38 research outputs found

    - Frequency Sharing of Wi-Fi/LTE-U -

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2020. 8. Jorn Altmann .5์„ธ๋Œ€(5G) ์ด๋™ํ†ต์‹  ํ™•์‚ฐ์„ ์œ„ํ•˜์—ฌ ์ •๋ถ€๋Š” '๋น„๋ฉดํ—ˆ 5G' ์ฃผํŒŒ์ˆ˜ ๋Œ€์—ญ์ธ 6GHz ๋Œ€์—ญํญ์— ์ตœ๋Œ€ 1.2ใŽ“ ํญ์„ ๊ณต๊ธ‰ํ•  ์˜ˆ์ •์ด๋‹ค. 4์ฐจ ์‚ฐ์—…์— ๋Œ€์‘ํ•˜์—ฌ ๊ธฐ์—…์ด ์Šค๋งˆํŠธ ๊ณต์žฅ์— ์ ํ•ฉํ•œ ๋งž์ถคํ˜• 5G๋ง์„ ๊ตฌ์ถ•ํ•˜๊ธฐ ์œ„ํ•œ ๋ชฉ์ ์ด๋‹ค. ๊ณผํ•™๊ธฐ์ˆ ์ •๋ณดํ†ต์‹ ๋ถ€(MSIT)๋Š” 6ใŽ“ ๋Œ€์—ญ ๋Œ€์ƒ์œผ๋กœ ๋น„๋ฉดํ—ˆ ์ฃผํŒŒ์ˆ˜ ๋Œ€์—ญ์˜ 5G ์—ฐ๊ตฌ๋ฐ˜์„ ๊ฐ€๋™, ์ฃผํŒŒ์ˆ˜ ๋ถ„๋ฐฐ ๋ฐฉ์•ˆ ์ˆ˜๋ฆฝ๊ณผ ๊ธฐ์ˆ  ๊ธฐ์ค€ ์ œ์ •์— ์ฐฉ์ˆ˜ํ•œ ๊ฒƒ์œผ๋กœ ํ™•์ธ๋๋‹ค. ๋น„๋ฉดํ—ˆ 5G(NR-U)๋Š” ์ •๋ถ€๊ฐ€ ๊ฐœ๋ฐฉํ•œ ๋น„๋ฉดํ—ˆ ์ฃผํŒŒ์ˆ˜ ๋Œ€์—ญ์— 5G ์ฝ”์–ด๋ง๊ณผ ๊ธฐ์ง€๊ตญ ๋“ฑ ํ‘œ์ค€ ๊ธฐ์ˆ ์„ ์ ์šฉํ•ด ์ดˆ์ €์ง€์—ฐ์ดˆ๊ณ ์† ์„ฑ๋Šฅ์„ ๊ตฌํ˜„ํ•˜๋Š” ๊ธฐ์ˆ ๋กœ, 6์›” ๊ตญ์ œ๋ฏผ๊ฐ„ํ‘œ์ค€ํ™”๊ธฐ๊ตฌ(3GPP) ์ƒ์šฉํ™”๋ฅผ ์•ž๋‘๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ 5/6GHz ๋Œ€์—ญ์˜ ์ฃผํŒŒ์ˆ˜๋Š” ์™€์ดํŒŒ์ด์™€ LTE-LAA๊ฐ€ ๊ณต์กดํ•˜๊ฒŒ ๋  ์˜์—ญ์œผ๋กœ ์ดํ•ด๊ด€๊ณ„์ž๋“ค์˜ ์ฃผ์žฅ์ด ์—‡๊ฐˆ๋ฆฌ๊ณ  ์žˆ๋‹ค. LBT (Listen-Before-Talk)๋Š” ๋น„๋ฉดํ—ˆ ์ฃผํŒŒ์ˆ˜๋ฅผ ๊ณต์œ ํ•˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ ๊ธฐ์ˆ ์ /ํ–‰์ •์  ๋ฐฉ์•ˆ์œผ๋กœ, ์ฑ„๋„์„ ๋ถ„์„ํ•˜์—ฌ ๋ฐ์ดํ„ฐ๋ฅผ ์ „์†กํ•˜๊ธฐ ๊ฐ€์žฅ ์ ์ ˆํ•œ ์ฃผํŒŒ์ˆ˜๋Œ€์—ญ์„ ์„ ์ถœํ•˜๋Š” ๋ฐฉ๋ฒ•์ค‘์— ํ•˜๋‚˜์ด๋‹ค. ์ด ๋Œ€์—ญ์„ ์„ ์ถœํ•˜๊ธฐ ์œ„ํ•œ ๊ธฐ์ˆ  ๋ฐฉ๋ฒ•์€ CCA (Clear Channel Assessment)๋ผ๊ณ  ํ•œ๋‹ค. ์ด ๊ธฐ์ˆ ์€ LTE ํ†ต์‹ ์‚ฌ์—…์ž๋“ค์ด ๊ฐœ๋ฐœํ•œ ๊ธฐ์ˆ ๋กœ ๊ตฌ์ฒดํ™”๋œ ๊ธฐ์ˆ ํ‘œ์ค€์„ ์š”๊ตฌํ•˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ, ์ด ๊ธฐ์ˆ ์„ ์™€์ดํŒŒ์ด ์‚ฌ์—…์ž ์ž…์žฅ์—์„œ๋Š” ์ด ๊ธฐ์ˆ ์˜ ์‹ค์ œ ์‹ค์šฉ๊ฐ€๋Šฅ ์—ฌ๋ถ€์— ๋Œ€ํ•ด ์˜๊ตฌ์‹ฌ์„ ๊ฐ–๊ณ  ์žˆ๋‹ค. ๊ทธ ์ด์œ ๋Š” ํ•ด๋‹น ๊ธฐ์ˆ ์ด ์ง€๋‚˜์น˜๊ฒŒ LTE ํ†ต์‹ ์‚ฌ์—…์ž๋“ค์—๊ฒŒ ์œ ๋ฆฌํ•˜๊ฒŒ ๊ฐœ๋ฐœ๋œ ๊ธฐ์ˆ ์ด๋ผ๋Š” ๋…ผ์Ÿ์ด ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ด์— ํ†ต์‹ ๊ธฐ์ˆ ์ •์ฑ… ๊ฒฐ์ •์ž๋Š” ์–‘์ธก์ด ๊ณต์กดํ•  ์ˆ˜ ์žˆ๋Š” ๋ฒ• ์ œ์ •๋ฐฉ์•ˆ์„ ๊ฐ•๊ตฌํ•ด์•ผ ํ•˜๋ฉฐ, ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ๊ธฐ์กด ์™€์ดํŒŒ์ด ์‚ฌ์šฉ์ž๋“ค์„ ๋ณดํ˜ธํ•˜๋ฉฐ ํ†ต์‹  ์‚ฌ์—…์ž๋“ค ์—ญ์‹œ ์ฃผํŒŒ์ˆ˜๋ฅผ ๊ณต์œ ํ•  ์ˆ˜ ์žˆ๋Š” ์ œ๋„ ์ •์ฑ…์˜ ๋ฐฉ๋ฒ•์„ ์ •์ฑ…์ž ์ž…์žฅ์—์„œ ์—ฐ๊ตฌํ•ด๋ณด์ž ํ•œ๋‹ค. ๋น„๋ฉดํ—ˆ ์ฃผํŒŒ์ˆ˜๋ฅผ ๊ฐœ๋ฐฉํ•˜๊ฒŒ ๋˜์—ˆ์„ ๋•Œ ์ฑ„๋„ ์‚ฌ์šฉ์ž ๋น„์œจ๊ณผ ๋ผ์ด์„ ์Šค ๋ฐฐ๋ถ€ ๋น„์œจ ์‚ฌ์ด์˜ ํšจ์œจ์„ฑ์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ , ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•œ ์ฑ„๋„์„ ๋น„๊ตํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ๊ธฐ์กด์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๋น„๊ตํ•˜๊ณ  ์ƒˆ๋กœ์šด ๋ชจ๋ธ์„ ๊ฐœ๋ฐœ ํ–ˆ๋‹ค. ๋˜ ๊ธฐ์ˆ  ํ‘œ์ค€์œผ๋กœ ์ด๋ฏธ ์ œ์ • ๋œ ๋…ธ์ด์ฆˆ ํ•œ๊ณ„์น˜๊ฐ€ ์‹ค์ œ ๊ณต์œ ๊ฐ€ ๋์„ ๋•Œ ์–ด๋–ค์‹์œผ๋กœ ์ž‘์šฉ๋˜๋Š” ์ง€๋ฅผ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ถ„์„์„ ํ†ตํ•ด ์˜ˆ์ธกํ•ด ๋ณด์•˜๋‹ค.A spectrum-sharing policy is yet to be established for unlicensed bands. There are several controversies related to providing 5-GHz (5G) band service with the coexistence of Wi-Fi and LTEโ€“LAA (License Assisted Access). LTEโ€“LAA is a technical and political scheme for sharing spectrum bands. Using this technique, the channel can be evaluated, and a proper channel can be selected before data transmission. This mechanism is called listen before talk (LBT), and the channel selection procedure is called clear channel assessment (CCA). LTE service providers suggest standardizing this technology using an unlicensed band. However, existing Wi-Fi users are skeptical about the sharing process. In this paper, we describe proper spectrum-sharing mechanisms based on policy-based channel selection algorithms. Based on the results, proper regulation of the 5G LTE-U is required to avoid conflicts among service providers. This work uses a policy-based mechanism to understand the role of the government in managing these bands. The main idea is that stricter sharing-policy regulations and higher thresholds must be implemented to protect the rights of existing users.1. Introduction 1 1.1 Research Background 1 1.2 Problem Description 2 1.2.1 Controversies on Wi-Fi and LTE-U Coexistence 2 1.3 Research Question 4 1.3.1. Fairness and Efficiency of Sharing Policy 4 1.3.2. Evaluation and Simulation of Sharing Technology 5 1.4 Contribution 5 2. Literature Review 6 2.1 Spectrum-Sharing Policy 6 2.1.2 Case of South Korea 8 2.2 Spectrum-Sharing Technologies 10 2.2.1 Licensed Assisted Access (LAA) 11 2.2.2 Listen Before Talk (LBT) 13 2.2.2.1 Qualcomm Evaluation 13 2.2.2.2 3GPP Evaluation 16 2.3 Comparison with Previous Work 17 3. Modeling 20 3.1 General Assumption 22 3.2 Flow Chart 23 3.2.1 License Type Evaluation (Guard Interval Evaluation) 23 3.2.2 Energy Detection Evaluation 26 4. Simulation 29 4.1 Parameter setting 29 4.2 Simulation Result 31 5. Conclusion 36 5.1 Overall Implication 36 5.2 Limitation 37 5.3 Further study 37 Appendix 39 Bibliography 47 Abstract (Korean) 50Maste

