사물 통신에서 압축 센싱을 이용한 대규모 연결방법에 대한 연구

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2019. 2. 이광복.Massive machine-type communication (mMTC) has become one of the most important requirements for next generation (5G) communication systems with the advent of the Internet-of-Things (IoT). In the mMTC scenarios, grant-free non-orthogonal multiple access (NOMA) on the transmission side and compressive sensing-based multi-user detection (CS-MUD) on the reception side are promising because many users sporadically transmit small data packets at low rates. In this dissertation, we propose a novel CS-MUD algorithm for active user and data detection for the mMTC systems. The proposed scheme consists of a MAP-based active user detector (MAP-AUD) and a MAP-based data detector (MAP-DD). By exchanging extrinsic information between MAP-AUD and MAP-DD, the proposed algorithm improves the performance of the active user detection and the reliability of the data detection. In addition, we extend the proposed algorithm to exploit group sparsity. By jointly processing the multiple received data with common activity, the proposed algorithm demonstrates dramatically improved performance. We show by numerical experiments that the proposed algorithm achieves a substantial performance gain over existing algorithms.사물 인터넷 (Internet of Things, IoT) 시대의 도래와 함께, 대규모 사물 통신(massive machine-type communications, mMTC)은 차세대 무선 통신 표준의 주요 요구 사항들 중의 하나가 되었다. 대규모 사물 통신 환경에서는 많은 수의 사물 기기(machine-type device)들이 대부분 비활성 상태로 데이터를 전송하지 않다가 가끔씩 활성 상태로 전환되어 작은 크기의 데이터를 전송한다. 그러므로, 기지국(base station, BS)으로부터의 스케쥴링을 기반으로 직교(orthogonal) 시간/주파수 자원을 할당받은 후 데이터 송수신이 이루어지는 기존의 통신 방식은, 실제 전송하려는 데이터 대비 많은 부가적인 제어 정보를 필요로 하고 또한 데이터의 지연을 유발시키므로 대규모 사물 통신에 적합하지 않다. 대신, 전송단에서는 스케쥴링 없이, 즉 기지국으로 부터의 승인 없이(grant-free), 비직교 자원에 다중 접속하고(non-orthogonal multiple access, NOMA), 수신단에서는 다중 사용자 검출(multi-user detection, MUD)을 이용하여 데이터의 충돌을 복조해 내는 방식이 대규모 사물 통신에 적합하다. 이 때, 사물 기기들이 전송하는 데이터의 희소 특성을 감안하면, 압축 센싱 기반의 다중 사용자 검출 방법(compressive sensing-based multi-user detection, CS-MUD)이 일반적인 다중 사용자 검출 방법보다 더 좋은 성능을 발휘할 수 있다. 본 논문에서는, 기존 방식보다 더 좋은 성능을 가진 새로운 압축 센싱 기반의 다중 사용자 검출 방법을 제안한다. 부연 설명하면, 가장 큰 사후 활성 확률을 가진 사용자를 찾고(maximum a posteriori probability-based active user detection, MAP-AUD), 역시 사후 확률 관점에서 가장 확률이 높은 데이터를 추정한다(maximum a posteriori probability-based data detection, MAP-DD). 이 때, MAP-AUD와 MAP-DD 블록은 서로 외재적 정보(extrinsic information)만을 주고 받는데, 이 외재적 정보는 상대방의 사전 정보가 되고, 이 사전 정보를 이용하여 다시 사후 확률 관점에서 최적의 해를 구한다. 이러한 반복 수행을 통해 각 블록은 검출의 정확도와 성능을 높여 나간다. 이 방법은 패킷 단위로 확장될 수 있다. 각각의 사물 기기들이 전송하려는 데이터는 여러개의 심볼로 구성된 패킷이며, 한 패킷 내의 각각의 심볼은 공통된 활성도를 가지게 된다. 여기서, 이 공통된 활성도를 이용하면, 활성 사물 기기와 이들의 전송 데이터 추정의 정확도를 높일 수 있다. 하지만, 이는 공동 최적화(joint optimization) 문제로 매우 복잡한 연산을 필요로 한다. 본 논문에서는, 패킷 내의 임의의 하나의 심볼에서 추정된 사물 기기의 활성도는 다른 심볼의 활성도를 추정하는데 사전 정보(a priori information)로 이용될 수 있다는 점에 착안하여, 복잡한 공동 최적화 문제를 비교적 연산량이 적은 부분 최적화(subproblem optimization) 문제로 단순화 시키고, 이들 간에 메시지 전달 (massage-passing) 기법을 통해 공동 최적화의 해에 근접한 해를 구하는 방법을 제안한다. 이 때, 부분 최적화 문제의 해법이 바로 앞에 설명한 MAP-AUD/MAP-DD 방법이다. 마지막으로, 모의 실험을 통해 제안하는 방법이 기존 방법과 비교했을 때 매우 크게 성능이 향상됨을 보였다. 특히, 제안하는 방법은 전체 사용자 수 대비 이용 가능한 자원이 적을 때일수록 더 큰 성능 향상이 있는데, 이는 차세대 무선 통신에서 사물 통신이 고려하는 단위 면적당 사물 기기의 수(10^6개/km^2)를 고려했을 때, 제안하는 방법이 대규모 사물 통신에 아주 효용성이 큼을 보여준다.Contents Abstract i Contents ii List of Tables iv List of Figures v 1 Introduction 1 2 MAP-based Active User and Data Detection 7 2.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 MAP-based Active User and Data Detection . . . . . . . . . . . . . . 9 2.2.1 Activity Log-Likelihood Ratios . . . . . . . . . . . . . . . . 11 2.2.2 MAP-based Active User Detection . . . . . . . . . . . . . . . 12 2.2.3 MAP-based Data Detection . . . . . . . . . . . . . . . . . . 16 2.2.4 Inversion of Covariance Matrices . . . . . . . . . . . . . . . 20 2.2.5 Comments on Complexity . . . . . . . . . . . . . . . . . . . 23 3 Group Sparsity-Aware Active User and Data Detection 28 3.1 Extraction of Extrinsic User Activity Information . . . . . . . . . . . 28 3.2 Modified Active User and Data Detection . . . . . . . . . . . . . . . 32 4 Numerical Results 35 4.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 5 Conclusion 55 Abstract (In Korean) 61 Acknowlegement 63Docto

