4,952 research outputs found
H2-ARQ-relaying: spectrum and energy efficiency perspectives
In this paper, we propose novel Hybrid Automatic Repeat re-Quest (HARQ) strategies used in conjunction with hybrid relaying schemes, named as H2-ARQ-Relaying. The strategies allow the relay to dynamically switch between amplify-and-forward/compress-and-forward and decode-and-forward schemes according to its decoding status. The performance analysis is conducted from both the spectrum and energy efficiency perspectives. The spectrum efficiency of the proposed strategies, in terms of the maximum throughput, is significantly improved compared with their non-hybrid counterparts under the same constraints. The consumed energy per bit is optimized by manipulating the node activation time, the transmission energy and the power allocation between the source and the relay. The circuitry energy consumption of all involved nodes is taken into consideration. Numerical results shed light on how and when the energy efficiency can be improved in cooperative HARQ. For instance, cooperative HARQ is shown to be energy efficient in long distance transmission only. Furthermore, we consider the fact that the compress-and-forward scheme requires instantaneous signal to noise ratios of all three constituent links. However, this requirement can be impractical in some cases. In this regard, we introduce an improved strategy where only partial and affordable channel state information feedback is needed
Adaptive Bit Allocation With Reduced Feedback for Wireless Multicarrier Transceivers
With the increasing demand in the wireless mobile applications came a growing need to transmit information quickly and accurately, while consuming more and more bandwidth. To address this need, communication engineers started employing multicarrier modulation in their designs, which is suitable for high data rate transmission. Multicarrier modulation reduces the system's susceptibility to the frequency-selective fading channel, by transforming it into a collection of approximately flat subchannels. As a result, this makes it easier to compensate for the distortion introduced by the channel. This thesis concentrates on techniques for saving bandwidth usage when employing adaptive multicarrier modulation, where subcarrier parameters (bit and energy allocations) are modulated based on the channel state information feedback obtained from previous burst. Although bit and energy allocations can substantially increase error robustness and throughput of the system, the feedback information required at both ends of the transceiver can be large. The objective of this work is to compare different feedback compression techniques that could reduce the amount of feedback information required to perform adaptive bit and energy allocation in multicarrier transceivers. This thesis employs an approach for reducing the number of feedback transmissions by exploiting the time-correlation properties of a wireless channel and placing a threshold check on bit error rate (BER) values. Using quantization and source coding techniques, such as Huffman coding, Run length encoding and LZWalgorithms, the amount of feedback information has been compressed. These calculations have been done for different quantization levels to understand the relationship between quantization levels and system performance. These techniques have been applied to both OFDM and MIMO-OFDM systems
