21 research outputs found

    Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication

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    The advent of the sixth-generation (6G) of wireless communications has given rise to the necessity to connect vast quantities of heterogeneous wireless devices, which requires advanced system capabilities far beyond existing network architectures. In particular, such massive communication has been recognized as a prime driver that can empower the 6G vision of future ubiquitous connectivity, supporting Internet of Human-Machine-Things for which massive access is critical. This paper surveys the most recent advances toward massive access in both academic and industry communities, focusing primarily on the promising compressive sensing-based grant-free massive access paradigm. We first specify the limitations of existing random access schemes and reveal that the practical implementation of massive communication relies on a dramatically different random access paradigm from the current ones mainly designed for human-centric communications. Then, a compressive sensing-based grant-free massive access roadmap is presented, where the evolutions from single-antenna to large-scale antenna array-based base stations, from single-station to cooperative massive multiple-input multiple-output systems, and from unsourced to sourced random access scenarios are detailed. Finally, we discuss the key challenges and open issues to shed light on the potential future research directions of grant-free massive access.Comment: Accepted by IEEE IoT Journa

    Joint User and Data Detection in Grant-Free NOMA with Attention-based BiLSTM Network

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    We consider the multi-user detection (MUD) problem in uplink grant-free non-orthogonal multiple access (NOMA), where the access point has to identify the total number and correct identity of the active Internet of Things (IoT) devices and decode their transmitted data. We assume that IoT devices use complex spreading sequences and transmit information in a random-access manner following the burst-sparsity model, where some IoT devices transmit their data in multiple adjacent time slots with a high probability, while others transmit only once during a frame. Exploiting the temporal correlation, we propose an attention-based bidirectional long short-term memory (BiLSTM) network to solve the MUD problem. The BiLSTM network creates a pattern of the device activation history using forward and reverse pass LSTMs, whereas the attention mechanism provides essential context to the device activation points. By doing so, a hierarchical pathway is followed for detecting active devices in a grant-free scenario. Then, by utilising the complex spreading sequences, blind data detection for the estimated active devices is performed. The proposed framework does not require prior knowledge of device sparsity levels and channels for performing MUD. The results show that the proposed network achieves better performance compared to existing benchmark schemes

    Signal Processing and Learning for Next Generation Multiple Access in 6G

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    Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed

    Modeling, Analysis, and Optimization of Grant-Free NOMA in Massive MTC via Stochastic Geometry

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    Massive machine-type communications (mMTC) is a crucial scenario to support booming Internet of Things (IoTs) applications. In mMTC, although a large number of devices are registered to an access point (AP), very few of them are active with uplink short packet transmission at the same time, which requires novel design of protocols and receivers to enable efficient data transmission and accurate multi-user detection (MUD). Aiming at this problem, grant-free non-orthogonal multiple access (GF-NOMA) protocol is proposed. In GF-NOMA, active devices can directly transmit their preambles and data symbols altogether within one time frame, without grant from the AP. Compressive sensing (CS)-based receivers are adopted for non-orthogonal preambles (NOP)-based MUD, and successive interference cancellation is exploited to decode the superimposed data signals. In this paper, we model, analyze, and optimize the CS-based GF-MONA mMTC system via stochastic geometry (SG), from an aspect of network deployment. Based on the SG network model, we first analyze the success probability as well as the channel estimation error of the CS-based MUD in the preamble phase and then analyze the average aggregate data rate in the data phase. As IoT applications highly demands low energy consumption, low infrastructure cost, and flexible deployment, we optimize the energy efficiency and AP coverage efficiency of GF-NOMA via numerical methods. The validity of our analysis is verified via Monte Carlo simulations. Simulation results also show that CS-based GF-NOMA with NOP yields better MUD and data rate performances than contention-based GF-NOMA with orthogonal preambles and CS-based grant-free orthogonal multiple access.Comment: This paper is submitted to IEEE Internet Of Things Journa

