ฉด์„œ, ๋žœ๋ค ์•ก์„ธ์Šค๋ถ€ํ•˜๋ฅผ 30.5% ์ค„์ธ๋‹ค. ๋‘˜์งธ, ํ”„๋ฆฌ์•ฐ๋ธ” ์ถฉ๋Œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์ž์ฒด ์ƒํ–ฅ๋งํฌ ๋™๊ธฐํ™” ํ”„๋ ˆ์ž„์›Œํฌ์ธ EsTA๋ฅผ ๊ฐœ๋ฐœํ•œ๋‹ค. ํ”„๋ฆฌ์•ฐ๋ธ” ์ถฉ๋Œ์€ ์—ฌ๋Ÿฌ ์žฅ์น˜๊ฐ€ ๋™์ผํ•œ ํ”„๋ฆฌ์•ฐ๋ธ”์„ ์ „์†กํ•  ๋•Œ ๋ฐœ์ƒํ•œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ๋‹จ๋ง์ด ์‹ฌ์ธต ์‹ ๊ฒฝ๋ง ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ timing advance(TA) command๋ฅผ ์ถ”์ •ํ•˜๊ณ  TA๊ฐ’์„ ๊ฒฐ์ •ํ•˜๋Š” ํ”„๋ ˆ์ž„ ์›Œํฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ๋„คํŠธ์›Œํฌ ์‹œ์Šคํ…œ์˜ ๋ถ€๋ฐ˜์†กํŒŒ ๊ฐ„๊ฒฉ์ด 30 ๋ฐ 60 kHz ์ผ ๋•Œ, TA command ์ถ”์ • ์ •ํ™•๋„๋Š”98โ€“99%๋ฅผ ๋‹ฌ์„ฑ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋ฐ€๋ฆฌ๋ฏธํ„ฐํŒŒ ๋„คํŠธ์›Œํฌ์—์„œ ๊ฐ„์„ญ์„ ์ค„์ด๊ธฐ ์œ„ํ•œ ๊ฐ„์„ญ ์ธ์‹ ๋น” ์กฐ์ • ๋ฐฉ๋ฒ•์ธ IBA๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์‹œ๊ฐ„๊ณผ ์ฃผํŒŒ์ˆ˜ ์ž์›์„ ๋‹ค๋ฅด๊ฒŒ ์˜ˆ์•ฝํ•˜์—ฌ ๊ฐ„์„ญ์„ ์ค„์ด๋Š” ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๊ณผ ๋‹ฌ๋ฆฌ IBA๋Š” ๋น” ์กฐ์ •์„ ํ†ตํ•ด ๊ฐ„์„ญ์„ ์ œ์–ดํ•œ๋‹ค.์ด ๋•Œ, ๊ฐ„์„ญ์„ ์ค„์ด๊ธฐ ์œ„ํ•ด ์ƒˆ๋กœ์šด ๋น” ์Œ์„ ์ฐพ๋Š” ๊ฒ€์ƒ‰ ๊ณต๊ฐ„์„ ์ค„์ด๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค.ํ˜„์‹ค์ ์œผ๋กœ ๋ชจ๋“  ๋น” ์Œ์˜ ์กฐํ•ฉ์„ ๊ฒ€์ƒ‰ํ•˜๋Š” ๊ฒƒ์€ ๋ถˆ๊ฐ€๋Šฅํ•˜๋‹ค. ๋”ฐ๋ผ์„œ IBA๋Š” Monte Carlo ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๊ฒ€์ƒ‰ ๊ณต๊ฐ„์„ ์ถ•์†Œํ•˜์—ฌ local optimum์„ ๋‹ฌ์„ฑํ•˜๋„๋ก ์„ค๊ณ„๋˜์–ด์•ผํ•œ๋‹ค. IBA๋Š” 5G ํ‘œ์ค€์˜ ๋น” ์กฐ์ • ๋ฐฉ๋ฒ•๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ, ํ•˜์œ„ 50% throughput์˜ ์ค‘๊ฐ„๊ฐ’์ด์ตœ๋Œ€ 50%๊นŒ์ง€ ํ–ฅ์ƒ๋œ๋‹ค. ์š”์•ฝํ•˜๋ฉด, ์šฐ๋ฆฌ๋Š” 5G ๋ฐ ๊ทธ ์ดํ›„์˜ ๋‹ค์–‘ํ•œ ์‚ฌ์šฉ ์‚ฌ๋ก€๋ฅผ ์œ„ํ•ด์„œ 2 ๋‹จ๊ณ„ ๋žœ๋ค ์•ก์„ธ์Šค, ์ž์ฒด ์ƒํ–ฅ๋งํฌ ๋™๊ธฐํ™” ํ”„๋ ˆ์ž„ ์›Œํฌ, ๊ทธ๋ฆฌ๊ณ  ๊ฐ„์„ญ ์ธ์‹ ๋น”์กฐ์ • ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ตœ์‹  ๊ธฐ์ˆ ์— ๋น„ํ•ด ์ง€์—ฐ์‹œ๊ฐ„ ๋ฐ ์ฒ˜๋ฆฌ๋Ÿ‰๊ณผ ๊ฐ™์€๋„คํŠธ์›Œํฌ ์„ฑ๋Šฅ์ด ํ–ฅ์ƒ๋œ๋‹ค.1 Introduction 1 1.1 5G Vision, Applications, and Keywords 1 1.2 Overview of Existing Approach 3 1.3 Main Contributions 4 1.3.1 RAPID: Two-Step Random Access 4 1.3.2 EsTA: Self-Uplink Synchronization 5 1.3.3 IBA: Interference-Aware Beam Adjustment 5 1.4 Organization of the Dissertation 6 2 RAPID: Contention Resolution-based Random Access Procedure using Context ID for IoT 7 2.1 