17 research outputs found

    A Reliable Multiple Access Scheme Based on Chirp Spread Spectrum and Turbo Codes

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    Nowadays, smart devices are the indispensable part of everyone's life and they play an important role in the advancement of industries and businesses.These devices are able to communicate with themselves and build the super network of the Internet of Things(IoT). Therefore, the need for the underlying structure of wireless data communications gains momentum. We require a wireless communication to support massive connectivity with ultra-fast data transmission rate and ultra-low latency. This research explores two possible methods of tackling the issues of the current communication systems for getting closer to the realization of the IoT. First, a grant-free scheme for uplink communication is proposed. The idea is to the combine the control signals with data signals by superimposing them on top of each other with minimal degradation of both signals. Moreover, it is well-established that orthogonal multiple access schemes cannot support the massive connectivity. Ergo, the second part of this research investigates a Non-Orthogonal Multiple Access(NOMA) scheme that exploits the powerful notion of turbo codes for separating the signals in a slow fading channel. It has been shown that in spite of the simplicity of the design, it has the potentials to surpass the performance of Sparse Code Multiple Access(SCMA) scheme

    ์‚ฌ๋ฌผ ํ†ต์‹ ์—์„œ ์••์ถ• ์„ผ์‹ฑ์„ ์ด์šฉํ•œ ๋Œ€๊ทœ๋ชจ ์—ฐ๊ฒฐ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2019. 2. ์ด๊ด‘๋ณต.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.์‚ฌ๋ฌผ ์ธํ„ฐ๋„ท (Internet of Things, IoT) ์‹œ๋Œ€์˜ ๋„๋ž˜์™€ ํ•จ๊ป˜, ๋Œ€๊ทœ๋ชจ ์‚ฌ๋ฌผ ํ†ต์‹ (massive machine-type communications, mMTC)์€ ์ฐจ์„ธ๋Œ€ ๋ฌด์„  ํ†ต์‹  ํ‘œ์ค€์˜ ์ฃผ์š” ์š”๊ตฌ ์‚ฌํ•ญ๋“ค ์ค‘์˜ ํ•˜๋‚˜๊ฐ€ ๋˜์—ˆ๋‹ค. ๋Œ€๊ทœ๋ชจ ์‚ฌ๋ฌผ ํ†ต์‹  ํ™˜๊ฒฝ์—์„œ๋Š” ๋งŽ์€ ์ˆ˜์˜ ์‚ฌ๋ฌผ ๊ธฐ๊ธฐ(machine-type device)๋“ค์ด ๋Œ€๋ถ€๋ถ„ ๋น„ํ™œ์„ฑ ์ƒํƒœ๋กœ ๋ฐ์ดํ„ฐ๋ฅผ ์ „์†กํ•˜์ง€ ์•Š๋‹ค๊ฐ€ ๊ฐ€๋”์”ฉ ํ™œ์„ฑ ์ƒํƒœ๋กœ ์ „ํ™˜๋˜์–ด ์ž‘์€ ํฌ๊ธฐ์˜ ๋ฐ์ดํ„ฐ๋ฅผ ์ „์†กํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ, ๊ธฐ์ง€๊ตญ(base station, BS)์œผ๋กœ๋ถ€ํ„ฐ์˜ ์Šค์ผ€์ฅด๋ง์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์ง๊ต(orthogonal) ์‹œ๊ฐ„/์ฃผํŒŒ์ˆ˜ ์ž์›์„ ํ• ๋‹น๋ฐ›์€ ํ›„ ๋ฐ์ดํ„ฐ ์†ก์ˆ˜์‹ ์ด ์ด๋ฃจ์–ด์ง€๋Š” ๊ธฐ์กด์˜ ํ†ต์‹  ๋ฐฉ์‹์€, ์‹ค์ œ ์ „์†กํ•˜๋ ค๋Š” ๋ฐ์ดํ„ฐ ๋Œ€๋น„ ๋งŽ์€ ๋ถ€๊ฐ€์ ์ธ ์ œ์–ด ์ •๋ณด๋ฅผ ํ•„์š”๋กœ ํ•˜๊ณ  ๋˜ํ•œ ๋ฐ์ดํ„ฐ์˜ ์ง€์—ฐ์„ ์œ ๋ฐœ์‹œํ‚ค๋ฏ€๋กœ ๋Œ€๊ทœ๋ชจ ์‚ฌ๋ฌผ ํ†ต์‹ ์— ์ ํ•ฉํ•˜์ง€ ์•Š๋‹ค. ๋Œ€์‹ , ์ „์†ก๋‹จ์—์„œ๋Š” ์Šค์ผ€์ฅด๋ง ์—†์ด, ์ฆ‰ ๊ธฐ์ง€๊ตญ์œผ๋กœ ๋ถ€ํ„ฐ์˜ ์Šน์ธ ์—†์ด(grant-free), ๋น„์ง๊ต ์ž์›์— ๋‹ค์ค‘ ์ ‘์†ํ•˜๊ณ (non-orthogonal multiple access, NOMA), ์ˆ˜์‹ ๋‹จ์—์„œ๋Š” ๋‹ค์ค‘ ์‚ฌ์šฉ์ž ๊ฒ€์ถœ(multi-user detection, MUD)์„ ์ด์šฉํ•˜์—ฌ ๋ฐ์ดํ„ฐ์˜ ์ถฉ๋Œ์„ ๋ณต์กฐํ•ด ๋‚ด๋Š” ๋ฐฉ์‹์ด ๋Œ€๊ทœ๋ชจ ์‚ฌ๋ฌผ ํ†ต์‹ ์— ์ ํ•ฉํ•˜๋‹ค. ์ด ๋•Œ, ์‚ฌ๋ฌผ ๊ธฐ๊ธฐ๋“ค์ด ์ „์†กํ•˜๋Š” ๋ฐ์ดํ„ฐ์˜ ํฌ์†Œ ํŠน์„ฑ์„ ๊ฐ์•ˆํ•˜๋ฉด, ์••์ถ• ์„ผ์‹ฑ ๊ธฐ๋ฐ˜์˜ ๋‹ค์ค‘ ์‚ฌ์šฉ์ž ๊ฒ€์ถœ ๋ฐฉ๋ฒ•(compressive sensing-based multi-user detection, CS-MUD)์ด ์ผ๋ฐ˜์ ์ธ ๋‹ค์ค‘ ์‚ฌ์šฉ์ž ๊ฒ€์ถœ ๋ฐฉ๋ฒ•๋ณด๋‹ค ๋” ์ข‹์€ ์„ฑ๋Šฅ์„ ๋ฐœํœ˜ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š”, ๊ธฐ์กด ๋ฐฉ์‹๋ณด๋‹ค ๋” ์ข‹์€ ์„ฑ๋Šฅ์„ ๊ฐ€์ง„ ์ƒˆ๋กœ์šด ์••์ถ• ์„ผ์‹ฑ ๊ธฐ๋ฐ˜์˜ ๋‹ค์ค‘ ์‚ฌ์šฉ์ž ๊ฒ€์ถœ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋ถ€์—ฐ ์„ค๋ช…ํ•˜๋ฉด, ๊ฐ€์žฅ ํฐ ์‚ฌํ›„ ํ™œ์„ฑ ํ™•๋ฅ ์„ ๊ฐ€์ง„ ์‚ฌ์šฉ์ž๋ฅผ ์ฐพ๊ณ (maximum a posteriori probability-based active user detection, MAP-AUD), ์—ญ์‹œ ์‚ฌํ›„ ํ™•๋ฅ  ๊ด€์ ์—์„œ ๊ฐ€์žฅ ํ™•๋ฅ ์ด ๋†’์€ ๋ฐ์ดํ„ฐ๋ฅผ ์ถ”์ •ํ•œ๋‹ค(maximum a posteriori probability-based data detection, MAP-DD). ์ด ๋•Œ, MAP-AUD์™€ MAP-DD ๋ธ”๋ก์€ ์„œ๋กœ ์™ธ์žฌ์  ์ •๋ณด(extrinsic information)๋งŒ์„ ์ฃผ๊ณ  ๋ฐ›๋Š”๋ฐ, ์ด ์™ธ์žฌ์  ์ •๋ณด๋Š” ์ƒ๋Œ€๋ฐฉ์˜ ์‚ฌ์ „ ์ •๋ณด๊ฐ€ ๋˜๊ณ , ์ด ์‚ฌ์ „ ์ •๋ณด๋ฅผ ์ด์šฉํ•˜์—ฌ ๋‹ค์‹œ ์‚ฌํ›„ ํ™•๋ฅ  ๊ด€์ ์—์„œ ์ตœ์ ์˜ ํ•ด๋ฅผ ๊ตฌํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐ˜๋ณต ์ˆ˜ํ–‰์„ ํ†ตํ•ด ๊ฐ ๋ธ”๋ก์€ ๊ฒ€์ถœ์˜ ์ •ํ™•๋„์™€ ์„ฑ๋Šฅ์„ ๋†’์—ฌ ๋‚˜๊ฐ„๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ํŒจํ‚ท ๋‹จ์œ„๋กœ ํ™•์žฅ๋  ์ˆ˜ ์žˆ๋‹ค. ๊ฐ๊ฐ์˜ ์‚ฌ๋ฌผ ๊ธฐ๊ธฐ๋“ค์ด ์ „์†กํ•˜๋ ค๋Š” ๋ฐ์ดํ„ฐ๋Š” ์—ฌ๋Ÿฌ๊ฐœ์˜ ์‹ฌ๋ณผ๋กœ ๊ตฌ์„ฑ๋œ ํŒจํ‚ท์ด๋ฉฐ, ํ•œ ํŒจํ‚ท ๋‚ด์˜ ๊ฐ๊ฐ์˜ ์‹ฌ๋ณผ์€ ๊ณตํ†ต๋œ ํ™œ์„ฑ๋„๋ฅผ ๊ฐ€์ง€๊ฒŒ ๋œ๋‹ค. ์—ฌ๊ธฐ์„œ, ์ด ๊ณตํ†ต๋œ ํ™œ์„ฑ๋„๋ฅผ ์ด์šฉํ•˜๋ฉด, ํ™œ์„ฑ ์‚ฌ๋ฌผ ๊ธฐ๊ธฐ์™€ ์ด๋“ค์˜ ์ „์†ก ๋ฐ์ดํ„ฐ ์ถ”์ •์˜ ์ •ํ™•๋„๋ฅผ ๋†’์ผ ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ, ์ด๋Š” ๊ณต๋™ ์ตœ์ ํ™”(joint optimization) ๋ฌธ์ œ๋กœ ๋งค์šฐ ๋ณต์žกํ•œ ์—ฐ์‚ฐ์„ ํ•„์š”๋กœ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š”, ํŒจํ‚ท ๋‚ด์˜ ์ž„์˜์˜ ํ•˜๋‚˜์˜ ์‹ฌ๋ณผ์—์„œ ์ถ”์ •๋œ ์‚ฌ๋ฌผ ๊ธฐ๊ธฐ์˜ ํ™œ์„ฑ๋„๋Š” ๋‹ค๋ฅธ ์‹ฌ๋ณผ์˜ ํ™œ์„ฑ๋„๋ฅผ ์ถ”์ •ํ•˜๋Š”๋ฐ ์‚ฌ์ „ ์ •๋ณด(a priori information)๋กœ ์ด์šฉ๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์— ์ฐฉ์•ˆํ•˜์—ฌ, ๋ณต์žกํ•œ ๊ณต๋™ ์ตœ์ ํ™” ๋ฌธ์ œ๋ฅผ ๋น„๊ต์  ์—ฐ์‚ฐ๋Ÿ‰์ด ์ ์€ ๋ถ€๋ถ„ ์ตœ์ ํ™”(subproblem optimization) ๋ฌธ์ œ๋กœ ๋‹จ์ˆœํ™” ์‹œํ‚ค๊ณ , ์ด๋“ค ๊ฐ„์— ๋ฉ”์‹œ์ง€ ์ „๋‹ฌ (massage-passing) ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ๊ณต๋™ ์ตœ์ ํ™”์˜ ํ•ด์— ๊ทผ์ ‘ํ•œ ํ•ด๋ฅผ ๊ตฌํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ๋•Œ, ๋ถ€๋ถ„ ์ตœ์ ํ™” ๋ฌธ์ œ์˜ ํ•ด๋ฒ•์ด ๋ฐ”๋กœ ์•ž์— ์„ค๋ช…ํ•œ MAP-AUD/MAP-DD ๋ฐฉ๋ฒ•์ด๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋ชจ์˜ ์‹คํ—˜์„ ํ†ตํ•ด ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ๊ธฐ์กด ๋ฐฉ๋ฒ•๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ ๋งค์šฐ ํฌ๊ฒŒ ์„ฑ๋Šฅ์ด ํ–ฅ์ƒ๋จ์„ ๋ณด์˜€๋‹ค. ํŠนํžˆ, ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์€ ์ „์ฒด ์‚ฌ์šฉ์ž ์ˆ˜ ๋Œ€๋น„ ์ด์šฉ ๊ฐ€๋Šฅํ•œ ์ž์›์ด ์ ์„ ๋•Œ์ผ์ˆ˜๋ก ๋” ํฐ ์„ฑ๋Šฅ ํ–ฅ์ƒ์ด ์žˆ๋Š”๋ฐ, ์ด๋Š” ์ฐจ์„ธ๋Œ€ ๋ฌด์„  ํ†ต์‹ ์—์„œ ์‚ฌ๋ฌผ ํ†ต์‹ ์ด ๊ณ ๋ คํ•˜๋Š” ๋‹จ์œ„ ๋ฉด์ ๋‹น ์‚ฌ๋ฌผ ๊ธฐ๊ธฐ์˜ ์ˆ˜(10^6๊ฐœ/km^2)๋ฅผ ๊ณ ๋ คํ–ˆ์„ ๋•Œ, ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ๋Œ€๊ทœ๋ชจ ์‚ฌ๋ฌผ ํ†ต์‹ ์— ์•„์ฃผ ํšจ์šฉ์„ฑ์ด ํผ์„ ๋ณด์—ฌ์ค€๋‹ค.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

