4 research outputs found

    Survey of Spectrum Sharing for Inter-Technology Coexistence

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    Increasing capacity demands in emerging wireless technologies are expected to be met by network densification and spectrum bands open to multiple technologies. These will, in turn, increase the level of interference and also result in more complex inter-technology interactions, which will need to be managed through spectrum sharing mechanisms. Consequently, novel spectrum sharing mechanisms should be designed to allow spectrum access for multiple technologies, while efficiently utilizing the spectrum resources overall. Importantly, it is not trivial to design such efficient mechanisms, not only due to technical aspects, but also due to regulatory and business model constraints. In this survey we address spectrum sharing mechanisms for wireless inter-technology coexistence by means of a technology circle that incorporates in a unified, system-level view the technical and non-technical aspects. We thus systematically explore the spectrum sharing design space consisting of parameters at different layers. Using this framework, we present a literature review on inter-technology coexistence with a focus on wireless technologies with equal spectrum access rights, i.e. (i) primary/primary, (ii) secondary/secondary, and (iii) technologies operating in a spectrum commons. Moreover, we reflect on our literature review to identify possible spectrum sharing design solutions and performance evaluation approaches useful for future coexistence cases. Finally, we discuss spectrum sharing design challenges and suggest future research directions

    LTE-LAA ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ์œ„ํ•œ MAC ๊ณ„์ธต ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2019. 2. ์ตœ์„ฑํ˜„.3GPP long term evolution (LTE) operation in unlicensed spectrum is emerging as a promising technology in achieving higher data rate with LTE since ultra-wide unlicensed spectrum, e.g., about 500 MHz at 5โ€“6 GHz range, is available in most countries. Recently, 3GPP has finalized standardization of licensed-assisted access (LAA) for LTE operation in 5 GHz unlicensed spectrum, which has been a playground only for Wi-Fi. In this dissertation, we propose the following three strategies to enhance the performance of LAA: (1) Receiver-aware COT adaptation, (2) Collision-aware link adaptation, and (3) Power and energy detection threshold adaptation. First, LAA has a fixed maximum channel occupancy time (MCOT), which is the maximum continuous transmission time after channel sensing, while Wi-Fi may transmit for much shorter time duration. As a result, when Wi-Fi coexists with LAA, Wi-Fi airtime and throughput can be much less than those