딥러닝과 최적화를 활용한 무선 통신 시스템 설계

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2021.8. 캄포싸이.최근 5G 시스템의 등장으로 고신뢰 저지연 통신(ultra reliable low-latency communications, URLLC)과 대규모 사물 통신(massive machine-type communications, mMTC)이 주목을 받고 있다. 의료 서비스, 커넥티드 카, 로봇 공학, 제조업, 자유 시점 비디오 등 다양한 서비스들이 저지연 통신에서 예상되고, 이들은 1 ms 정도의 극도로 낮은 지연 시간을 요구한다. 한편, 대규모 사물 통신은 기지국에서 많은 기기(예를 들어 센서, 로봇, 자동차, 기계)의 방대한 연결성에 관한 것이다. 기존 통신 시스템(예를 들어 Long-Term Evolution (LTE))은 저지연 통신과 대규모 사물 통신의 요구 사항을 만족하기 어렵기에 이 통신 환경에 적합한 새로운 기술이 필요하다. 본 학위논문에서는 대규모 사물 통신과 저지연 통신을 위한 세 가지 기술을 제안한다. 논문의 첫 부분에서는 많은 기기가 비직교 확산 시퀀스를 사용해 기지국에 접속하는 대규모 사물 통신을 지원하는 딥러닝 기반의 확산 시퀀스 설계 및 활성 사용자 검출(active user detection, AUD) 방법을 제안한다. 검출 오류를 최소화하는 전체 통신 시스템을 설계하기 위해, 종단 간 심층 신경 네트워크(deep neural network, DNN)를 활용한다. 이 신경 네트워크에서 확산 네트워크는 송신기를 모델링하고 검출 네트워크는 활성 기기를 추정한다. 검출 오류를 손실 함수로 사용함으로써, 확산 시퀀스를 포함한 네트워크 변수들은 검출 오류를 최소화하도록 학습된다. 시뮬레이션 결과에서는 제안한 방법으로 얻어진 확산 시퀀스가 압축센싱 기반의 검출 기법과 제안한 검출 기법 모두에서 기존의 시퀀스보다 더 좋은 검출 성능을 달성하는 것을 보여준다. 논문의 두 번째 부분에서는 직교 주파수 분할 다중 방식(orthogonal frequency division multiplexing, OFDM) 시스템에서 프리코딩된 채널의 RMS (root mean square) 지연 확산을 줄이는 프리코딩 기법을 제안한다. OFDM 시스템에서 오버헤드를 증가시키지 않으면서 지연을 줄이기 위해서는 채널의 지연 확산과 그로 인한 CP (cyclic prefix)의 길이를 줄이는 것이 무엇보다 중요하다. 제안하는 기법에서는 RMS 지연 확산의 상한을 목적 함수로 하고 각 부반송파의 신호 대 잡음비를 제약조건으로 하는 최적화 문제를 설정한다. 최적화된 프리코딩을 찾을 수 있도록 원래 문제를 볼록 문제로 변환하기 위해 SDR (semi-definite relaxation) 기법을 사용한다. 시뮬레이션 결과에서는 제안한 프리코딩 설계가 특히 기지국에서 안테나의 수가 많을 때 RMS 지연 확산을 크게 줄이는 것을 보여준다. 논문의 마지막 부분에서는 저지연 OFDM 시스템에서 전송률 최대화를 위한 선형 프리코딩 설계를 다룬다. 저지연 통신에서 짧아지는 심볼 주기로 인한 CP의 오버헤드를 완화하기 위해 5G 무선 시스템은 짧은 CP를 사용할 필요가 있다. 채널의 지연 확산은 CP 길이보다 짧아야 하므로 먼저 실질적인 RMS 지연 확산과 달성 가능한 전송률을 제로 포싱 조건을 사용하여 유도한다. 다음으로 지연 확산 제약조건을 만족하는 전송률 최적화 문제를 사용자마다 정립하고 SDR 기법으로 해결 가능한 볼록 문제로 변환한다. 모든 사용자에 대해 최적화 문제를 푸는 것으로 전체 프리코딩 행렬을 얻는다. 시뮬레이션 결과에서는 제안한 기법이 작은 RMS 지연 확산과 함께 기존의 전송률 최적화보다 월등한 성능을 달성하는 것을 보여준다.With the advent of 5G wireless systems, ultra reliable low-latency communications (URLLC) and massive machine-type communications (mMTC) have recently attracted growing attention. Applications in health care, connected cars, robotics, manufacturing, and free-viewpoint video are expected in low-latency communications, and they demand extremely short round-trip latency levels as low as 1 ms. On the other hand, mMTC mainly concerns the massive connectivity of a large number of devices (e.g. sensors, robots, vehicles, and machines) to the base station (BS). Since conventional communications systems (e.g. Long-Term Evolution (LTE)) are difficult to meet the requirements of low-latency communications or mMTC, novel techniques suitable for these communications environments are required. This dissertation proposes three techniques for mMTC or low-latency communications. In the first part of the dissertation, we propose a deep learning-based spreading sequence design and active user detection (AUD) to support mMTC where a large number of devices access the base station using non-orthogonal spreading sequences. To design the whole communications system minimizing AUD error, we employ an end-to-end deep neural network (DNN) where the spreading network models the transmitter side and the AUD network estimates active devices. By using the AUD error as a loss function, network parameters including the spreading sequences are learned to minimize the AUD error. Numerical results reveal that the spreading sequences obtained from the proposed approach achieve higher AUD performance than the