Multiuser MIMO techniques with feedback
Kooperative Antennenanlagen haben vor kurzem einen heiĂen Forschungsthema geworden, da Sie deutlich höhere spektrale Effizienz als herkömmliche zellulĂ€re Systeme versprechen. Der Gewinn wird durch die Eliminierung von Inter-Zelle Störungen (ICI) durch Koordinierung der-Antenne Ăbertragungen erworben. Vor kurzem, verteilte Organisation Methoden vorgeschlagen. Eine der gröĂten Herausforderungen fĂŒr das Dezentrale kooperative Antennensystem ist KanalschĂ€tzung fĂŒr den Downlink Kanal besonders wenn FDD verwendet wird. Alle zugehörigen Basisstationen im genossenschaftlichen Bereich mĂŒssen die vollstĂ€ndige Kanal Informationen zu Wissen, die entsprechenden precoding Gewicht Matrix zu berechnen. Diese Information ist von mobilen Stationen ĂŒbertragen werden Stationen mit Uplink Ressourcen zu stĂŒtzen. Wird als mehrere Basisstationen und mehreren mobilen Stationen in kooperativen Antennensysteme und jede Basisstation und Mobilstation beteiligt sind, können mit mehreren Antennen ausgestattet sein, die Anzahl der Kanal Parameter wieder gefĂŒttert werden erwartet, groĂ zu sein. In dieser Arbeit wird ein effizientes Feedback Techniken der downlink Kanal Informationen sind fĂŒr die Multi-user Multiple Input Multiple Output Fall vorgeschlagen, der insbesondere auf verteilte kooperative Antennensysteme zielt. Zuerst wird ein Unterraum-basiertes Kanalquantisierungsverfahren vorgeschlagen, das ein vorbestimmtes Codebuch verwendet. Ein iterativer Codebuchentwurfsalgorithmus wird vorgeschlagen, der zu einem lokalen optimalen Codebuch konvergiert. DarĂŒber hinaus werden Feedback-Overhead-Reduktionsverfahren entwickelt, die die zeitliche Korrelation des Kanals ausnutzen. Es wird gezeigt, dass das vorgeschlagene adaptive Codebuchverfahren in Verbindung mit einem Datenkomprimierungsschema eine Leistung nahe an dem perfekten Kanalfall erzielt, was viel weniger RĂŒckkopplungsoverhead im Vergleich zu anderen Techniken erfordert. Das auf dem Unterraum basierende Kanalquantisierungsverfahren wird erweitert, indem mehrere Antennen auf der Senderseite und/oder auf der EmpfĂ€ngerseite eingefĂŒhrt werden, und die Leistung eines Vorcodierungs- (/Decodierungs-) Schemas mit regulierter Blockdiagonalisierung (RBD) wurde untersucht. Es wird ein kosteneffizientes Decodierungsmatrixquantisierungsverfahren vorgeschlagen, dass eine komplexe Berechnung an der Mobilstation vermeiden kann, wĂ€hrend es nur eine leichte Verschlechterung zeigt. Die Arbeit wird abgeschlossen, indem die vorgeschlagenen Feedback-Methoden hinsichtlich ihrer Leistung, ihres erforderlichen Feedback-Overheads und ihrer RechenkomplexitĂ€t verglichen werden.Cooperative antenna systems have recently become a hot research topic, as they promise significantly higher spectral efficiency than conventional cellular systems. The gain is acquired by eliminating inter-cell interference (ICI) through coordination of the base antenna transmissions. Recently, distributed organization methods have been suggested. One of the main challenges of the distributed cooperative antenna system is channel estimation for the downlink channel especially when FDD is used. All of the associated base stations in the cooperative