Ph.D. DISSERTATION
Researches on Capacity and Key Techniques of Distributed Wireless Communication Systems (DWCS)
[
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(written in Chinese)
| Abstract: |
|
The major
challenge in today's wireless communication is how to serve the explosively
increasing demand of multimedia service within the limited bandwidth.
This drives the developments of new techniques, such as multiple-antenna
space-time processing and multiuser detection, to increase the frequency
efficiency for band-limited wireless channels. However, the existing
cellular infrastructure imposes restrictions on the effective application
of these techniques.
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| In this thesis, a new architecture, Distributed Wireless Communication System (DWCS), is proposed, which breaks through the barrier of traditional cells. It adopts distributed antennas and distributed processing, and thus makes full use of space resources and multidimensional information. Compared with the cellular system, enormous capacity advantage can be gained with DWCS. |
|
We first
introduce the background and basic concepts of DWCS, and compare it
with the existing Distributed Antenna System (DAS). Then we illustrate
its logical structure, new concepts, new characteristics and our analytical
model in details.
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| In order to prove its capacity advantage, we further focus on the capacity analysis, including information-theoretic capacity and user capacity. Based on the recent research about the capacity analysis in the MIMO system, the channel capacity analysis was conducted for distributed multiple-antenna channels in DWCS. In the user capacity analysis, we derived the analytical outage probability expressions on the CDMA assumption. We demonstrated that DWCS is superior to the cellular system with antenna array in both channel capacity and user capacity. |
|
Furthermore,
a novel bandwidth efficient transmit diversity scheme (C-MIMO) based
on layered space-time architecture for DWCS is presented, in which channel
state information (CSI) is fully utilized to maximize channel capacity
following water-filling principle. The average receive SNR and frame
error rate (FER) are selected as the performance indicators. Compared
with V-BLAST, C-MIMO can maintain the same high bandwidth efficiency,
but achieve much better performance thanks to more effective transmission
power allocation and diversity gain. Moreover, the performance gap becomes
even larger in distributed channels. When the channel matrix is singular
(or ill-conditional), the performance of V-BLAST degrades very quickly,
while C-MIMO still maintains superior performance. In order to further
improve the performance of C-MIMO, we drew inspiration from Linear Dispersion
Codes and combine spatial processing with temporal processing. Besides,
we also analyze the effect of feedback error on the performance of C-MIMO.
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| Finally we put forward some ideas about the future research on the key techniques of DWCS. |
| Key Words: Distributed Wireless Communication System (DWCS), distributed antennas, MIMO, channel capacity, user capacity, CDMA, transmit diversity |
| Outline: | |||
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1.
|
Introduction | ||
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1.1
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Main features of wireless communication | ||
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1.2
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Overview of cellular communication systems | ||
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1.3
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Thesis motivation and outline | ||
| PART 1 Concepts of DWCS | |||
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2.
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Overview of DWCS | ||
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2.1
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Background | ||
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2.2
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Introduction to Distributed Antenna Systems (DAS) | ||
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2.3
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Comparison of DAS and DWCS | ||
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3.
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Concepts of DWCS | ||
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3.1
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Logical framework | ||
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3.2
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New concepts | ||
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3.2.1
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Distributed processing | ||
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3.2.2
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Virtual cell | ||
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3.2.3
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Virtual base station | ||
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3.2.4
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Virtual tunnel | ||
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3.3
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Features | ||
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3.4
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Research directions | ||
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3.5
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My contributions | ||
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3.6
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Model used throughout this thesis | ||
| PART 2 Capacity analysis of DWCS | |||
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4.
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Introduction to PART 2 | ||
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4.1
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Definition of capacity | ||
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4.2
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Motivation and results | ||
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5.
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Information-theoretic capacity | ||
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5.1
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Introduction | ||
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5.1.1
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Research on MIMO capacity | ||
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5.1.2
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New features in distributed channels | ||
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5.2
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Preliminary | ||
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5.2.1
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Open-loop MIMO capacity | ||
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5.2.2
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Closed-loop MIMO capacity | ||
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5.3
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Capacity analysis in distributed channels | ||
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5.3.1
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Model and assumptions | ||
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5.3.2
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Simulation results and discussions | ||
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5.4
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Comparison with MIMO capacity results | ||
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5.5
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Conclusions and future research directions | ||
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6.
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User capacity | ||
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6.1
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Introduction | ||
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6.1.1
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Overview of user capacity | ||
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6.1.2
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Macrodiversity in distributed systems | ||
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6.2
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Preliminary | ||
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6.2.1
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Outage probability | ||
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6.2.2
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Traditional user capacity analysis of cellular systems | ||
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6.3
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Reverse-link capacity | ||
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6.3.1
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Model and assumptions | ||
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6.3.2
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Simulation results and discussions | ||
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6.3.3
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Comparison with reverse-link capacity of cellular systems with antenna array | ||
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6.4
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Forward-link capacity | ||
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6.4.1
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Model and assumptions | ||
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6.4.2
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Simulation results and discussions | ||
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6.4.3
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Comparison with forward-link capacity of cellular systems with antenna array | ||
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6.5
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Conclusions and future research directions | ||
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7.
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Summary and remarks | ||
| PART 3 Multiple-antenna transmit diversity algorithm for the forward-link of DWCS | |||
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8.
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Introduction to PART 3 | ||
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8.1
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Background | ||
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8.2
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Motivation and results | ||
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9.
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New transmit diversity algorithm ---- C-MIMO | ||
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9.1
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Introduction and overview | ||
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9.1.1
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Bandwidth efficiency and diversity gain | ||
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9.1.2
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BLAST, space-time coding and Linear Dispersion codes | ||
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9.1.3
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Transmit diversity with and without feedback | ||
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9.1.4
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Applications in 3G systems | ||
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9.2
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Preliminary | ||
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9.2.1
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System model of BLAST | |
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9.2.2
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Layered transmit structure of BLAST | |
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9.2.3
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Multi-user detection algorithms used in BLAST | |
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9.3
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Algorithm description | ||
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9.3.1
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Model and assumptions | |
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9.3.2
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Principle of C-MIMO and simplified algorithm | |
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9.4
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Performance evaluation | ||
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9.4.1
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Average receive SNR | |
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9.4.2
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Frame error rate | |
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9.5
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Performance in distributed channels | ||
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9.5.1
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Modification of channel model | |
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9.5.2
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Simulation results | |
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9.6
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Improved C-MIMO | ||
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9.6.1
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Utilization of time diversity | |
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9.6.2
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Performance of improved C-MIMO | |
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9.7
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Effect of feedback error | ||
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9.8
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Conclusions and future research directions | ||
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10.
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Summary and remarks | ||
| PART 4 Conclusions | |||
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11.
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Future research | ||
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11.1
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Multi-user detection for DWCS | ||
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11.2
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Power control for DWCS | ||
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11.3
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OFDM applied in DWCS | ||
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11.4
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Random spreading applied in DWCS | ||
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12.
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Summary and concluding remarks | ||
| Appendix
A: Proof of Equation (6-17) Appendix B: Proof of Theorem (6-1) Appendix C: Proof of Theorem (6-2) Appendix D: Proof of Equation (9-10) Appendix E: Form of cyclic orthogonal matrix Appendix F: Proof of Inequation (9-19) |
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| Bibliography | |||