48 research outputs found
Detection for 5G-NOMA: An Online Adaptive Machine Learning Approach
Non-orthogonal multiple access (NOMA) has emerged as a promising radio access
technique for enabling the performance enhancements promised by the
fifth-generation (5G) networks in terms of connectivity, low latency, and high
spectrum efficiency. In the NOMA uplink, successive interference cancellation
(SIC) based detection with device clustering has been suggested. In the case of
multiple receive antennas, SIC can be combined with the minimum mean-squared
error (MMSE) beamforming. However, there exists a tradeoff between the NOMA
cluster size and the incurred SIC error. Larger clusters lead to larger errors
but they are desirable from the spectrum efficiency and connectivity point of
view. We propose a novel online learning based detection for the NOMA uplink.
In particular, we design an online adaptive filter in the sum space of linear
and Gaussian reproducing kernel Hilbert spaces (RKHSs). Such a sum space design
is robust against variations of a dynamic wireless network that can deteriorate
the performance of a purely nonlinear adaptive filter. We demonstrate by
simulations that the proposed method outperforms the MMSE-SIC based detection
for large cluster sizes.Comment: Accepted at ICC 201
Wireless for Machine Learning
As data generation increasingly takes place on devices without a wired
connection, Machine Learning over wireless networks becomes critical. Many
studies have shown that traditional wireless protocols are highly inefficient
or unsustainable to support Distributed Machine Learning. This is creating the
need for new wireless communication methods. In this survey, we give an
exhaustive review of the state of the art wireless methods that are
specifically designed to support Machine Learning services. Namely,
over-the-air computation and radio resource allocation optimized for Machine
Learning. In the over-the-air approach, multiple devices communicate
simultaneously over the same time slot and frequency band to exploit the
superposition property of wireless channels for gradient averaging
over-the-air. In radio resource allocation optimized for Machine Learning,
Active Learning metrics allow for data evaluation to greatly optimize the
assignment of radio resources. This paper gives a comprehensive introduction to
these methods, reviews the most important works, and highlights crucial open
problems.Comment: Corrected typo in author name. From the incorrect Maitron to the
correct Mairto
Over-the-Air Federated Learning Over MIMO Channels: A Sparse-Coded Multiplexing Approach
The communication bottleneck of over-the-air federated learning (OA-FL) lies
in uploading the gradients of local learning models. In this paper, we study
the reduction of the communication overhead in the gradients uploading by using
the multiple-input multiple-output (MIMO) technique. We propose a novel
sparse-coded multiplexing (SCoM) approach that employs sparse-coding
compression and MIMO multiplexing to balance the communication overhead and the
learning performance of the FL model. We derive an upper bound on the learning
performance loss of the SCoM-based MIMO OA-FL scheme by quantitatively
characterizing the gradient aggregation error. Based on the analysis results,
we show that the optimal number of multiplexed data streams to minimize the
upper bound on the FL learning performance loss is given by the minimum of the
numbers of transmit and receive antennas. We then formulate an optimization
problem for the design of precoding and post-processing matrices to minimize
the gradient aggregation error. To solve this problem, we develop a
low-complexity algorithm based on alternating optimization (AO) and alternating
direction method of multipliers (ADMM), which effectively mitigates the impact
of the gradient aggregation error. Numerical results demonstrate the superb
performance of the proposed SCoM approach
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
Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computation
Federated learning (FL) as a promising edge-learning framework can
effectively address the latency and privacy issues by featuring distributed
learning at the devices and model aggregation in the central server. In order
to enable efficient wireless data aggregation, over-the-air computation
(AirComp) has recently been proposed and attracted immediate attention.
However, fading of wireless channels can produce aggregate distortions in an
AirComp-based FL scheme. To combat this effect, the concept of dynamic learning
rate (DLR) is proposed in this work. We begin our discussion by considering
multiple-input-single-output (MISO) scenario, since the underlying optimization
problem is convex and has closed-form solution. We then extend our studies to
more general multiple-input-multiple-output (MIMO) case and an iterative method
is derived. Extensive simulation results demonstrate the effectiveness of the
proposed scheme in reducing the aggregate distortion and guaranteeing the
testing accuracy using the MNIST and CIFAR10 datasets. In addition, we present
the asymptotic analysis and give a near-optimal receive beamforming design
solution in closed form, which is verified by numerical simulations
Federated Learning in Wireless Networks
Artificial intelligence (AI) is transitioning from a long development period into reality. Notable instances like AlphaGo, Teslaโs self-driving cars, and the recent innovation of ChatGPT stand as widely recognized exemplars of AI applications. These examples collectively enhance the quality of human life. An increasing number of AI applications are expected to integrate seamlessly into our daily lives, further enriching our experiences.
