6 research outputs found

    Optimization of Grant-Free NOMA With Multiple Configured-Grants for mURLLC

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    15 pages, 15 figures, submitted to IEEE JSAC SI on Next Generation Multiple Access. arXiv admin note: text overlap with arXiv:2101.0051515 pages, 15 figures, submitted to IEEE JSAC SI on Next Generation Multiple Access. arXiv admin note: text overlap with arXiv:2101.0051

    A Framework of Non-Orthogonal Slotted Aloha (NOSA) Protocol for TDMA-Based Random Multiple Access in IoT-Oriented Satellite Networks

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    There is an urgent demand for massive machine-type terminals to have access into time-division multiple access (TDMA)-based satellite networks by means of random multiple access (RMA). Several RMA protocols have been proposed by exploiting packet repetitions and interference cancellation to achieve high throughput. In this paper, a framework of non-orthogonal slotted aloha (NOSA) protocol is reported to achieve even higher throughput. With a specifically designed tile-based frame structure, it introduces the intra-tile sparse mapping as a special kind of pre-coded packet repetitions and exploits the joint multi-packet detection to blindly detect superimposed packets. By further employing inter-tile packet repetitions and interference cancellation, the NOSA protocol is able to achieve high throughput with affordable complexity while keeping the same transmission efficiency as and comparable power consumptions to available protocols. Simulation results show that the NOSA prototype has the potential in providing RMA for massive machine-type terminals in practical TDMA-based satellite networks

    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

    Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives

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    ยฉ 1998-2012 IEEE. Future 5th generation networks are expected to enable three key services-enhanced mobile broadband, massive machine type communications and ultra-reliable and low latency communications (URLLC). As per the 3rd generation partnership project URLLC requirements, it is expected that the reliability of one transmission of a 32 byte packet will be at least 99.999% and the latency will be at most 1 ms. This unprecedented level of reliability and latency will yield various new applications, such as smart grids, industrial automation and intelligent transport systems. In this survey we present potential future URLLC applications, and summarize the corresponding reliability and latency requirements. We provide a comprehensive discussion on physical (PHY) and medium access control (MAC) layer techniques that enable URLLC, addressing both licensed and unlicensed bands. This paper evaluates the relevant PHY and MAC techniques for their ability to improve the reliability and reduce the latency. We identify that enabling long-term evolution to coexist in the unlicensed spectrum is also a potential enabler of URLLC in the unlicensed band, and provide numerical evaluations. Lastly, this paper discusses the potential future research directions and challenges in achieving the URLLC requirements

    Compressive Sensing Based Grant-Free Communication

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    Grant-free communication, where each user can transmit data without following the strict access grant process, is a promising technique to reduce latency and support massive users. In this thesis, compressive sensing (CS), which exploits signal sparsity to recover data from a small sample, is investigated for user activity detection (UAD), channel estimation, and signal detection in grant-free communication, in order to extract information from the signals received by base station (BS). First, CS aided UAD is investigated by utilizing the property of quasi-time-invariant channel tap delays as the prior information for the burst users in internet of things (IoT). Two UAD algorithms are proposed, which are referred to as gradient based and time-invariant channel tap delays assisted CS (g-TIDCS) and mean value based and TIDCS (m-TIDCS), respectively. In particular, g-TIDCS and m-TIDCS do not require any prior knowledge of the number of active users like the existing approaches and therefore are more practical. Second, periodic communication as one of the salient features of IoT is considered. Two schemes, namely periodic block orthogonal matching pursuit (PBOMP) and periodic block sparse Bayesian learning (PBSBL), are proposed to exploit the non-continuous temporal correlation of the received signal for joint UAD, channel estimation, and signal detection. The theoretical analysis and simulation results show that the PBOMP and PBSBL outperform the existing schemes in terms of the success rate of UAD, bit error rate (BER), and accuracy in period estimation and channel estimation. Third, UAD and channel estimation for grant-free communication in the presence of massive users that are actively connected to the BS is studied. An iteratively UAD and signal detection approach for the burst users is proposed, where the interference of the connected users on the burst users is reduced by applying a preconditioning matrix to the received signals at the BS. The proposed approach is capable of providing significant performance gains over the existing algorithms in terms of the success of UAD and BER. Last but not least, since the physical layer security becomes a critical issue for grant-free communication, the channel reciprocity in time-division duplex systems is utilized to design environment-aware (EA) pilots derived from transmission channels to prevent eavesdroppers from acquiring usersโ€™ channel information. The proposed EA-pilots based approach possesses a high level of security by scrambling the eavesdropperโ€™s normalized mean square error performance of channel estimation
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