虚拟天线阵列在接力和多跳网络中的应用

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"Virtual Antenna Arrays - Mischa Dohler - PHD Thesis - London University" 这篇博士论文由Mischa Dohler撰写，于2003年11月提交伦敦大学，旨在研究电气与电子工程领域的虚拟天线阵列（Virtual Antenna Arrays，简称VAA）及其在中继和多跳网络中的应用。该研究对于理解现代无线通信系统中的信号处理和网络架构具有重要意义。虚拟天线阵列是一种创新的无线通信技术，它通过软件定义无线电（Software Defined Radio, SDR）和智能信号处理算法，模拟多个物理天线的效果，实现更高效、灵活的无线通信。这一技术可以显著提升通信系统的空间分集、频率复用和多用户接入能力，从而提高网络容量和覆盖范围。在多跳网络中，中继节点是连接各个通信节点的关键，它们可以延长信号传输距离，改善信号质量，并在无法直接通信的节点间建立通信链路。虚拟天线阵列在中继网络中的应用，能够优化中继策略，减少能量消耗，同时增强网络的鲁棒性和可靠性。论文详细探讨了虚拟天线阵列的理论基础，包括天线理论、多径传播、信号检测与估计、以及多用户干扰管理等核心概念。此外，还可能涵盖了阵列信号处理技术，如波束形成、空时编码和多输入多输出（MIMO）系统设计。这些技术都是为了实现更高效的无线资源利用，适应复杂的无线环境变化。作者在论文中可能还分析了虚拟天线阵列在实际无线网络部署中的挑战，例如硬件限制、功率控制、同步问题以及动态网络环境下的自适应性。可能还讨论了未来的研究方向，如将VAA技术与认知无线电、物联网（IoT）和5G通信系统相结合的可能性。论文最后的致谢部分表达了作者对伦敦这个城市的感慨，以及对一路上给予他支持的人们的感谢。特别提到了Gemma、他的母亲和Hamid，他们在他面临困难时给予了最大的支持。这篇博士论文深入研究了虚拟天线阵列的理论与应用，对于无线通信领域的研究者和技术开发者来说，是一份宝贵的资源，有助于推动无线网络技术的进步。

3.26 Comparison between exact and approximate throughput-maximising outage

probability versus the number of transmit elements for a MISO channel. . . 118

3.27 Achieved end-to-end throughput of various fractional resource allocation strate-

gies utilising a simple numerical optimisation of the rate in each stage for a

2-stagenetwork. ................................. 123

3.28 Achieved end-to-end throughput of various fractional resource allocation strate-

gies utilising an approximation for the rate in each stage for a 2-stage

network....................................... 123

3.29 Achieved end-to-end throughput of various fractional resource allocation strate-

giesfora2-stagenetwork............................. 124

3.30 Achieved end-to-end throughput of various fractional resource allocation strate-

gies for a 3-stage network with

resourcereuse. ................. 124

3.31 Achieved end-to-end throughput of various fractional resource allocation strate-

giesoverRayleighO-MIMOchannelsfora2-stagenetwork.......... 127

3.32 Achieved end-to-end throughput versus SNR in the ﬁrst link for a 3-stage

O-MIMO relaying network with unequal channel gains as speciﬁed in

Figure3.24..................................... 127

3.33 Achieved end-to-end capacity with quantised fractional bandwidth for a 2-

stagerelayingnetworkoverO-MIMORayleighchannels............ 131

3.34 Achieved end-to-end capacity with quantised fractional bandwidth of power

of two for a 2-stage relaying network over O-MIMO Rayleigh channels. . . . 131

3.35 Outage probability for optimum, near-optimum and non-optimised transmit

power distribution for deployed Alamouti scheme with one receive antenna. 138

3.36 Outage probability for optimum, near-optimum and non-optimised transmit

power distribution for deployed Alamouti scheme with two receive antennas. 138

4.1 Functional blocks of the source VAA (top), the v

relaying VAA (middle)