area need to know the full channel state information to calculate the corresponding precoding weight matrix. This information has to be transferred from mobile stations to base stations by using uplink resources. As several base stations and several mobile stations are involved in cooperative antenna systems and each base station and mobile station may be equipped with multiple antennas, the number of channel state parameters to be fed back is expected to be big. In this thesis, efficient feedback techniques of the downlink channel state information are proposed for the multi-user multiple-input multiple-output case, targeting distributed cooperative antenna systems in particular. First, a subspace based channel quantization method is proposed which employs a predefined codebook. An iterative codebook design algorithm is proposed which converges to a local optimum codebook. Furthermore, feedback overhead reduction methods are devised exploiting temporal correlation of the channel. It is shown that the proposed adaptive codebook method in conjunction with a data compression scheme achieves a performance close to the perfect channel case, requiring much less feedback overhead compared with other techniques. The subspace based channel quantization method is extended by introducing multiple antennas at the transmitter side and/or at the receiver side and the performance of a regularized block diagonalization (RBD) precoding(/decoding) scheme has been investigated as well as a zero-forcing (ZF) precoding scheme. A cost-efficient decoding matrix quantization method is proposed which can avoid a complex computation at the mobile station while showing only a slight degradation. The thesis is concluded by comparing the proposed feedback methods in terms of their performance, their required feedback overhead, and their computational complexity. The techniques that are developed in this thesis can be useful and applicable for 5G, which is envisioned to support the high granularity/resolution codebook and its efficient deployment schemes. Keywords: MU-MIMO, COOPA, limited feedback, CSI, CQ, feedback overhead reduction, Givens rotatio
íŹììžì§ë„Œ ìŽì©í ì ìĄêž°ì ì°ê”Ź
íìë
ŒëŹž (ë°ìŹ)-- ììžëíê” ëíì : êł”êłŒëí ì Ʞ·ì ëłŽêł”íë¶, 2019. 2. ìŹëłíš.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.5ìžë 돎ì í”ì ìì€í
ì ìëĄìŽ êž°ì íì ì ëŹŽìž ì°šë ë° íêł”êž°, ì€ë§íž ëì ë° êł”ì„, ìêČ© ìëŁ ì§ëš ë° ìì , ìžêł” ì§ë„ êž°ë° ë§ì¶€í ì§ìêłŒ ê°ì ì ëĄ ìë ìëčì€ ë° ìì©íëĄê·žëšìŒëĄ ë¶ìíêł ìë€. ìŽëŹí ìëĄìŽ ì í늏ìŒìŽì
ë° ìëčì€ì êŽë šë í”ì ë°©ìì ëêž° ìê°, ìëì§ íšìšì±, ì ëą°ì±, ì ì°ì± ë° ì°êČ° ë°ë ìžĄë©Žìì êž°ìĄŽ í”ì êłŒ ë§€ì° ë€ë„Žë€. íìŹì 돎ì ìĄìžì€ ë°©ìì ëč륯í ìą
ëì ì ê·ŒëČì ìŽëŹí ìê”Ź ìŹíì ë§ìĄ±í ì ìêž° ë돞ì ì”ê·Œì sparse processingêłŒ ê°ì ìëĄìŽ ì ê·Œ ë°©ëČìŽ ì°ê”Źëêł ìë€. ìŽ ìëĄìŽ ì ê·Œ ë°©ëČì íëłž ì¶ì¶, ê°ì§, ìì¶, íê° ë° íì§ë„Œ íŹíší êž°ìĄŽì ì 볎 ìČ늏ì ëí íšìšì ìž ëìČŽêž°ì ëĄ íì©ëêł ìë€. ì§ë 10ë
ëì compressed sensing (CS)êž°ëČì ìëŁìì, êž°êłíì”, íì§, 컎íší° êłŒí, í”êł ë° êž°í ìŹëŹ ë¶ìŒìì ëč ë„ŽêČ íì°ëìë€. ëí, ì ížì íŹìì±(sparsity)ë„Œ ìŽì©íë CS êž°ëČì ë€ìí 돎ì í”ì ìŽ ì°ê”Źëìë€. ìŁŒëȘ©í ë§í ìëĄë ì±ë ì¶ì , ê°ì ì ê±°, ê°ë ì¶ì , ë° ì€íížëŒ ê°ì§ê° ììŒë©° íìŹêčì§ ì°ê”Źë ìŁŒìŽì§ ì ížê° ê°ì§êł ìë ëłžëì íŹìì±ì ìŁŒëȘ©íììŒë ëłž ë
ŒëŹžììë êž°ìĄŽì ì ê·Œ ë°©ëČêłŒ ëŹëŠŹ ìžìì ìŒëĄ ì€êłë íŹìì±ì ìŽì©íìŹ í”ì ìì€í
ì ì±ë„ì í„ììí€ë ë°©ëČì ì ìíë€.
ì°ì ëłž ë
ŒëŹžì ë€ìŽë§íŹ ì ìĄìì íŹì ì íž ë§€íì í”í ë°ìŽí° ì ìĄ ë°©ëČì ì ìíë©° 짧ì íší· (short packet) ì ìĄì ì í©í CS ì ê·ŒëČì íì©íë êž°ì ì ì ìíë€. ì ìíë êž°ì ìž íŹìëČĄí°ìœë© (sparse vector coding, SVC)ì ë°ìŽí° ì ëłŽê° ìžêł”ì ìž íŹìëČĄí°ì nonzero elementì ììčì 맀ííìŹ ì ìĄë ë°ìŽí° íší·ì íŹìëČĄí°ì 0ìŽ ìë ììčë„Œ ìëłíšìŒëĄ ìì íž ëł”ììŽ ê°ë„íë€. ë¶ìêłŒ ì럏ë ìŽì
ì í”íŽ ì ìíë SVC êž°ëČì íší· ì€ë„ë„ ì ultra-reliable and low latency communications (URLLC) ìëčì€ë„Œ ì§ìì ìíŽ ìŹì©ëë ì±ëìœë©ë°©ìëłŽë€ ì°ìí ì±ë„ì 볎ìŹì€ë€. ëí, ëłž ë
ŒëŹžì SVCêž°ì ì ë€ìì ìžê°ì§ ìììŒëĄ íì„íìë€. ìČ«ì§žëĄ, ìŹëŹ ê°ì SVC êž°ë° íší·ì ëìŒí ììì êČčìčêČ ì ìĄíšìŒëĄ ìí„ë§íŹìì ëê·ëȘš ì ìĄì ì§ìíë ë°©ëČì ì ìíë€. ì€ìČ©ë íŹìëČĄí°ë„Œ ë€ì€ìŹì©ì CS ëìœë© ë°©ìì ìŹì©íìŹ ì±ë ê°ìì ê°ìží ì±ë„ì ì êł”íêł ëčì§ê” ë€ì€ ì ì (NOMA) ë°©ìêłŒ ì ìŹí ì±ë„ì ì êł”íë€. ë짞ëĄ, SVC êž°ì ìŽ íŹì ëČĄí°ì supportë§ì ìëłíë€ë ìŹì€ì ìŽì©íìŹ íìŒëż ì ìĄìŽ íììë pilotless-SVC ì ìĄ ë°©ëČì ì ìíë€. ì±ë ì ëłŽê° ìë êČœì°ìë íŹì ëČĄí°ì supportì íŹêž°ë ì±ëì íŹêž°ì ëčëĄíêž° ë돞ì pilotììŽ ëł”ììŽ ê°ë„íë€. ì
짞ëĄ, íŹìëČĄí°ì supportì íŹêž°ì ì¶ê° ì ëłŽë„Œ ì ìĄíšìŒëĄ ëł”ì ì±ë„ì í„ì ìí€ë enhanced SVC (E-SVC)ë„Œ ì ìíë€. ì ìë E-SVC ì ìĄ ë°©ìì í”ìŹ ìëëìŽë 짧ì íší·ì ì ìĄëë ì ëłŽë„Œ íŹì ëČĄí°ëĄ ëłííêł ì 볎 ëł”ìì ëłŽìĄ°íë ì¶ê° ì ëłŽë„Œ íŹì ëČĄí°ì íŹêž° (magnitude)ëĄ ë§€ííë êČìŽë€. ë§ì§ë§ìŒëĄ, SVC êž°ì ì íìŒëż ì ìĄì íì©íë ë°©ëČì ì ìíë€. íčí, ì±ë ì¶ì ì ìíŽ ì±ë ìíì€ ìë”ì ì ížë„Œ íŹìííë í늏ìœë© êž°ëČì ì ìíë€. íìŒëż ì ížì íëĄìœë© ììŽ ì ìĄëë êž°ìĄŽì ë°©ìêłŒ ëŹëŠŹ, ì ìë êž°ì ììë íìŒëż ì ížë„Œ ëčíŹë°íìŹ ì ìĄíë€. ì ìë êž°ëČì êž°ì§ê”ìì ë€ì€ ìí
ëë„Œ íì©íìŹ ì±ë ìë”ì 0ìŽ ìë ììë„Œ ì”ìííë ìê° ìì íŹì í늏ìœë©ì ì ì©íìë€. ìŽë„Œ í”íŽ ë ì íí ì±ë ì¶ì ì ê°ë„íë©° ë ì ì íìŒëż ì€ëČí€ëëĄ ì±ë ì¶ì ìŽ ê°ë„íë€.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
Dynamic bandwidth allocation in ATM networks
Includes bibliographical references.This thesis investigates bandwidth allocation methodologies to transport new emerging bursty traffic types in ATM networks. However, existing ATM traffic management solutions are not readily able to handle the inevitable problem of congestion as result of the bursty traffic from the new emerging services. This research basically addresses bandwidth allocation issues for bursty traffic by proposing and exploring the concept of dynamic bandwidth allocation and comparing it to the traditional static bandwidth allocation schemes
Quality of service differentiation for multimedia delivery in wireless LANs
Delivering multimedia content to heterogeneous devices over a variable networking environment while maintaining high quality levels involves many technical challenges. The research reported in this thesis presents a solution for Quality of Service (QoS)-based service differentiation when delivering multimedia content over the wireless LANs. This thesis has three major contributions outlined below:
1. A Model-based Bandwidth Estimation algorithm (MBE), which estimates the available bandwidth based on novel TCP and UDP throughput models over IEEE 802.11 WLANs. MBE has been modelled, implemented, and tested through simulations and real life testing. In comparison with other bandwidth estimation techniques, MBE shows better performance in terms of error rate, overhead, and loss.
