Semi-grant-free non-orthogonal multiple access for tactile Internet of Things

Ultra-low latency connections for a massive number of devices are one of the main requirements of the next-generation tactile Internet-of-Things (TIoT). Grant-free non-orthogonal multiple access (GF-NOMA) is a novel paradigm that leverages the advantages of grant-free access and non-orthogonal transmissions, to deliver ultra-low latency connectivity. In this work, we present a joint channel assignment and power allocation solution for semi-GF-NOMA systems, which provides access to both grant-based (GB) and grant-free (GF) devices, maximizes the network throughput, and is capable of ensuring each device’s throughput requirements. In this direction, we provide the mathematical formulation of the aforementioned problem. After explaining that it is not convex, we propose a solution strategy based on the Lagrange multipliers and subgradient method. To evaluate the performance of our solution, we carry out system-level Monte Carlo simulations. The simulation results indicate that the proposed solution can optimize the total system throughput and achieve a high association rate, while taking into account the minimum throughput requirements of both GB and GF devices

Non-Orthogonal Multiple Access for 6G: Performance Analysis With Stochastic Geometry

Driven by an immense escalation of the wireless capacity requirements, ranging from conventional mobile services to machine-type devices and virtual reality, Sixth-Generation (6G) wireless networks are facing arduous challenges in enhancing massive connectivity with high reliability while low latency. As one of the promising solutions, non-orthogonal multiple access (NOMA), compared to orthogonal multiple access (OMA), allows multiple users to share the same time or frequency resource while being allocated at the transmitter with different codes or power levels and split at the receiver exploiting successive interference cancellation (SIC) techniques. Additionally, the emerging technologies of Fifth-Generation (5G) and 6G communications have excellent compatibility with NOMA, which further meets the requirements of massive connectivity, low latency, and multi-functional communication. In particular, NOMA-aided grant-free (GF) transmission balances the tradeoff between high quality-of-service (QoS) and low latency; reconfigurable intelligent surfaces (RISs) flexibly adjust the SIC detecting orders in NOMA networks as a new degree of freedom; and NOMA facilitates integrated sensing and communication (ISaC) networks achieve simultaneously coexistence of wireless connection and sensing functions in the same resource blocks. With the above potentials, this thesis focuses on NOMA networks with promising technologies from protocol designs to 6G massive connectivity scenarios, such as 6G massive machine-type communication (mMTC) connectivity, 6G full coverage connectivity, and 6G multi-functional connectivity for ultra-high frequency communications. As for the main mathematical tools, this thesis exploits stochastic geometry models to facilitate the performance evaluation and to derive performance metrics as insights, including diversity gains, high signal-to-noise ratio, etc. Finally, the contributions are highlighted in the conclusion to achieve massive connectivity

Deep Inception-based Siamese Network for Active User Detection in Grant-free NOMA System

Recent years have seen a rapid growth and development in the field of wireless communication networks. Specifically, the grant-free access and non-orthogonal multiple access (NOMA) in connection with deep learning algorithms. Which facilitate massive machine-type communication devices and improve performance in terms of active user detection (AUD). The detection procedure in the grant-free NOMA systems is difficult due to the signal being received is superimposed. Existing studies focused on deep learning methods to increase the detection performance. However, the models show limitations over the computational complexity. Integration of LSTM and GRUs can only handle temporal modeling not the spatial correlations. The aim of this paper is to add inception modules with Siamese network. The proposed S-net goes wider instead of deeper which reduces computational complexity and increase detection performance Furthermore, parameter sharing characteristics of S-Net helps in generalizing the performance for large sparse matrices with varying SNR values. The comparative analysis show that the proposed S-Net outperforms existing state-of-the-art methods in an effective manner

Active Terminal Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA-OTFS in LEO Satellite Internet-of-Things