achieved when Wi-Fi coexists with another Wi-Fi. To guarantee fair airtime and improve throughput of Wi-Fi, we propose a receiver-aware channel occupancy time (COT) adaptation ( RACOTA ) algorithm, which observes Wi-Fi aggregate MAC protocol data unit (A-MPDU) frames and matches LAAs COT to the duration of A-MPDU frames when any Wi-Fi receiver has more data to receive. Moreover, RACOTA detects saturation of Wi-Fi traffic and adjusts COT only if Wi-Fi traffic is saturated. We prototype saturation detection algorithm of RACOTA with commercial off-the-shelf Wi-Fi device and show that RACOTA detects saturation of Wi-Fi networks accurately. Through ns-3 simulations, we demonstrate that RACOTA provides airtime fairness between LAA and Wi-Fi while achieves up to 334% Wi-Fi throughput gain. Second, the link adaptation scheme of the conventional LTE, adaptive modulation and coding (AMC), cannot operate well in the unlicensed band due to intermittent collisions. Intermittent collisions make LAA eNB lower modulation and coding scheme (MCS) for the subsequent transmission and such unnecessarily lowered MCS significantly degrades spectral efficiency. To address this problem, we propose a collision-aware link adaptation algorithm ( COALA ). COALA exploits k-means unsupervised clustering algorithm to discriminate channel quality indicator (CQI) reports which are measured with collision interference and selects the most suitable MCS for the next transmission. By prototype-based experiments, we demonstrate that COALA detects collisions accurately, and by conducting ns-3 simulations in various scenarios, we also show that COALA achieves up to 74.9% higher user perceived throughput than AMC. Finally, we propose PETAL to mitigate the negative impact of spatial reuse (SR) operation. We first design the baseline algorithm, which operates SR aggressively, and show that the baseline algorithm degrades the throughput performance severely when the UE is close to an interferer. Our proposed algorithm PETAL estimates and compares the spectral efficiency for the SR operation and non-SR operation. Then, PETAL operates SR only if the spectral efficiency of SR operation is expected to be higher than the case of non-SR operation. Our simulation verifies the performance of PETAL in various scenarios. When two pair of an eNB and a UE coexists, PETAL improves the throughput by up to 329% over the baseline algorithm. In summary, we identify interesting problems that appeared with LAA and shows the impact of the problems through the extensive simulations and propose compelling algorithms to solve the problems. The airtime fairness between Wi-Fi and LAA is improved with COT adaptation. Furthermore, link adaptation accuracy and SR operation are improved by exploiting CQI reports history. The performance of the proposed schemes is verified by system level simulation.