conventional spreading sequences in the compressive sensing-based AUD schemes, as well as in the proposed AUD scheme. In the second part of the dissertation, a precoding scheme to reduce the root mean square (RMS) delay spread of precoded channels in a orthogonal frequency division multiplexing (OFDM) system is proposed. In order to reduce latency in OFDM systems while not increasing the overhead, it is of primary importance to reduce the effective delay spread of the channel and thus the length of the cyclic prefix (CP). We formulate an optimization problem with an upper bound of the RMS delay spread as the objective function and a signal-to-noise ratio for each subcarrier as constraints. Semi-definite relaxation (SDR) technique is used to convert the problem into a convex problem so as to find the optimal precoding vector. Numerical results confirm that the proposed precoding design provides a significant reduction in the RMS delay spread, especially when there are a large number of antennas at the base station. In the last part of the dissertation, we addresses linear precoding design for sum rate maximization in low-latency OFDM systems. In order to mitigate the overhead of CP originating from shortened symbol duration for low-latency communications, 5G wireless systems need to adopt short CP lengths. As channel delay spread must be less than the CP length, we first derive the effective RMS delay spread and the achievable rate using the zero-forcing assumption. We construct a sum rate optimization problem for each user subject to delay spread constraints and then convert the problem into a solvable convex problem along with a SDR technique. The precoding matrix is finally obtained by solving optimization problems for all users. Numerical results reveal that the proposed scheme attains superior performance to the conventional sum rate optimization, as well as small RMS delay spread.1 INTRODUCTION 1 1.1 Deep Learning-based Spreading Sequence Design and Active User Detection for Massive Machine-Type Communications 2 1.2 Precoding Design for Cyclic Prefix Overhead Reduction in a MISO-OFDM System 5 1.3 Sum Rate Maximization with Shortened Cyclic Prefix in a MIMO-OFDM System 6 2 DEEP LEARNING-BASED SPREADING SEQUENCE DESIGN AND ACTIVE USER DETECTION FOR MASSIVE MACHINE-TYPE COMMUNICATIONS 8 2.1 System Model 8 2.2 DNN-based Spreading Sequence Design and Active User Detection 10 2.2.1 SN Architecture 13 2.2.2 AUDN Architecture 15 2.2.3 Operation 17 2.3 Numerical Results 18 2.3.1 Simulation Setup 18 2.3.2 Homogeneous Activities 19 2.3.3 Heterogeneous Activities 21 3 PRECODING DESIGN FOR CYCLIC PREFIX OVERHEAD REDUCTION IN A MISO-OFDM SYSTEM 26 3.1 System Model 26 3.2 Precoding Design 27 3.2.1 Effective RMS Delay Spread and SNR 28 3.2.2 Precoding Optimization 29 3.3 Numerical Results 31 4 SUM RATE MAXIMIZATION WITH SHORTENED CYCLIC PREFIX IN A MIMO-OFDM SYSTEM 37 4.1 System Model 37 4.2 Preliminaries for Precoding Design 38 4.2.1 Zero-Forcing Conditions 38 4.2.2 Effective RMS Delay Spread 40 4.2.3 Achievable Rate 41 4.3 Precoding Optimization 42 4.4 Numerical Results 43 5 CONCLUSION 53 5.1 Deep Learning-based Spreading Sequence Design and Active User Detection for Massive Machine-Type Communications 53 5.2 Precoding Design for Cyclic Prefix Overhead Reduction in a MISO-OFDM System 54 5.3 Sum Rate Maximization with Shortened Cyclic Prefix in a MIMO-OFDM System 54 Abstract (In Korean) 60 Acknowledgments 62박

