On the Reliability of LTE Random Access: Performance Bounds for Machine-to-Machine Burst Resolution Time

Random Access Channel (RACH) has been identified as one of the major bottlenecks for accommodating massive number of machine-to-machine (M2M) users in LTE networks, especially for the case of burst arrival of connection requests. As a consequence, the burst resolution problem has sparked a large number of works in the area, analyzing and optimizing the average performance of RACH. However, the understanding of what are the probabilistic performance limits of RACH is still missing. To address this limitation, in the paper, we investigate the reliability of RACH with access class barring (ACB). We model RACH as a queuing system, and apply stochastic network calculus to derive probabilistic performance bounds for burst resolution time, i.e., the worst case time it takes to connect a burst of M2M devices to the base station. We illustrate the accuracy of the proposed methodology and its potential applications in performance assessment and system dimensioning.Comment: Presented at IEEE International Conference on Communications (ICC), 201

Enabling LTE RACH Collision Multiplicity Detection via Machine Learning

The collision resolution mechanism in the Random Access Channel (RACH) procedure of the Long-Term Evolution (LTE) standard is known to represent a serious bottleneck in case of machine-type traffic. Its main drawbacks are seen in the facts that Base Stations (eNBs) typically cannot infer the number of collided User Equipments (UEs) and that collided UEs learn about the collision only implicitly, through the lack of the feedback in the later stage of the RACH procedure. The collided UEs then restart the procedure, thereby increasing the RACH load and making the system more prone to collisions. In this paper, we leverage machine learning techniques to design a system that outperforms the state-of-the-art schemes in preamble detection for the LTE RACH procedure. Most importantly, our scheme can also estimate the collision multiplicity, and thus gather information about how many devices chose the same preamble. This data can be used by the eNB to resolve collisions, increase the supported system load and reduce transmission latency. The presented approach is applicable to novel 3GPP standards that target massive IoT, e.g., LTE-M and NB-IoT.Comment: Submitted to IEEE GLOBECOM 201

An Efficient FPGA-Based Frequency Shifter for LTE/LTE-A Systems

The Physical Random Access Channel plays an important role in LTE and LTE-A systems. Through this channel, the user equipment aligns its uplink transmissions to the eNodeB’s uplink and gains access to the network. One of the initial operations executed by the receiver at eNodeB side is the translation of the channel’s signal back to base-band. This operation is a necessary step for preamble detection and can be executed through a time-domain frequency-shift operation. Therefore, in this paper, we present the hardware architecture and design details of an optimised and configurable FPGA-based time-domain frequency shifter. The proposed architecture is based on a customised Numerically Controlled Oscillator that is employed for creating complex exponential samples using only plain logical resources. The main advantage of the proposed architecture is that it completely removes the necessity of saving in memory a huge number of long complex exponentials by making use of a Look-Up Table and exploiting the quarter-wave symmetry of the basis waveform. The results demonstrate that the proposed architecture provides high Spurious Free Dynamic Range signals employing only a minimal number of FPGA resources. Additionally, the proposed architecture presents spur-suppression ranging from 62.13 to 153.58 dB without employing any correction

