259 research outputs found
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Performance Modelling and Analysis of a New CoMP-based Handover Scheme for Next Generation Wireless Networks. Performance Modelling and Analysis for the Design and Development of a New Handover Scheme for Cell Edge Users in Next Generation Wireless Networks (NGWNs) Based on the Coordinated Multi-Point (CoMP) Joint Transmission (JT) Technique
Inter-Cell Interference (ICI) will be one of main problems for degrading the performance of future wireless networks at cell edge. This adverse situation will become worst in the presence of dense deployment of micro and macro cells. In this context, the Coordinated Multi-Point (CoMP) technique was introduced to mitigate ICI in Next Generation Wireless Networks (NGWN) and increase their network performance at cell edge. Even though the CoMP technique provides satisfactory solutions of various problems at cell edge, nevertheless existing CoMP handover schemes do not prevent unnecessary handover initialisation decisions and never discuss the drawbacks of CoMP handover technique such as excessive feedback and resource sharing among UEs. In this research, new CoMP-based handover schemes are proposed in order to minimise unnecessary handover decisions at cell edge and determine solution of drawbacks of CoMP technique in conjunction with signal measurements such as Reference Signal Received Power (RSRP) and Received Signal Received Quality (RSRQ). A combination of calculations of RSRP and RSRQ facilitate a credible decision making process of CoMP mode and handover mode at cell edge. Typical numerical experiments indicate that by triggering the CoMP mode along with solutions of drawbacks, the overall network performance is constantly increase as the number of unnecessary handovers is progressively reduced
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Self-organising network management for heterogeneous LTE-advanced networks
This thesis was submitted for the award of Doctor of Philosophy and awarded by Brunel University LondonSince 2004, when the Long Term Evolution (LTE) was first proposed to be publicly available in the year 2009, a plethora of new characteristics, techniques and applications have been constantly enhancing it since its first release, over the past decade. As a result, the research aims for LTE-Advanced (LTE-A) have been released to create a ubiquitous and supportive network for mobile users. The incorporation of heterogeneous networks (HetNets) has been proposed as one of the main enhancements of LTE-A systems over the existing LTE releases, by proposing the deployment of small-cell applications, such as femtocells, to provide more coverage and quality of service (QoS) within the network, whilst also reducing capital expenditure. These principal advantages can be obtained at the cost of new challenges such as inter-cell interference, which occurs when different network applications share the same frequency channel in the network. In this thesis, the main challenges of HetNets in LTE-A platform have been addressed and novel solutions are proposed by using self-organising network (SON) management approaches, which allows the cooperative cellular systems to observe, decide and amend their ongoing operation based on network conditions. The novel SON algorithms are modelled and simulated in OPNET modeler simulation software for the three processes of resource allocation, mobility management and interference coordination in multi-tier macro-femto networks. Different channel allocation methods based on cooperative transmission, frequency reuse and dynamic spectrum access are investigated and a novel SON sub-channel allocation method is proposed based on hybrid fractional frequency reuse (HFFR) scheme to provide dynamic resource allocation between macrocells and femtocells, while avoiding co-tier and cross-tier interference. Mobility management is also addressed as another important issue in HetNets, especially in hand-ins from macrocell to femtocell base stations. The existing research considers a limited number of methods for handover optimisation, such as signal strength and call admission control (CAC) to avoid unnecessary handovers, while our novel SON handover management method implements a comprehensive algorithm that performs sensing process, as well as resource availability and user residence checks to initiate the handover process at the optimal time. In addition to this, the novel femto over macro priority (FoMP) check in this process also gives the femtocell target nodes priority over the congested macrocells in order to improve the QoS at both the network tiers. Inter-cell interference, as the key challenge of HetNets, is also investigated by research on the existing time-domain, frequency-domain and power control methods. A novel SON interference mitigation algorithm is proposed, which is based on enhanced inter-cell interference coordination (eICIC) with power control process. The 3-phase power control algorithm contains signal to interference plus noise ratio (SINR) measurements, channel quality indicator (CQI) mapping and transmission power amendments to avoid the occurrence of interference due to the effects of high transmission power. The results of this research confirm that if heterogeneous systems are backed-up with SON management strategies, not only can improve the network capacity and QoS, but also the new network challenges such as inter-cell interference can also be mitigated in new releases of LTE-A network
LTE uplink MIMO receiver with low complexity interference cancellation
In LTE/LTE-A uplink receiver, frequency
domain equalizers (FDE) are adopted to achieve good
performance. However, in multi-tap channels, the residual
inter-symbol and inter-antenna interference still exist after
FDE and degrade the performance. Conventional interference
cancellation schemes can minimize this interference
by using frequency domain interference cancellation.
However, those schemes have high complexity and large
feedback latency, especially when adopting a large number
of iterations. These result in low throughput and require a
large amount of resource in software defined radio implementation.