Single-Frequency Network Terrestrial Broadcasting with 5GNR Numerology

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Iterative pre-distortion of the non-linear satellite channel

Digital Video Broadcasting - Satellite - Second Generation (DVB-S2) is the current European standard for satellite broadcast and broadband communications. It relies on high order modulations up to 32-amplitude/phase-shift-keying (APSK) in order to increase the system spectral efficiency. Unfortunately, as the modulation order increases, the receiver becomes more sensitive to physical layer impairments, and notably to the distortions induced by the power amplifier and the channelizing filters aboard the satellite. Pre-distortion of the non-linear satellite channel has been studied for many years. However, the performance of existing pre-distortion algorithms generally becomes poor when high-order modulations are used on a non-linear channel with a long memory. In this paper, we investigate a new iterative method that pre-distorts blocks of transmitted symbols so as to minimize the Euclidian distance between the transmitted and received symbols. We also propose approximations to relax the pre-distorter complexity while keeping its performance acceptable

Estimation and detection techniques for doubly-selective channels in wireless communications

A fundamental problem in communications is the estimation of the channel. The signal transmitted through a communications channel undergoes distortions so that it is often received in an unrecognizable form at the receiver. The receiver must expend significant signal processing effort in order to be able to decode the transmit signal from this received signal. This signal processing requires knowledge of how the channel distorts the transmit signal, i.e. channel knowledge. To maintain a reliable link, the channel must be estimated and tracked by the receiver. The estimation of the channel at the receiver often proceeds by transmission of a signal called the 'pilot' which is known a priori to the receiver. The receiver forms its estimate of the transmitted signal based on how this known signal is distorted by the channel, i.e. it estimates the channel from the received signal and the pilot. This design of the pilot is a function of the modulation, the type of training and the channel. [Continues.

Cardiac electrophysiology and mechanoelectric feedback : modeling and simulation

Cardiac arrhythmia such as atrial and ventricular fibrillation are characterized by rapid and irregular electrical activity, which may lead to asynchronous contraction and a reduced pump function. Besides experimental and clinical studies, computer simulations are frequently applied to obtain insight in the onset and perpetuation of cardiac arrhythmia. In existing models, the excitable tissue is often modeled as a continuous two-phase medium, representing the intracellular and interstitial domains, respectively. A possible drawback of continuous models is the lack of flexibility when modeling discontinuities in the cardiac tissue. We introduce a discrete bidomain model in which the cardiac tissue is subdivided in segments, each representing a small number of cardiac cells. Active membrane behavior as well as intracellular coupling and interstitial currents are described by this model. Compared with the well-known continuous bidomain equations, our Cellular Bidomain Model is better aimed at modeling the structure of cardiac tissue, in particular anisotropy, myofibers, fibrosis, and gap junction remodeling. An important aspect of our model is the strong coupling between cardiac electrophysiology and cardiomechanics. Mechanical behavior of a single segment is modeled by a contractile element, a series elastic element, and a parallel elastic element. Active force generated by the sarcomeres is represented by the contractile element together with the series elastic element. The parallel elastic element describes mechanical behavior when the segment is not electrically stimulated. Contractile force is related to the intracellular calcium concentration, the sarcomere length, and the velocity of sarcomere shortening. By incorporating the influence of mechanical deformation on electrophysiology, mechanoelectric feedback can be studied. In our model, we consider the immediate influence of stretch on the action potential by modeling a stretch-activated current. Furthermore, we consider the adap- tation of ionic membrane currents triggered by changes in mechanical load. The strong coupling between cardiac electrophysiology and cardiac mechanics is a unique property of our model, which is reflected by its application to obtain more insight in the cause and consequences of mechanical feedback on cardiac electrophysiology. In this thesis, we apply the Cellular Bidomain Model in five different simulation studies to cardiac electrophysiology and mechanoelectric feedback. In the first study, the effect of field stimulation on virtual electrode polarization is studied in uniform, decoupled, and nonuniform cardiac tissue. Field stimulation applied on nonuniform tissue results in more virtual electrodes compared with uniform tissue. Spiral waves can be terminated in decoupled tissue, but not in uniform, homogeneous tissue. By gradually increasing local differences in intracellular conductivities, the amount and spread of virtual electrodes increases and spiral waves can be terminated. We conclude that the clinical success of defibrillation may be explained by intracellular decoupling and spatial heterogeneity present in normal and in pathological cardiac tissue. In the second study, the role of the hyperpolarization-activated inward current If is investigated on impulse propagation in normal and in pathological tissue. The effect of diffuse fibrosis and gap junction remodeling is simulated by reducing cellular coupling nonuniformly. As expected, the conduction velocity decreases when cellular coupling is reduced. In the presence of If, the conduction velocity increases both in normal and in pathological tissue. In our simulations, ectopic activity is present in regions with high expression of If and is facilitated by cellular uncoupling. We also found that an increased If may facilitate propagation of the action potential. Hence, If may prevent conduction slowing and block. Overexpression of If may lead to ectopic activity, especially when cellular coupling is reduced under pathological conditions. In the third study, the influence of the stretch-activated current Isac is investigated on impulse propagation in cardiac fibers composed of segments that are electrically and mechanically coupled. Simulations of homogeneous and inhomogeneous cardiac fibers have been performed to quantify the relation between conduction velocity and Isac under stretch. Conduction slowing and block are related to the amount of stretch and are enhanced by contraction of early-activated segments. Our observations are in agreement with experimental results and explain the large differences in intra-atrial conduction, as well as the increased inducibility of atrial fibrillation in acutely dilated atria. In the fourth study, we investigate the hypothesis that electrical remodeling is triggered by changes in mechanical work. Stroke work is determined for each segment by simulating the cardiac cycle. Electrical remodeling is simulated by adapting the L-type Ca2+ current ICa,L such that a homogeneous distribution of stroke work is obtained. With electrical remodeling, a more homogeneous shortening of the fiber is obtained, while heterogeneity in APD increases and the repolarization wave reverses. These results are in agreement with experimentally observed distributions of strain and APD and indicate that electrical remodeling leads to more homogeneous shortening during ejection. In the fifth study, we investigate the effect of stretch on the vulnerability to AF. The human atria are represented by a triangular mesh obtained from MRI data. To model acute dilatation, overall stretch is applied to the atria. In the presence of Isac, the membrane potential depolarizes, which causes inactivation of the sodium channels and results in conduction slowing or block. Inducibility of AF increases under stretch, which is explained by an increased dispersion in refractory period, conduction slowing, and local conduction block. Our observations explain the large differences in intra-atrial conduction measured in experiments and provide insight in the vulnerability to AF in dilated atria. In conclusion, our model is well-suited to describe cardiac electrophysiology and mechanoelectric feedback. For future applications, the model may be improved by taking into account new insights from cellular physiology, a more accurate geometry, and hemodynamics

