717 research outputs found

    Short Block-length Codes for Ultra-Reliable Low-Latency Communications

    Full text link
    This paper reviews the state of the art channel coding techniques for ultra-reliable low latency communication (URLLC). The stringent requirements of URLLC services, such as ultra-high reliability and low latency, have made it the most challenging feature of the fifth generation (5G) mobile systems. The problem is even more challenging for the services beyond the 5G promise, such as tele-surgery and factory automation, which require latencies less than 1ms and failure rate as low as 10โˆ’910^{-9}. The very low latency requirements of URLLC do not allow traditional approaches such as re-transmission to be used to increase the reliability. On the other hand, to guarantee the delay requirements, the block length needs to be small, so conventional channel codes, originally designed and optimised for moderate-to-long block-lengths, show notable deficiencies for short blocks. This paper provides an overview on channel coding techniques for short block lengths and compares them in terms of performance and complexity. Several important research directions are identified and discussed in more detail with several possible solutions.Comment: Accepted for publication in IEEE Communications Magazin

    ํฌ์†Œ์ธ์ง€๋ฅผ ์ด์šฉํ•œ ์ „์†ก๊ธฐ์ˆ  ์—ฐ๊ตฌ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2019. 2. ์‹ฌ๋ณ‘ํšจ.The new wave of the technology revolution, named the fifth wireless systems, is changing our daily life dramatically. These days, unprecedented services and applications such as driverless vehicles and drone-based deliveries, smart cities and factories, remote medical diagnosis and surgery, and artificial intelligence-based personalized assistants are emerging. Communication mechanisms associated with these new applications and services are way different from traditional communications in terms of latency, energy efficiency, reliability, flexibility, and connection density. Since the current radio access mechanism cannot support these diverse services and applications, a new approach to deal with these relentless changes should be introduced. This compressed sensing (CS) paradigm is very attractive alternative to the conventional information processing operations including sampling, sensing, compression, estimation, and detection. To apply the CS techniques to wireless communication systems, there are a number of things to know and also several issues to be considered. In the last decade, CS techniques have spread rapidly in many applications such as medical imaging, machine learning, radar detection, seismology, computer science, statistics, and many others. Also, various wireless communication applications exploiting the sparsity of a target signal have been studied. Notable examples include channel estimation, interference cancellation, angle estimation, spectrum sensing, and symbol detection. The distinct feature of this work, in contrast to the conventional approaches exploiting naturally acquired sparsity, is to exploit intentionally designed sparsity to improve the quality of the communication systems. In the first part of the dissertation, we study the mapping data information into the sparse signal in downlink systems. We propose an approach, called sparse vector coding (SVC), suited for the short packet transmission. In SVC, since the data information is mapped to the position of sparse vector, whole data packet can be decoded by idenitifying nonzero