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    Signal Processing and Learning for Next Generation Multiple Access in 6G

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    Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed

    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

    Review of Recent Trends

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    This work was partially supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Centre (CENTRO 2020) of the Portugal 2020 framework, through projects SOCA (CENTRO-01-0145-FEDER-000010) and ORCIP (CENTRO-01-0145-FEDER-022141). Fernando P. Guiomar acknowledges a fellowship from โ€œla Caixaโ€ Foundation (ID100010434), code LCF/BQ/PR20/11770015. Houda Harkat acknowledges the financial support of the Programmatic Financing of the CTS R&D Unit (UIDP/00066/2020).MIMO-OFDM is a key technology and a strong candidate for 5G telecommunication systems. In the literature, there is no convenient survey study that rounds up all the necessary points to be investigated concerning such systems. The current deeper review paper inspects and interprets the state of the art and addresses several research axes related to MIMO-OFDM systems. Two topics have received special attention: MIMO waveforms and MIMO-OFDM channel estimation. The existing MIMO hardware and software innovations, in addition to the MIMO-OFDM equalization techniques, are discussed concisely. In the literature, only a few authors have discussed the MIMO channel estimation and modeling problems for a variety of MIMO systems. However, to the best of our knowledge, there has been until now no review paper specifically discussing the recent works concerning channel estimation and the equalization process for MIMO-OFDM systems. Hence, the current work focuses on analyzing the recently used algorithms in the field, which could be a rich reference for researchers. Moreover, some research perspectives are identified.publishersversionpublishe

    5G URLLC๋ฅผ ์œ„ํ•œ ์ €์ง€์—ฐ ํ†ต์‹  ํ”„๋กœํ† ์ฝœ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2020. 2. ์‹ฌ๋ณ‘ํšจ.2020๋…„ IMT ๋น„์ „์— ๋”ฐ๋ฅด๋ฉด 5 ์„ธ๋Œ€ (5G) ์ด๋™ ํ†ต์‹  ์„œ๋น„์Šค๋Š” eMBB (Enhanced Mobile Broadband), mMTC (Massive Machine Type Communication) ๋ฐ URLLC (Ultra Reliability and Low Latency Communication)์˜ ์„ธ ๊ฐ€์ง€ ์„œ๋น„์Šค๋กœ ๋ถ„๋ฅ˜๋œ๋‹ค. ๋‚ฎ์€ ์ง€์—ฐ ์‹œ๊ฐ„๊ณผ ๋†’์€ ์‹ ๋ขฐ๋„๋ฅผ ๋™์‹œ์— ๋ณด์žฅํ•˜๋Š” ๊ฒƒ์€ ์‹ค์‹œ๊ฐ„ ์„œ๋น„์Šค ๋ฐ ์‘์šฉ ํ”„๋กœ๊ทธ๋žจ์˜ ์ƒ์šฉํ™”๋ฅผ ์œ„ํ•˜์—ฌ ํ•„์š”ํ•œ ํ•ต์‹ฌ ๊ธฐ์ˆ ์ด๊ณ , 3 ๊ฐœ์˜ 5G ์„œ๋น„์Šค ์ค‘ URLLC๋Š” ๊ฐ€์žฅ ์–ด๋ ค์šด ์‹œ๋‚˜๋ฆฌ์˜ค๋กœ ์—ฌ๊ฒจ์ง€๊ณ  ์žˆ๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” URLLC ์„œ๋น„์Šค๋ฅผ ์ง€์›ํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์Œ๊ณผ ๊ฐ™์€ 3๊ฐ€์ง€ ์ €์ง€์—ฐ ํ†ต์‹  ํ”„๋กœํ† ์ฝœ์„ ์ œ์•ˆํ•œ๋‹ค: (i) 2-way ํ•ธ๋“œ์‰์ดํฌ ๊ธฐ๋ฐ˜ ๋žœ๋ค ์•ก์„ธ์Šค, (ii) Fast Grant Multiple Access ๋ฐ (iii) UE๊ฐ€ ์‹œ์ž‘ํ•˜๋Š” ํ•ธ๋“œ ์˜ค๋ฒ„ ๋ฐฉ์‹. ์ฒซ์งธ, 5G์—์„œ ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ์„ฑ๋Šฅ ์ง€ํ‘œ๋Š” ๋ฐ์ดํ„ฐ ์ „์†ก๋ฅ ์˜ ์ฆ๊ฐ€๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ง€์—ฐ ์‹œ๊ฐ„์„ ๊ฐ์†Œ์‹œํ‚ค๋Š” ๊ฒƒ๋„ ํฌํ•จํ•˜๊ณ  ์žˆ๋‹ค. ํ˜„์žฌ LTE-Advanced ์‹œ์Šคํ…œ์€ ๋žœ๋ค ์•ก์„ธ์Šค ๋ฐ ์ƒํ–ฅ ๋งํฌ ์ „์†ก ์ ˆ์ฐจ์—์„œ 4๊ฐœ์˜ ๋ฉ”์‹œ์ง€ ๊ตํ™˜์„ ํ•„์š”๋กœํ•˜๊ณ , ์ด๋Š” ๋†’์€ ์ง€์—ฐ ์‹œ๊ฐ„์„ ์•ผ๊ธฐํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ์ง€์—ฐ ์‹œ๊ฐ„์„ ํšจ๊ณผ์ ์œผ๋กœ ์ค„์ด๊ธฐ ์œ„ํ•˜์—ฌ 2-way ๋žœ๋ค ์•ก์„ธ์Šค ๋ฐฉ์‹์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•œ 2-way ๋žœ๋ค ์•ก์„ธ์Šค ๊ธฐ์ˆ ์€ ํ”„๋ฆฌ์•ฐ๋ธ”์˜ ์ˆ˜๋ฅผ ์ฆ๊ฐ€์‹œํ‚ด์œผ๋กœ์จ ํ•ด๋‹น ์ ˆ์ฐจ๋ฅผ ์™„๋ฃŒํ•˜๋Š”๋ฐ ๋‹จ 2๊ฐœ์˜ ๋ฉ”์‹œ์ง€ ๋งŒ ํ•„์š”ํ•˜๋‹ค. ์šฐ๋ฆฌ๋Š” ์ด๋Ÿฌํ•œ ํ”„๋ฆฌ์•ฐ๋ธ”์„ ์ƒ์„ฑํ•˜๊ณ  ํ™œ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์—ฐ๊ตฌํ–ˆ๊ณ , ๋‹ค์–‘ํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•˜์—ฌ ์ œ์•ˆํ•œ ๋žœ๋ค ์•ก์„ธ์Šค ๋ฐฉ์‹์ด ๊ธฐ์กด ๊ธฐ์ˆ ๊ณผ ๋น„๊ตํ•˜์—ฌ ์ง€์—ฐ ์‹œ๊ฐ„์„ ์ตœ๋Œ€ 43% ์ค„์ด๋Š” ๊ฒƒ ์„ ํ™•์ธํ–ˆ๋‹ค. ๋˜ํ•œ ์ œ์•ˆํ•œ ๋žœ๋ค ์•ก์„ธ์Šค๋Š” ๊ณ„์‚ฐ ๋ณต์žก๋„๊ฐ€ ์•ฝ๊ฐ„ ์ฆ๊ฐ€ํ•˜์ง€๋งŒ, ๋„คํŠธ์›Œํฌ ๋กœ๋“œ๋Š” ๊ธฐ์กด ๊ธฐ์ˆ ์— ๋น„ํ•ด ์ ˆ๋ฐ˜ ์ด์ƒ ๊ฐ์†Œํ•œ๋‹ค. ๋‘˜์งธ,์›๊ฒฉ ๋™์ž‘,์ž์œจ ์ฃผํ–‰,๋ชฐ์ž…ํ˜• ๊ฐ€์ƒ ํ˜„์‹ค ๋“ฑ๊ณผ ๊ฐ™์€ ๋‹ค์–‘ํ•œ ๋ฏธ์…˜ ํฌ๋ฆฌํ‹ฐ์ปฌ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์ด ๋“ฑ์žฅํ•˜๊ณ  ์žˆ๋‹ค. ๋‹ค์–‘ํ•œ URLLC ํŠธ๋ž˜ํ”ฝ์€ ๋‹ค์–‘ํ•œ ์ง€์—ฐ ์‹œ๊ฐ„ ๋ฐ ์‹ ๋ขฐ๋„ ์ˆ˜์ค€์„ ์š”๊ตฌ ์‚ฌํ•ญ์œผ๋กœ ๊ฐ€์ง€๊ณ  ์žˆ๊ณ , ์ด์™€ ํ•จ๊ป˜ ํ•„์š”ํ•œ ๋ฐ์ดํ„ฐ ํฌ๊ธฐ ๋ฐ ํŒจํ‚ท์˜ ๋ฐœ์ƒ์œจ ๋“ฑ์˜ ์ธก๋ฉด์—์„œ ๋‹ค์–‘ํ•œ ํŠน์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ๋ฏธ์…˜ ํฌ๋ฆฌํ‹ฐ์ปฌ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์˜ ๋‹ค์–‘ํ•œ ์š”๊ตฌ ์‚ฌํ•ญ์„ ์ง€์›ํ•˜๊ธฐ ์œ„ํ•ด ์ƒํ–ฅ ๋งํฌ ์ „์†ก์— ์ค‘์ ์„ ๋‘” FGMA(Fast Grant Multiple Access)๋ฅผ ์ œ์•ˆํ–ˆ๋‹ค. FGMA๋Š” ์Šน์ธ ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜, ๋™์  ํ”„๋ฆฌ์•ฐ๋ธ” ๊ตฌ์กฐ, ์ƒํ–ฅ ๋งํฌ ์Šค์ผ€์ค„๋ง ๋ฐ ์ ์‘์  ๋Œ€์—ญํญ ์กฐ์ ˆ์˜ ๋„ค ๊ฐ€์ง€ ๋ถ€๋ถ„์œผ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. FGMA์—์„œ๋Š” ์ง€์—ฐ ์‹œ๊ฐ„์„ ์ตœ์†Œํ™” ํ•˜๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ์ž์› ํ• ๋‹น์„ ํ•œ๋‹ค. ์ด ๋ฐฉ๋ฒ•์„ ํ™œ์šฉํ•˜๋ฉด ์ ์‘์  ๋Œ€์—ญํญ ์กฐ์ ˆ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ†ตํ•ด ์ง€์—ฐ ์‹œ๊ฐ„ ์š”๊ตฌ ์‚ฌํ•ญ์ด ๋‹ค๋ฅธ ํŠธ๋ž˜ํ”ฝ์˜ ๋ถˆ๊ท ํ˜•์„ ์™„ํ™” ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์Šน์ธ ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ†ตํ•ด FGMA ์‹œ์Šคํ…œ์— ์ด๋ฏธ ์Šน์ธ๋œ ๋ชจ๋“  UE๋“ค์— ๋Œ€ํ•œ ์š”๊ตฌ ์‚ฌํ•ญ์„ ํ•ญ์ƒ ๋ณด์žฅํ•œ๋‹ค. FGMA๋Š” ์‹œ๊ฐ„์— ๋”ฐ๋ผ ๋ณ€ํ•˜๋Š” ํ™˜๊ฒฝ์—์„œ๋„ UE์˜ QoS ์š”๊ตฌ ์‚ฌํ•ญ์„ ํšจ์œจ์ ์œผ๋กœ ๋ณด์žฅํ•œ๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์†Œํ˜• ์…€์€ ์…€๋ฃฐ๋Ÿฌ ์„œ๋น„์Šค ๋ฒ”์œ„๋ฅผ ๊ฐœ์„ ํ•˜๊ณ  ์‹œ์Šคํ…œ ์šฉ๋Ÿ‰์„ ํ–ฅ์ƒ ์‹œ ํ‚ค๊ณ , ๋งŽ์€ ์ˆ˜์˜ ๋ฌด์„  ๋‹จ๋ง์„ ์ง€์›ํ•˜๋Š” ํ•ต์‹ฌ ๊ธฐ์ˆ ๋กœ ๋– ์˜ค๋ฅด๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์…€์˜ ์„œ๋น„์Šค ๋ฒ”์œ„์˜ ๊ฐ์†Œ๋Š” ๋นˆ๋ฒˆํ•œ ํ•ธ๋“œ์˜ค๋ฒ„๋ฅผ ์œ ๋„ํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ํšจ๊ณผ์ ์ธ ํ•ธ๋“œ์˜ค๋ฒ„ ๋ฐฉ์‹์ดURLLC ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ์ง€์›ํ•˜๊ธฐ ์œ„ํ•ด์„œ ํ•„์š”ํ•˜๋‹ค. ๋”ฐ๋ผ์„œ, URLLC์„œ๋น„์Šค๋ฅผ ์š”๊ตฌํ•˜๋Š” ์ด๋™์„ฑ์ด ์žˆ๋Š” UE๋ฅผ ์„œ๋น„์Šคํ•˜๊ธฐ ์œ„ํ•ด ์ ์‘์  ํ•ธ๋“œ์˜ค๋ฒ„ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์„ ํƒ ๋ฐ ๋‹จ๋ง์˜ ๋™์ž‘์„ ๋ฏธ๋ฆฌ ์ค€๋น„ํ•ด ๋†“๋Š” ๋ฐฉ์‹์„ ์ ์šฉํ•œ ๋‹จ๋ง์ด ์‹œ์ž‘ํ•˜๋Š” ํ•ธ๋“œ์˜ค๋ฒ„ ๋ฐฉ์‹์„ ์ œ์•ˆํ•œ๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ๋Š” ์ œ์•ˆํ•œ ํ•ธ๋“œ์˜ค๋ฒ„๊ฐ€ ์ˆ˜์œจ์„ ํ–ฅ์ƒ์‹œํ‚ด๊ณผ ๋™์‹œ์— ์ €์ง€์—ฐ์„ ๋‹ฌ์„ฑํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ชฌ์„ ๊ฐ„๋žตํžˆ ์š”์•ฝํ•˜๋ฉด ์ง€์—ฐ ์‹œ๊ฐ„์˜ ์ข…๋ฅ˜๋ฅผ ๋žœ๋ค ์•ก์„ธ์Šค ์ง€์—ฐ ์‹œ๊ฐ„, ์ƒํ–ฅ ๋งํฌ ๋ฐ์ดํ„ฐ ์ „์†ก ์ง€์—ฐ ์‹œ๊ฐ„ ๋ฐ ํ•ธ๋“œ์˜ค๋ฒ„ ์ง€์—ฐ ์‹œ๊ฐ„๊ณผ ๊ฐ™์ด 3๊ฐ€์ง€๋กœ ๊ตฌ๋ถ„ํ•˜์˜€๋‹ค. 3๊ฐ€์ง€ ์ข…๋ฅ˜์˜ ์ง€์—ฐ ์‹œ๊ฐ„์— ๋Œ€ํ•ด์„œ ๊ฐ๊ฐ ์ €์ง€์—ฐ์„ ๋‹ฌ์„ฑ ํ•  ์ˆ˜ ์žˆ๋Š” ํ”„๋กœํ† ์ฝœ๊ณผ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์˜€๋‹ค.According to IMT vision for 2020, the fifth generation (5G) wireless services are classified into three categories, namely, Enhanced Mobile Broadband (eMBB), Massive Machine Type Communication (mMTC), and Ultra Reliable and Low Latency Communication (URLLC). Among three 5G service categories, URLLC is considered as the most challenging scenario. Thus, ensuring the latency and reliability is a key to the success of real-time services and applications. In this dissertation, we propose the following three latency reduction protocols to support the URLLC services: (i)2-way handshake-based random access, (ii) Fast grant multiple access, and (iii) UE-initiated handover