Introduction 7 2.2 Background 10 2.2.1 RRC State 10 2.2.2 Random Access Procedure 11 2.2.3 Uplink Latency in RRC INACTIVE State 13 2.2.4 Related Work 14 2.3 RAPID: Proposed Random Access Procedure 15 2.3.1 Overview 15 2.3.2 Criterion of Applying RAPID 16 2.3.3 Preamble Set and RACH Period Allocation 17 2.3.4 Preamble Transmission 18 2.3.5 RAR Transmission 19 2.3.6 AS Context ID Allocation 21 2.3.7 Number of Preambles for RAPID 22 2.4 Access Pattern Analyzer 22 2.4.1 Overview 22 2.4.2 APA Operation 23 2.4.3 Margin Value 26 2.4.4 Offset Index Decision 26 2.5 Random Access Load Analysis 27 2.5.1 System Model 28 2.5.2 Markov Chain Model for 4-Step RA 29 2.5.3 Average Random Access Load for 4-Step RA 34 2.5.4 Markov Chain Model for RAPID 34 2.5.5 Average Random Access Load for RAPID 37 2.5.6 Validation of Analysis 38 2.5.7 Optimization Problem 41 2.6 Performance Evaluation 42 2.6.1 Simulation Setup 42 2.6.2 Number of Preambles for RAPID 43 2.6.3 Performance of RAPID 43 2.6.4 Performance of APA 48 2.7 Summary 48 3 EsTA: Self-Uplink Synchronization in 2-Step Random Access 49 3.1 Introduction 49 3.2 Background 51 3.2.1 Overview of 2-Step CBRA 51 3.2.2 Channel Structure for msgA 52 3.2.3 TA Handling for the Payload 54 3.2.4 2-Step Random Access in Recent Literature 56 3.3 Challenges of 2-Step Random Access 57 3.3.1 Preamble Allocation 57 3.3.2 Resource Mapping for msgA 58 3.3.3 DFT Operation in gNB 58 3.3.4 Detected Collision Problem 58 3.4 EsTA: Proposed Self-UL Synchronization Procedure 59 3.4.1 Overview 60 3.4.2 Overall Procedures 60 3.4.3 Performance Evaluation 61 3.4.4 Future Research Perspectives 65 3.5 Summary 65 4 IBA: Interference-Aware Beam Adjustment for 5G mmWave Networks 67 4.1 Introduction 67 4.2 Background 68 4.2.1 Beam Management in 5G NR 68 4.2.2 System-Level Simulation and 3D Beamforming for 5G NR 70 4.3 Motivation 70 4.3.1 Throughput Degradation by Interference 70 4.4 IBA: Proposed Interference Management Scheme 72 4.4.1 Overall Procedure 72 4.4.2 Reduction of Search Space 72 4.4.3 Algorithm for IBA 75 4.5 Performance Evaluation 76 4.6 Summary 78 5 Concluding Remarks 79 5.1 Research Contributions 79 5.2 Future Work 80 Abstract (In Korean) 89 ๊ฐ์‚ฌ์˜ ๊ธ€ 92Docto
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