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    Fluid Antenna System: New Insights on Outage Probability and Diversity Gain

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    To enable innovative applications and services, both industry and academia are exploring new technologies for sixth generation (6G) communications. One of the promising candidates is fluid antenna system (FAS). Unlike existing systems, FAS is a novel communication technology where its antenna can freely change its position and shape within a given space. Compared to the traditional systems, this unique capability has the potential of providing higher diversity and interference-free communications. Nevertheless, the performance limits of FAS remain unclear as its system properties are difficult to analyze. To address this, we approximate the outage probability and diversity gain of FAS in closed-form expressions. We then propose a suboptimal FAS with N * ports, where a significant gain can be obtained over FAS with N * - 1 ports whilst FAS with N * + 1 ports only yields marginal improvement over the proposed suboptimal FAS. In this paper, we also provide analytical and simulation results to unfold the key factors that affect the performance of FAS. Limited to systems with one active radio frequency (RF)-chain, we show that the proposed suboptimal FAS outperforms single-antenna (SISO) system and selection combining (SC) system in terms of outage probability. Interestingly, when the given space is ฮป/2, the outage probability of the proposed suboptimal FAS with one active RF-chain achieves near to that of the maximal ratio combining (MRC) system with multiple active RF-chains

    Machine learning enabled Wi-Fi saturation sensing for fair coexistence in unlicensed spectrum

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    In the past few years, machine learning (ML) techniques have been extensively applied to provide efficient solutions to complex wireless network problems. As such, Convolutional Neural Network (CNN) and Q-learning based ML techniques are most popular to achieve harmonized coexistence of Wi-Fi with other co-located technologies such as LTE. In the existing coexistence schemes, a co-located technology selects its transmission time based on the level of Wi-Fi traffic generated in its collision domain which is determined by either sniffing the Wi-Fi packets or using a central coordinator that can communicate with the co-located networks to exchange their status and requirements through a collaboration protocol. However, such approaches for sensing traffic status increase cost, complexity, traffic overhead, and reaction time of the coexistence schemes. As a solution to this problem, this work applies a ML-based approach that is capable to determine the saturation status of a Wi-Fi network based on real-time and over-the-air collection of medium occupation statistics about the Wi-Fi frames without the need for decoding. In particular, inter-frame spacing statistics of Wi-Fi frames are used to develop a CNN model that can determine Wi-Fi network saturation. The results demonstrate that the proposed ML-based approach can accurately classify whether a Wi-Fi network is saturated or not

    Predicting Software Performance with Divide-and-Learn

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    Predicting the performance of highly configurable software systems is the foundation for performance testing and quality assurance. To that end, recent work has been relying on machine/deep learning to model software performance. However, a crucial yet unaddressed challenge is how to cater for the sparsity inherited from the configuration landscape: the influence of configuration options (features) and the distribution of data samples are highly sparse. In this paper, we propose an approach based on the concept of 'divide-and-learn', dubbed DaLDaL. The basic idea is that, to handle sample sparsity, we divide the samples from the configuration landscape into distant divisions, for each of which we build a regularized Deep Neural Network as the local model to deal with the feature sparsity. A newly given configuration would then be assigned to the right model of division for the final prediction. Experiment results from eight real-world systems and five sets of training data reveal that, compared with the state-of-the-art approaches, DaLDaL performs no worse than the best counterpart on 33 out of 40 cases (within which 26 cases are significantly better) with up to 1.94ร—1.94\times improvement on accuracy; requires fewer samples to reach the same/better accuracy; and producing acceptable training overhead. Practically, DaLDaL also considerably improves different global models when using them as the underlying local models, which further strengthens its flexibility. To promote open science, all the data, code, and supplementary figures of this work can be accessed at our repository: https://github.com/ideas-labo/DaL.Comment: This paper has been accepted by The ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 202
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