    사물 ν†΅μ‹ μ—μ„œ μ••μΆ• 센싱을 μ΄μš©ν•œ λŒ€κ·œλͺ¨ 연결방법에 λŒ€ν•œ 연ꡬ

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    ν•™μœ„λ…Όλ¬Έ (박사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : κ³΅κ³ΌλŒ€ν•™ 전기·정보곡학뢀, 2019. 2. 이광볡.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) μ‹œλŒ€μ˜ λ„λž˜μ™€ ν•¨κ»˜, λŒ€κ·œλͺ¨ 사물 톡신(massive machine-type communications, mMTC)은 μ°¨μ„ΈλŒ€ 무선 톡신 ν‘œμ€€μ˜ μ£Όμš” μš”κ΅¬ 사항듀 μ€‘μ˜ ν•˜λ‚˜κ°€ λ˜μ—ˆλ‹€. λŒ€κ·œλͺ¨ 사물 톡신 ν™˜κ²½μ—μ„œλŠ” λ§Žμ€ 수의 사물 κΈ°κΈ°(machine-type device)듀이 λŒ€λΆ€λΆ„ λΉ„ν™œμ„± μƒνƒœλ‘œ 데이터λ₯Ό μ „μ†‘ν•˜μ§€ μ•Šλ‹€κ°€ 가끔씩 ν™œμ„± μƒνƒœλ‘œ μ „ν™˜λ˜μ–΄ μž‘μ€ 크기의 데이터λ₯Ό μ „μ†‘ν•œλ‹€. κ·ΈλŸ¬λ―€λ‘œ, 기지ꡭ(base station, BS)μœΌλ‘œλΆ€ν„°μ˜ μŠ€μΌ€μ₯΄λ§μ„ 기반으둜 직ꡐ(orthogonal) μ‹œκ°„/주파수 μžμ›μ„ 할당받은 ν›„ 데이터 μ†‘μˆ˜μ‹ μ΄ μ΄λ£¨μ–΄μ§€λŠ” 기쑴의 톡신 방식은, μ‹€μ œ μ „μ†‘ν•˜λ €λŠ” 데이터 λŒ€λΉ„ λ§Žμ€ 뢀가적인 μ œμ–΄ 정보λ₯Ό ν•„μš”λ‘œ ν•˜κ³  λ˜ν•œ λ°μ΄ν„°μ˜ 지연을 μœ λ°œμ‹œν‚€λ―€λ‘œ λŒ€κ·œλͺ¨ 사물 톡신에 μ ν•©ν•˜μ§€ μ•Šλ‹€. λŒ€μ‹ , μ „μ†‘λ‹¨μ—μ„œλŠ” μŠ€μΌ€μ₯΄λ§ 없이, 즉 κΈ°μ§€κ΅­μœΌλ‘œ λΆ€ν„°μ˜ 승인 없이(grant-free), 비직ꡐ μžμ›μ— 닀쀑 μ ‘μ†ν•˜κ³ (non-orthogonal multiple access, NOMA), μˆ˜μ‹ λ‹¨μ—μ„œλŠ” 닀쀑 μ‚¬μš©μž κ²€μΆœ(multi-user detection, MUD)을 μ΄μš©ν•˜μ—¬ λ°μ΄ν„°μ˜ μΆ©λŒμ„ 볡쑰해 λ‚΄λŠ” 방식이 λŒ€κ·œλͺ¨ 사물 톡신에 μ ν•©ν•˜λ‹€. 이 λ•Œ, 사물 기기듀이 μ „μ†‘ν•˜λŠ” λ°μ΄ν„°μ˜ ν¬μ†Œ νŠΉμ„±μ„ κ°μ•ˆν•˜λ©΄, μ••μΆ• μ„Όμ‹± 기반의 닀쀑 μ‚¬μš©μž κ²€μΆœ 방법(compressive sensing-based multi-user detection, CS-MUD)이 일반적인 닀쀑 μ‚¬μš©μž κ²€μΆœ 방법보닀 더 쒋은 μ„±λŠ₯을 λ°œνœ˜ν•  수 μžˆλ‹€. λ³Έ λ…Όλ¬Έμ—μ„œλŠ”, κΈ°μ‘΄ 방식보닀 더 쒋은 μ„±λŠ₯을 가진 μƒˆλ‘œμš΄ μ••μΆ• μ„Όμ‹± 기반의 닀쀑 μ‚¬μš©μž κ²€μΆœ 방법을 μ œμ•ˆν•œλ‹€. λΆ€μ—° μ„€λͺ…ν•˜λ©΄, κ°€μž₯ 큰 사후 ν™œμ„± ν™•λ₯ μ„ 가진 μ‚¬μš©μžλ₯Ό μ°Ύκ³ (maximum a posteriori probability-based active user detection, MAP-AUD), μ—­μ‹œ 사후 ν™•λ₯  κ΄€μ μ—μ„œ κ°€μž₯ ν™•λ₯ μ΄ 높은 데이터λ₯Ό μΆ”μ •ν•œλ‹€(maximum a posteriori probability-based data detection, MAP-DD). 