๋น„๋ฉดํ—ˆ ๋Œ€์—ญ์—์„œ์˜ LTE ๋™์ž‘์€ ๋” ๋†’์€ ๋ฐ์ดํ„ฐ ์ „์†ก๋ฅ ์„ ๋‹ฌ์„ฑํ•˜๋Š” ์œ ๋งํ•œ ๊ธฐ์ˆ ๋กœ ๋ถ€๊ฐ๋˜๊ณ  ์žˆ๋‹ค. ์ตœ๊ทผ 3GPP๋Š” ๊ธฐ์กด Wi-Fi ๊ธฐ์ˆ ์ด ์‚ฌ์šฉํ•˜๋˜ 5 GHz ๋น„๋ฉดํ—ˆ ๋Œ€์—ญ์—์„œ LTE๋ฅผ ์‚ฌ์šฉํ•˜๋Š” licensed-assisted access (LAA) ๊ธฐ์ˆ ์˜ ํ‘œ์ค€ํ™”๋ฅผ ์™„๋ฃŒํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์šฐ๋ฆฌ๋Š” LAA์˜ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์„ธ ๊ฐ€์ง€ ์ „๋žต์„ ์ œ์•ˆํ•œ๋‹ค: (1) ์ˆ˜์‹ ๊ธฐ ์ธ์‹ ์ฑ„๋„ ์ ์œ  ์‹œ๊ฐ„ ์ ์‘, (2) ์ถฉ๋Œ ์ธ์‹ ๋งํฌ ์ ์‘, (3) ์ „๋ ฅ ๋ฐ ์—๋„ˆ์ง€ ๊ฒ€์ถœ ์—ญ์น˜ ์ ์‘. ์ฒซ์งธ, LAA๋Š” ๊ณ ์ •๋œ ์ตœ๋Œ€ ์ฑ„๋„ ์ ์œ  ์‹œ๊ฐ„์„ ๊ฐ€์ง€๊ณ  ์žˆ๊ณ  ๊ทธ ์‹œ๊ฐ„ ๋งŒํผ ์—ฐ์†์ ์œผ๋กœ ์ „์†กํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐ˜๋ฉด, Wi-Fi๋Š” ๋น„๊ต์  ์งง์€ ์‹œ๊ฐ„ ๋™์•ˆ๋งŒ ์—ฐ์†์ ์œผ๋กœ ์ „์†กํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ Wi-Fi๊ฐ€ LAA์™€ ๊ณต์กดํ•  ๋•Œ Wi-Fi์˜ airtime๊ณผ ์ˆ˜์œจ ์„ฑ๋Šฅ์€ Wi-Fi๊ฐ€ ๋˜ ๋‹ค๋ฅธ Wi-Fi์™€ ๊ณต์กดํ•  ๋•Œ์— ๋น„ํ•˜์—ฌ ์ €ํ•˜๋˜๊ฒŒ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ์šฐ๋ฆฌ๋Š” Wi-Fi์˜ airtime๊ณผ ์ˆ˜์œจ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•˜์—ฌ Wi-Fi์˜ A-MPDU ํ”„๋ ˆ์ž„ ์ „์†ก ์‹œ๊ฐ„์— ๋งž์ถ”์–ด LAA์˜ ์ฑ„๋„ ์ ์œ  ์‹œ๊ฐ„์„ ์กฐ์ ˆํ•˜๋Š” ์ˆ˜์‹ ๊ธฐ ์ธ์‹ ์ฑ„๋„ ์ ์œ  ์‹œ๊ฐ„ ์ ์‘ ๊ธฐ๋ฒ•์ธ RACOTA๋ฅผ ์ œ์•ˆํ•œ๋‹ค. RACOTA ๋Š” ํฌํ™” ๊ฐ์ง€ ๊ฒฐ๊ณผ Wi-Fi ์ˆ˜์‹ ๊ธฐ๊ฐ€ ๋” ๋ฐ›์„ ๋ฐ์ดํ„ฐ๊ฐ€ ์žˆ๋‹ค๊ณ  ํŒ๋‹จ๋  ๋•Œ์—๋งŒ ์ฑ„๋„ ์ ์œ  ์‹œ๊ฐ„์„ ์กฐ์ ˆํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” RACOTA ์˜ ํฌํ™” ๊ฐ์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ƒ์šฉ Wi-Fi ์žฅ๋น„์— ๊ตฌํ˜„ํ•˜์—ฌ ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์‹ค์ธก์„ ํ†ตํ•ด RACOTA ๊ฐ€ ๊ณต์กดํ•˜๋Š” Wi-Fi์˜ ํฌํ™” ์—ฌ๋ถ€๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ๊ฐ์ง€ํ•ด๋ƒ„์„ ๋ณด์ธ๋‹ค. ๋˜ํ•œ ์šฐ๋ฆฌ๋Š” ns-3 ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•˜์—ฌ RACOTA ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” LAA๊ฐ€ ๊ณต์กดํ•˜๋Š” Wi-Fi์—๊ฒŒ ๊ณต์ •ํ•œ airtime์„ ์ œ๊ณตํ•˜๊ณ  ๊ธฐ์กด LAA์™€ ๊ณต์กดํ•˜๋Š” Wi-Fi ๋Œ€๋น„ ์ตœ๋Œ€ 334%์˜ Wi-Fi ์ˆ˜์œจ ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ๊ฐ€์ ธ์˜ด์„ ๋ณด์ธ๋‹ค. ๋‘˜์งธ, ๊ฐ„ํ—์ ์ธ ์ถฉ๋Œ์ด ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ๋น„๋ฉดํ—ˆ ๋Œ€์—ญ์—์„œ๋Š” ๊ธฐ์กด LTE์˜ ๋งํฌ ์ ์‘ ๊ธฐ๋ฒ•์ธ adaptive modulation and coding (AMC)์ด ์ž˜ ๋™์ž‘ํ•˜์ง€ ์•Š์„ ์ˆ˜ ์žˆ๋‹ค. ๊ฐ„ํ—์ ์ธ ์ถฉ๋Œ์€ LAA ๊ธฐ์ง€๊ตญ์œผ๋กœ ํ•˜์—ฌ๊ธˆ modulation and coding scheme (MCS)์„ ๋‚ฎ์ถ”์–ด์„œ ๋‹ค์Œ ์ „์†ก์„ ํ•˜๋„๋ก ํ•˜๋Š”๋ฐ ๋‹ค์Œ ์ „์†ก ์‹œ์— ์ถฉ๋Œ์ด ๋ฐœ์ƒํ•˜์ง€ ์•Š๋Š”๋‹ค๋ฉด ๋ถˆํ•„์š”ํ•˜๊ฒŒ ๋‚ฎ์ถ˜ MCS๋กœ ์ธํ•ด ์ฃผํŒŒ์ˆ˜ ํšจ์œจ์ด ํฌ๊ฒŒ ์ €ํ•˜๋œ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ์œ„ํ•ด ์šฐ๋ฆฌ๋Š” ์ถฉ๋Œ ์ธ์‹ ๋งํฌ ์ ์‘ ๊ธฐ๋ฒ•์ธ COALA ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. COALA ๋Š” k-means ๋ฌด๊ฐ๋… ํด๋Ÿฌ์Šคํ„ฐ๋ง ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์‚ฌ์šฉํ•˜์—ฌ channel quality indicator (CQI) ๋ฆฌํฌํŠธ ์ค‘ ์ถฉ๋Œ ๊ฐ„์„ญ์— ์˜ํ–ฅ์„ ๋ฐ›์€ CQI ๋ฆฌํฌํŠธ๋“ค์„ ๊ตฌ๋ณ„ํ•ด๋‚ด๊ณ  ์ด๋ฅผ ํ†ตํ•ด ๋‹ค์Œ ์ „์†ก์„ ์œ„ํ•œ ์ตœ์ ์˜ MCS๋ฅผ ์„ ํƒํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ์‹ค์ธก์„ ํ†ตํ•˜์—ฌ COALA ๊ฐ€ ์ •ํ™•ํ•˜๊ฒŒ ์ถฉ๋Œ์„ ๊ฐ์ง€ํ•ด๋ƒ„์„ ๋ณด์ธ๋‹ค. ๋˜ํ•œ ์šฐ๋ฆฌ๋Š” ๋‹ค์–‘ํ•œ ํ™˜๊ฒฝ์—์„œ์˜ ns-3 ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•˜์—ฌ COALA ๊ฐ€ AMC ๋Œ€๋น„ ์ตœ๋Œ€ 74.9%์˜ ์‚ฌ์šฉ์ž ์ธ์‹ ์ˆ˜์œจ ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ๊ฐ€์ ธ์˜ด์„ ๋ณด์ธ๋‹ค. ์…‹์งธ, ์šฐ๋ฆฌ๋Š” ๊ณต๊ฐ„ ์žฌ์‚ฌ์šฉ ๋™์ž‘์˜ ๋ถ€์ž‘์šฉ์„ ์ตœ์†Œํ™”ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ˆ˜์‹  ๋‹จ๋ง์„ ๊ณ ๋ คํ•˜์—ฌ ์ „์†ก ํŒŒ์›Œ ๋ฐ ์—๋„ˆ์ง€ ๊ฒ€์ถœ ์—ญ์น˜๋ฅผ ์ ์‘์ ์œผ๋กœ ์กฐ์ ˆํ•˜๋Š” PETAL ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ๋จผ์ € ์ˆ˜์‹  ๋‹จ๋ง์„ ๊ณ ๋ คํ•˜์ง€ ์•Š๊ณ  ๊ณต๊ฒฉ์ ์œผ๋กœ ๊ณต๊ฐ„ ์žฌ์‚ฌ์šฉ ๋™์ž‘์„ ํ•˜๋Š” baseline ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์„ค๊ณ„ํ•˜๊ณ  ๋‹ค์–‘ํ•œ ํ™˜๊ฒฝ์—์„œ์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•˜์—ฌ ์ˆ˜์‹  ๋‹จ๋ง์ด ๊ฐ„์„ญ์›์— ๊ฐ€๊นŒ์šด ๊ฒฝ์šฐ baseline ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์„ฑ๋Šฅ์ด ์‹ฌ๊ฐํ•˜๊ฒŒ ์—ดํ™”๋จ์„ ๋ณด์ธ๋‹ค. ์ œ์•ˆํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์ธ PETAL ์€ ์ˆ˜์‹  ๋‹จ๋ง๋กœ๋ถ€ํ„ฐ ๋ฐ›์€ CQI ๋ฆฌํฌํŠธ ์ •๋ณด์™€ ์ฑ„๋„ ์ ์œ  ์‹œ์ ๊นŒ์ง€์˜ ํ‰๊ท  ๋Œ€๊ธฐ ์‹œ๊ฐ„์„ ์ด์šฉํ•˜์—ฌ ๊ณต๊ฐ„ ์žฌ์‚ฌ์šฉ ๋™์ž‘์„ ํ•  ๋•Œ ์˜ˆ์ƒ๋˜๋Š” ์ฃผํŒŒ์ˆ˜ ํšจ์œจ๊ณผ ๊ณต๊ฐ„ ์žฌ์‚ฌ์šฉ ๋™์ž‘์„ ํ•˜์ง€ ์•Š์„ ๋•Œ ์˜ˆ์ƒ๋˜๋Š” ์ฃผํŒŒ์ˆ˜ ํšจ์œจ์„ ๋น„๊ตํ•˜์—ฌ ๊ณต๊ฐ„ ์žฌ์‚ฌ์šฉ ๋™์ž‘์„ ํ•  ๋•Œ ์˜ˆ์ƒ๋˜๋Š” ์ฃผํŒŒ์ˆ˜ ํšจ์œจ์ด ๋” ํด ๋•Œ์—๋งŒ ๊ณต๊ฐ„ ์žฌ์‚ฌ์šฉ ๋™์ž‘์„ ํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ๋‹ค์–‘ํ•œ ํ™˜๊ฒฝ์—์„œ์˜ ns-3 ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•˜์—ฌ PETAL ์ด baseline ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๋Œ€๋น„ ์ตœ๋Œ€ 329%์˜ ์ˆ˜์œจ ์„ฑ๋Šฅ ํ–ฅ์ƒ์„ ๊ฐ€์ ธ์˜ด์„ ๋ณด์ธ๋‹ค. ์š”์•ฝํ•˜์ž๋ฉด, ์šฐ๋ฆฌ๋Š” LAA์˜ ๋“ฑ์žฅ๊ณผ ํ•จ๊ป˜ ์ƒˆ๋กญ๊ฒŒ ๋Œ€๋‘๋˜๋Š” ํฅ๋ฏธ๋กœ์šด ๋ฌธ์ œ๋“ค์„ ํ™•์ธํ•˜๊ณ  ๋ฌธ์ œ๋“ค์˜ ์‹ฌ๊ฐ์„ฑ์„ ๋‹ค์–‘ํ•œ ํ™˜๊ฒฝ์—์„œ์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•˜์—ฌ ์‚ดํŽด๋ณด๊ณ  ์ด ๋Ÿฌํ•œ ๋ฌธ์ œ๋“ค์„ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜๋“ค์„ ์ œ์•ˆํ•œ๋‹ค. Wi-Fi์™€ LAA ์‚ฌ์ด์˜ airtime ๊ณต์ •์„ฑ์€ LAA์˜ ์—ฐ์† ์ „์†ก ์‹œ๊ฐ„์„ ์ ์‘์ ์œผ๋กœ ์กฐ์ ˆํ•˜์—ฌ ๊ฐœ์„ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ๋งํฌ ์ ์‘ ์ •ํ™•๋„์™€ ๊ณต๊ฐ„ ์žฌ์‚ฌ์šฉ ๋™์ž‘์˜ ํšจ์œจ์„ฑ์€ CQI ๋ฆฌํฌํŠธ๋“ค์˜ ๋ถ„ํฌ๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ฐœ์„ ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ œ์•ˆํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜๋“ค์˜ ์„ฑ๋Šฅ์€ ์‹œ์Šคํ…œ ๋ ˆ๋ฒจ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•˜์—ฌ ๊ฒ€์ฆ๋˜์—ˆ๋‹ค.1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Overview of Existing Approaches . . . . . . . . . . . . . . . . . . . 