69 research outputs found
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
Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions
The ever-increasing number of resource-constrained Machine-Type Communication
(MTC) devices is leading to the critical challenge of fulfilling diverse
communication requirements in dynamic and ultra-dense wireless environments.
Among different application scenarios that the upcoming 5G and beyond cellular
networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the
unique technical challenge of supporting a huge number of MTC devices, which is
the main focus of this paper. The related challenges include QoS provisioning,
handling highly dynamic and sporadic MTC traffic, huge signalling overhead and
Radio Access Network (RAN) congestion. In this regard, this paper aims to
identify and analyze the involved technical issues, to review recent advances,
to highlight potential solutions and to propose new research directions. First,
starting with an overview of mMTC features and QoS provisioning issues, we
present the key enablers for mMTC in cellular networks. Along with the
highlights on the inefficiency of the legacy Random Access (RA) procedure in
the mMTC scenario, we then present the key features and channel access
mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT.
Subsequently, we present a framework for the performance analysis of
transmission scheduling with the QoS support along with the issues involved in
short data packet transmission. Next, we provide a detailed overview of the
existing and emerging solutions towards addressing RAN congestion problem, and
then identify potential advantages, challenges and use cases for the
applications of emerging Machine Learning (ML) techniques in ultra-dense
cellular networks. Out of several ML techniques, we focus on the application of
low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss
some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future
publication in IEEE Communications Surveys and Tutorial
D4.3 Final Report on Network-Level Solutions
Research activities in METIS reported in this document focus on proposing solutions
to the network-level challenges of future wireless communication networks. Thereby, a large variety of scenarios is considered and a set of technical concepts is proposed to serve the needs envisioned for the 2020 and beyond.
This document provides the final findings on several network-level aspects and groups of
solutions that are considered essential for designing future 5G solutions. Specifically, it
elaborates on:
-Interference management and resource allocation schemes
-Mobility management and robustness enhancements
-Context aware approaches
-D2D and V2X mechanisms
-Technology components focused on clustering
-Dynamic reconfiguration enablers
These novel network-level technology concepts are evaluated against requirements defined
by METIS for future 5G systems. Moreover, functional enablers which can support the
solutions mentioned aboveare proposed.
We find that the network level solutions and technology components developed during the course of METIS complement the lower layer technology components and thereby effectively contribute to meeting 5G requirements and targets.Aydin, O.; Valentin, S.; Ren, Z.; Botsov, M.; Lakshmana, TR.; Sui, Y.; Sun, W.... (2015). D4.3 Final Report on Network-Level Solutions. http://hdl.handle.net/10251/7675
Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication
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
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