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Massive MIMO for Internet of Things (IoT) Connectivity
Massive MIMO is considered to be one of the key technologies in the emerging
5G systems, but also a concept applicable to other wireless systems. Exploiting
the large number of degrees of freedom (DoFs) of massive MIMO essential for
achieving high spectral efficiency, high data rates and extreme spatial
multiplexing of densely distributed users. On the one hand, the benefits of
applying massive MIMO for broadband communication are well known and there has
been a large body of research on designing communication schemes to support
high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT)
is still a developing topic, as IoT connectivity has requirements and
constraints that are significantly different from the broadband connections. In
this paper we investigate the applicability of massive MIMO to IoT
connectivity. Specifically, we treat the two generic types of IoT connections
envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable
low-latency communication (URLLC). This paper fills this important gap by
identifying the opportunities and challenges in exploiting massive MIMO for IoT
connectivity. We provide insights into the trade-offs that emerge when massive
MIMO is applied to mMTC or URLLC and present a number of suitable communication
schemes. The discussion continues to the questions of network slicing of the
wireless resources and the use of massive MIMO to simultaneously support IoT
connections with very heterogeneous requirements. The main conclusion is that
massive MIMO can bring benefits to the scenarios with IoT connectivity, but it
requires tight integration of the physical-layer techniques with the protocol
design.Comment: Submitted for publicatio
ํฌ์์ธ์ง๋ฅผ ์ด์ฉํ ์ ์ก๊ธฐ์ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ ๋ณด๊ณตํ๋ถ, 2019. 2. ์ฌ๋ณํจ.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.5์ธ๋ ๋ฌด์ ํต์ ์์คํ