positions of the sparse vector. From our simulations, we show that the packet error rate of SVC outperforms the conventional channel coding schemes at the URLLC regime. Moreover, we discuss the SVC transmission for the massive MTC access by overlapping multiple SVC-based packets into the same resources. Using the spare vector overlapping and multiuser CS decoding scheme, SVC-based transmission provides robustness against the co-channel interference and also provide comparable performance than other non-orthogonal multiple access (NOMA) schemes. By using the fact that SVC only identifies the support of sparse vector, we extend the SVC transmission without pilot transmission, called pilot-less SVC. Instead of using the support, we further exploit the magnitude of sparse vector for delivering additional information. This scheme is referred to as enhanced SVC. The key idea behind the proposed E-SVC transmission scheme is to transform the small information into a sparse vector and map the side-information into a magnitude of the sparse vector. Metaphorically, E-SVC can be thought as a standing a few poles to the empty table. As long as the number of poles is small enough and the measurements contains enough information to find out the marked cell positions, accurate recovery of E-SVC packet can be guaranteed. In the second part of this dissertation, we turn our attention to make sparsification of the non-sparse signal, especially for the pilot transmission and channel estimation. Unlike the conventional scheme where the pilot signal is transmitted without modification, the pilot signals are sent after the beamforming in the proposed technique. This work is motivated by the observation that the pilot overhead must scale linearly with the number of taps in CIR vector and the number of transmit antennas so that the conventional pilot transmission is not an appropriate option for the IoT devices. Primary goal of the proposed scheme is to minimize the nonzero entries of a time-domain channel vector by the help of multiple antennas at the basestation. To do so, we apply the time-domain sparse precoding, where each precoded channel propagates via fewer tap than the original channel vector. The received channel vector of beamformed pilots can be jointly estimated by the sparse recovery algorithm.5์„ธ๋Œ€ ๋ฌด์„ ํ†ต์‹  ์‹œ์Šคํ…œ์˜ ์ƒˆ๋กœ์šด ๊ธฐ์ˆ  ํ˜์‹ ์€ ๋ฌด์ธ ์ฐจ๋Ÿ‰ ๋ฐ ํ•ญ๊ณต๊ธฐ, ์Šค๋งˆํŠธ ๋„์‹œ ๋ฐ ๊ณต์žฅ, ์›๊ฒฉ ์˜๋ฃŒ ์ง„๋‹จ ๋ฐ ์ˆ˜์ˆ , ์ธ๊ณต ์ง€๋Šฅ ๊ธฐ๋ฐ˜ ๋งŸ์ถคํ˜• ์ง€์›๊ณผ ๊ฐ™์€ ์ „๋ก€ ์—†๋Š” ์„œ๋น„์Šค ๋ฐ ์‘์šฉํ”„๋กœ๊ทธ๋žจ์œผ๋กœ ๋ถ€์ƒํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ƒˆ๋กœ์šด ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜ ๋ฐ ์„œ๋น„์Šค์™€ ๊ด€๋ จ๋œ ํ†ต์‹  ๋ฐฉ์‹์€ ๋Œ€๊ธฐ ์‹œ๊ฐ„, ์—๋„ˆ์ง€ ํšจ์œจ์„ฑ, ์‹ ๋ขฐ์„ฑ, ์œ ์—ฐ์„ฑ ๋ฐ ์—ฐ๊ฒฐ ๋ฐ€๋„ ์ธก๋ฉด์—์„œ ๊ธฐ์กด ํ†ต์‹ ๊ณผ ๋งค์šฐ ๋‹ค๋ฅด๋‹ค. ํ˜„์žฌ์˜ ๋ฌด์„  ์•ก์„ธ์Šค ๋ฐฉ์‹์„ ๋น„๋กฏํ•œ ์ข…๋ž˜์˜ ์ ‘๊ทผ๋ฒ•์€ ์ด๋Ÿฌํ•œ ์š”๊ตฌ ์‚ฌํ•ญ์„ ๋งŒ์กฑํ•  ์ˆ˜ ์—†๊ธฐ ๋•Œ๋ฌธ์— ์ตœ๊ทผ์— sparse processing๊ณผ ๊ฐ™์€ ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ๋ฒ•์ด ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ์ด ์ƒˆ๋กœ์šด ์ ‘๊ทผ ๋ฐฉ๋ฒ•์€ ํ‘œ๋ณธ ์ถ”์ถœ, ๊ฐ์ง€, ์••์ถ•, ํ‰๊ฐ€ ๋ฐ ํƒ์ง€๋ฅผ ํฌํ•จํ•œ ๊ธฐ์กด์˜ ์ •๋ณด ์ฒ˜๋ฆฌ์— ๋Œ€ํ•œ ํšจ์œจ์ ์ธ ๋Œ€์ฒด๊ธฐ์ˆ ๋กœ ํ™œ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์ง€๋‚œ 10๋…„ ๋™์•ˆ compressed sensing (CS)๊ธฐ๋ฒ•์€ ์˜๋ฃŒ์˜์ƒ, ๊ธฐ๊ณ„ํ•™์Šต, ํƒ์ง€, ์ปดํ“จํ„ฐ ๊ณผํ•™, ํ†ต๊ณ„ ๋ฐ ๊ธฐํƒ€ ์—ฌ๋Ÿฌ ๋ถ„์•ผ์—์„œ ๋น ๋ฅด๊ฒŒ ํ™•์‚ฐ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ์‹ ํ˜ธ์˜ ํฌ์†Œ์„ฑ(sparsity)๋ฅผ ์ด์šฉํ•˜๋Š” CS ๊ธฐ๋ฒ•์€ ๋‹ค์–‘ํ•œ ๋ฌด์„  ํ†ต์‹ ์ด ์—ฐ๊ตฌ๋˜์—ˆ๋‹ค. ์ฃผ๋ชฉํ• ๋งŒํ•œ ์˜ˆ๋กœ๋Š” ์ฑ„๋„ ์ถ”์ •, ๊ฐ„์„ญ ์ œ๊ฑฐ, ๊ฐ๋„ ์ถ”์ •, ๋ฐ ์ŠคํŽ™ํŠธ๋Ÿผ ๊ฐ์ง€๊ฐ€ ์žˆ์œผ๋ฉฐ ํ˜„์žฌ๊นŒ์ง€ ์—ฐ๊ตฌ๋Š” ์ฃผ์–ด์ง„ ์‹ ํ˜ธ๊ฐ€ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ๋ณธ๋ž˜์˜ ํฌ์†Œ์„ฑ์— ์ฃผ๋ชฉํ•˜์˜€์œผ๋‚˜ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ธฐ์กด์˜ ์ ‘๊ทผ ๋ฐฉ๋ฒ•๊ณผ ๋‹ฌ๋ฆฌ ์ธ์œ„์ ์œผ๋กœ ์„ค๊ณ„๋œ ํฌ์†Œ์„ฑ์„ ์ด์šฉํ•˜์—ฌ ํ†ต์‹  ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์šฐ์„  ๋ณธ ๋…ผ๋ฌธ์€ ๋‹ค์šด๋งํฌ ์ „์†ก์—์„œ ํฌ์†Œ ์‹ ํ˜ธ ๋งคํ•‘์„ ํ†ตํ•œ ๋ฐ์ดํ„ฐ ์ „์†ก ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜๋ฉฐ ์งง์€ ํŒจํ‚ท (short packet) ์ „์†ก์— ์ ํ•ฉํ•œ CS ์ ‘๊ทผ๋ฒ•์„ ํ™œ์šฉํ•˜๋Š” ๊ธฐ์ˆ ์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๊ธฐ์ˆ ์ธ ํฌ์†Œ๋ฒกํ„ฐ์ฝ”๋”ฉ (sparse vector coding, SVC)์€ ๋ฐ์ดํ„ฐ ์ •๋ณด๊ฐ€ ์ธ๊ณต์ ์ธ ํฌ์†Œ๋ฒกํ„ฐ์˜ nonzero element์˜ ์œ„์น˜์— ๋งคํ•‘ํ•˜์—ฌ ์ „์†ก๋œ ๋ฐ์ดํ„ฐ ํŒจํ‚ท์€ ํฌ์†Œ๋ฒกํ„ฐ์˜ 0์ด ์•„๋‹Œ ์œ„์น˜๋ฅผ ์‹๋ณ„ํ•จ์œผ๋กœ ์›์‹ ํ˜ธ ๋ณต์›์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ๋ถ„์„๊ณผ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ์ œ์•ˆํ•˜๋Š” SVC ๊ธฐ๋ฒ•์˜ ํŒจํ‚ท ์˜ค๋ฅ˜๋ฅ ์€ ultra-reliable and low latency communications (URLLC) ์„œ๋น„์Šค๋ฅผ ์ง€์›์„ ์œ„ํ•ด ์‚ฌ์šฉ๋˜๋Š” ์ฑ„๋„์ฝ”๋”ฉ๋ฐฉ์‹๋ณด๋‹ค ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋˜ํ•œ, ๋ณธ ๋…ผ๋ฌธ์€ SVC๊ธฐ์ˆ ์„ ๋‹ค์Œ์˜ ์„ธ๊ฐ€์ง€ ์˜์—ญ์œผ๋กœ ํ™•์žฅํ•˜์˜€๋‹ค. ์ฒซ์งธ๋กœ, ์—ฌ๋Ÿฌ ๊ฐœ์˜ SVC ๊ธฐ๋ฐ˜ ํŒจํ‚ท์„ ๋™์ผํ•œ ์ž์›์— ๊ฒน์น˜๊ฒŒ ์ „์†กํ•จ์œผ๋กœ ์ƒํ–ฅ๋งํฌ์—์„œ ๋Œ€๊ทœ๋ชจ ์ „์†ก์„ ์ง€์›ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ค‘์ฒฉ๋œ ํฌ์†Œ๋ฒกํ„ฐ๋ฅผ ๋‹ค์ค‘์‚ฌ์šฉ์ž CS ๋””์ฝ”๋”ฉ ๋ฐฉ์‹์„ ์‚ฌ์šฉํ•˜์—ฌ ์ฑ„๋„ ๊ฐ„์„ญ์— ๊ฐ•์ธํ•œ ์„ฑ๋Šฅ์„ ์ œ๊ณตํ•˜๊ณ  ๋น„์ง๊ต ๋‹ค์ค‘ ์ ‘์† (NOMA) ๋ฐฉ์‹๊ณผ ์œ ์‚ฌํ•œ ์„ฑ๋Šฅ์„ ์ œ๊ณตํ•œ๋‹ค. ๋‘˜์งธ๋กœ, SVC ๊ธฐ์ˆ ์ด ํฌ์†Œ ๋ฒกํ„ฐ์˜ support๋งŒ์„ ์‹๋ณ„ํ•œ๋‹ค๋Š” ์‚ฌ์‹ค์„ ์ด์šฉํ•˜์—ฌ ํŒŒ์ผ๋Ÿฟ ์ „์†ก์ด ํ•„์š”์—†๋Š” pilotless-SVC ์ „์†ก ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ฑ„๋„ ์ •๋ณด๊ฐ€ ์—†๋Š” ๊ฒฝ์šฐ์—๋„ ํฌ์†Œ ๋ฒกํ„ฐ์˜ support์˜ ํฌ๊ธฐ๋Š” ์ฑ„๋„์˜ ํฌ๊ธฐ์— ๋น„๋ก€ํ•˜๊ธฐ ๋•Œ๋ฌธ์— pilot์—†์ด ๋ณต์›์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ์…‹์งธ๋กœ, ํฌ์†Œ๋ฒกํ„ฐ์˜ support์˜ ํฌ๊ธฐ์— ์ถ”๊ฐ€ ์ •๋ณด๋ฅผ ์ „์†กํ•จ์œผ๋กœ ๋ณต์› ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ ์‹œํ‚ค๋Š” enhanced SVC (E-SVC)๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ E-SVC ์ „์†ก ๋ฐฉ์‹์˜ ํ•ต์‹ฌ ์•„๋””๋””์–ด๋Š” ์งง์€ ํŒจํ‚ท์„ ์ „์†ก๋˜๋Š” ์ •๋ณด๋ฅผ ํฌ์†Œ ๋ฒกํ„ฐ๋กœ ๋ณ€ํ™˜ํ•˜๊ณ  ์ •๋ณด ๋ณต์›์„ ๋ณด์กฐํ•˜๋Š” ์ถ”๊ฐ€ ์ •๋ณด๋ฅผ ํฌ์†Œ ๋ฒกํ„ฐ์˜ ํฌ๊ธฐ (magnitude)๋กœ ๋งคํ•‘ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, SVC ๊ธฐ์ˆ ์„ ํŒŒ์ผ๋Ÿฟ ์ „์†ก์— ํ™œ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ํŠนํžˆ, ์ฑ„๋„ ์ถ”์ •์„ ์œ„ํ•ด ์ฑ„๋„ ์ž„ํŽ„์Šค ์‘๋‹ต์˜ ์‹ ํ˜ธ๋ฅผ ํฌ์†Œํ™”ํ•˜๋Š” ํ”„๋ฆฌ์ฝ”๋”ฉ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ํŒŒ์ผ๋Ÿฟ ์‹ ํ˜ธ์„ ํ”„๋กœ์ฝ”๋”ฉ ์—†์ด ์ „์†ก๋˜๋Š” ๊ธฐ์กด์˜ ๋ฐฉ์‹๊ณผ ๋‹ฌ๋ฆฌ, ์ œ์•ˆ๋œ ๊ธฐ์ˆ ์—์„œ๋Š” ํŒŒ์ผ๋Ÿฟ ์‹ ํ˜ธ๋ฅผ ๋น”ํฌ๋ฐํ•˜์—ฌ ์ „์†กํ•œ๋‹ค. ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•์€ ๊ธฐ์ง€๊ตญ์—์„œ ๋‹ค์ค‘ ์•ˆํ…Œ๋‚˜๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ฑ„๋„ ์‘๋‹ต์˜ 0์ด ์•„๋‹Œ ์š”์†Œ๋ฅผ ์ตœ์†Œํ™”ํ•˜๋Š” ์‹œ๊ฐ„ ์˜์—ญ ํฌ์†Œ ํ”„๋ฆฌ์ฝ”๋”ฉ์„ ์ ์šฉํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋” ์ ํ™•ํ•œ ์ฑ„๋„ ์ถ”์ •์„ ๊ฐ€๋Šฅํ•˜๋ฉฐ ๋” ์ ์€ ํŒŒ์ผ๋Ÿฟ ์˜ค๋ฒ„ํ—ค๋“œ๋กœ ์ฑ„๋„ ์ถ”์ •์ด ๊ฐ€๋Šฅํ•˜๋‹ค.Abstract i Contents iv List of Tables viii List of Figures ix 1 INTRODUCTION 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Three Key Services in 5G systems . . . . . . . . . . . . . . . 