2 1.3 Main Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3.1 RACOTA: Receiver-Aware Channel Occupancy Time Adaptation for LTE-LAA . . . . . . . 2 1.3.2 COALA: Collision-Aware Link Adaptation for LTE-LAA . . 3 1.3.3 PETAL: Power and Energy Detection Threshold Adaptation for LAA . . . . . . . . . . . . . . 4 1.4 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . 5 2 RACOTA:Receiver-AwareChannelOccupancyTimeAdaptationforLTE- LAA 6 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3 MAC Mechanisms of Wi-Fi and LAA . . . . . . . . . . . . . . . . . 10 2.3.1 Wi-Fi MAC Operation . . . . . . . . . . . . . . . . . . . . . 10 2.3.2 LAA Listen-Before-Talk (LBT) Mechanism . . . . . . . . . . 11 2.3.3 Wide Bandwidth Operation . . . . . . . . . . . . . . . . . . 13 2.4 Coexistence performance of LAA and Wi-Fi . . . . . . . . . . . . . . 14 2.4.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 14 2.4.2 Unfairness between LAA and Wi-Fi . . . . . . . . . . . . . . 15 2.5 Receiver-Aware COT Adaptation Algorithm . . . . . . . . . . . . . . 17 2.5.1 Saturation Detection (SD) . . . . . . . . . . . . . . . . . . . 20 2.5.2 Receiver-Aware COT Decision . . . . . . . . . . . . . . . . . 22 2.6 Performance Evaluation of SD Algorithm . . . . . . . . . . . . . . . 22 2.6.1 Measurement Setup . . . . . . . . . . . . . . . . . . . . . . . 22 2.6.2 PPDUMaxTime Detection . . . . . . . . . . . . . . . . . . . 24 2.6.3 Saturation Detection Performance . . . . . . . . . . . . . . . 26 2.7 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.7.1 Saturated Traffic Scenario . . . . . . . . . . . . . . . . . . . 28 2.7.2 Unsaturated Traffic Scenario . . . . . . . . . . . . . . . . . . 30 2.7.3 Bursty Traffic Scenario . . . . . . . . . . . . . . . . . . . . . 30 2.7.4 Heterogeneous Wi-Fi Traffic Generation Scenario . . . . . . 31 2.7.5 Multiple Node Scenario . . . . . . . . . . . . . . . . . . . . 34 2.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3 COALA: Collision-Aware Link Adaptation for LTE-LAA 36 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.2 Backgound and Related Work . . . . . . . . . . . . . . . . . . . . . 38 3.2.1 LAA and LBT . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2.2 AMC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2.3 Inter-Cell Interference Cancellation . . . . . . . . . . . . . . 39 3.2.4 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.3 Impact of Collision to Link Adaptation . . . . . . . . . . . . . . . . . 41 3.4 COALA: Collision-aware Link Adaptation . . . . . . . . . . . . . . . 47 3.4.1 CQI Clustering Algorithm . . . . . . . . . . . . . . . . . . . 