무선 네트워크에서의 TCP 성능 향상 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 박세웅.TCP (Transmission Control Protocol), one of the most essential protocol for the Internet, has carried the most of the Internet traffic since its birth. With the deployment of various types of wireless networks and proliferation of smart devices, a rapid increase in mobile data traffic volume has been observed and TCP has still carried the majority of mobile traffic, thus leading to huge attention again on TCP performance in wireless networks. In this dissertation, we tackle three different problems that aim to improve TCP performance in wireless networks. Firstly, we dealt with the downstream bufferbloat problem in wireless access networks such as LTE and Wi-Fi. We clarify the downstream bufferbloat problem in resource competitive environments such as Wi-Fi, and design a receiver-side countermeasure for easy deployment that does not require any modification at the sender or intermediate routers. Exploiting TCP and AQM dynamics, our scheme competes for shared resource in a fair manner with conventional TCP flow control methods and prevents bufferbloat. We implement our scheme in commercial smart devices and verify its performance through real experiments in LTE and Wi-Fi networks. Secondly, we consider the upstream bufferbloat problem in LTE networks. We clarify that the upstream bufferbloat problem can significantly degrade multitasking users QoE in LTE networks and design a packet scheduler that aims to separate delay-sensitive packets from non delay-sensitive packets without computational overhead. We implement the proposed packet scheduler in commercial smart devices and evaluate the performance of our proposed scheme through real experiments in LTE networks. Lastly, we investigate the TCP fairness problem in low-power and lossy networks (LLNs). We confirm severe throughput unfairness among nodes with different hop counts and propose dynamic TX period adjustment scheme to enhance TCP fairness in LLNs. Through experiments on the testbed, we evaluate how much the proposed scheme enhances fairness index.1 Introduction 1 1.1 Motivation 1 1.2 Background and Related Work 3 1.3 Outline 7 2 Receiver-side TCP Countermeasure to Bufferbloat in Wireless Access Networks 8 2.1 Introduction 8 2.2 Dynamics of TCP and AQM 11 2.3 Receiver-side TCP Adaptive Queue Control 14 2.3.1 Receiver-side Window Control 15 2.3.2 Delay Measurement and Queue Length Estimation 17 2.3.3 Configuration of RTAC 19 2.4 Experimental Setup and Configuration 20 2.4.1 Receiver Measurement Errors and Configuration 21 2.5 Experimental Results 27 2.5.1 Bufferbloat in Wireless Access Networks 27 2.5.2 Prevention of Bufferbloat 31 2.5.3 Fairness of TCP Flows with Various Receiver Types 32 2.5.4 The Impact of TCP Variants 39 2.5.5 The Impact of Upload Bufferbloat 46 2.5.6 Coexistence with the Unlimited Sender 48 2.6 Summary 48 3 Dual Queue Approach for Improving User QoE in LTE Networks 51 3.1 Introduction 51 3.2 User QoE Degradation in Multitasking Scenarios 54 3.2.1 Unnecessary Large Upload Queueing delay 54 3.2.2 Negative Impact on Performance in Multitasking Scenarios 55 3.3 SOR based Packet Classification with Multiple Transmit Queue 58 3.3.1 Dual Transmit Queue 59 3.3.2 SOR based Packet Classification Algorithm 61 3.4 Experiment Results 63 3.4.1 Packet Classification Metric: Sendbuffer Occupancy Ratio (SOR) 64 3.4.2 Improving RTT performance of Interactive Applications 68 3.4.3 Improving Download Performance 69 3.4.4 Fairness among Competing Upload Flows 71 3.5 Summary 74 4 Uplink Congestion Control in Low-power and Lossy Networks 75 4.1 Introduction 75 4.2 System Model 78 4.3 Proposed Scheme 79 4.3.1 Tx Period 79 4.3.2 Dynamic TX Period Adjustment 80 4.4 Experimental Results 82 4.4.1 Experimental Setup 82 4.4.2 Throughput analysis vs. Measurement 84 4.4.3 TCP Performance in Low-power Lossy Networks 87 4.4.4 Fairness improvement of DTPA 89 4.5 Summary 92 5 Conclusion 93 5.1 Research Contributions 93 5.2 Future Research Directions 95Docto

A fast engineering approach to high efficiency power amplifier linearization for avionics applications

