Serial Interference Cancellation for Improving uplink in LoRa-like Networks

In this paper, we present a new receiver design, which significantly improves performance in the Internet of Things networks such as LoRa, i.e., having a chirp spread spectrum modulation. The proposed receiver is able to demodulate multiple users simultaneously transmitted over the same frequency channel with the same spreading factor. From a non-orthogonal multiple access point of view, it is based on the power domain and uses serial interference cancellation. Simulation results show that the receiver allows a significant increase in the number of connected devices in the network

A Maximum-Likelihood-based Multi-User LoRa Receiver Implemented in GNU Radio

LoRa is a popular low-power wide-area network (LPWAN) technology that uses spread-spectrum to achieve long-range connectivity and resilience to noise and interference. For energy efficiency reasons, LoRa adopts a pure ALOHA access scheme, which leads to reduced network throughput due to packet collisions at the gateways. To alleviate this issue, in this paper we analyze and implement a LoRa receiver that is able to decode LoRa packets from two interfering users. Our main contribution is a two-user detector derived in a maximum-likelihood fashion using a detailed interference model. As the complexity of the maximum-likelihood sequence estimation is prohibitive, a complexity-reduction technique is introduced to enable a practical implementation of the proposed two-user detector. This detector has been implemented along with an interference-robust synchronization algorithm on the GNU Radio Software-Defined-Radio (SDR) platform. The SDR implementation shows the effectiveness of the proposed method and also allows its experimental evaluation. Measurements indicate that our detector inherently leverages the time offset between the two colliding users to separate and demodulate their contributions.Comment: 2020 Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, US

Performance Analysis of LEO Satellite-Based IoT Networks in the Presence of Interference

This paper explores a star-of-star topology for an internet-of-things (IoT) network using mega low Earth orbit constellations where the IoT users broadcast their sensed information to multiple satellites simultaneously over a shared channel. The satellites use amplify-and-forward relaying to forward the received signal to the ground station (GS), which then combines them coherently using maximal ratio combining. A comprehensive outage probability (OP) analysis is performed for the presented topology. Stochastic geometry is used to model the random locations of satellites, thus making the analysis general and independent of any constellation. The satellites are assumed to be visible if their elevation angle is greater than a threshold, called a mask angle. Statistical characteristics of the range and the number of visible satellites are derived for a given mask angle. Successive interference cancellation (SIC) and capture model (CM)-based decoding schemes are analyzed at the GS to mitigate interference effects. The average OP for the CM-based scheme, and the OP of the best user for the SIC scheme are derived analytically. Simulation results are presented that corroborate the derived analytical expressions. Moreover, insights on the effect of various system parameters like mask angle, altitude, number of satellites and decoding order are also presented. The results demonstrate that the explored topology can achieve the desired OP by leveraging the benefits of multiple satellites. Thus, this topology is an attractive choice for satellite-based IoT networks as it can facilitate burst transmissions without coordination among the IoT users.Comment: Submitted to IEEE IoT Journa

Scalable coexistence of eMBB, URLLC and mMTC enabled by non-orthogonal multiple access and network slicing

Abstract. The 5G systems feature three use cases: enhanced Mobile BroadBand (eMBB), massive Machine-Type Communications (mMTC) and Ultra-Reliable and Low-Latency Communications (URLLC). The diverse requirements of the corresponding services in terms of achievable data-rate, number of connected devices, latency and reliability can lead to sub-optimal use of the 5G resources, thus network slicing emerges as a promising alternative that customizes slices of the network specifically designed to meet specific requirements. By employing network slicing, the radio resources can be shared via orthogonal and non-orthogonal schemes. Motivated by the Industrial Internet of Things (IIoT) paradigm where a large number of sensors may require connectivity with stringent requirements of latency and reliability, we propose and evaluate the joint use of network slicing and Non-Orthogonal Multiple Access (NOMA) with Successive Interference Cancellation (SIC) in two different uplink scenarios. In the first scenario, eMBB coexists with URLLC in the same Radio Access Network (RAN) and, in order to improve the number of concurrent URLLC connections to the same base station (BS), they transmit simultaneously and across multiple frequency channels. In the second scenario, eMBB coexists with mMTC and, to provide connectivity to a massive number of devices, the BS has multiple receive antennas. In both cases, we set the reliability requirements for the services and compare the performance of both orthogonal and non-orthogonal network slicing schemes in terms of maximum achievable data rates and connected users. Our results show that, even with overlapping transmissions from multiple devices, network slicing, NOMA and SIC techniques allow us simultaneously satisfy all the heterogeneous requirements of the 5G services