์ ์๋ก์ด ๊ธฐ์ ํ์ ์ ๋ฌด์ธ ์ฐจ๋ ๋ฐ ํญ๊ณต๊ธฐ, ์ค๋งํธ ๋์ ๋ฐ ๊ณต์ฅ, ์๊ฒฉ ์๋ฃ ์ง๋จ ๋ฐ ์์ , ์ธ๊ณต ์ง๋ฅ ๊ธฐ๋ฐ ๋ง์ถคํ ์ง์๊ณผ ๊ฐ์ ์ ๋ก ์๋ ์๋น์ค ๋ฐ ์์ฉํ๋ก๊ทธ๋จ์ผ๋ก ๋ถ์ํ๊ณ ์๋ค. ์ด๋ฌํ ์๋ก์ด ์ ํ๋ฆฌ์ผ์ด์
๋ฐ ์๋น์ค์ ๊ด๋ จ๋ ํต์ ๋ฐฉ์์ ๋๊ธฐ ์๊ฐ, ์๋์ง ํจ์จ์ฑ, ์ ๋ขฐ์ฑ, ์ ์ฐ์ฑ ๋ฐ ์ฐ๊ฒฐ ๋ฐ๋ ์ธก๋ฉด์์ ๊ธฐ์กด ํต์ ๊ณผ ๋งค์ฐ ๋ค๋ฅด๋ค. ํ์ฌ์ ๋ฌด์ ์ก์ธ์ค ๋ฐฉ์์ ๋น๋กฏํ ์ข
๋์ ์ ๊ทผ๋ฒ์ ์ด๋ฌํ ์๊ตฌ ์ฌํญ์ ๋ง์กฑํ ์ ์๊ธฐ ๋๋ฌธ์ ์ต๊ทผ์ sparse processing๊ณผ ๊ฐ์ ์๋ก์ด ์ ๊ทผ ๋ฐฉ๋ฒ์ด ์ฐ๊ตฌ๋๊ณ ์๋ค. ์ด ์๋ก์ด ์ ๊ทผ ๋ฐฉ๋ฒ์ ํ๋ณธ ์ถ์ถ, ๊ฐ์ง, ์์ถ, ํ๊ฐ ๋ฐ ํ์ง๋ฅผ ํฌํจํ ๊ธฐ์กด์ ์ ๋ณด ์ฒ๋ฆฌ์ ๋ํ ํจ์จ์ ์ธ ๋์ฒด๊ธฐ์ ๋ก ํ์ฉ๋๊ณ ์๋ค. ์ง๋ 10๋
๋์ compressed sensing (CS)๊ธฐ๋ฒ์ ์๋ฃ์์, ๊ธฐ๊ณํ์ต, ํ์ง, ์ปดํจํฐ ๊ณผํ, ํต๊ณ ๋ฐ ๊ธฐํ ์ฌ๋ฌ ๋ถ์ผ์์ ๋น ๋ฅด๊ฒ ํ์ฐ๋์๋ค. ๋ํ, ์ ํธ์ ํฌ์์ฑ(sparsity)๋ฅผ ์ด์ฉํ๋ CS ๊ธฐ๋ฒ์ ๋ค์ํ ๋ฌด์ ํต์ ์ด ์ฐ๊ตฌ๋์๋ค. ์ฃผ๋ชฉํ ๋งํ ์๋ก๋ ์ฑ๋ ์ถ์ , ๊ฐ์ญ ์ ๊ฑฐ, ๊ฐ๋ ์ถ์ , ๋ฐ ์คํํธ๋ผ ๊ฐ์ง๊ฐ ์์ผ๋ฉฐ ํ์ฌ๊น์ง ์ฐ๊ตฌ๋ ์ฃผ์ด์ง ์ ํธ๊ฐ ๊ฐ์ง๊ณ ์๋ ๋ณธ๋์ ํฌ์์ฑ์ ์ฃผ๋ชฉํ์์ผ๋ ๋ณธ ๋
ผ๋ฌธ์์๋ ๊ธฐ์กด์ ์ ๊ทผ ๋ฐฉ๋ฒ๊ณผ ๋ฌ๋ฆฌ ์ธ์์ ์ผ๋ก ์ค๊ณ๋ ํฌ์์ฑ์ ์ด์ฉํ์ฌ ํต์ ์์คํ
์ ์ฑ๋ฅ์ ํฅ์์ํค๋ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค.
์ฐ์ ๋ณธ ๋
ผ๋ฌธ์ ๋ค์ด๋งํฌ ์ ์ก์์ ํฌ์ ์ ํธ ๋งคํ์ ํตํ ๋ฐ์ดํฐ ์ ์ก ๋ฐฉ๋ฒ์ ์ ์ํ๋ฉฐ ์งง์ ํจํท (short packet) ์ ์ก์ ์ ํฉํ CS ์ ๊ทผ๋ฒ์ ํ์ฉํ๋ ๊ธฐ์ ์ ์ ์ํ๋ค. ์ ์ํ๋ ๊ธฐ์ ์ธ ํฌ์๋ฒกํฐ์ฝ๋ฉ (sparse vector coding, SVC)์ ๋ฐ์ดํฐ ์ ๋ณด๊ฐ ์ธ๊ณต์ ์ธ ํฌ์๋ฒกํฐ์ nonzero element์ ์์น์ ๋งคํํ์ฌ ์ ์ก๋ ๋ฐ์ดํฐ ํจํท์ ํฌ์๋ฒกํฐ์ 0์ด ์๋ ์์น๋ฅผ ์๋ณํจ์ผ๋ก ์์ ํธ ๋ณต์์ด ๊ฐ๋ฅํ๋ค. ๋ถ์๊ณผ ์๋ฎฌ๋ ์ด์
์ ํตํด ์ ์ํ๋ SVC ๊ธฐ๋ฒ์ ํจํท ์ค๋ฅ๋ฅ ์ ultra-reliable and low latency communications (URLLC) ์๋น์ค๋ฅผ ์ง์์ ์ํด ์ฌ์ฉ๋๋ ์ฑ๋์ฝ๋ฉ๋ฐฉ์๋ณด๋ค ์ฐ์ํ ์ฑ๋ฅ์ ๋ณด์ฌ์ค๋ค. ๋ํ, ๋ณธ ๋
ผ๋ฌธ์ SVC๊ธฐ์ ์ ๋ค์์ ์ธ๊ฐ์ง ์์ญ์ผ๋ก ํ์ฅํ์๋ค. ์ฒซ์งธ๋ก, ์ฌ๋ฌ ๊ฐ์ SVC ๊ธฐ๋ฐ ํจํท์ ๋์ผํ ์์์ ๊ฒน์น๊ฒ ์ ์กํจ์ผ๋ก ์ํฅ๋งํฌ์์ ๋๊ท๋ชจ ์ ์ก์ ์ง์ํ๋ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ์ค์ฒฉ๋ ํฌ์๋ฒกํฐ๋ฅผ ๋ค์ค์ฌ์ฉ์ CS ๋์ฝ๋ฉ ๋ฐฉ์์ ์ฌ์ฉํ์ฌ ์ฑ๋ ๊ฐ์ญ์ ๊ฐ์ธํ ์ฑ๋ฅ์ ์ ๊ณตํ๊ณ ๋น์ง๊ต ๋ค์ค ์ ์ (NOMA) ๋ฐฉ์๊ณผ ์ ์ฌํ ์ฑ๋ฅ์ ์ ๊ณตํ๋ค. ๋์งธ๋ก, SVC ๊ธฐ์ ์ด ํฌ์ ๋ฒกํฐ์ support๋ง์ ์๋ณํ๋ค๋ ์ฌ์ค์ ์ด์ฉํ์ฌ ํ์ผ๋ฟ ์ ์ก์ด ํ์์๋ pilotless-SVC ์ ์ก ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ์ฑ๋ ์ ๋ณด๊ฐ ์๋ ๊ฒฝ์ฐ์๋ ํฌ์ ๋ฒกํฐ์ support์ ํฌ๊ธฐ๋ ์ฑ๋์ ํฌ๊ธฐ์ ๋น๋กํ๊ธฐ ๋๋ฌธ์ pilot์์ด ๋ณต์์ด ๊ฐ๋ฅํ๋ค. ์