This PhD thesis provides a fast engineering approach to the design of digital predistortion (DPD) linearizers from several perspectives: i) enhancing the off-line training performance of open-loop DPD, ii) providing robustness and reducing the computational complexity of the parameters identification subsystem and, iii) importing machine learning techniques to favor the automatic tuning of power amplifiers (PAs) and DPD linearizers with several free-parameters to maximize power efficiency while meeting the linearity specifications. One of the essential parts of unmanned aerial vehicles (UAV) is the avionics, being the radio control one of the earliest avionics present in the UAV. Unlike the control signal, for transferring user data (such as images, video, etc.) real-time from the drone to the ground station, large transmission rates are required. The PA is a key element in the transmitter chain to guarantee the data transmission (video, photo, etc.) over a long range from the ground station. The more linear output power, the better the coverage or alternatively, with the same coverage, better SNR allows the use of high-order modulation schemes and thus higher transmission rates are achieved. In the context of UAV wireless communications, the power consumption, size and weight of the payload is of significant importance. Therefore, the PA design has to take into account the compromise among bandwidth, output power, linearity and power efficiency (very critical in battery-supplied devices). The PA can be designed to maximize its power efficiency or its linearity, but not both. Therefore, a way to deal with this inherent trade-off is to design high efficient amplification topologies and let the PA linearizers take care of the linearity requirements. Among the linearizers, DPD linearization is the preferred solution to both academia and industry, for its high flexibility and linearization performance. In order to save as many computational and power resources as possible, the implementation of an open-loop DPD results a very attractive solution for UAV applications. This thesis contributes to the PA linearization, especially on off-line training for open-loop DPD, by presenting two different methods for reducing the design and operating costs of an open-loop DPD, based on the analysis of the DPD function. The first method focuses on the input domain analysis, proposing mesh-selecting (MeS) methods to accurately select the proper samples for a computationally efficient DPD parameter estimation. Focusing in the MeS method with better performance, the memory I-Q MeS method is combined with feature extraction dimensionality reduction technique to allow a computational complexity reduction in the identification subsystem by a factor of 65, in comparison to using the classical QR-LS solver and consecutive samples selection. In addition, the memory I-Q MeS method has been proved to be of crucial interest when training artificial neural networks (ANN) for DPD purposes, by significantly reducing the ANN training time. The second method involves the use of machine learning techniques in the DPD design procedure to enlarge the capacity of the DPD algorithm when considering a high number of free parameters to tune. On the one hand, the adaLIPO global optimization algorithm is used to find the best parameter configuration of a generalized memory polynomial behavioral model for DPD. On the other hand, a methodology to conduct a global optimization search is proposed to find the optimum values of a set of key circuit and system level parameters, that properly combined with DPD linearization and crest factor reduction techniques, can exploit at best dual-input PAs in terms of maximizing power efficiency along wide bandwidths while being compliant with the linearity specifications. The advantages of these proposed techniques have been validated through experimental tests and the obtained results are analyzed and discussed along this thesis.Aquesta tesi doctoral proporciona unes pautes per al disseny de linealitzadors basats en predistorsió digital (DPD) des de diverses perspectives: i) millorar el rendiment del DPD en llaç obert, ii) proporcionar robustesa i reduir la complexitat computacional del subsistema d'identificació de paràmetres i, iii) incorporació de tècniques d'aprenentatge automàtic per afavorir l'auto-ajustament d'amplificadors de potència (PAs) i linealitzadors DPD amb diversos graus de llibertat per poder maximitzar l’eficiència energètica i al mateix temps acomplir amb les especificacions de linealitat. Una de les parts essencials dels vehicles aeris no tripulats (UAV) _es l’aviònica, sent el radiocontrol un dels primers sistemes presents als UAV. Per transferir dades d'usuari (com ara imatges, vídeo, etc.) en temps real des del dron a l’estació terrestre, es requereixen taxes de transmissió grans. El PA _es un element clau de la cadena del transmissor per poder garantir la transmissió de dades a grans distàncies de l’estació terrestre. A major potència de sortida, més cobertura o, alternativament, amb la mateixa cobertura, millor relació senyal-soroll (SNR) la qual cosa permet l’ús d'esquemes de modulació d'ordres superiors i, per tant, aconseguir velocitats de transmissió més altes. En el context de les comunicacions sense fils en UAVs, el consum de potència, la mida i el pes de la càrrega útil són de vital importància. Per tant, el disseny del PA ha de tenir en compte el compromís entre ample de banda, potència de sortida, linealitat i eficiència energètica (molt crític en dispositius alimentats amb bateries). El PA es pot dissenyar per maximitzar la seva eficiència energètica o la seva linealitat, però no totes dues. Per tant, per afrontar aquest compromís s'utilitzen topologies amplificadores d'alta eficiència i es deixa que el linealitzador s'encarregui de garantir els nivells necessaris de linealitat. Entre els linealitzadors, la linealització DPD és la solució preferida tant per al món acadèmic com per a la indústria, per la seva alta flexibilitat i rendiment. Per tal d'estalviar tant recursos computacionals com consum de potència, la implementació d'un DPD en lla_c obert resulta una solució molt atractiva per a les aplicacions UAV. Aquesta tesi contribueix a la linealització del PA, especialment a l'entrenament fora de línia de linealitzadors DPD en llaç obert, presentant dos mètodes diferents per reduir el cost computacional i augmentar la fiabilitat dels DPDs en llaç obert. El primer mètode se centra en l’anàlisi de l’estadística del senyal d'entrada, proposant mètodes de selecció de malla (MeS) per seleccionar les mostres més significatives per a una estimació computacionalment eficient dels paràmetres del DPD. El mètode proposat IQ MeS amb memòria es pot combinar amb tècniques de reducció del model del DPD i d'aquesta manera poder aconseguir una reducció de la complexitat computacional en el subsistema d’identificació per un factor de 65, en comparació amb l’ús de l'algoritme clàssic QR-LS i selecció de mostres d'entrenament consecutives. El segon mètode consisteix en l’ús de tècniques d'aprenentatge automàtic pel disseny del DPD quan es considera un gran nombre de graus de llibertat (paràmetres) per sintonitzar. D'una banda, l'algorisme d’optimització global adaLIPO s'utilitza per trobar la millor configuració de paràmetres d'un model polinomial amb memòria generalitzat per a DPD. D'altra banda, es proposa una estratègia per l’optimització global d'un conjunt de paràmetres clau per al disseny a nivell de circuit i sistema, que combinats amb linealització DPD i les tècniques de reducció del factor de cresta, poden maximitzar l’eficiència de PAs d'entrada dual de gran ample de banda, alhora que compleixen les especificacions de linealitat. Els avantatges d'aquestes tècniques proposades s'han validat mitjançant proves experimentals i els resultats obtinguts s'analitzen i es discuteixen al llarg d'aquesta tesi