이 λ•Œ, MAP-AUD와 MAP-DD 블둝은 μ„œλ‘œ μ™Έμž¬μ  정보(extrinsic information)λ§Œμ„ μ£Όκ³  λ°›λŠ”λ°, 이 μ™Έμž¬μ  μ •λ³΄λŠ” μƒλŒ€λ°©μ˜ 사전 정보가 되고, 이 사전 정보λ₯Ό μ΄μš©ν•˜μ—¬ λ‹€μ‹œ 사후 ν™•λ₯  κ΄€μ μ—μ„œ 졜적의 ν•΄λ₯Ό κ΅¬ν•œλ‹€. μ΄λŸ¬ν•œ 반볡 μˆ˜ν–‰μ„ 톡해 각 블둝은 κ²€μΆœμ˜ 정확도와 μ„±λŠ₯을 λ†’μ—¬ λ‚˜κ°„λ‹€. 이 방법은 νŒ¨ν‚· λ‹¨μœ„λ‘œ ν™•μž₯될 수 μžˆλ‹€. 각각의 사물 기기듀이 μ „μ†‘ν•˜λ €λŠ” λ°μ΄ν„°λŠ” μ—¬λŸ¬κ°œμ˜ μ‹¬λ³Όλ‘œ κ΅¬μ„±λœ νŒ¨ν‚·μ΄λ©°, ν•œ νŒ¨ν‚· λ‚΄μ˜ 각각의 심볼은 κ³΅ν†΅λœ ν™œμ„±λ„λ₯Ό κ°€μ§€κ²Œ λœλ‹€. μ—¬κΈ°μ„œ, 이 κ³΅ν†΅λœ ν™œμ„±λ„λ₯Ό μ΄μš©ν•˜λ©΄, ν™œμ„± 사물 기기와 μ΄λ“€μ˜ 전솑 데이터 μΆ”μ •μ˜ 정확도λ₯Ό 높일 수 μžˆλ‹€. ν•˜μ§€λ§Œ, μ΄λŠ” 곡동 μ΅œμ ν™”(joint optimization) 문제둜 맀우 λ³΅μž‘ν•œ 연산을 ν•„μš”λ‘œ ν•œλ‹€. λ³Έ λ…Όλ¬Έμ—μ„œλŠ”, νŒ¨ν‚· λ‚΄μ˜ μž„μ˜μ˜ ν•˜λ‚˜μ˜ μ‹¬λ³Όμ—μ„œ μΆ”μ •λœ 사물 기기의 ν™œμ„±λ„λŠ” λ‹€λ₯Έ μ‹¬λ³Όμ˜ ν™œμ„±λ„λ₯Ό μΆ”μ •ν•˜λŠ”λ° 사전 정보(a priori information)둜 이용될 수 μžˆλ‹€λŠ” 점에 μ°©μ•ˆν•˜μ—¬, λ³΅μž‘ν•œ 곡동 μ΅œμ ν™” 문제λ₯Ό 비ꡐ적 μ—°μ‚°λŸ‰μ΄ 적은 λΆ€λΆ„ μ΅œμ ν™”(subproblem optimization) 문제둜 λ‹¨μˆœν™” μ‹œν‚€κ³ , 이듀 간에 λ©”μ‹œμ§€ 전달 (massage-passing) 기법을 톡해 곡동 μ΅œμ ν™”μ˜ 해에 κ·Όμ ‘ν•œ ν•΄λ₯Ό κ΅¬ν•˜λŠ” 방법을 μ œμ•ˆν•œλ‹€. 이 λ•Œ, λΆ€λΆ„ μ΅œμ ν™” 문제의 해법이 λ°”λ‘œ μ•žμ— μ„€λͺ…ν•œ MAP-AUD/MAP-DD 방법이닀. λ§ˆμ§€λ§‰μœΌλ‘œ, λͺ¨μ˜ μ‹€ν—˜μ„ 톡해 μ œμ•ˆν•˜λŠ” 방법이 κΈ°μ‘΄ 방법과 λΉ„κ΅ν–ˆμ„ λ•Œ 맀우 크게 μ„±λŠ₯이 ν–₯상됨을 λ³΄μ˜€λ‹€. 특히, μ œμ•ˆν•˜λŠ” 방법은 전체 μ‚¬μš©μž 수 λŒ€λΉ„ 이용 κ°€λŠ₯ν•œ μžμ›μ΄ 적을 λ•ŒμΌμˆ˜λ‘ 더 큰 μ„±λŠ₯ ν–₯상이 μžˆλŠ”λ°, μ΄λŠ” μ°¨μ„ΈλŒ€ 무선 ν†΅μ‹ μ—μ„œ 사물 톡신이 κ³ λ €ν•˜λŠ” λ‹¨μœ„ 면적당 사물 기기의 수(10^6개/km^2)λ₯Ό κ³ λ €ν–ˆμ„ λ•Œ, μ œμ•ˆν•˜λŠ” 방법이 λŒ€κ·œλͺ¨ 사물 톡신에 μ•„μ£Ό νš¨μš©μ„±μ΄ 큼을 보여쀀닀.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
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