48 3.4.2 Collision Detection and Collision Probability Estimation . . . 48 3.4.3 Suitable MCS Selection . . . . . . . . . . . . . . . . . . . . 49 3.5 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.5.1 Prototype-based Feasibility Study . . . . . . . . . . . . . . . 51 3.5.2 Contention Collision with LAA eNBs . . . . . . . . . . . . . 53 3.5.3 Hidden Collision . . . . . . . . . . . . . . . . . . . . . . . . 57 3.5.4 Bursty Hidden Collision . . . . . . . . . . . . . . . . . . . . 58 3.5.5 Contention Collision with Wi-Fi Transmitters . . . . . . . . . 58 3.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4 PETAL: Power and Energy Detection Threshold Adaptation for LAA 62 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.2 Backgound and Related Work . . . . . . . . . . . . . . . . . . . . . 64 4.2.1 Energy Detection Threshold . . . . . . . . . . . . . . . . . . 64 4.2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.3 Baseline Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.3.1 Design of the Baseline Algorithm . . . . . . . . . . . . . . . 65 4.3.2 Performance of the Baseline Algorithm . . . . . . . . . . . . 66 4.4 PETAL: Power and Energy Detection Threshold Adaptation . . . . . 68 4.4.1 CQI Management . . . . . . . . . . . . . . . . . . . . . . . . 69 4.4.2 Success Probability and Airtime Ratio Estimation . . . . . . . 69 4.4.3 CQI Clustering Algorithm . . . . . . . . . . . . . . . . . . . 71 4.4.4 SR Decision . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.5 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.5.1 Two Cell Scenario . . . . . . . . . . . . . . . . . . . . . . . 73 4.5.2 Coexistence with Standard LAA . . . . . . . . . . . . . . . . 75 4.5.3 Four Cell Scenario . . . . . . . . . . . . . . . . . . . . . . . 76 4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5 Concluding Remarks 79 5.1 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 79 Abstract (In Korean) 88 ๊ฐ์‚ฌ์˜ ๊ธ€ 92Docto

    MAC-PHY Frameworks For LTE And WiFi Networks\u27 Coexistence Over The Unlicensed Band

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    The main focus of this dissertation is to address these issues and to analyze the interactions between LTE and WiFi coexisting on the unlicensed spectrum. This can be done by providing some improvements in the first two communication layers in both technologies. Regarding the physical (PHY) layer, efficient spectrum sensing and data fusion techniques that consider correlated spectrum sensing readings at the LTE/WiFi users (sensors) are needed. Failure to consider such correlation has been a major shortcoming of the literature. This resulted in poorly performing spectrum sensing systems if such correlation is not considered in correlated-measurements environments
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