์งธ๋ก, ํฌ์๋ฒกํฐ์ support์ ํฌ๊ธฐ์ ์ถ๊ฐ ์ ๋ณด๋ฅผ ์ ์กํจ์ผ๋ก ๋ณต์ ์ฑ๋ฅ์ ํฅ์ ์ํค๋ enhanced SVC (E-SVC)๋ฅผ ์ ์ํ๋ค. ์ ์๋ E-SVC ์ ์ก ๋ฐฉ์์ ํต์ฌ ์๋๋์ด๋ ์งง์ ํจํท์ ์ ์ก๋๋ ์ ๋ณด๋ฅผ ํฌ์ ๋ฒกํฐ๋ก ๋ณํํ๊ณ ์ ๋ณด ๋ณต์์ ๋ณด์กฐํ๋ ์ถ๊ฐ ์ ๋ณด๋ฅผ ํฌ์ ๋ฒกํฐ์ ํฌ๊ธฐ (magnitude)๋ก ๋งคํํ๋ ๊ฒ์ด๋ค. ๋ง์ง๋ง์ผ๋ก, SVC ๊ธฐ์ ์ ํ์ผ๋ฟ ์ ์ก์ ํ์ฉํ๋ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ํนํ, ์ฑ๋ ์ถ์ ์ ์ํด ์ฑ๋ ์ํ์ค ์๋ต์ ์ ํธ๋ฅผ ํฌ์ํํ๋ ํ๋ฆฌ์ฝ๋ฉ ๊ธฐ๋ฒ์ ์ ์ํ๋ค. ํ์ผ๋ฟ ์ ํธ์ ํ๋ก์ฝ๋ฉ ์์ด ์ ์ก๋๋ ๊ธฐ์กด์ ๋ฐฉ์๊ณผ ๋ฌ๋ฆฌ, ์ ์๋ ๊ธฐ์ ์์๋ ํ์ผ๋ฟ ์ ํธ๋ฅผ ๋นํฌ๋ฐํ์ฌ ์ ์กํ๋ค. ์ ์๋ ๊ธฐ๋ฒ์ ๊ธฐ์ง๊ตญ์์ ๋ค์ค ์ํ
๋๋ฅผ ํ์ฉํ์ฌ ์ฑ๋ ์๋ต์ 0์ด ์๋ ์์๋ฅผ ์ต์ํํ๋ ์๊ฐ ์์ญ ํฌ์ ํ๋ฆฌ์ฝ๋ฉ์ ์ ์ฉํ์๋ค. ์ด๋ฅผ ํตํด ๋ ์ ํํ ์ฑ๋ ์ถ์ ์ ๊ฐ๋ฅํ๋ฉฐ ๋ ์ ์ ํ์ผ๋ฟ ์ค๋ฒํค๋๋ก ์ฑ๋ ์ถ์ ์ด ๊ฐ๋ฅํ๋ค.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
Joint Domain Based Massive Access for Small Packets Traffic of Uplink Wireless Channel
The fifth generation (5G) communication scenarios such as the cellular
network and the emerging machine type communications will produce massive small
packets. To support massive connectivity and avoid signaling overhead caused by
the transmission of those small packets, this paper proposes a novel method to
improve the transmission efficiency for massive connections of wireless uplink
channel. The proposed method combines compressive sensing (CS) with power
domain NOMA jointly, especially neither the scheduling nor the centralized
power allocation is necessary in the method. Both the analysis and simulation
show that the method can support up to two or three times overloading.Comment: 6 pages, 5 figures.submitted to globecom 201
Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach
A key challenge of massive MTC (mMTC), is the joint detection of device