희소인지를 이용한 전송기술 연구

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2019. 2. 심병효.The new wave of the technology revolution, named the fifth wireless systems, is changing our daily life dramatically. These days, unprecedented services and applications such as driverless vehicles and drone-based deliveries, smart cities and factories, remote medical diagnosis and surgery, and artificial intelligence-based personalized assistants are emerging. Communication mechanisms associated with these new applications and services are way different from traditional communications in terms of latency, energy efficiency, reliability, flexibility, and connection density. Since the current radio access mechanism cannot support these diverse services and applications, a new approach to deal with these relentless changes should be introduced. This compressed sensing (CS) paradigm is very attractive alternative to the conventional information processing operations including sampling, sensing, compression, estimation, and detection. To apply the CS techniques to wireless communication systems, there are a number of things to know and also several issues to be considered. In the last decade, CS techniques have spread rapidly in many applications such as medical imaging, machine learning, radar detection, seismology, computer science, statistics, and many others. Also, various wireless communication applications exploiting the sparsity of a target signal have been studied. Notable examples include channel estimation, interference cancellation, angle estimation, spectrum sensing, and symbol detection. The distinct feature of this work, in contrast to the conventional approaches exploiting naturally acquired sparsity, is to exploit intentionally designed sparsity to improve the quality of the communication systems. In the first part of the dissertation, we study the mapping data information into the sparse signal in downlink systems. We propose an approach, called sparse vector coding (SVC), suited for the short packet transmission. In SVC, since the data information is mapped to the position of sparse vector, whole data packet can be decoded by idenitifying nonzero positions of the sparse vector. From our simulations, we show that the packet error rate of SVC outperforms the conventional channel coding schemes at the URLLC regime. Moreover, we discuss the SVC transmission for the massive MTC access by overlapping multiple SVC-based packets into the same resources. Using the spare vector overlapping and multiuser CS decoding scheme, SVC-based transmission provides robustness against the co-channel interference and also provide comparable performance than other non-orthogonal multiple access (NOMA) schemes. By using the fact that SVC only identifies the support of sparse vector, we extend the SVC transmission without pilot transmission, called pilot-less SVC. Instead of using the support, we further exploit the magnitude of sparse vector for delivering additional information. This scheme is referred to as enhanced SVC. The key idea behind the proposed E-SVC transmission scheme is to transform the small information into a sparse vector and map the side-information into a magnitude of the sparse vector. Metaphorically, E-SVC can be thought as a standing a few poles to the empty table. As long as the number of poles is small enough and the measurements contains enough information to find out the marked cell positions, accurate recovery of E-SVC packet can be guaranteed. In the second part of this dissertation, we turn our attention to make sparsification of the non-sparse signal, especially for the pilot transmission and channel estimation. Unlike the conventional scheme where the pilot signal is transmitted without modification, the pilot signals are sent after the beamforming in the proposed technique. This work is motivated by the observation that the pilot overhead must scale linearly with the number of taps in CIR vector and the number of transmit antennas so that the conventional pilot transmission is not an appropriate option for the IoT devices. Primary goal of the proposed scheme is to minimize the nonzero entries of a time-domain channel vector by the help of multiple antennas at the basestation. To do so, we apply the time-domain sparse precoding, where each precoded channel propagates via fewer tap than the original channel vector. The received channel vector of beamformed pilots can be jointly estimated by the sparse recovery algorithm.5세대 무선통신 시스템의 새로운 기술 혁신은 무인 차량 및 항공기, 스마트 도시 및 공장, 원격 의료 진단 및 수술, 인공 지능 기반 맟춤형 지원과 같은 전례 없는 서비스 및 응용프로그램으로 부상하고 있다. 