Active User Detection for Massive Machine-type Communications via Dimension Spreading Deep Neural Network

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2019. 2. 심병효.대용량 사물 통신 (massive machine type communication, mMTC)은 다수의 사물 통신 기기들이 기지국에 접속하는 상황과 관계가 있다. 대규모 연결성을 지원하기 위하여 최근에 비승인 접속과 비직교 다중 접속 (non-orthogonal multiple access, NOMA)이 고려되었다. 비승인 기반 전송 시, 각 기기는 승인 절차 없이 정보를 전송하기 때문에 기지국은 모든 기기들 중 활성 상태에 있는 기기들만을 검출하는 과정을 수행해야 한다. 이러한 절차를 활성 기기 검출 (active user detection, AUD)이라고 하며, 비직교 다중 접속 기반의 시스템에서는 수신 신호에 활성 기기들의 신호들이 중첩되어 있기 때문에 활성 기기를 검출하는 것은 어려운 문제이다. 본 논문에서는 전체 기기의 수가 매우 많은 대용량 사물 통신에 적합한 새로운 방식의 활성 기기 검출 기술을 제안한다. 이 기술을 차원 확장 심층 신경망 기반의 활성 기기 검출 (dimension spreading deep neural network based active user detection, DSDNN-AUD)이라고 명명하며, 본 기술의 핵심적인 특징은 은닉층의 차원을 송신 정보 벡터의 크기보다 크게 설정함으로써 서포트 검출 능력을 향상시키는 것이다. 모의 실험 결과를 통해 제안하는 활성 기기 검출 기술이 기존의 기법들보다 활성 기기 검출 성공 확률과 스루풋 성능 관점에서 우수함을 확인했다.Massive machine-type communication (mMTC) concerns the access of massive machine-type communication devices to the basestation. To support the massive connectivity, grant-free access and non-orthogonal multiple access (NOMA) have been recently introduced. In the grant-free transmission, each device transmits information without the granting process so that the basestation needs to identify the active devices among all potential devices. This process, called an active user detection (AUD), is a challenging problem in the NOMA-based systems since it is difficult to find out the active devices from the superimposed received signal. An aim of this paper is to propose a new type of AUD scheme suitable for the highly overloaded mMTC, referred to as dimension spreading deep neural network-based AUD (DSDNN-AUD). The key feature of DSDNN-AUD is to set the dimension of hidden layers being larger than the size of a transmit vector to improve the representation quality of the support. Numerical results demonstrate that the proposed AUD scheme outperforms the conventional approaches in both AUD success probability and throughput performance.1 Introduction 2 Grant-free Non-orthogonal Multiple Access 2.1 AUD System Model 2.2 Conventional AUD 3 Support Function Approximation via DNN 3.1 Network Description 3.2 Training Issue in DSDNN 4 Simulations and Discussions 4.1 Simulation Setup 4.2 Simulation Results 5 ConclusionMaste

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In next-generation mobile radio systems, multiple access schemes will support a massive number of uncoordinated devices exhibiting sporadic traffic, transmitting short packets to a base station. Grant-free non-orthogonal multiple access (NOMA) has been introduced to provide services to a large number of devices and to reduce the communication overhead in massive machine-type communication (mMTC) scenarios. In grant-free communication, there is no coordination between the device and base station (BS) before the data transmission; therefore, the challenging task of active users detection (AUD) must be conducted at the BS. For NOMA with sparse spreading, we propose a deep neural network (DNN)-based approach for AUD called active users enumeration and identification (AUEI). It consists of two phases: firstly, a DNN is used to estimate the number of active users; then in the second phase, another DNN identifies them. To speed up the training process of the DNNs, we propose a multi-stage transfer learning technique. Our numerical results show a remarkable performance improvement of AUEI in comparison to previously proposed approaches

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