2 1.1.2 Sparse Processing in Wireless Communications . . . . . . . . 4 1.2 Contributions and Organization . . . . . . . . . . . . . . . . . . . . . 7 1.3 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Sparse Vector Coding for Downlink Ultra-reliable and Low Latency Communications 12 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2 URLLC Service Requirements . . . . . . . . . . . . . . . . . . . . . 15 2.2.1 Latency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.2 Ultra-High Reliability . . . . . . . . . . . . . . . . . . . . . 17 2.2.3 Coexistence . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3 URLLC Physical Layer in 5G NR . . . . . . . . . . . . . . . . . . . 18 2.3.1 Packet Structure . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3.2 Frame Structure and Latency-sensitive Scheduling Schemes . 20 2.3.3 Solutions to the Coexistence Problem . . . . . . . . . . . . . 22 2.4 Short-sized Packet in LTE-Advanced Downlink . . . . . . . . . . . . 24 2.5 Sparse Vector Coding . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.5.1 SVC Encoding and Transmission . . . . . . . . . . . . . . . 25 2.5.2 SVC Decoding . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.5.3 Identification of False Alarm . . . . . . . . . . . . . . . . . . 33 2.6 SVC Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . 36 2.7 Implementation Issues . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.7.1 Codebook Design . . . . . . . . . . . . . . . . . . . . . . . . 48 2.7.2 High-order Modulation . . . . . . . . . . . . . . . . . . . . . 49 2.7.3 Diversity Transmission . . . . . . . . . . . . . . . . . . . . . 50 2.7.4 SVC without Pilot . . . . . . . . . . . . . . . . . . . . . . . 50 2.7.5 Threshold to Prevent False Alarm Event . . . . . . . . . . . . 51 2.8 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 52 2.8.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 52 2.8.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 53 2.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3 Sparse Vector Coding for Uplink Massive Machine-type Communications 59 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3.2 Uplink NOMA transmission for mMTC . . . . . . . . . . . . . . . . 61 3.3 Sparse Vector Coding based NOMA for mMTC . . . . . . . . . . . . 63 3.3.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.3.2 Joint Multiuser Decoding . . . . . . . . . . . . . . . . . . . . 66 3.4 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 68 3.4.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 68 3.4.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 69 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4 Pilot-less Sparse Vector Coding for Short Packet Transmission 72 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4.2 Pilot-less Sparse Vector Coding Processing . . . . . . . . . . . . . . 75 4.2.1 SVC Processing with Pilot Symbols . . . . . . . . . . . . . . 75 4.2.2 Pilot-less SVC . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.2.3 PL-SVC Decoding in Multiple Basestation Antennas . . . . . 78 4.3 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 80 4.3.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 80 4.3.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 81 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5 Joint Analog and Quantized Feedback via Sparse Vector Coding 84 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.2 System Model for Joint Spase Vector Coding . . . . . . . . . . . . . 