LTE IN UNLICENSED BANDS: A RIVAL OR COLLABORATOR TO WI-FI?

Due to the rapidly increasing demand for internet traffic, mobile operators have faced a problem of bandwidth availability. Since only licensed spectrum has been previously utilized by wireless networks, moving LTE to the 5 GHz unlicensed bands has become a popular research initiative, known as LTELicensed Assisted Access (LTE-LAA). This thesis studies the feasibility and implementation of LTE-LAA, and sets a goal of confirming the effectiveness of this technology. An alternative implementation of a Listen-Before-Talk channel contention mechanism is tested in this work with the use of LTE-A Vienna Link Level Simulator. The obtained results suggest that LTE-LAA is capable of boosting network throughput while providing harmonious coexistence with the IEEE 802.11 standard operating in the same unlicensed spectrum

LTE and Wi-Fi Coexistence in Unlicensed Spectrum with Application to Smart Grid: A Review

Long Term Evolution (LTE) is expanding its utilization in unlicensed band by deploying LTE Unlicensed (LTEU) and Licensed Assisted Access LTE (LTE-LAA) technology. Smart Grid can take the advantages of unlicensed bands for achieving two-way communication between smart meters and utility data centers by using LTE-U/LTE-LAA. However, both schemes must co-exist with the incumbent Wi-Fi system. In this paper, several co-existence schemes of Wi-Fi and LTE technology is comprehensively reviewed. The challenges of deploying LTE and Wi-Fi in the same band are clearly addressed based on the papers reviewed. Solution procedures and techniques to resolve the challenging issues are discussed in a short manner. The performance of various network architectures such as listenbefore- talk (LBT) based LTE, carrier sense multiple access with collision avoidance (CSMA/CA) based Wi-Fi is briefly compared. Finally, an attempt is made to implement these proposed LTEWi- Fi models in smart grid technology.Comment: submitted in 2018 IEEE PES T&

Towards More Efficient 5G Networks via Dynamic Traffic Scheduling

Department of Electrical EngineeringThe 5G communications adopt various advanced technologies such as mobile edge computing and unlicensed band operations, to meet the goal of 5G services such as enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (URLLC). Specifically, by placing the cloud resources at the edge of the radio access network, so-called mobile edge cloud, mobile devices can be served with lower latency compared to traditional remote-cloud based services. In addition, by utilizing unlicensed spectrum, 5G can mitigate the scarce spectrum resources problem thus leading to realize higher throughput services. To enhance user-experienced service quality, however, aforementioned approaches should be more fine-tuned by considering various network performance metrics altogether. For instance, the mechanisms for mobile edge computing, e.g., computation offloading to the edge cloud, should not be optimized in a specific metric's perspective like latency, since actual user satisfaction comes from multi-domain factors including latency, throughput, monetary cost, etc. Moreover, blindly combining unlicensed spectrum resources with licensed ones does not always guarantee the performance enhancement, since it is crucial for unlicensed band operations to achieve peaceful but efficient coexistence with other competing technologies (e.g., Wi-Fi). This dissertation proposes a focused resource management framework for more efficient 5G network operations as follows. First, Quality-of-Experience is adopted to quantify user satisfaction in mobile edge computing, and the optimal transmission scheduling algorithm is derived to maximize user QoE in computation offloading scenarios. Next, regarding unlicensed band operations, two efficient mechanisms are introduced to improve the coexistence performance between LTE-LAA and Wi-Fi networks. In particular, we develop a dynamic energy-detection thresholding algorithm for LTE-LAA so that LTE-LAA devices can detect Wi-Fi frames in a lightweight way. In addition, we propose AI-based network configuration for an LTE-LAA network with which an LTE-LAA operator can fine-tune its coexistence parameters (e.g., CAA threshold) to better protect coexisting Wi-Fi while achieving enhanced performance than the legacy LTE-LAA in the standards. Via extensive evaluations using computer simulations and a USRP-based testbed, we have verified that the proposed framework can enhance the efficiency of 5G.clos