Massive MIMO for Internet of Things (IoT) Connectivity

Massive MIMO is considered to be one of the key technologies in the emerging 5G systems, but also a concept applicable to other wireless systems. Exploiting the large number of degrees of freedom (DoFs) of massive MIMO essential for achieving high spectral efficiency, high data rates and extreme spatial multiplexing of densely distributed users. On the one hand, the benefits of applying massive MIMO for broadband communication are well known and there has been a large body of research on designing communication schemes to support high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT) is still a developing topic, as IoT connectivity has requirements and constraints that are significantly different from the broadband connections. In this paper we investigate the applicability of massive MIMO to IoT connectivity. Specifically, we treat the two generic types of IoT connections envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC). This paper fills this important gap by identifying the opportunities and challenges in exploiting massive MIMO for IoT connectivity. We provide insights into the trade-offs that emerge when massive MIMO is applied to mMTC or URLLC and present a number of suitable communication schemes. The discussion continues to the questions of network slicing of the wireless resources and the use of massive MIMO to simultaneously support IoT connections with very heterogeneous requirements. The main conclusion is that massive MIMO can bring benefits to the scenarios with IoT connectivity, but it requires tight integration of the physical-layer techniques with the protocol design.Comment: Submitted for publicatio

Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach

A key challenge of massive MTC (mMTC), is the joint detection of device activity and decoding of data. The sparse characteristics of mMTC makes compressed sensing (CS) approaches a promising solution to the device detection problem. However, utilizing CS-based approaches for device detection along with channel estimation, and using the acquired estimates for coherent data transmission is suboptimal, especially when the goal is to convey only a few bits of data. First, we focus on the coherent transmission and demonstrate that it is possible to obtain more accurate channel state information by combining conventional estimators with CS-based techniques. Moreover, we illustrate that even simple power control techniques can enhance the device detection performance in mMTC setups. Second, we devise a new non-coherent transmission scheme for mMTC and specifically for grant-free random access. We design an algorithm that jointly detects device activity along with embedded information bits. The approach leverages elements from the approximate message passing (AMP) algorithm, and exploits the structured sparsity introduced by the non-coherent transmission scheme. Our analysis reveals that the proposed approach has superior performance compared to application of the original AMP approach.Comment: Submitted to IEEE Transactions on Communication

Low-complexity iterative receiver design for high spectral efficiency communication systems

University of Technology Sydney. Faculty of Engineering and Information Technology.With the rapid development of the modern society, people have an increasing demand of higher data rate. Due to the limited available bandwidth, how to improve the spectral efficiency becomes a key issue in the next generation wireless systems. Recent researches show that, compared to the conventional orthogonal communication systems, the non-orthogonal system can transmit more information with the same resources by introducing non-orthogonality. The non-orthogonal communication systems can be achieved by using faster-than-Nyquist (FTN) signaling to transmit more data symbols in the same time period. On the other hand, by designing appropriate codebook, the sparse code multiple access (SCMA) system can support more users while preserving the same resource elements. Utilisation of these new technologies leads to challenge in receiver design, which becomes severer in complex channel environments. This thesis studies the receiver design for high spectral efficiency communication systems. The main contributions are as follows: 1. A hybrid message passing algorithm is proposed for faster-than-Nyquist, which solves the problem of joint data detection and channel estimation when the channel coefficients are unknown. To fully exploit the known ISI imposed by FTN signaling, the interference induced by FTN signaling and channel fading are intentionally separated. 2. Gaussian message passing and variational inference based estimation algorithms are proposed for faster-than-Nyquist signaling detection in doubly selective channels. Iterative receivers using mean field and Bethe approximations based on variational inference framework are proposed. Moreover, a novel Gaussian message passing based FTN signaling detection algorithm is proposed. 3. An energy minimisation based SCMA decoding algorithm is proposed and convergence analysis of the proposed algorithm is derived. Following optimisation theory and variational free energy framework, the posterior distribution of data symbol is derived in closed form. Then, the convergence property of the proposed algorithm is analysed. 4. A stretched factor graph is designed for MIMO-SCMA system in order to reduce the receiver complexity. Then, a convergence guaranteed message passing algorithm is proposed by convexifying the Bethe free energy. Finally, cooperative communication methods based on belief consensus and alternative direction method of multipliers are proposed. 5. A low complexity detection algorithm is proposed for faster-than-Nyquist SCMA system, which enables joint channel estimation, decoding and user activity detection in grant-free systems. The combination of FTN signaling with SCMA to further enhance the spectral efficiency is first considered. Then, a merging belief propagation and expectation propagation algorithm is proposed to estimate channel state and perform SCMA decoding