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

Joint Domain Based Massive Access for Small Packets Traffic of Uplink Wireless Channel

The fifth generation (5G) communication scenarios such as the cellular network and the emerging machine type communications will produce massive small packets. To support massive connectivity and avoid signaling overhead caused by the transmission of those small packets, this paper proposes a novel method to improve the transmission efficiency for massive connections of wireless uplink channel. The proposed method combines compressive sensing (CS) with power domain NOMA jointly, especially neither the scheduling nor the centralized power allocation is necessary in the method. Both the analysis and simulation show that the method can support up to two or three times overloading.Comment: 6 pages, 5 figures.submitted to globecom 201

Sparse Signal Processing Concepts for Efficient 5G System Design

As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges and what are the key drivers is part of intensive, ongoing discussions. Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years. In this paper we will describe a variety of scenarios in which signal sparsity arises naturally in 5G wireless systems. Signal sparsity and the associated rich collection of tools and algorithms will thus be a viable source for innovation in 5G wireless system design. We will discribe applications of this sparse signal processing paradigm in MIMO random access, cloud radio access networks, compressive channel-source network coding, and embedded security. We will also emphasize important open problem that may arise in 5G system design, for which sparsity will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces

Compressive Sensing Based Massive Access for IoT Relying on Media Modulation Aided Machine Type Communications

A fundamental challenge of the large-scale Internet-of-Things lies in how to support massive machine-type communications (mMTC). This letter proposes a media modulation based mMTC solution for increasing the throughput, where a massive multi-input multi-output based base station (BS) is used for enhancing the detection performance. For such a mMTC scenario, the reliable active device detection and data decoding pose a serious challenge. By leveraging the sparsity of the uplink access signals of mMTC received at the BS, a compressive sensing based massive access solution is proposed for tackling this challenge. Specifically, we propose a block sparsity adaptive matching pursuit algorithm for detecting the active devices, whereby the block-sparsity of the uplink access signals exhibited across the successive time slots and the structured sparsity of media modulated symbols are exploited for enhancing the detection performance. Moreover, a successive interference cancellation based structured subspace pursuit algorithm is conceived for data demodulation of the active devices, whereby the structured sparsity of media modulation based symbols found in each time slot is exploited for improving the detection performance. Finally, our simulation results verify the superiority of the proposed scheme over state-of-the-art solutions.Comment: submitted to IEEE Transactions on Vehicular Technology [Major Revision

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