86 5.3 Sparse Recovery Algorithm and Performance Analysis . . . . . . . . 90 5.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5.4.1 Linear Interpolation of Sensing Information . . . . . . . . . . 96 5.4.2 Linear Combined Feedback . . . . . . . . . . . . . . . . . . 96 5.4.3 One-shot Packet Transmission . . . . . . . . . . . . . . . . . 96 5.5 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.5.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.5.2 Results and Discussions . . . . . . . . . . . . . . . . . . . . 98 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 6 Sparse Beamforming for Enhanced Mobile Broadband Communications 101 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 6.1.1 Increase the number of transmit antennas . . . . . . . . . . . 102 6.1.2 2D active antenna system (AAS) . . . . . . . . . . . . . . . . 103 6.1.3 3D channel environment . . . . . . . . . . . . . . . . . . . . 104 6.1.4 RS transmission for CSI acquisition . . . . . . . . . . . . . . 106 6.2 System Design and Standardization of FD-MIMO Systems . . . . . . 107 6.2.1 Deployment scenarios . . . . . . . . . . . . . . . . . . . . . 108 6.2.2 Antenna configurations . . . . . . . . . . . . . . . . . . . . . 108 6.2.3 TXRU architectures . . . . . . . . . . . . . . . . . . . . . . 109 6.2.4 New CSI-RS transmission strategy . . . . . . . . . . . . . . . 112 6.2.5 CSI feedback mechanisms for FD-MIMO systems . . . . . . 114 6.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6.3.1 Basic System Model . . . . . . . . . . . . . . . . . . . . . . 116 6.3.2 Beamformed Pilot Transmission . . . . . . . . . . . . . . . . 117 6.4 Sparsification of Pilot Beamforming . . . . . . . . . . . . . . . . . . 118 6.4.1 Time-domain System Model without Pilot Beamforming . . . 119 6.4.2 Pilot Beamforming . . . . . . . . . . . . . . . . . . . . . . . 120 6.5 Channel Estimation of Beamformed Pilots . . . . . . . . . . . . . . . 124 6.5.1 Recovery using Multiple Measurement Vector . . . . . . . . . 124 6.5.2 MSE Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 128 6.6 Simulations and Discussion . . . . . . . . . . . . . . . . . . . . . . . 129 6.6.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 129 6.6.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 130 6.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 7 Conclusion 136 7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 7.2 Future Research Directions . . . . . . . . . . . . . . . . . . . . . . . 139 Abstract (In Korean) 152Docto

    Reliable Packet Streams with Multipath Network Coding

    Get PDF
    With increasing computational capabilities and advances in robotics, technology is at the verge of the next industrial revolution. An growing number of tasks can be performed by artificial intelligence and agile robots. This impacts almost every part of the economy, including agriculture, transportation, industrial manufacturing and even social interactions. In all applications of automated machines, communication is a critical component to enable cooperation between machines and exchange of sensor and control signals. The mobility and scale at which these automated machines are deployed also challenges todays communication systems. These complex cyber-physical systems consisting of up to hundreds of mobile machines require highly reliable connectivity to operate safely and efficiently. Current automation systems use wired communication to guarantee low latency connectivity. But wired connections cannot be used to connect mobile robots and are also problematic