Although AI has demonstrated remarkable performance, it is accompanied by numerous challenges. At the forefront of AIโs advancement lies machine learning (ML), a cutting-edge technique that acquires knowledge by emulating the human brainโs cognitive processes. Like humans, ML requires a substantial amount of data to build its knowledge repository. Computational capabilities have surged in alignment with Mooreโs law, leading to the realization of cloud computing services like Amazon AWS. Presently, we find ourselves in the era of the IoT, characterized by the ubiquitous presence of smartphones, smart speakers, and intelligent vehicles. This landscape facilitates decentralizing data processing tasks, shifting them from the cloud to local devices. At the same time, a growing emphasis on privacy protection has emerged, as individuals are increasingly concerned with sharing personal data with corporate giants such as Google and Meta. Federated learning (FL) is a new distributed machine learning paradigm. It fosters a scenario where clients collaborate by sharing learned models rather than raw data, thus safeguarding client data privacy while providing a collaborative and resilient model.
FL has promised to address privacy concerns. However, it still faces many challenges, particularly within wireless networks. Within the FL landscape, four main challenges stand out: high communication costs, system heterogeneity, statistical heterogeneity, and privacy and security. When many clients participate in the learning process, and the wireless communication resources remain constrained, accommodating all participating clients becomes very complex. The contemporary realm of deep learning relies on models encompassing millions and, in some cases, billions of parameters, exacerbating communication overhead when transmitting these parameters. The heterogeneity of the system manifests itself across device disparities, deployment scenarios, and connectivity capabilities. Simultaneously, statistical heterogeneity encompasses variations in data distribution and model composition. Furthermore, the distributed architecture makes FL susceptible to attacks inside and outside the system.
This dissertation presents a suite of algorithms designed to address the challenges effectively. Mew communication schemes are introduced, including Non-Orthogonal Multiple Access (NOMA), over-the-air computation, and approximate communication. These techniques are coupled with gradient compression, client scheduling, and power allocation, each significantly mitigating communication overhead. Implementing asynchronous FL is a suitable remedy to solve the intricate issue of system heterogeneity. Independent and identically distributed (IID) and non-IID data in statistical heterogeneity are considered in all scenarios. Finally, the aggregation of model updates and individual client model initialization collaboratively address security and privacy issues
Towards Efficient Communications in Federated Learning: A Contemporary Survey
In the traditional distributed machine learning scenario, the user's private
data is transmitted between nodes and a central server, which results in great
potential privacy risks. In order to balance the issues of data privacy and
joint training of models, federated learning (FL) is proposed as a special
distributed machine learning with a privacy protection mechanism, which can
realize multi-party collaborative computing without revealing the original
data. However, in practice, FL faces many challenging communication problems.
This review aims to clarify the relationship between these communication
problems, and focus on systematically analyzing the research progress of FL
communication work from three perspectives: communication efficiency,
communication environment, and communication resource allocation. Firstly, we
sort out the current challenges existing in the communications of FL. Secondly,
we have compiled articles related to FL communications, and then describe the
development trend of the entire field guided by the logical relationship
between them. Finally, we point out the future research directions for
communications in FL
Federated Learning for Physical Layer Design
Model-free techniques, such as machine learning
(ML), have recently attracted much interest towards the physical
layer design, e.g., symbol detection, channel estimation, and
beamforming. Most of these ML techniques employ centralized
learning (CL) schemes and assume the availability of datasets at a
parameter server (PS), demanding the transmission of data from
edge devices, such as mobile phones, to the PS. Exploiting the data
generated at the edge, federated learning (FL) has been proposed
recently as a distributed learning scheme, in which each device
computes the model parameters and sends them to the PS for
model aggregation while the datasets are kept intact at the edge.
Thus, FL is more communication-efficient and privacy-preserving
than CL and applicable to the wireless communication scenarios,
wherein the data are generated at the edge devices. This article
presents the recent advances in FL-based training for physical
layer design problems. Compared to CL, the effectiveness of FL
is presented in terms of communication overhead with a slight
performance loss in the learning accuracy. The design challenges,
such as model, data, and hardware complexity, are also discussed
in detail along with possible solutions