    C-RAN CoMP Methods for MPR Receivers

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    The growth in mobile network traffic due to the increase in MTC (Machine Type Communication) applications, brings along a series of new challenges in traffic routing and management. The goals are to have effective resolution times (less delay), low energy consuption (given that wide sensor networks which are included in the MTC category, are built to last years with respect to their battery consuption) and extremely reliable communication (low Packet Error Rates), following the fifth generation (5G) mobile network demands. In order to deal with this type of dense traffic, several uplink strategies can be devised, where diversity variables like space (several Base Stations deployed), time (number of retransmissions of a given packet per user) and power spreading (power value diversity at the receiver, introducing the concept of SIC and Power-NOMA) have to be handled carefully to fulfill the requirements demanded in Ultra-Reliable Low-Latency Communication (URLLC). This thesis, besides being restricted in terms of transmission power and processing of a User Equipment (UE), works on top of an Iterative Block Decision Feedback Equalization Reciever that allows Multi Packet Reception to deal with the diversity types mentioned earlier. The results of this thesis explore the possibility of fragmenting the processing capabilities in an integrated cloud network (C-RAN) environment through an SINR estimation at the receiver to better understand how and where we can break and distribute our processing needs in order to handle near Base Station users and cell-edge users, the latters being the hardest to deal with in dense networks like the ones deployed in a MTC environment