andthetargetVAA(bottom). ......................... 142

4.2 Distributed Encoder and Decoder. . ...................... 143

4.3 Aspace-timeblockencodedMIMOsystem(O-MIMO). ........... 149

4.4 SER versus SNR labelled on the number of receive antennas for systems

operatingat2bits/s/Hz. ............................ 153

4.5 SER versus SNR labelled on the number of receive antennas for systems

operatingat3bits/s/Hz. ............................ 153

4.6 SER versus SNR labelled on the number of receive antennas for systems

operatingat4bits/s/Hz. ............................ 154

4.7 SER versus SNR labelled on the number of receive antennas for systems

operatingat6bits/s/Hz. ............................ 154

4.8 SER versus SNR for a distributed Alamouti system operating at 2 bits/s/Hz;

+ γ

≡ 2andγ

: γ

=2:1. ......................... 156

4.9 SER versus the normalised power γ

in the ﬁrst link for a distributed Alam-

outi system operating at 2 bits/s/Hz; SNR=30dB and γ

=2− γ

...... 156

4.10 SER versus SNR for a distributed Alamouti system operating at 2 bits/s/Hz;

+ γ

≡ 2andγ

: γ

=2:1. ......................... 158

4.11 SER versus the Nakagami fading parameter f for systems operating at

3bits/s/Hz;SNR=20dB. ............................ 158

4.12 Exact and approximate BER versus SNR for systems with two receive an-

tennasoperatingat2bits/s/Hz. ........................ 160

4.13 Exact (simulation) and approximate (analysis) FER versus SNR for systems

with two receive antennas operating at 2 bits/s/Hz over fast-fading channels. 160

4.14 Comparison between optimum and near-optimum, as well as non-optimised

end-to-end BER for various conﬁgurations of a two-stage relaying network. 167

4.15 Comparison between optimum and near-optimum, as well as non-optimised

end-to-end BER for various conﬁgurations of a three-stage relaying network. 167

4.16 Comparison between optimum and near-optimum, as well as non-optimised

end-to-end throughput for various conﬁgurations of a two-stage relaying net-

work......................................... 168

4.17 Numerically optimised modulation index where M

1,2

=(2, 4, 16, 64, 256) to

yield near-optimum end-to-end throughput, compared to non-optimised sys-

tems......................................... 168

4.18 3-stage distributed O-MIMO communication system without cooperation. . 170

4.19 Numerically obtained and derived end-to-end BER versus the SNR in the

ﬁrstlinkforatwo-stagenetworkwithoutcooperation. ............ 172

4.20 Numerically obtained and derived end-to-end BER versus the SNR in the

ﬁrst link for a three-stage network without cooperation. . .......... 172

4.21 Comparison between optimum, near-optimum and non-optimised end-to-end

throughput of a two-stage relaying network, assuming stage-by-stage

detection. ..................................... 176

4.22 Optimum, near-optimum and non-optimised end-to-end throughput for a

two-stagerelayingnetworkwithﬁxedmodulationindex............ 176

4.23 Numerically optimised modulation index where M

1,2

=(2, 4, 16, 64, 256) to

yield near-optimum end-to-end throughput, compared to non-optimised sys-

tems......................................... 177

Chapter 1

Overview

1.1 Introduction

It would be foolish to believe that the capacity oﬀered by the wireless medium is limited.

With Thousands of THz of electromagnetic spectrum known, auctioning 140MHz of band-

width for about £21bn on the grounds of scarce spectrum seems to be beyond ridicule. It

could be compared to somebody sitting in a small shelter in London and claiming there is

not enough space in the universe.

Unfortunately, such an auction has happened in the UK. It marked the beginning of

an era which is generally referred to as 3G. It also marked the beginning of an era where

we have only just begun to understand what freedom of information means, where we have

grasped the idea of having access to any information anywhere anyhow at any cost, but

where we have only started to explore ways of delivering this information. We are, to my

belief, only in the cradle of an information society.

There is a long path to pace until we are entitled to call the development of transmission

technologies to be mature. There is little we can predict at this stage, except that the way

the electromagnetic spectrum is handled has to change. Hopefully, in a couple of decades

it will be understood that the wireless medium is not the data bottleneck as frequently

claimed. It will then be the time when research can move on to more important things than

designing transceivers which operate as close as possible to the predicted capacity limits.

Until this will happen, however, the existence of researchers like me is justiﬁed and the

work which I expose within this thesis will be read. Indeed, this thesis is about increasing

the capacity of wireless systems under the constraints of limited bandwidth and limited

transmission power.

In its original formulation, the capacity of a communications channel characterises the

maximum amount of error-free information (in bits) which can be transmitted over that

channel in a given time (in seconds) over a given bandwidth (in Hertz). Shannon was the

ﬁrst to associate the entropy of a random signal with its information contents, which allowed

him to analytically derive the capacity for an additive Gaussian noise channel [1].

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