A Reliable Multiple Access Scheme Based on Chirp Spread Spectrum and Turbo Codes

Nowadays, smart devices are the indispensable part of everyone's life and they play an important role in the advancement of industries and businesses.These devices are able to communicate with themselves and build the super network of the Internet of Things(IoT). Therefore, the need for the underlying structure of wireless data communications gains momentum. We require a wireless communication to support massive connectivity with ultra-fast data transmission rate and ultra-low latency. This research explores two possible methods of tackling the issues of the current communication systems for getting closer to the realization of the IoT. First, a grant-free scheme for uplink communication is proposed. The idea is to the combine the control signals with data signals by superimposing them on top of each other with minimal degradation of both signals. Moreover, it is well-established that orthogonal multiple access schemes cannot support the massive connectivity. Ergo, the second part of this research investigates a Non-Orthogonal Multiple Access(NOMA) scheme that exploits the powerful notion of turbo codes for separating the signals in a slow fading channel. It has been shown that in spite of the simplicity of the design, it has the potentials to surpass the performance of Sparse Code Multiple Access(SCMA) scheme

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

C-RAN CoMP Methods for MPR Receivers

The growth in mobile network traffic due to the increase in MTC (Machine Type Communication) applications, brings along a series of new challenges in traffic routing and management. The goals are to have effective resolution times (less delay), low energy consuption (given that wide sensor networks which are included in the MTC category, are built to last years with respect to their battery consuption) and extremely reliable communication (low Packet Error Rates), following the fifth generation (5G) mobile network demands. In order to deal with this type of dense traffic, several uplink strategies can be devised, where diversity variables like space (several Base Stations deployed), time (number of retransmissions of a given packet per user) and power spreading (power value diversity at the receiver, introducing the concept of SIC and Power-NOMA) have to be handled carefully to fulfill the requirements demanded in Ultra-Reliable Low-Latency Communication (URLLC). This thesis, besides being restricted in terms of transmission power and processing of a User Equipment (UE), works on top of an Iterative Block Decision Feedback Equalization Reciever that allows Multi Packet Reception to deal with the diversity types mentioned earlier. The results of this thesis explore the possibility of fragmenting the processing capabilities in an integrated cloud network (C-RAN) environment through an SINR estimation at the receiver to better understand how and where we can break and distribute our processing needs in order to handle near Base Station users and cell-edge users, the latters being the hardest to deal with in dense networks like the ones deployed in a MTC environment

Toward High-Performance Implementation of 5G SCMA Algorithms

International audienceThe recent evolution of mobile communication systems toward a 5G network is associated with the search for new types of non-orthogonal modulations such as Sparse Code Multiple Access (SCMA). Such modulations are proposed in response to demands for increasing the number of connected users. SCMA is a non-orthogonal multiple access technique that offers improved Bit Error Rate (BER) performance and higher spectral efficiency than other comparable techniques, but these improvements come at the cost of complex decoders. There are many challenges in designing near-optimum high throughput SCMA decoders. This paper explores means to enhance the performance of SCMA decoders. To achieve this goal, various improvements to the MPA algorithms are proposed. They notably aim at adapting SCMA decoding to the Single Instruction Multiple Data (SIMD) paradigm. An approximate modeling of noise is performed to reduce the complexity of floating-point calculations. The effects of Forward Error Corrections (FEC) such as polar, turbo and LDPC codes, as well as different ways of accessing memory and improving power efficiency of modified MPAs are investigated. The results show that the throughput of a SCMA decoder can be increased by 3.1 to 21 times when compared to the original MPA on different computing platforms using the suggested improvements

Research on PDMA system based on complementary sequence and low complexity detection algorithm

With the intensive deployment of mobile networks and the vigorous development of new multimedia services, video has gradually become the mainstream of cultural consumption. The contradiction between the proliferation of video data services and the scarcity of spectrum resources has brought great challenges to the current network resource allocation. Non-orthogonal multiple access (NOMA) can be used to solve this problem by signal superposition and spectrum multiplexing to improve system access capability. As a new type of joint optimization design of transmitter and receiver side, PDMA has high research value. In this paper, a framework of PDMA video transmission system based on H.264 video compression coding (HVC-PDMA) is proposed. Poly complementary sequence (PCS) spread spectrum coding is performed on the transmission codebook in order to improve the transmission accuracy. Meanwhile, a low complexity serial sphere compensated Max-log MPA (SSCM-MPA) algorithm is proposed to reduce the complexity of the multi-user detection algorithm. Simulation results show that the PCS spread spectrum can improve system throughput and peak signal-to-noise ratio (PSNR) while reducing bit error rate (BER). SSCM-MPA algorithm can greatly reduce the complexity and improve the transmission efficiency

Symbol Detection in 5G and Beyond Networks

Beyond 5G networks are expected to provide excellent quality of service in terms of delay and reliability for users, where they could travel with high mobility (e.g., 500 km/h) and achieve better spectral efficiency. To support these demands, advanced wireless architectures have been proposed, i.e., orthogonal time frequency space (OTFS) modulation and multiple-input multiple-output (MIMO), which are used to handle high mobility communications and increase the network’s spectral efficiency, respectively. Symbol detection in these advanced wireless architectures is essential to satisfy reliability requirements. On the one hand, the optimal maximum likelihood symbol detector is prohibitively complex as its complexity is non-deterministic polynomial-time (NP)-hard. On the other hand, conventional low-complexity symbol detectors pose a significant performance loss compared to the optimal detector. Thus they cannot be used to satisfy high-reliability requirements. One solution to this problem is to develop a low-complexity algorithm that can achieve near-optimal performance in a particular scenario (e.g., M-MIMO). Nevertheless, there are some cases where we cannot design low-complexity algorithms. To alleviate this issue, deep learning networks can be integrated into an existing algorithm and trained using a dataset obtained by simulating a corresponding scenario. In this thesis, we design symbol detectors for advanced wireless architectures (i.e., MIMO and OTFS) to support an excellent quality of service in terms of delay and reliability and better spectral efficiency beyond 5G networks

Journal of Telecommunications and Information Technology

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