A cuckoo search optimization-based forward consecutive mean excision model for threshold adaptation in cognitive radio

The forward consecutive mean excision (FCME) algorithm is one of the most effective adaptive threshold estimation algorithms presently deployed for threshold adaptation in cognitive radio (CR) systems. However, its effectiveness is often limited by the manual parameter tuning process and by the lack of prior knowledge pertaining to the actual noise distribution considered during the parameter modeling process of the algorithm. In this paper, we propose a new model that can automatically and accurately tune the parameters of the FCME algorithm based on a novel integration with the cuckoo search optimization (CSO) algorithm. Our model uses the between-class variance function of the Otsu’s algorithm as the objective function in the CSO algorithm in order to auto-tune the parameters of the FCME algorithm. We compared and selected the CSO algorithm based on its relatively better timing and accuracy performance compared to some other notable metaheuristics such as the particle swarm optimization, artificial bee colony (ABC), genetic algorithm, and the differential evolution (DE) algorithms. Following close performance values, our findings suggest that both the DE and ABC algorithms can be adopted as favorable substitutes for the CSO algorithm in our model. Further simulation results show that our model achieves reasonably lower probability of false alarm and higher probability of detection as compared to the baseline FCME algorithm under different noise-only and signal-plus-noise conditions. In addition, we compared our model with some other known autonomous methods with results demonstrating improved performance. Thus, based on our new model, users are relieved from the cumbersome process involved in manually tuning the parameters of the FCME algorithm; instead, this can be done accurately and automatically for the user by our model. Essentially, our model presents a fully blind signal detection system for use in CR and a generic platform deployable to convert other parameterized adaptive threshold algorithms into fully autonomous algorithms.http://link.springer.com/journal/5002020-11-03hj2020Electrical, Electronic and Computer Engineerin

Advanced Technique and Future Perspective for Next Generation Optical Fiber Communications

Optical fiber communication industry has gained unprecedented opportunities and achieved rapid progress in recent years. However, with the increase of data transmission volume and the enhancement of transmission demand, the optical communication field still needs to be upgraded to better meet the challenges in the future development. Artificial intelligence technology in optical communication and optical network is still in its infancy, but the existing achievements show great application potential. In the future, with the further development of artificial intelligence technology, AI algorithms combining channel characteristics and physical properties will shine in optical communication. This reprint introduces some recent advances in optical fiber communication and optical network, and provides alternative directions for the development of the next generation optical fiber communication technology

The 2017 Terahertz Science and Technology Roadmap

Science and technologies based on terahertz frequency electromagnetic radiation (100GHz-30THz) have developed rapidly over the last 30 years. For most of the 20th century, terahertz radiation, then referred to as sub-millimeter wave or far-infrared radiation, was mainly utilized by astronomers and some spectroscopists. Following the development of laser based terahertz time-domain spectroscopy in the 1980s and 1990s the field of THz science and technology expanded rapidly, to the extent that it now touches many areas from fundamental science to “real world” applications. For example THz radiation is being used to optimize materials for new solar cells, and may also be a key technology for the next generation of airport security scanners. While the field was emerging it was possible to keep track of all new developments, however now the field has grown so much that it is increasingly difficult to follow the diverse range of new discoveries and applications that are appearing. At this point in time, when the field of THz science and technology is moving from an emerging to a more established and interdisciplinary field, it is apt to present a roadmap to help identify the breadth and future directions of the field. The aim of this roadmap is to present a snapshot of the present state of THz science and technology in 2016, and provide an opinion on the challenges and opportunities that the future holds. To be able to achieve this aim, we have invited a group of international experts to write 17 sections that cover most of the key areas of THz Science and Technology. We hope that The 2016 Roadmap on THz Science and Technology will prove to be a useful resource by providing a wide ranging introduction to the capabilities of THz radiation for those outside or just entering the field as well as providing perspective and breadth for those who are well established. We also feel that this review should serve as a useful guide for government and funding agencies