    Toward High-Performance Implementation of 5G SCMA Algorithms

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    International audienceThe recent evolution of mobile communication systems toward a 5G network is associated with the search for new types of non-orthogonal modulations such as Sparse Code Multiple Access (SCMA). Such modulations are proposed in response to demands for increasing the number of connected users. SCMA is a non-orthogonal multiple access technique that offers improved Bit Error Rate (BER) performance and higher spectral efficiency than other comparable techniques, but these improvements come at the cost of complex decoders. There are many challenges in designing near-optimum high throughput SCMA decoders. This paper explores means to enhance the performance of SCMA decoders. To achieve this goal, various improvements to the MPA algorithms are proposed. They notably aim at adapting SCMA decoding to the Single Instruction Multiple Data (SIMD) paradigm. An approximate modeling of noise is performed to reduce the complexity of floating-point calculations. The effects of Forward Error Corrections (FEC) such as polar, turbo and LDPC codes, as well as different ways of accessing memory and improving power efficiency of modified MPAs are investigated. The results show that the throughput of a SCMA decoder can be increased by 3.1 to 21 times when compared to the original MPA on different computing platforms using the suggested improvements

    Research on PDMA system based on complementary sequence and low complexity detection algorithm

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    With the intensive deployment of mobile networks and the vigorous development of new multimedia services, video has gradually become the mainstream of cultural consumption. The contradiction between the proliferation of video data services and the scarcity of spectrum resources has brought great challenges to the current network resource allocation. Non-orthogonal multiple access (NOMA) can be used to solve this problem by signal superposition and spectrum multiplexing to improve system access capability. As a new type of joint optimization design of transmitter and receiver side, PDMA has high research value. In this paper, a framework of PDMA video transmission system based on H.264 video compression coding (HVC-PDMA) is proposed. Poly complementary sequence (PCS) spread spectrum coding is performed on the transmission codebook in order to improve the transmission accuracy. Meanwhile, a low complexity serial sphere compensated Max-log MPA (SSCM-MPA) algorithm is proposed to reduce the complexity of the multi-user detection algorithm. Simulation results show that the PCS spread spectrum can improve system throughput and peak signal-to-noise ratio (PSNR) while reducing bit error rate (BER). SSCM-MPA algorithm can greatly reduce the complexity and improve the transmission efficiency

    Symbol Detection in 5G and Beyond Networks

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    Beyond 5G networks are expected to provide excellent quality of service in terms of delay and reliability for users, where they could travel with high mobility (e.g., 500 km/h) and achieve better spectral efficiency. To support these demands, advanced wireless architectures have been proposed, i.e., orthogonal time frequency space (OTFS) modulation and multiple-input multiple-output (MIMO), which are used to handle high mobility communications and increase the networkโ€™s spectral efficiency, respectively. Symbol detection in these advanced wireless architectures is essential to satisfy reliability requirements. On the one hand, the optimal maximum likelihood symbol detector is prohibitively complex as its complexity is non-deterministic polynomial-time (NP)-hard. On the other hand, conventional low-complexity symbol detectors pose a significant performance loss compared to the optimal detector. Thus they cannot be used to satisfy high-reliability requirements. One solution to this problem is to develop a low-complexity algorithm that can achieve near-optimal performance in a particular scenario (e.g., M-MIMO). Nevertheless, there are some cases where we cannot design low-complexity algorithms. To alleviate this issue, deep learning networks can be integrated into an existing algorithm and trained using a dataset obtained by simulating a corresponding scenario. In this thesis, we design symbol detectors for advanced wireless architectures (i.e., MIMO and OTFS) to support an excellent quality of service in terms of delay and reliability and better spectral efficiency beyond 5G networks
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