to deploy at scale. Therefore, wireless connectivity is a necessity. On the other hand, it is subject to many external influences and cannot reach the same level of reliability as the wired communication systems. This thesis aims to address this problem by proposing methods to combine multiple unreliable wireless connections to a stable channel. The foundation for this work is Caterpillar Random Linear Network Coding (CRLNC), a new variant of network code designed to achieve low latency. CRLNC performs similar to block codes in recovery of lost packets, but with a significantly decreased latency. CRLNC with Feedback (CRLNC-FB) integrates a Selective-Repeat ARQ (SR-ARQ) to optimize the tradeoff between delay and throughput of reliable communication. The proposed protocol allows to slightly increase the overhead to reduce the packet delay at the receiver. With CRLNC, delay can be reduced by more than 50 % with only a 10 % reduction in throughput. Finally, CRLNC is combined with a statistical multipath scheduler to optimize the reliability and service availability in wireless network with multiple unreliable paths. This multipath CRLNC scheme improves the reliability of a fixed-rate packet stream by 10 % in a system model based on real-world measurements of LTE and WiFi. All the proposed protocols have been implemented in the software library NCKernel. With NCKernel, these protocols could be evaluated in simulated and emulated networks, and were also deployed in several real-world testbeds and demonstrators.:Abstract 2 Acknowledgements 6 1 Introduction 7 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2 Use Cases and Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3 Opportunities of Multipath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.4 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2 State of the Art of Multipath Communication 19 2.1 Physical Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2 Data Link Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.3 Network Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.4 Transport Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.5 Application Layer and Session Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.6 Research Gap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3 NCKernel: Network Coding Protocol Framework 27 3.1 Theory that matters! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.3.1 Socket Buffers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.3.2 En-/Re-/Decoder API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3.3 Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.4 Timers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.5 Tracing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.5 Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4 Low-Latency Network Coding 35 4.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 Random Linear Network Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.3 Low Latency Network Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.4 CRLNC: Caterpillar Random Linear Network Coding . . . . . . . . . . . . . . . . . . 38 4.4.1 Encoding and Packet Format . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.4.2 Decoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.4.3 Computational Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.5.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.5.2 Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.5.3 Packet Loss Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.5.4 Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.5.5 Window Size Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5 Delay-Throughput Tradeoff 55 5.