scheme. First, the performance target includes not only increasing data rate, but also reducing latency in 5G cellular networks. The current LTE-Advanced systems require four message exchanges in the random access and uplink transmission procedure, thus inducing high latency. We propose a 2-way random access scheme which effectively reduces the latency. The proposed 2-way random access requires only two messages to complete the procedure at the cost of increased number of preambles. We study how to generate such preambles and how to utilize them. According to extensive simulation results, the proposed random access scheme significantly outperforms conventional schemes by reducing latency by up to 43%. We also demonstrate that computational complexity slightly increases in the proposed scheme, while network load is reduced more than a half compared to the conventional schemes. Second, various mission-critical applications are emerging such as teleoperation, autonomous driving, immersive virtual reality, and so on. A variety of URLLC traffic has various characteristics in terms of required data sizes and arrival rates with a variety of requirements of latency and reliability. To support the various requirements of the mission-critical applications, We propose a fast grant multiple access (FGMA) focusing on the uplink transmission. FGMA consists of four important parts, namely, admission control, dynamic preamble structure, the uplink scheduling, and bandwidth adaptation. The latency minimization scheduling policy is adopted in FGMA. Taking advantage of this method, the bandwidth adaptation algorithm makes even for the imbalanced arrival of the traffic requiring different latency requirements. With the proposed admission control, FGMA guarantee the requirements to all admitted UEs in the systems. We observe that the proposed FGMA efficiently guarantee the QoS requirements of the UEs even with the dynamic time-varying environment. Finally, small cells are considered a promising solution for improving cellular coverage, enhancing system capacity and supporting the massive number of things. Reduction of the cell coverage induced the frequent handover, so that the effective handover scheme is of importance in the presence of the URLLC applications. Thus, we propose a UE-initiated handover to deal with the mobile UEs requiring URLLC services taking into account the adaptive handover parameter selection and the logic of preparing in advance. The simulation results show that the proposed