이러한 새로운 애플리케이션 및 서비스와 관련된 통신 방식은 대기 시간, 에너지 효율성, 신뢰성, 유연성 및 연결 밀도 측면에서 기존 통신과 매우 다르다. 현재의 무선 액세스 방식을 비롯한 종래의 접근법은 이러한 요구 사항을 만족할 수 없기 때문에 최근에 sparse processing과 같은 새로운 접근 방법이 연구되고 있다. 이 새로운 접근 방법은 표본 추출, 감지, 압축, 평가 및 탐지를 포함한 기존의 정보 처리에 대한 효율적인 대체기술로 활용되고 있다. 지난 10년 동안 compressed sensing (CS)기법은 의료영상, 기계학습, 탐지, 컴퓨터 과학, 통계 및 기타 여러 분야에서 빠르게 확산되었다. 또한, 신호의 희소성(sparsity)를 이용하는 CS 기법은 다양한 무선 통신이 연구되었다. 주목할만한 예로는 채널 추정, 간섭 제거, 각도 추정, 및 스펙트럼 감지가 있으며 현재까지 연구는 주어진 신호가 가지고 있는 본래의 희소성에 주목하였으나 본 논문에서는 기존의 접근 방법과 달리 인위적으로 설계된 희소성을 이용하여 통신 시스템의 성능을 향상시키는 방법을 제안한다. 우선 본 논문은 다운링크 전송에서 희소 신호 매핑을 통한 데이터 전송 방법을 제안하며 짧은 패킷 (short packet) 전송에 적합한 CS 접근법을 활용하는 기술을 제안한다. 제안하는 기술인 희소벡터코딩 (sparse vector coding, SVC)은 데이터 정보가 인공적인 희소벡터의 nonzero element의 위치에 매핑하여 전송된 데이터 패킷은 희소벡터의 0이 아닌 위치를 식별함으로 원신호 복원이 가능하다. 분석과 시뮬레이션을 통해 제안하는 SVC 기법의 패킷 오류률은 ultra-reliable and low latency communications (URLLC) 서비스를 지원을 위해 사용되는 채널코딩방식보다 우수한 성능을 보여준다. 또한, 본 논문은 SVC기술을 다음의 세가지 영역으로 확장하였다. 첫째로, 여러 개의 SVC 기반 패킷을 동일한 자원에 겹치게 전송함으로 상향링크에서 대규모 전송을 지원하는 방법을 제안한다. 중첩된 희소벡터를 다중사용자 CS 디코딩 방식을 사용하여 채널 간섭에 강인한 성능을 제공하고 비직교 다중 접속 (NOMA) 방식과 유사한 성능을 제공한다. 둘째로, SVC 기술이 희소 벡터의 support만을 식별한다는 사실을 이용하여 파일럿 전송이 필요없는 pilotless-SVC 전송 방법을 제안한다. 채널 정보가 없는 경우에도 희소 벡터의 support의 크기는 채널의 크기에 비례하기 때문에 pilot없이 복원이 가능하다. 셋째로, 희소벡터의 support의 크기에 추가 정보를 전송함으로 복원 성능을 향상 시키는 enhanced SVC (E-SVC)를 제안한다. 제안된 E-SVC 전송 방식의 핵심 아디디어는 짧은 패킷을 전송되는 정보를 희소 벡터로 변환하고 정보 복원을 보조하는 추가 정보를 희소 벡터의 크기 (magnitude)로 매핑하는 것이다. 마지막으로, SVC 기술을 파일럿 전송에 활용하는 방법을 제안한다. 특히, 채널 추정을 위해 채널 임펄스 응답의 신호를 희소화하는 프리코딩 기법을 제안한다. 파일럿 신호을 프로코딩 없이 전송되는 기존의 방식과 달리, 제안된 기술에서는 파일럿 신호를 빔포밍하여 전송한다. 제안된 기법은 기지국에서 다중 안테나를 활용하여 채널 응답의 0이 아닌 요소를 최소화하는 시간 영역 희소 프리코딩을 적용하였다. 이를 통해 더 적확한 채널 추정을 가능하며 더 적은 파일럿 오버헤드로 채널 추정이 가능하다.Abstract i Contents iv List of Tables viii List of Figures ix 1 INTRODUCTION 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Three Key Services in 5G systems . . . . . . . . . . . . . . . 2 1.1.2 Sparse Processing in Wireless Communications . . . . . . . . 4 1.2 Contributions and Organization . . . . . . . . . . . . . . . . . . . . . 7 1.3 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Sparse Vector Coding for Downlink Ultra-reliable and Low Latency Communications 12 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2 URLLC Service Requirements . . . . . . . . . . . . . . . . . . . . . 15 2.2.1 Latency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.2 Ultra-High Reliability . . . . . . . . . . . . . . . . . . . . . 17 2.2.3 Coexistence . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3 URLLC Physical Layer in 5G NR . . . . . . . . . . . . . . . . . . . 18 2.3.1 Packet Structure . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3.2 Frame Structure and Latency-sensitive Scheduling Schemes . 20 2.3.3 Solutions to the Coexistence Problem . . . . . . . . . . . . . 22 2.4 Short-sized Packet in LTE-Advanced Downlink . . . . . . . . . . . . 24 2.5 Sparse Vector Coding . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.5.1 SVC Encoding and Transmission . . . . . . . . . . . . . . . 25 2.5.2 SVC Decoding . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.5.3 Identification of False Alarm . . . . . . . . . . . . . . . . . . 33 2.6 SVC Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . 36 2.7 Implementation Issues . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.7.1 Codebook Design . . . . . . . . . . . . . . . . . . . . . . . . 48 2.7.2 High-order Modulation . . . . . . . . . . . . . . . . . . . . . 49 2.7.3 Diversity Transmission . . . . . . . . . . . . . . . . . . . . . 50 2.7.4 SVC without Pilot . . . . . . . . . . . . . . . . . . . . . . . 50 2.7.5 Threshold to Prevent False Alarm Event . . . . . . . . . . . . 51 2.8 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 52 2.8.