This paper investigates the massive connectivity of low Earth orbit (LEO) satellite-based Internet-of-Things (IoT) for seamless global coverage. We propose to integrate the grant-free non-orthogonal multiple access (GF-NOMA) paradigm with the emerging orthogonal time frequency space (OTFS) modulation to accommodate the massive IoT access, and mitigate the long round-trip latency and severe Doppler effect of terrestrial-satellite links (TSLs). On this basis, we put forward a two-stage successive active terminal identification (ATI) and channel estimation (CE) scheme as well as a low-complexity multi-user signal detection (SD) method. Specifically, at the first stage, the proposed training sequence aided OTFS (TS-OTFS) data frame structure facilitates the joint ATI and coarse CE, whereby both the traffic sparsity of terrestrial IoT terminals and the sparse channel impulse response are leveraged for enhanced performance. Moreover, based on the single Doppler shift property for each TSL and sparsity of delay-Doppler domain channel, we develop a parametric approach to further refine the CE performance. Finally, a least square based parallel time domain SD method is developed to detect the OTFS signals with relatively low complexity. Simulation results demonstrate the superiority of the proposed methods over the state-of-the-art solutions in terms of ATI, CE, and SD performance confronted with the long round-trip latency and severe Doppler effect.Comment: 20 pages, 9 figures, accepted by IEEE Transactions on Wireless Communication

사물 통신에서 딥러닝을 이용한 확산코드 설계

학위논문(석사)--서울대학교 대학원 :공과대학 전기·정보공학부,2019. 8. 이광복.Massive machine-type communications (mMTC) have been drawing a lot of attentions because the number of MTC devices is expected to be increasing in the next generation (5G) communication systems with a variety of Internet-of-Things (IoT) applications. For effective uplink transmission in the mMTC, the grant-free non-orthogonal multiple access (NOMA) scheme has been a promising solution to overcome high signaling overhead and latency problems. Due to instant transmissions, active user detection (AUD) is an important task for grant-free NOMA. In the transmitter, data symbols are spread by user-specific spreading sequences. However, the most research papers have focused on designing the effective detection algorithms, but not given much attention to the transmitter design. In this dissertation, the generation of spreading sequences via deep learning is proposed. With sufficient training data, the proposed spreading sequences show the close performance to the mathematically optimized sequences. In particular, we show the capabilities of learning sequences by demonstrating that learned sequences can have different cross-correlations depending on the activity probability of each user.5세대 이동통신에서 사물 통신기기들의 수가 폭발적으로 증가할것이라 예상되면서, 대규모 사물 통신(massive machine-type communications, mMTC)은 많은 관심을 받고있다. 효과적인 상향링크를 위해서 최근 무허가 방식의 비직교 다중접속(non-orthogonal multiple access, NOMA) 기술이 높은 신호 오버헤드와 지연 시간 문제를 해결하기 위한 대안으로 주목받고 있다. 특히 무허가 비직교 다중접속에서는 스케쥴링 없이 즉각적인 전송이 이루어지기 때문에 활성 기기 검출(active user detection, AUD)이 중요한 문제가 된다. 대규모 사물 통신의 상향링크에서 송신기는 데이터 심볼에 기기마다 다른 확산 코드(spreading sequence)를 이용해 데이터를 확산해서 보낸다. 그러나 대부분의 기존 연구들은 수신기의 검출 알고리즘 연구에 치우쳐져 있고 송신기에서 어떠한 확산 코드를 설계해서 보내야 하는지는 미흡했다. 본 논문에서는 딥러닝을 기반으로 확산 코드를 설계하는 기법을 제안한다. 충분한 학습 데이터을 이용해서 학습된 확산 코드는 수학적으로 상관 관계가 최적화된 확산 코드와 비슷한 활성 기기 검출 성능을 보여주었다. 특히 기기들이 서로 다른 활성 확률을 가지는 환경에서는 확산 코드가 활성 확률에 따라 서로 다른 상호상관관계을 가지도록 학습되고, 높은 SNR에서 약 1.4배에서 2배의 성능의 향상을 보여준다.1 Introduction 1 2 System Model 4 3 Design of Spreading Sequences using Deep Learning 6 3.1 Entire Network Structure of the System 6 3.2 Spreading Layer 7 3.3 Active User Detection Layer 7 4 Simulation Results 12 4.1 Simulation Setup 12 4.2 Simulation Results and Interpretation 13 5 Conclusion 19 Abstract (In Korean) 22 Acknowlegement 23Maste