5G 이후 무선 네트워크를 위한 무선 접속 기술 향상 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 박세웅.Recently, operators are creating services using 5G systems in various fields, e.g., manufacturing, automotive, health care, etc. 5G use cases include transmission of small packets using IoT devices to high data rate transmission such as high-definition video streaming. When a large-scale IoT device transmits a small packet, power saving is important, so it is necessary to disconnect from the base station and then establish a connection through random access to transmit data. However, existing random access procedures are difficult to satisfy various latency requirements. It is attractive to use a wide bandwidth of the millimeter wave spectrum for high data rate transmission. In order to overcome the channel characteristics, beamforming technology is applied. However, when determining a beam pair between a transmitter and a receiver, interference is not considered. In this dissertation, we consider the following three enhancements to enable 5G and beyond use cases: (i) Two-step random access procedure for delay-sensitive devices, (ii) self-uplink synchronization framework for solving preamble collision problem, and (iii) interference-aware beam adjustment for interference coordination. First, RAPID, two-step random access for delay-sensitive devices, is proposed to reduce latency requirement value for satisfying specific reliability. When devices, performing RAPID and contention-based random access, coexist, it is important to determine a value that is the number of preambles for RAPID to reduce random access load. Simulation results show that RAPID achieves 99.999% reliability with 80.8% shorter uplink latency, and also decreases random access load by 30.5% compared with state-of-the-art techniques. Second, in order to solve preamble collision problem, we develop self-uplink synchronization framework called EsTA. Preamble collision occurs when multiple devices transmit the same preamble. Specifically, we propose a framework that helps the UE to estimate the timing advance command using a deep neural network model and to determine the TA value. Estimation accuracy can achieve 98–99% when subcarrier spacing is 30 and 60 kHz. Finally, we propose IBA, which is interference-aware beam adjustment method to reduce interference in millimeter wave networks. Unlike existing methods of reducing interference by scheduling time and frequency resources differently, interference is controlled through beam adjustment. In IBA, it is important to reduce search space of finding new beam pair to reduce interference. In practical, it is impossible to search beam pair of all combinations. Therefore, through Monte Carlo method, we can reduce search space to achieve local optimum. IBA achieve enhancement of lower 50%throughput up to 50% compared with only applying beam adjustment. In summary, we propose a two-step random access, a self-uplink synchronization framework, and interference-aware beam adjustment for 5G and beyond use cases. Through these researches, we achieve enhancements of network performance such as latency and throughput compared with state-of-the-art techniques.최근 사업자는 제조, 자동차, 헬스 케어 등 다양한 분야에서 5G 시스템을 사용하여 서비스를 만들고 있다. 5G 사용 사례에는 IoT 장치를 이용한 작은 패킷 전송에서고화질 비디오 스트리밍과 같은 고속 데이터 전송까지 포함된다. 대규모 IoT 장치가작은 패킷을 전송하는 경우 전력 소모 절약이 중요하므로 기지국과의 연결을 끊은다음 랜덤 액세스를 통해 다시 기지국과 연결하여 데이터를 전송해야한다. 그러나기존의 랜덤 액세스 절차는 다양한 지연시간 요건을 만족시키기 어렵다. 한편, 높은데이터 전송 속도를 위해 넓은 대역폭의 밀리미터파 대역을 사용한다. 이때, 밀리미터파 대역 채널 특성을 극복하기 위해 빔포밍 기술이 적용된다. 그러나 현재 5G표준에서 송신기와 수신기 사이의 빔 쌍을 결정할 때, 간섭은 고려되지 않는다. 이논문에서는 5G 및 그 이후의 네트워크에서 다양한 사용 사례를 지원하기 위해 다음세 가지 개선 사항을 고려한다. (i) 지연에 민감한 장치를 위한 2 단계 랜덤 액세스절차, (ii) 프리앰블 충돌 문제를 해결하기 위한 자체 상향링크 동기화 프레임 워크,그리고 (iii) 간섭을 줄이기 위한 간섭 인식 빔 조정이다. 첫째, 지연에 민감한 장치를 위한 2 단계 랜덤 액세스인 RAPID는 특정 신뢰도를 만족시키기 위한 지연시간을 줄이기 위해 제안되었다. RAPID와 경합 기반 랜덤 액세스를 수행하는 장치가 공존할 경우 RAPID가 랜덤 액세스 부하를 줄이기 위해 RAPID를 위해 할당되는 프리앰블 수를 결정하는 것이 중요하다. 시뮬레이션 결과에 따르면 RAPID는 99.999%의신뢰도를 만족시키는 지연시간을 최신 기술에 비해 80.8% 줄이면서, 랜덤 액세스부하를 30.5% 줄인다. 둘째, 프리앰블 충돌 문제를 해결하기 위해 자체 상향링크 동기화 프레임워크인 EsTA를 개발한다. 프리앰블 충돌은 여러 장치가 동일한 프리앰블을 전송할 때 발생한다. 구체적으로, 단말이 심층 신경망 모델을 사용하여 timing advance(TA) command를 추정하고 TA값을 결정하는 프레임 워크를 제안한다. 네트워크 시스템의 부반송파 간격이 30 및 60 kHz 일 때, TA command 추정 정확도는98–99%를 달성 할 수 있다. 마지막으로, 밀리미터파 네트워크에서 간섭을 줄이기 위한 간섭 인식 빔 조정 방법인 IBA를 제안한다. 시간과 주파수 자원을 다르게 예약하여 간섭을 줄이는 기존의 방법과 달리 IBA는 빔 조정을 통해 간섭을 제어한다.이 때, 간섭을 줄이기 위해 새로운 빔 쌍을 찾는 검색 공간을 줄이는 것이 중요하다.현실적으로 모든 빔 쌍의 조합을 검색하는 것은 불가능하다. 따라서 IBA는 Monte Carlo 방법을 통해 검색 공간을 축소하여 local optimum을 달성하도록 설계되어야한다. IBA는 5G 표준의 빔 조정 방법과 비교했을 때, 하위 50% throughput의 중간값이최대 50%까지 향상된다. 요약하면, 우리는 5G 및 그 이후의 다양한 사용 사례를 위해서 2 단계 랜덤 액세스, 자체 상향링크 동기화 프레임 워크, 그리고 간섭 인식 빔조정 방법을 제안한다. 이 연구를 통해 최신 기술에 비해 지연시간 및 처리량과 같은네트워크 성능이 향상된다.1 Introduction 1 1.1 5G Vision, Applications, and Keywords 1 1.2 Overview of Existing Approach 3 1.3 Main Contributions 4 1.3.1 RAPID: Two-Step Random Access 4 1.3.2 EsTA: Self-Uplink Synchronization 5 1.3.3 IBA: Interference-Aware Beam Adjustment 5 1.4 Organization of the Dissertation 6 2 RAPID: Contention Resolution-based Random Access Procedure using Context ID for IoT 7 2.1 Introduction 7 2.2 Background 10 2.2.1 RRC State 10 2.2.2 Random Access Procedure 11 2.2.3 Uplink Latency in RRC INACTIVE State 13 2.2.4 Related Work 14 2.3 RAPID: Proposed Random Access Procedure 15 2.3.1 Overview 15 2.3.2 Criterion of Applying RAPID 16 2.3.3 Preamble Set and RACH Period Allocation 17 2.3.4 Preamble Transmission 18 2.3.5 RAR Transmission 19 2.3.6 AS Context ID Allocation 21 2.3.7 Number of Preambles for RAPID 22 2.4 Access Pattern Analyzer 22 2.4.1 Overview 22 2.4.2 APA Operation 23 2.4.3 Margin Value 26 2.4.4 Offset Index Decision 26 2.5 Random Access Load Analysis 27 2.5.1 System Model 28 2.5.2 Markov Chain Model for 4-Step RA 29 2.5.3 Average Random Access Load for 4-Step RA 34 2.5.4 Markov Chain Model for RAPID 34 2.5.5 Average Random Access Load for RAPID 37 2.5.6 Validation of Analysis 38 2.5.7 Optimization Problem 41 2.6 Performance Evaluation 42 2.6.1 Simulation Setup 42 2.6.2 Number of Preambles for RAPID 43 2.6.3 Performance of RAPID 43 2.6.4 Performance of APA 48 2.7 Summary 48 3 EsTA: Self-Uplink Synchronization in 2-Step Random Access 49 3.1 Introduction 49 3.2 Background 51 3.2.1 Overview of 2-Step CBRA 51 3.2.2 Channel Structure for msgA 52 3.2.3 TA Handling for the Payload 54 3.2.4 2-Step Random Access in Recent Literature 56 3.3 Challenges of 2-Step Random Access 57 3.3.1 Preamble Allocation 57 3.3.2 Resource Mapping for msgA 58 3.3.3 DFT Operation in gNB 58 3.3.4 Detected Collision Problem 58 3.4 EsTA: Proposed Self-UL Synchronization Procedure 59 3.4.1 Overview 60 3.4.2 Overall Procedures 60 3.4.3 Performance Evaluation 61 3.4.4 Future Research Perspectives 65 3.5 Summary 65 4 IBA: Interference-Aware Beam Adjustment for 5G mmWave Networks 67 4.1 Introduction 67 4.2 Background 68 4.2.1 Beam Management in 5G NR 68 4.2.2 System-Level Simulation and 3D Beamforming for 5G NR 70 4.3 Motivation 70 4.3.1 Throughput Degradation by Interference 70 4.4 IBA: Proposed Interference Management Scheme 72 4.4.1 Overall Procedure 72 4.4.2 Reduction of Search Space 72 4.4.3 Algorithm for IBA 75 4.5 Performance Evaluation 76 4.6 Summary 78 5 Concluding Remarks 79 5.1 Research Contributions 79 5.2 Future Work 80 Abstract (In Korean) 89 감사의 글 92Docto