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 52 2.8.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 53 2.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3 Sparse Vector Coding for Uplink Massive Machine-type Communications 59 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.2 Uplink NOMA transmission for mMTC . . . . . . . . . . . . . . . . 61 3.3 Sparse Vector Coding based NOMA for mMTC . . . . . . . . . . . . 63 3.3.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.3.2 Joint Multiuser Decoding . . . . . . . . . . . . . . . . . . . . 66 3.4 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 68 3.4.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 68 3.4.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 69 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4 Pilot-less Sparse Vector Coding for Short Packet Transmission 72 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.2 Pilot-less Sparse Vector Coding Processing . . . . . . . . . . . . . . 75 4.2.1 SVC Processing with Pilot Symbols . . . . . . . . . . . . . . 75 4.2.2 Pilot-less SVC . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.2.3 PL-SVC Decoding in Multiple Basestation Antennas . . . . . 78 4.3 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 80 4.3.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 80 4.3.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 81 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5 Joint Analog and Quantized Feedback via Sparse Vector Coding 84 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.2 System Model for Joint Spase Vector Coding . . . . . . . . . . . . . 86 5.3 Sparse Recovery Algorithm and Performance Analysis . . . . . . . . 90 5.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5.4.1 Linear Interpolation of Sensing Information . . . . . . . . . . 96 5.4.2 Linear Combined Feedback . . . . . . . . . . . . . . . . . . 96 5.4.3 One-shot Packet Transmission . . . . . . . . . . . . . . . . . 96 5.5 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.5.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.5.2 Results and Discussions . . . . . . . . . . . . . . . . . . . . 98 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 6 Sparse Beamforming for Enhanced Mobile Broadband Communications 101 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 6.1.1 Increase the number of transmit antennas . . . . . . . . . . . 102 6.1.2 2D active antenna system (AAS) . . . . . . . . . . . . . . . . 103 6.1.3 3D channel environment . . . . . . . . . . . . . . . . . . . . 104 6.1.4 RS transmission for CSI acquisition . . . . . . . . . . . . . . 106 6.2 System Design and Standardization of FD-MIMO Systems . . . . . . 107 6.2.1 Deployment scenarios . . . . . . . . . . . . . . . . . . . . . 108 6.2.2 Antenna configurations . . . . . . . . . . . . . . . . . . . . . 108 6.2.3 TXRU architectures . . . . . . . . . . . . . . . . . . . . . . 109 6.2.4 New CSI-RS transmission strategy . . . . . . . . . . . . . . . 112 6.2.5 CSI feedback mechanisms for FD-MIMO systems . . . . . . 114 6.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6.3.1 Basic System Model . . . . . . . . . . . . . . . . . . . . . . 116 6.3.2 Beamformed Pilot Transmission . . . . . . . . . . . . . . . . 117 6.4 Sparsification of Pilot Beamforming . . . . . . . . . . . . . . . . . . 118 6.4.1 Time-domain System Model without Pilot Beamforming . . . 119 6.4.2 Pilot Beamforming . . . . . . . . . . . . . . . . . . . . . . . 120 6.5 Channel Estimation of Beamformed Pilots . . . . . . . . . . . . . . . 124 6.5.1 Recovery using Multiple Measurement Vector . . . . . . . . . 124 6.5.2 MSE Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 128 6.6 Simulations and Discussion . . . . . . . . . . . . . . . . . . . . . . . 129 6.6.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 129 6.6.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 130 6.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 7 Conclusion 136 7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 7.2 Future Research Directions . . . . . . . . . . . . . . . . . . . . . . . 139 Abstract (In Korean) 152Docto