2. An intelligent Prioritized Adaptive Scheme (iPAS), which provides QoS service differentiation for multimedia delivery in wireless networks. iPAS assigns dynamic priorities to various streams and determines their bandwidth share by employing a probabilistic approach-which makes use of stereotypes. The total bandwidth to be allocated is estimated using MBE. The priority level of individual stream is variable and dependent on stream-related characteristics and delivery QoS parameters. iPAS can be deployed seamlessly over the original IEEE 802.11 protocols and can be included in the IEEE 802.21 framework in order to optimize the control signal communication. iPAS has been modelled, implemented, and evaluated via simulations. The results demonstrate that iPAS achieves better performance than the equal channel access mechanism over IEEE 802.11 DCF and a service differentiation scheme on top of IEEE 802.11e EDCA, in terms of fairness, throughput, delay, loss, and estimated PSNR. Additionally, both objective and subjective video quality assessment have been performed using a prototype system.
3. A QoS-based Downlink/Uplink Fairness Scheme, which uses the stereotypes-based structure to balance the QoS parameters (i.e. throughput, delay, and loss) between downlink and uplink VoIP traffic. The proposed scheme has been modelled and tested through simulations. The results show that, in comparison with other downlink/uplink fairness-oriented solutions, the proposed scheme performs better in terms of VoIP capacity and fairness level between downlink and uplink traffic
A Learnable Optimization and Regularization Approach to Massive MIMO CSI Feedback
Channel state information (CSI) plays a critical role in achieving the
potential benefits of massive multiple input multiple output (MIMO) systems. In
frequency division duplex (FDD) massive MIMO systems, the base station (BS)
relies on sustained and accurate CSI feedback from the users. However, due to
the large number of antennas and users being served in massive MIMO systems,
feedback overhead can become a bottleneck. In this paper, we propose a
model-driven deep learning method for CSI feedback, called learnable
optimization and regularization algorithm (LORA). Instead of using l1-norm as
the regularization term, a learnable regularization module is introduced in
LORA to automatically adapt to the characteristics of CSI. We unfold the
conventional iterative shrinkage-thresholding algorithm (ISTA) to a neural
network and learn both the optimization process and regularization term by
end-toend training. We show that LORA improves the CSI feedback accuracy and
speed. Besides, a novel learnable quantization method and the corresponding
training scheme are proposed, and it is shown that LORA can operate
successfully at different bit rates, providing flexibility in terms of the CSI
feedback overhead. Various realistic scenarios are considered to demonstrate
the effectiveness and robustness of LORA through numerical simulations
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