activity and decoding of data. The sparse characteristics of mMTC makes
compressed sensing (CS) approaches a promising solution to the device detection
problem. However, utilizing CS-based approaches for device detection along with
channel estimation, and using the acquired estimates for coherent data
transmission is suboptimal, especially when the goal is to convey only a few
bits of data.
First, we focus on the coherent transmission and demonstrate that it is
possible to obtain more accurate channel state information by combining
conventional estimators with CS-based techniques. Moreover, we illustrate that
even simple power control techniques can enhance the device detection
performance in mMTC setups.
Second, we devise a new non-coherent transmission scheme for mMTC and
specifically for grant-free random access. We design an algorithm that jointly
detects device activity along with embedded information bits. The approach
leverages elements from the approximate message passing (AMP) algorithm, and
exploits the structured sparsity introduced by the non-coherent transmission
scheme. Our analysis reveals that the proposed approach has superior
performance compared to application of the original AMP approach.Comment: Submitted to IEEE Transactions on Communication
๊ณ ์ ๋ขฐ ์งง์ ํจํท ์ ์ก์ ์ํ ๊น์ ์ ๊ฒฝ๋ง์ ์ด์ฉํ ํฌ์ ๋ฒกํฐ ๋ณตํธ์ ๊ดํ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ ๋ณด๊ณตํ๋ถ, 2019. 2. ์ฌ๋ณํจ.Ultra-reliable and low latency communication (URLLC) is one of the prospective service categories in 5G to be useful in the future hyper-connective industrial field. To support its requirements, 3rd Generation Partnership Project (3GPP) sets an aggressive standard that a packet should be delivered within 1 ms transmission period with an accuracy of 99.999%. Since the current 4G systems designed to maximize the coding gain by transmitting capacity achieving long codeblock resulting in an increase of the latency. A recently proposed approach for the short packet transmission