Enable Reliable and Secure Data Transmission in Resource-Constrained Emerging Networks

The increasing deployment of wireless devices has connected humans and objects all around the world, benefiting our daily life and the entire society in many aspects. Achieving those connectivity motivates the emergence of different types of paradigms, such as cellular networks, large-scale Internet of Things (IoT), cognitive networks, etc. Among these networks, enabling reliable and secure data transmission requires various resources including spectrum, energy, and computational capability. However, these resources are usually limited in many scenarios, especially when the number of devices is considerably large, bringing catastrophic consequences to data transmission. For example, given the fact that most of IoT devices have limited computational abilities and inadequate security protocols, data transmission is vulnerable to various attacks such as eavesdropping and replay attacks, for which traditional security approaches are unable to address. On the other hand, in the cellular network, the ever-increasing data traffic has exacerbated the depletion of spectrum along with the energy consumption. As a result, mobile users experience significant congestion and delays when they request data from the cellular service provider, especially in many crowded areas. In this dissertation, we target on reliable and secure data transmission in resource-constrained emerging networks. The first two works investigate new security challenges in the current heterogeneous IoT environment, and then provide certain countermeasures for reliable data communication. To be specific, we identify a new physical-layer attack, the signal emulation attack, in the heterogeneous environment, such as smart home IoT. To defend against the attack, we propose two defense strategies with the help of a commonly found wireless device. In addition, to enable secure data transmission in large-scale IoT network, e.g., the industrial IoT, we apply the amply-and-forward cooperative communication to increase the secrecy capacity by incentivizing relay IoT devices. Besides security concerns in IoT network, we seek data traffic alleviation approaches to achieve reliable and energy-efficient data transmission for a group of users in the cellular network. The concept of mobile participation is introduced to assist data offloading from the base station to users in the group by leveraging the mobility of users and the social features among a group of users. Following with that, we deploy device-to-device data offloading within the group to achieve the energy efficiency at the user side while adapting to their increasing traffic demands. In the end, we consider a perpendicular topic - dynamic spectrum access (DSA) - to alleviate the spectrum scarcity issue in cognitive radio network, where the spectrum resource is limited to users. Specifically, we focus on the security concerns and further propose two physical-layer schemes to prevent spectrum misuse in DSA in both additive white Gaussian noise and fading environments

6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities

Mobile communications have been undergoing a generational change every ten years or so. However, the time difference between the so-called "G's" is also decreasing. While fifth-generation (5G) systems are becoming a commercial reality, there is already significant interest in systems beyond 5G, which we refer to as the sixth-generation (6G) of wireless systems. In contrast to the already published papers on the topic, we take a top-down approach to 6G. We present a holistic discussion of 6G systems beginning with lifestyle and societal changes driving the need for next generation networks. This is followed by a discussion into the technical requirements needed to enable 6G applications, based on which we dissect key challenges, as well as possibilities for practically realizable system solutions across all layers of the Open Systems Interconnection stack. Since many of the 6G applications will need access to an order-of-magnitude more spectrum, utilization of frequencies between 100 GHz and 1 THz becomes of paramount importance. As such, the 6G eco-system will feature a diverse range of frequency bands, ranging from below 6 GHz up to 1 THz. We comprehensively characterize the limitations that must be overcome to realize working systems in these bands; and provide a unique perspective on the physical, as well as higher layer challenges relating to the design of next generation core networks, new modulation and coding methods, novel multiple access techniques, antenna arrays, wave propagation, radio-frequency transceiver design, as well as real-time signal processing. We rigorously discuss the fundamental changes required in the core networks of the future that serves as a major source of latency for time-sensitive applications. While evaluating the strengths and weaknesses of key 6G technologies, we differentiate what may be achievable over the next decade, relative to what is possible.Comment: Accepted for Publication into the Proceedings of the IEEE; 32 pages, 10 figures, 5 table