handover enhances the throughput performance as well as achieving low latency. In summary, we identify interesting problem in terms of latency. We classify three latency, random access latency, data transmission latency, and handover latency. With compelling protocols and algorithms, we resolve the above three problems.1 Introduction 1 1.1 Motivation 1 1.2 Main Contributions 2 1.2.1 Low Latency Random Access for Small Cell Toward Future Cellular Networks 2 1.2.2 Fast Grant Multiple Access in Large-Scale Antenna Systems for URLLC Services 3 1.2.3 UE-initiated Handover for Low Latency Communications 4 1.3 Organization of the Dissertation 4 2 Low Latency Random Access for Small Cell Toward Future Cellular Networks 6 2.1 Introduction 6 2.2 Related Work 9 2.3 Random Access and Uplink Transmission Procedure in LTE-A 11 2.3.1 Random Access in LTE-A 12 2.3.2 Uplink Transmission Procedure 14 2.3.3 Latency Issue in LTE-A 15 2.4 Proposed Random Access 16 2.4.1 Key Idea . 17 2.4.2 Proposed Preamble and Categorization 18 2.5 Preamble Sequence Analysis 23 2.5.1 Preamble Sequence Generation in LTE-A 23 2.5.2 Proposed Preamble Sequence Generation 25 2.5.3 Proposed Preamble Detection 26 2.6 Performance Evaluation 31 2.6.1 Network Latency 32 2.6.2 One-way Latency 33 2.6.3 Network Load 36 2.6.4 Computational Complexity 37 2.7 Conclusion 39 3 Fast Grant Multiple Access in Large-Scale Antenna Systems for URLLC Services 40 3.1 Introduction 40 3.2 Related Work 43 3.3 System Model 44 3.3.1 QoS Information and Service Category 45 3.3.2 Channel Structure 47 3.3.3 Frame Structure 48 3.4 Fast Grant Multiple Access 49 3.4.1 The Uplink Scheduling Policy 51 3.4.2 Dynamic Preamble Structure 53 3.4.3 Admission Control 54 3.4.4 Bandwidth Adaptation 55 3.5 Performance Evaluation 57 3.5.1 Impact of admission control 59 3.5.2 Impact of bandwidth adaptation 61 3.5.3 FGMA performance 62 3.6 Conclusion 64 4 UE-initiated Handover for Low Latency Communications 67 4.1 Introduction 67 4.2 Background and Motivation 69 4.2.1 Handover Decision Principle 69 4.2.2 Handover Procedure 70 4.2.3 Summary of the latency issues 72 4.3 UE-initiated Handover 73 4.3.1 The proposed handover design principles 73 4.3.2 The proposed handover procedure 75 4.4 Performance Evaluation 77 4.4.1 Low mobility environment 77 4.4.2 Low mobility environment 78 4.4.3 High mobility environment 80 4.5 Conclusion 82 5 ConcludingRemarks 84 5.1 Research Contributions 84 Abstract (InKorean) 92Docto
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