Lisanssız LTE-LAA ve Wi-Fi birlikte varoluş sorunları ve çözüm yaklaşımı

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Long Term Evolution-License Assisted Access, Uzun Süreli Evrim-Lisansı Destekli Erişim (LTE-LAA), hücresel verilerin boşaltılması için en iyi çözümlerden birisi ve aynı zamanda hızla büyüyen hücresel ağ trafiği talebine uygun bir teknolojidir. LTE-LAA, LTE'nin lisanssız spektrumuna uzantısıdır. Lisanssız spektrum ise kullanıcılara yüksek hızlı veri sağlamak için ikincil kaynak olarak kullanılır. LTE-LAA, Wi-Fi gibi diğer bazı teknolojiler tarafından işgal edilmiş 5GHz'lik lisanssız bant genişliğinde çalışmaktadır. Mevcut Wi-Fi kanal erişim mekanizması, "Carrier Sense Multiple Access/ Collision Avoidance, Çarpışmadan Kaçınma ile Taşıyıcı Algılama Çoklu Erişim" (CSMA/CA) mekanizmasına dayalıdır. Yani kullanıcı kanalın meşgul olduğunu ya olmadığını belirlemek için kanalı ilk olarak algılar. Ayrıca iletim kanalında çarpışmayı önlemek için rastgele geri çekilme mekanizması kullanılır. Diğer taraftan, LTE-LAA "Listen Before Talk, Konuşmadan Önce Dinle" (LBT), LTE-LAA kullanıcının ilk önce verileri göndermeden önce kanalı dinlediği, CSMA/CA (Wi-Fi kanal erişim mekanizması) kanal erişim mekanizmasına benzer bir mekanizma kullanır. Ancak, bu iki teknolojinin (LTE-LAA ve Wi-Fi) aynı 5GHz kanalında çalıştığı durumlarda bu 5GHz kanalının dengesiz/uyumsuz bir şekilde paylaşma sorunuyla karşı karşıya geleceklerdir. Bu tez çalışması, bu sorunu iyileştirmek için Wi-Fi ve LTE-LAA kullanıcıları arasındaki uygun "birlikte-varoluşu" sağlamak adına uygun bir mekanizmayı "Hybrid Automatic Repeat Request and Channel Monitoring based Collision Probability" (HCmbp) önermektedir. Daha özellikli olarak, eNB, HCmbp'nin mekanizmasına dayalı çarpışma olasılığını gözlemleyecektir. Ayrıca, kanalda çarpışma olduğu veya olmadığı zaman çekişme, pencerenin artması ve azalması HCmbp mekanizmasına dayalıdır. Simülasyon sonuçları, önerilen HCmbp mekanizmasının, mevcut LBT mekanizmasına kıyasla daha iyi bir "birlikte-varoluş" sağladığını göstermektedir.Long Term Evolution-License Assisted Access (LTE-LAA) which is one of the best solution for offloading the cellular networks data and also a suit respond to the rapidly growing cellular network traffic demand. LTE-LAA is the extension of LTE to the unlicensed bandwidth, in which unlicensed spectrum is used as secondary resource for providing high speed data to the users. Since LTE-LAA operates in 5GHz unlicensed bandwidth where it is already in used by Wi-Fi. The current channel access mechanism of Wi-Fi is based on Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) where the station first sense the channel to specify if the channel is busy or free. And a random back-off is used to prevent the collision in transmission channel. From other hand LTE-LAA uses Listen Before Talk (LBT) a CSMA/CA (Wi-Fi channel access mechanism) like channel access mechanism where a LTE-LAA user first will listen to the channel before sending the data. However, there is a fair coexistence issue when these two technologies (LTE-LAA and Wi-Fi) share the same 5GHz channel. To overcome this coexistence issue in this thesis study we propose a joint, "Hybrid Automatic Repeat Request and Channel Monitoring based Collision Probability" (HCmbp) channel access mechanism to bring the fairness between LTE-LAA and Wi-Fi stations. More specifically, the eNB will observe the collision probability based on HCmbp mechanism. The contention window is updated based on HCmbp mechanism. Based on simulation results the proposed HCmbp mechanism brings a better fairness compared to the current mechanism of LBT

Simultaneous Transmission Opportunities for LTE-LAA Co existing with WiFi in Unlicensed Spectrum from Exploiting Spatial Domain

