Joint Fixed Power Allocation and Partial Relay Selection Schemes for Cooperative NOMA

 In the future wireless systems, non-orthogonal multiple-access (NOMA) with partial relay selection scheme is considered as developing research topic. In this paper, dual-hop relaying systems is deployed for NOMA, in which the signal is transfered with the assistance of decode-and-forward (DF) scheme. This paper presents exact expressions for outage probability over independent Rayleigh fading channels, and two partial relay selection schemes are provided. Using matching analytical result and Monte-Carlo method, we introduce forwarding strategy selection for fixed user allocation and exactness of derived formula is checked. The presented simulations confirm the the advantage of such considered NOMA, and the effectiveness of the proposed forwarding strategy

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

A NOMA-enhanced reconfigurable access scheme with device pairing for M2M networks

This paper aims to address the distinct requirements of machine-to-machine networks, particularly heterogeneity and massive transmissions. To this end, a reconfigurable medium access control (MAC) with the ability to choose a proper access scheme with the optimal configuration for devices based on the network status is proposed. In this scheme, in each frame, a separate time duration is allocated for each of the nonorthogonal multiple access (NOMA)-based, orthogonal multiple access (OMA)-based, and random access-based segments, where the length of each segment can be optimized. To solve this optimization problem, an iterative algorithm consisting of two sub-problems is proposed. The first sub-problem deals with selecting devices for the NOMA/OMA-based transmissions, while the second one optimizes the parameter of the random access scheme. To show the efficacy of the proposed scheme, the results are compared with the reconfigurable scheme which does not support NOMA. The results demonstrate that by using a proper device pairing scheme for the NOMA-based transmissions, the proposed reconfigurable scheme achieves better performance when NOMA is adopted

Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing

Support estimation (SE) of a sparse signal refers to finding the location indices of the non-zero elements in a sparse representation. Most of the traditional approaches dealing with SE problem are iterative algorithms based on greedy methods or optimization techniques. Indeed, a vast majority of them use sparse signal recovery techniques to obtain support sets instead of directly mapping the non-zero locations from denser measurements (e.g., Compressively Sensed Measurements). This study proposes a novel approach for learning such a mapping from a training set. To accomplish this objective, the Convolutional Support Estimator Networks (CSENs), each with a compact configuration, are designed. The proposed CSEN can be a crucial tool for the following scenarios: (i) Real-time and low-cost support estimation can be applied in any mobile and low-power edge device for anomaly localization, simultaneous face recognition, etc. (ii) CSEN's output can directly be used as "prior information" which improves the performance of sparse signal recovery algorithms. The results over the benchmark datasets show that state-of-the-art performance levels can be achieved by the proposed approach with a significantly reduced computational complexity