is sparse vector coding (SVC). In SVC, encoding is done by simple sparse mapping and spreading to formulate the system model into an underdetermined system and replaces the decoding process with a simple sparse recovery algorithm. In this paper, we propose a deep neural network-based approach, referred to as deep sparse vector decoding (deep-SVD), to enhance the performance of SVC to better meet the URLLCs extreme requirements. To this end, we reformulate the SVC-decoding process as a multi-label classification and build the network to learn the highly correlated relationship within codebook. Numerical results demonstrate that the proposed deep-SVD outperforms the conventional SVC decoding in both reliability and latency.URLLC (Ultra Reliability and Low Latency Communication)๋ ๋ฏธ๋์ ์ด์ฐ๊ฒฐ ์ฐ์
๋ถ์ผ์์ ์ฃผ๋ชฉ๋ฐ๋ 5G ์๋น์ค ์นดํ
๊ณ ๋ฆฌ ์ค ํ๋์ด๋ค. 3GPP (Third Generation Partnership Project)๋ URLLC์ ์คํํ๊ธฐ ์ํ ์๊ตฌ์กฐ๊ฑด์ผ๋ก 1ms ์ ์ก ์๊ฐ ๋ด์ 99.999%์ ์ ํ๋๋ก ํจํท์ ์ ์กํด์ผ ํ๋ค๋ ๋ค์ ๊น๋ค๋ก์ด ๊ธฐ์ค์ ์ค์ ํ์๋ค. ํ์ฌ์ 4G ๋ฌด์ ํต์ ์์คํ
์์๋ ๋ณต์กํ๊ณ ๊ธด ์ฝ๋ ๋ธ๋ก์ ์ ์กํจ์ผ๋ก์จ ์ฝ๋ฉ ์ด๋์ ์ต๋ํํ๋๋ก ์ค๊ณํ์ฌ ์ ํ์ฑ์ ๋์ด์ง๋ง ๊ทธ๋ก ์ธํ์ฌ ์ง์ฐ ์๊ฐ์ด ๊ธธ์ด์ง๋ค๋ ๋จ์ ์ด ์๋ค. ์ต๊ทผ URLLC๋ฅผ ๋์์ผ๋ก ์งง์ ํจํท ์ ์ก์ ์ํ SVC (Sparse Vector Coding) ๊ธฐ๋ฒ์ด ์ ์๋์๋ค. SVC์์์ ์ธ์ฝ๋ฉ์ ๋จ์ํ ํฌ์ ์ ํธ ๋งคํ ๋ฐ ํ์ฐ์ ํ์ฌ ํจํท์ ์ ์กํ๋ฉฐ ๋์ฝ๋ฉ์ ๊ฐ๋จํ ํฌ์ ๋ฒกํฐ ๋ณต์ ์๊ณ ๋ฆฌ์ฆ์ผ๋ก ๋์ฒดํ๋ค. ์ด ๋
ผ๋ฌธ์์๋ URLLC์ ๋์ ์๊ตฌ์กฐ๊ฑด์ ๋ง์กฑํ๊ธฐ ์ํด์ ๊น์ ์ ๊ฒฝ๋ง ๊ธฐ๋ฐ์ deep sparse vector decoding (Deep-SVC) ๊ธฐ๋ฒ์ ์ ์ํ๋ค. ์ด๋ฅผ ์ํด์, ์ฐ๋ฆฌ๋ SVC์ ๋์ฝ๋ฉ ๊ณผ์ ์ ๋ค์ค ๋ ์ด๋ธ ๋ถ๋ฅ (multi-label classification)์ผ๋ก ์ฌ๊ตฌ์ฑํ๋ค. ๊ทธ๋ฆฌ๊ณ ๊น์ ์ ๊ฒฝ๋ง์ ๊ตฌ์ฑํ์ฌ ์ฝ๋๋ถ ๋ด์ ๋์ ์๊ด๊ด๊ณ๋ฅผ ํ์ตํ์ฌ SVC ๋์ฝ๋ฉ ๊ณผ์ ์ ์ฑ๋ฅ์ ๋์ด์ฌ๋ฆฐ๋ค. ์คํ์ ํตํ์ฌ ์ฐ๋ฆฌ๋ ์ ์ํ๋ Deep-SVD ๊ธฐ๋ฒ์ด ๊ธฐ์กด์ SVC ๋์ฝ๋ฉ ๊ธฐ๋ฒ๋ณด๋ค ๋ ์ข์ ์ ํ์ฑ์ ๊ฐ์์ ๋ณด์ธ๋ค.1. Introduction
2. Short Packet Transmission Using Sparse Vector Coding
3. Sparse Vector Decoding via DNN
4. Simulation
5. ConclusionMaste
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