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.2 Network Coding with ARQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.3 CRLNC-FB: CRLNC with Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 5.3.1 Encoding and Packet Format . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.3.2 Decoding and Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.3.3 Retransmissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.4.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.4.2 Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.4.3 Systematic Retransmissions . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.4.4 Coded Packet Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 5.4.5 Comparison with other Protocols . . . . . . . . . . . . . . . . . . . . . . . . 67 5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6 Multipath for Reliable Low-Latency Packet Streams 73 6.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 6.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 6.3.1 Traffic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 6.3.2 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 6.3.3 Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.3.4 Reliability Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.4 Multipath CRLNC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 6.4.1 Window Size for Heterogeneous Paths . . . . . . . . . . . . . . . . . . . . . 77 6.4.2 Packet Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.5.1 Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.5.2 Preliminary Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 6.5.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 7 Conclusion 94 7.1 Results and Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 7.2 Future Research Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Acronyms 99 Publications 101 Bibliography 10

    Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions

    Full text link
    Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio (NR) and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency. Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices. The complexity of the digital processing has been viewed as a fundamental obstacle to the feasibility of Massive MIMO in the past. Recent advances on system-algorithm-hardware co-design have led to extremely energy-efficient implementations. These exploit opportunities in deeply-scaled silicon technologies and perform partly distributed processing to cope with the bottlenecks encountered in the interconnection of many signals. For example, prototype ASIC implementations have demonstrated zero-forcing precoding in real time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing of 8 terminals). Coarse and even error-prone digital processing in the antenna paths permits a reduction of consumption with a factor of 2 to 5. This article summarizes the fundamental technical contributions to efficient digital signal processing for Massive MIMO. The opportunities and constraints on operating on low-complexity RF and analog hardware chains are clarified. It illustrates how terminals can benefit from improved energy efficiency. The status of technology and real-life prototypes discussed. Open challenges and directions for future research are suggested.Comment: submitted to IEEE transactions on signal processin
    • โ€ฆ
    corecore