7,242 research outputs found

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

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2019. 2. ์‹ฌ๋ณ‘ํšจ.๋Œ€์šฉ๋Ÿ‰ ์‚ฌ๋ฌผ ํ†ต์‹  (massive machine type communication, mMTC)์€ ๋‹ค์ˆ˜์˜ ์‚ฌ๋ฌผ ํ†ต์‹  ๊ธฐ๊ธฐ๋“ค์ด ๊ธฐ์ง€๊ตญ์— ์ ‘์†ํ•˜๋Š” ์ƒํ™ฉ๊ณผ ๊ด€๊ณ„๊ฐ€ ์žˆ๋‹ค. ๋Œ€๊ทœ๋ชจ ์—ฐ๊ฒฐ์„ฑ์„ ์ง€์›ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ตœ๊ทผ์— ๋น„์Šน์ธ ์ ‘์†๊ณผ ๋น„์ง๊ต ๋‹ค์ค‘ ์ ‘์† (non-orthogonal multiple access, NOMA)์ด ๊ณ ๋ ค๋˜์—ˆ๋‹ค. ๋น„์Šน์ธ ๊ธฐ๋ฐ˜ ์ „์†ก ์‹œ, ๊ฐ ๊ธฐ๊ธฐ๋Š” ์Šน์ธ ์ ˆ์ฐจ ์—†์ด ์ •๋ณด๋ฅผ ์ „์†กํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๊ธฐ์ง€๊ตญ์€ ๋ชจ๋“  ๊ธฐ๊ธฐ๋“ค ์ค‘ ํ™œ์„ฑ ์ƒํƒœ์— ์žˆ๋Š” ๊ธฐ๊ธฐ๋“ค๋งŒ์„ ๊ฒ€์ถœํ•˜๋Š” ๊ณผ์ •์„ ์ˆ˜ํ–‰ํ•ด์•ผ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ ˆ์ฐจ๋ฅผ ํ™œ์„ฑ ๊ธฐ๊ธฐ ๊ฒ€์ถœ (active user detection, AUD)์ด๋ผ๊ณ  ํ•˜๋ฉฐ, ๋น„์ง๊ต ๋‹ค์ค‘ ์ ‘์† ๊ธฐ๋ฐ˜์˜ ์‹œ์Šคํ…œ์—์„œ๋Š” ์ˆ˜์‹  ์‹ ํ˜ธ์— ํ™œ์„ฑ ๊ธฐ๊ธฐ๋“ค์˜ ์‹ ํ˜ธ๋“ค์ด ์ค‘์ฒฉ๋˜์–ด ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ํ™œ์„ฑ ๊ธฐ๊ธฐ๋ฅผ ๊ฒ€์ถœํ•˜๋Š” ๊ฒƒ์€ ์–ด๋ ค์šด ๋ฌธ์ œ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ „์ฒด ๊ธฐ๊ธฐ์˜ ์ˆ˜๊ฐ€ ๋งค์šฐ ๋งŽ์€ ๋Œ€์šฉ๋Ÿ‰ ์‚ฌ๋ฌผ ํ†ต์‹ ์— ์ ํ•ฉํ•œ ์ƒˆ๋กœ์šด ๋ฐฉ์‹์˜ ํ™œ์„ฑ ๊ธฐ๊ธฐ ๊ฒ€์ถœ ๊ธฐ์ˆ ์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ๊ธฐ์ˆ ์„ ์ฐจ์› ํ™•์žฅ ์‹ฌ์ธต ์‹ ๊ฒฝ๋ง ๊ธฐ๋ฐ˜์˜ ํ™œ์„ฑ ๊ธฐ๊ธฐ ๊ฒ€์ถœ (dimension spreading deep neural network based active user detection, DSDNN-AUD)์ด๋ผ๊ณ  ๋ช…๋ช…ํ•˜๋ฉฐ, ๋ณธ ๊ธฐ์ˆ ์˜ ํ•ต์‹ฌ์ ์ธ ํŠน์ง•์€ ์€๋‹‰์ธต์˜ ์ฐจ์›์„ ์†ก์‹  ์ •๋ณด ๋ฒกํ„ฐ์˜ ํฌ๊ธฐ๋ณด๋‹ค ํฌ๊ฒŒ ์„ค์ •ํ•จ์œผ๋กœ์จ ์„œํฌํŠธ ๊ฒ€์ถœ ๋Šฅ๋ ฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๊ฒƒ์ด๋‹ค. ๋ชจ์˜ ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์ œ์•ˆํ•˜๋Š” ํ™œ์„ฑ ๊ธฐ๊ธฐ ๊ฒ€์ถœ ๊ธฐ์ˆ ์ด ๊ธฐ์กด์˜ ๊ธฐ๋ฒ•๋“ค๋ณด๋‹ค ํ™œ์„ฑ ๊ธฐ๊ธฐ ๊ฒ€์ถœ ์„ฑ๊ณต ํ™•๋ฅ ๊ณผ ์Šค๋ฃจํ’‹ ์„ฑ๋Šฅ ๊ด€์ ์—์„œ ์šฐ์ˆ˜ํ•จ์„ ํ™•์ธํ–ˆ๋‹ค.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

    Enumeration and Identification of Active Users for Grant-Free NOMA Using Deep Neural Networks

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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

    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

    A survey of machine learning techniques applied to self organizing cellular networks

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    In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

    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
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