LTE-LAA 성능 향상을 위한 MAC 계층 기법

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2019. 2. 최성현.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 5–6 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.비면허 대역에서의 LTE 동작은 더 높은 데이터 전송률을 달성하는 유망한 기술로 부각되고 있다. 최근 3GPP는 기존 Wi-Fi 기술이 사용하던 5 GHz 비면허 대역에서 LTE를 사용하는 licensed-assisted access (LAA) 기술의 표준화를 완료하였다. 본 논문에서 우리는 LAA의 성능을 향상시키기 위해 다음과 같은 세 가지 전략을 제안한다: (1) 수신기 인식 채널 점유 시간 적응, (2) 충돌 인식 링크 적응, (3) 전력 및 에너지 검출 역치 적응. 첫째, LAA는 고정된 최대 채널 점유 시간을 가지고 있고 그 시간 만큼 연속적으로 전송할 수 있는 반면, Wi-Fi는 비교적 짧은 시간 동안만 연속적으로 전송할 수 있다. 그 결과 Wi-Fi가 LAA와 공존할 때 Wi-Fi의 airtime과 수율 성능은 Wi-Fi가 또 다른 Wi-Fi와 공존할 때에 비하여 저하되게된다. 따라서 우리는 Wi-Fi의 airtime과 수율 성능을 향상시키기 위하여 Wi-Fi의 A-MPDU 프레임 전송 시간에 맞추어 LAA의 채널 점유 시간을 조절하는 수신기 인식 채널 점유 시간 적응 기법인 RACOTA를 제안한다. RACOTA 는 포화 감지 결과 Wi-Fi 수신기가 더 받을 데이터가 있다고 판단될 때에만 채널 점유 시간을 조절한다. 우리는 RACOTA 의 포화 감지 알고리즘을 상용 Wi-Fi 장비에 구현하여 이를 바탕으로 실측을 통해 RACOTA 가 공존하는 Wi-Fi의 포화 여부를 정확하게 감지해냄을 보인다. 또한 우리는 ns-3 시뮬레이션을 통하여 RACOTA 를 사용하는 LAA가 공존하는 Wi-Fi에게 공정한 airtime을 제공하고 기존 LAA와 공존하는 Wi-Fi 대비 최대 334%의 Wi-Fi 수율 성능 향상을 가져옴을 보인다. 둘째, 간헐적인 충돌이 발생할 수 있는 비면허 대역에서는 기존 LTE의 링크 적응 기법인 adaptive modulation and coding (AMC)이 잘 동작하지 않을 수 있다. 간헐적인 충돌은 LAA 기지국으로 하여금 modulation and coding scheme (MCS)을 낮추어서 다음 전송을 하도록 하는데 다음 전송 시에 충돌이 발생하지 않는다면 불필요하게 낮춘 MCS로 인해 주파수 효율이 크게 저하된다. 이러한 문제를 해결하기위해 우리는 충돌 인식 링크 적응 기법인 COALA 를 제안한다. COALA 는 k-means 무감독 클러스터링 알고리즘을 사용하여 channel quality indicator (CQI) 리포트 중 충돌 간섭에 영향을 받은 CQI 리포트들을 구별해내고 이를 통해 다음 전송을 위한 최적의 MCS를 선택한다. 우리는 실측을 통하여 COALA 가 정확하게 충돌을 감지해냄을 보인다. 또한 우리는 다양한 환경에서의 ns-3 시뮬레이션을 통하여 COALA 가 AMC 대비 최대 74.9%의 사용자 인식 수율 성능 향상을 가져옴을 보인다. 셋째, 우리는 공간 재사용 동작의 부작용을 최소화하기 위하여 수신 단말을 고려하여 전송 파워 및 에너지 검출 역치를 적응적으로 조절하는 PETAL 알고리즘을 제안한다. 우리는 먼저 수신 단말을 고려하지 않고 공격적으로 공간 재사용 동작을 하는 baseline 알고리즘을 설계하고 다양한 환경에서의 시뮬레이션을 통하여 수신 단말이 간섭원에 가까운 경우 baseline 알고리즘의 성능이 심각하게 열화됨을 보인다. 제안하는 알고리즘인 PETAL 은 수신 단말로부터 받은 CQI 리포트 정보와 채널 점유 시점까지의 평균 대기 시간을 이용하여 공간 재사용 동작을 할 때 예상되는 주파수 효율과 공간 재사용 동작을 하지 않을 때 예상되는 주파수 효율을 비교하여 공간 재사용 동작을 할 때 예상되는 주파수 효율이 더 클 때에만 공간 재사용 동작을 한다. 우리는 다양한 환경에서의 ns-3 시뮬레이션을 통하여 PETAL 이 baseline 알고리즘 대비 최대 329%의 수율 성능 향상을 가져옴을 보인다. 요약하자면, 우리는 LAA의 등장과 함께 새롭게 대두되는 흥미로운 문제들을 확인하고 문제들의 심각성을 다양한 환경에서의 시뮬레이션을 통하여 살펴보고 이 러한 문제들을 해결할 수 있는 알고리즘들을 제안한다. Wi-Fi와 LAA 사이의 airtime 공정성은 LAA의 연속 전송 시간을 적응적으로 조절하여 개선할 수 있다. 또한 링크 적응 정확도와 공간 재사용 동작의 효율성은 CQI 리포트들의 분포를 이용하여 개선할 수 있다. 제안하는 알고리즘들의 성능은 시스템 레벨 시뮬레이션을 통하여 검증되었다.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 감사의 글 92Docto

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