Signal-independent RFF Identification for LTE Mobile Devices via Ensemble Deep Learning

Radio frequency fingerprint (RFF)-based wireless device authentication is an emerging technique to prevent potential spoofing attacks in wireless communications. The random access preamble of the physical random access channel (PRACH) in Long Term Evolution (LTE) systems is the first message sent from a user equipment (UE). However, PRACH preambles change under different evolved Node B (eNB), which will affect the RFF extraction. In this paper, a signal-independent RFF extraction method is first proposed to extract varying LTE PRACH preambles under different LTE eNBs. Residual transient segment (RTS) features from the varying PRACH preambles are extracted for RFF identification. A convolutional neural network (CNN) based ensemble deep learning scheme is proposed to integrate benefits from different RFF features. An experimental system under real operator LTE eNB is designed to capture and identify real UE signals. Experimental results show that the classification accuracy of five UEs can reach more than 95% under the same eNB and 85% under different eNBs. Furthermore, longtime evaluations show that the UE RTS feature is robust over time

LTE Synchronization Algorithms

Third Generation Partnership Project (3GPP) Long Term Evolution(LTE) provides high spectral effciency, high peak data rates and frequency exibility with low latency and low cost. Usage of modulation techniques like OFDMA and SCFDMA enables LTE to achieve such requirements.However it is well known that OFDM systems like LTE are very sensitive when it comes to carrier frequency off- sets and symbol synchronization errors.Inorder to transfer data correctly, the User Equipment(UE) must perform both uplink and downlink synchronization with the Base station(eNodeB). In this thesis, Primary Synchronization Signal(PSS) and Secondary Synchronization signal(SSS) are used to detect the cell-ID of the best serving base station and also to achieve downlink synchronization whereas Physical Random Access Channel(PRACH) preamble is used to obtain uplink synchronization. PSS and SSS helps to achieve downlink synchronization by estimating the frame timing and carrier frequency offsets.In this thesis a non-coherent detection approach is followed for detection of both PSS and SSS signals