In this thesis, we first give an intensive review on the background of LTE-LAA technology, the research status of LTE-LAA and WiFi co-existence mechanisms and 3GPP Rel. 13 standardization on LTELAA. The existing co-existence designs focus on the time-domain, frequency-domain and power-domain to achieve fairness between two systems. Simultaneous transmissions are avoided to reduce collision probability. However, by exploiting the spatial domain, we discover the possibility of simultaneous LTE-LAA/WiFi transmission opportunities as long as the interference received at the WiFi receiver is well managed. We first show the feasibility of such simultaneous transmission opportunities considering AP/UE location diversity and various coverage overlap situations between LTE-LAA small cell and WiFi AP. Then, by utilizing multi-antenna beamforming capability, we propose a more practical co-existence scheme combing DoA estimation and null steering technologies. As the lack of direct communication link between LTE-LAA and WiFi systems, we also give our design of information exchange that requires minimal modifications on current WiFi standards and with little to none extra overhead. From the discussions and simulation results, we prove the existence of such simultaneous transmission opportunities that do not bring extra impact on WiFi networks. The channel occupancy time of LTE-LAA can be greatly improved. However, problems and challenges are also identified that require future investigations

Holistic Small Cell Traffic Balancing across Licensed and Unlicensed Bands

Due to the dramatic growth in mobile data traffic on one hand and the scarcity of the licensed spectrum on the other hand, mobile operators are considering the use of unlicensed bands (especially those in 5 GHz) as complementary spectrum for providing higher system capacity and better user experience. This approach is currently being standardized by 3GPP under the name of LTE Licensed-Assisted Access (LTE-LAA). In this paper, we take a holistic approach for LTE-LAA small cell traffic balancing by jointly optimizing the use of the licensed and unlicensed bands. We pose this traffic balancing as an optimization problem that seeks proportional fair coexistence of WiFi, small cell and macro cell users by adapting the transmission probability of the LTE-LAA small cell in the licensed and unlicensed bands. The motivation for this formulation is for the LTE-LAA small cell to switch between or aggregate licensed and unlicensed bands depending on the interference/traffic level and the number of active users in each band. We derive a closed form solution for this optimization problem and additionally propose a transmission mechanism for the operation of the LTE-LAA small cell on both bands. Through numerical and simulation results, we show that our proposed traffic balancing scheme, besides enabling better LTE-WiFi coexistence and efficient utilization of the radio resources relative to the existing traffic balancing scheme, also provides a better tradeoff between maximizing the total network throughput and achieving fairness among all network flows compared to alternative approaches.Comment: Accepted for publication at MSWiM 201

Long Term Evolution – License Assisted Access (LTE-LAA): Modeling and Coexistence Performance Analysis

Wireless communication built upon radio spectrum plays an instrumental role in today's modern world. With the explosive growth of mobile data traffic, mobile cellular networks need more spectrum to boost their system capacity. Long Term Evolution (LTE) technology leveraging the unlicensed band is anticipated to provide a solution to address this challenge. However, ensuring fair operation in terms of spectrum sharing with current unlicensed spectrum incumbents remains a key challenge for the success and viability of Unlicensed LTE (U-LTE). In particular, fair co-existence between unlicensed LTE and the Institute of Electrical and Electronics Engineers (IEEE) 802.11x standard, known as Wi-Fi, remains a principal concern, due to the ubiquitous, high-throughput and high capacity nature of both technologies. This work addresses the problem of modeling and evaluating the coexistence of LTE License-Assisted-Access (LTE-LAA) in the unlicensed band. The research work presents a novel analytical model using Markov Chain to accurately model the LAA Listen-Before-Talk (LBT) scheme, as specified in the final technical specification 36.213 of the 3rd Generation Partnership Project (3GPP) release 13 and 14. Model validation is demonstrated through numerical and simulation result comparison. Model performance evaluation is examined and contrasted with the IEEE 802.11 Distributed Coordination Function (DCF) and analysis results are subsequently presented and discussed herein. Finally, succeeding model development, a comprehensive coexistence performance analysis study is developed and completed examining the coexistence of homogeneous and heterogeneous network scenarios consisting of LTE-LAA and Wi-Fi nodes. As a result, the contribution of this work establishes a novel apparatus that facilitates numerical analysis of the LTE-LAA LBT mechanism and enables numerical comparison of future enhancements with the standardized LTE-LAA framework. In addition, this work delivers a delineating, unequivocal and in-depth examination of the effects and implications that the LTE-LAA LBT mechanism and its parameters have on coexistence performance of homogeneous and heterogeneous co-channel and co-located networks

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