In this paper, we propose a novel low complexity
interference cancellation scheme to minimize the
residual interference in LTE/LTE-A uplink. Our proposed
scheme can bring about 2 dB gains in different channels,
but only adds up to 7.2 % complexity to the receiver. The
scheme is further implemented on Xilinx FPGA. Compared
to other conventional interference cancellation schemes,
our scheme has less complexity, less data to store, and
shorter feedback latency.Renesas MobileTexas IntrumentsXilinxSamsungHuaweiNational Science Foundation (NSF
Internet of Things and Sensors Networks in 5G Wireless Communications
The Internet of Things (IoT) has attracted much attention from society, industry and academia as a promising technology that can enhance day to day activities, and the creation of new business models, products and services, and serve as a broad source of research topics and ideas. A future digital society is envisioned, composed of numerous wireless connected sensors and devices. Driven by huge demand, the massive IoT (mIoT) or massive machine type communication (mMTC) has been identified as one of the three main communication scenarios for 5G. In addition to connectivity, computing and storage and data management are also long-standing issues for low-cost devices and sensors. The book is a collection of outstanding technical research and industrial papers covering new research results, with a wide range of features within the 5G-and-beyond framework. It provides a range of discussions of the major research challenges and achievements within this topic
Internet of Things and Sensors Networks in 5G Wireless Communications
This book is a printed edition of the Special Issue Internet of Things and Sensors Networks in 5G Wireless Communications that was published in Sensors
Millimetre wave frequency band as a candidate spectrum for 5G network architecture : a survey
In order to meet the huge growth in global mobile data traffic in 2020 and beyond, the development of the 5th Generation (5G) system is required as the current 4G system is expected to fall short of the provision needed for such growth. 5G is anticipated to use a higher carrier frequency in the millimetre wave (mm-wave) band, within the 20 to 90 GHz, due to the availability of a vast amount of unexploited bandwidth. It is a revolutionary step to use these bands because of their different propagation characteristics, severe atmospheric attenuation, and hardware constraints. In this paper, we carry out a survey of 5G research contributions and proposed design architectures based on mm-wave communications. We present and discuss the use of mm-wave as indoor and outdoor mobile access, as a wireless backhaul solution, and as a key enabler for higher order sectorisation. Wireless standards such as IEE802.11ad, which are operating in mm-wave band have been presented. These standards have been designed for short range, ultra high data throughput systems in the 60 GHz band. Furthermore, this survey provides new insights regarding relevant and open issues in adopting mm-wave for 5G networks. This includes increased handoff rate and interference in Ultra-Dense Network (UDN), waveform consideration with higher spectral efficiency, and supporting spatial multiplexing in mm-wave line of sight. This survey also introduces a distributed base station architecture in mm-wave as an approach to address increased handoff rate in UDN, and to provide an alternative way for network densification in a time and cost effective manner
ํ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
Comunicaciones Mรณviles de Misiรณn Crรญtica sobre Redes LTE
Mission Critical Communications (MCC) have been typically provided by proprietary radio technologies, but, in the last years, the interest to use commercial-off-the-shelf mobile technologies has increased. In this thesis, we explore the use of LTE to support MCC. We analyse the feasibility of LTE networks employing an experimental platform, PerformNetworks. To do so, we extend the testbed to increase the number of possible scenarios and the tooling available. After exploring the Key Performance Indicators (KPIs) of LTE, we propose different architectures to support the performance and functional requirements demanded by MCC.
We have identified latency as one of the KPI to improve, so we have done several proposals to reduce it. These proposals follow the Mobile Edge Computing (MEC) paradigm, locating the services in what we called the fog, close to the base station to avoid the backhaul and transport networks. Our first proposal is the Fog Gateway, which is a MEC solution fully compatible with standard LTE networks that analyses the traffic coming from the base station to decide whether it has to be routed to the fog of processed normally by the SGW. Our second proposal is its natural evolution, the GTP Gateway that requires modifications on the base station. With this proposal, the base station will only transport over GTP the traffic not going to the fog.
Both proposals have been validated by providing emulated scenarios, and, in the case of the Fog Gateway, also with the implementation of different prototypes, proving its compatibility with standard LTE network and its performance. The gateways can reduce drastically the end-to-end latency, as they avoid the time consumed by the backhaul and transport networks, with a very low trade-off
5G: 2020 and Beyond
The future society would be ushered in a new communication era with the emergence of 5G. 5G would be significantly different, especially, in terms of architecture and operation in comparison with the previous communication generations (4G, 3G...). This book discusses the various aspects of the architecture, operation, possible challenges, and mechanisms to overcome them. Further, it supports users? interac- tion through communication devices relying on Human Bond Communication and COmmunication-NAvigation- SENsing- SErvices (CONASENSE).Topics broadly covered in this book are; โข Wireless Innovative System for Dynamically Operating Mega Communications (WISDOM)โข Millimeter Waves and Spectrum Managementโข Cyber Securityโข Device to Device Communicatio
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