Direction-Aware Adaptive Online Neural Speech Enhancement with an Augmented Reality Headset in Real Noisy Conversational Environments

This paper describes the practical response- and performance-aware development of online speech enhancement for an augmented reality (AR) headset that helps a user understand conversations made in real noisy echoic environments (e.g., cocktail party). One may use a state-of-the-art blind source separation method called fast multichannel nonnegative matrix factorization (FastMNMF) that works well in various environments thanks to its unsupervised nature. Its heavy computational cost, however, prevents its application to real-time processing. In contrast, a supervised beamforming method that uses a deep neural network (DNN) for estimating spatial information of speech and noise readily fits real-time processing, but suffers from drastic performance degradation in mismatched conditions. Given such complementary characteristics, we propose a dual-process robust online speech enhancement method based on DNN-based beamforming with FastMNMF-guided adaptation. FastMNMF (back end) is performed in a mini-batch style and the noisy and enhanced speech pairs are used together with the original parallel training data for updating the direction-aware DNN (front end) with backpropagation at a computationally-allowable interval. This method is used with a blind dereverberation method called weighted prediction error (WPE) for transcribing the noisy reverberant speech of a speaker, which can be detected from video or selected by a user's hand gesture or eye gaze, in a streaming manner and spatially showing the transcriptions with an AR technique. Our experiment showed that the word error rate was improved by more than 10 points with the run-time adaptation using only twelve minutes of observation.Comment: IEEE/RSJ IROS 202

Bridging the gap between computation and clinical biology: validation of cable theory in humans

Introduction: Computerized simulations of cardiac activity have significantly contributed to our understanding of cardiac electrophysiology, but techniques of simulations based on patient-acquired data remain in their infancy. We sought to integrate data acquired from human electrophysiological studies into patient-specific models, and validated this approach by testing whether electrophysiological responses to sequential premature stimuli could be predicted in a quantitatively accurate manner. Methods: Eleven patients with structurally normal hearts underwent electrophysiological studies. Semi-automated analysis was used to reconstruct activation and repolarization dynamics for each electrode. This S(2) extrastimuli data was used to inform individualized models of cardiac conduction, including a novel derivation of conduction velocity restitution. Activation dynamics of multiple premature extrastimuli were then predicted from this model and compared against measured patient data as well as data derived from the ten-Tusscher cell-ionic model. Results: Activation dynamics following a premature S(3) were significantly different from those after an S(2). Patient specific models demonstrated accurate prediction of the S(3) activation wave, (Pearson's R(2) = 0.90, median error 4%). Examination of the modeled conduction dynamics allowed inferences into the spatial dispersion of activation delay. Further validation was performed against data from the ten-Tusscher cell-ionic model, with our model accurately recapitulating predictions of repolarization times (R(2) = 0.99). Conclusions: Simulations based on clinically acquired data can be used to successfully predict complex activation patterns following sequential extrastimuli. Such modeling techniques may be useful as a method of incorporation of clinical data into predictive models