137 research outputs found

    Will SDN be part of 5G?

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    For many, this is no longer a valid question and the case is considered settled with SDN/NFV (Software Defined Networking/Network Function Virtualization) providing the inevitable innovation enablers solving many outstanding management issues regarding 5G. However, given the monumental task of softwarization of radio access network (RAN) while 5G is just around the corner and some companies have started unveiling their 5G equipment already, the concern is very realistic that we may only see some point solutions involving SDN technology instead of a fully SDN-enabled RAN. This survey paper identifies all important obstacles in the way and looks at the state of the art of the relevant solutions. This survey is different from the previous surveys on SDN-based RAN as it focuses on the salient problems and discusses solutions proposed within and outside SDN literature. Our main focus is on fronthaul, backward compatibility, supposedly disruptive nature of SDN deployment, business cases and monetization of SDN related upgrades, latency of general purpose processors (GPP), and additional security vulnerabilities, softwarization brings along to the RAN. We have also provided a summary of the architectural developments in SDN-based RAN landscape as not all work can be covered under the focused issues. This paper provides a comprehensive survey on the state of the art of SDN-based RAN and clearly points out the gaps in the technology.Comment: 33 pages, 10 figure

    Evolution Toward 5G Mobile Networks - A Survey on Enabling Technologies

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    In this paper, an extensive review has been carried out on the trends of existing as well as proposed potential enabling technologies that are expected to shape the fifth generation (5G) mobile wireless networks. Based on the classification of the trends, we develop a 5G network architectural evolution framework that comprises three evolutionary directions, namely, (1) radio access network node and performance enabler, (2) network control programming platform, and (3) backhaul network platform and synchronization. In (1), we discuss node classification including low power nodes in emerging machine-type communications, and network capacity enablers, e.g., millimeter wave communications and massive multiple-input multiple-output. In (2), both logically distributed cell/device-centric platforms, and logically centralized conventional/wireless software defined networking control programming approaches are discussed. In (3), backhaul networks and network synchronization are discussed. A comparative analysis for each direction as well as future evolutionary directions and challenges toward 5G networks are discussed. This survey will be helpful for further research exploitations and network operators for a smooth evolution of their existing networks toward 5G networks

    Timing and Carrier Synchronization in Wireless Communication Systems: A Survey and Classification of Research in the Last 5 Years

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    Timing and carrier synchronization is a fundamental requirement for any wireless communication system to work properly. Timing synchronization is the process by which a receiver node determines the correct instants of time at which to sample the incoming signal. Carrier synchronization is the process by which a receiver adapts the frequency and phase of its local carrier oscillator with those of the received signal. In this paper, we survey the literature over the last 5 years (2010โ€“2014) and present a comprehensive literature review and classification of the recent research progress in achieving timing and carrier synchronization in single-input single-output (SISO), multiple-input multiple-output (MIMO), cooperative relaying, and multiuser/multicell interference networks. Considering both single-carrier and multi-carrier communication systems, we survey and categorize the timing and carrier synchronization techniques proposed for the different communication systems focusing on the system model assumptions for synchronization, the synchronization challenges, and the state-of-the-art synchronization solutions and their limitations. Finally, we envision some future research directions

    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

    D6.6 Final report on the METIS 5G system concept and technology roadmap

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    This deliverable presents the METIS 5G system concept which was developed to fulfil the requirements of the beyond-2020 connected information society and to extend todayโ€™s wireless communication systems to include new usage scenarios. The METIS 5G system concept consists of three generic 5G services and four main enablers. The three generic 5G services are Extreme Mobile BroadBand (xMBB), Massive Machine- Type Communications (mMTC), and Ultra-reliable Machine-Type Communication (uMTC). The four main enablers are Lean System Control Plane (LSCP), Dynamic RAN, Localized Contents and Traffic Flows, and Spectrum Toolbox. An overview of the METIS 5G architecture is given, as well as spectrum requirements and considerations. System-level evaluation of the METIS 5G system concept has been conducted, and we conclude that the METIS technical objectives are met. A technology roadmap outlining further 5G development, including a timeline and recommended future work is given.Popovski, P.; Mange, G.; Gozalvez -Serrano, D.; Rosowski, T.; Zimmermann, G.; Agyapong, P.; Fallgren, M.... (2014). D6.6 Final report on the METIS 5G system concept and technology roadmap. http://hdl.handle.net/10251/7676
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