Interference Alignment and Cancellation in Wireless Communication Systems

206 Pages·2016·3.91 MB·English

Interference Alignment and
Cancellation in Wireless
Communication Systems
by
Refik Fatih Ustok
A thesis
submitted to the Victoria University of Wellington
in fulfilment of the
requirements for the degree of
Doctor of Philosophy
in Engineering.
Victoria University of Wellington
2016

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Abstract
The Shannon capacity of wireless networks has a fundamental importance for
network information theory. This area has recently seen remarkable progress
on a variety of problems including the capacity of interference networks, X net-
works, cellular networks, cooperative communication networks and cognitive
radio networks. While each communication scenario has its own characteristics,
a common reason of these recent developments is the new idea of interference
alignment. The idea of interference alignment is to consolidate the interference
into smaller dimensions of signal space at each receiver and use the remaining
dimensions to transmit the desired signals without any interference. However,
perfect alignment of interference requires certain assumptions, such as perfect
channel state information at transmitter and receiver, perfect synchronization
and feedback. Today’s wireless communication systems, on the other hand, do
not encounter such ideal conditions. In this thesis, we cover a breadth of topics
of interference alignment and cancellation schemes in wireless communication
systems such as multihop relay networks, multicell networks as well as cooper-
ation and optimisation in such systems. Our main contributions in this thesis
can be summarised as follows
• We derive analytical expressions for an interference alignment scheme in
a multihop relay network with imperfect channel state information, and
investigate the impact of interference on such systems where interference
could accumulate due to the misalignment at each hop.
• We also address the dimensionality problem in larger wireless commu-
nication systems such as multi-cellular systems. We propose precoding
schemes based on maximising signal power over interference and noise.
We show that these precoding vectors would dramatically improve the
rates for multi-user cellular networks in both uplink and downlink, with-
out requiring an excessive number of dimensions. Furthermore, we inves-
tigate how to improve the receivers which can mitigate interference more
efficiently.
• We also propose partial cooperation in an interference alignment and can-
cellation scheme. This enables us to assess the merits of varying mixture
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of cooperative and non-cooperative users and the gains achievable while
reducing the overhead of channel estimation. In addition to this, we an-
alytically derive expressions for the additional interference caused by im-
perfect channel estimation in such cooperative systems. We also show the
impact of imperfect channel estimation on cooperation gains.
• Furthermore, we propose jointly optimisation of interference alignment
and cancellation for multi-user multi-cellular networks in both uplink and
downlink. We find the optimum set of transceivers which minimise the
mean square error at each base station. We demonstrate that optimised
transceivers can outperform existing interference alignment and cancella-
tion schemes.
• Finally, we consider power adaptation and user selection schemes. The
simulation results indicate that user selection and power adaptation tech-
niques based on estimated rates can improve the overall system perfor-
mance significantly.
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Acknowledgments
Undertaking this PhD has been a truly life-changing experience for me and it
would not have been possible to do without the support and guidance that I
received from many people.
I would like to first say a very big thank you to my supervisors, Dr. Pawel
Dmochowski and Prof. Mansoor Shafi, for all the support and encouragement
they gave me during my studies. Without their guidance and constant feedback
this PhD would not have been achievable. Furthermore I would like to thank
Prof. Peter Smith for his valuable supervision, even though he was not my
supervisor officially. Besides my supervisors, I would like to thank the rest of
my thesis committee: Dr. Bryan Ng, Dr. Graeme Woodward and Prof. Jinhang
Yuan for their insightful comments and encouragement, but also for the hard
questions which incented me to widen my research from various perspectives.
I also gratefully acknowledge the funding received towards my PhD from the
Victoria University of Wellington Faculty of Graduate Research.
I would also like to thank my colleagues Jawad Mirza, Callum Neil and Harsh
Tataria for all their help, support and friendship. I wish to thank all faculty
and staff at Victoria who are providing such a great teaching and research
environment for students. Studying at Victoria was an amazing part of my life
regarding my both career and personal life, it have given me an instructive and
wonderful experience that I will never forget.
IamindebtedtomanypeopleinWellingtonwhobecamemyfamilyhere. Study-
ing towards a PhD was also an emotional journey that caused ups and downs
in my life. Here I first want to thank Brad Lawson, who has helped me make
Wellington my home since my first day in Wellington. My life in Wellington
would definitely be more difficult without him. Juan Pablo Lampe, my Ar-
gentinian brother, was always there whenever I needed help or support. Many
thanks for knowing when something is wrong with me, always giving me the
extra push I need and simply being a brother from another mother. I also would
like to thank Liviu Sas, Snehal Poojary and Peter Godden who all helped me in
numerous ways during various stages of my PhD. Very special thanks to Mar-
ilena Kouklaki, "H agaph mou kai to mwro mou", for patiently proofreading my
thesis before submission and dealing with me during the most stressful times of
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this journey.
I would also like to say a heartfelt thank you to my mum Nurefsan Ustok, my
father Dr. Nezih Ustok and my sister Dr. Isik Ustok for always believing in me
and encouraging me to follow my dreams. They always give me their love and
support in all aspects of my life and I can never afford to thank them enough.
Refik Fatih Ustok
Victoria University of Wellington
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Notation and Symbols
N(µ,σ2) Normal distribution with mean µ and variance σ2
CN(µ,σ2) Complex normal distribution with mean µ and variance σ2
|·| Absolute value
(cid:107)·(cid:107) Euclidean norm of a vector
(cid:107)·(cid:107) Frobenius norm of a matrix
F
E [ · ] Expected value
det(·) Determinant of a matrix
tr(·) Trace of a matrix
(·)T Matrix or vector transpose
null(·) Null space of a vector
span(·) The space spanned by the column vectors of a matrix
rank(·) Rank of a matrix
(·)−1 Inverse of a matrix
(·)∗ Conjugate transpose of a matrix and a vector
(cid:60) Real part of a complex number
(cid:61) Imaginary part of a complex number
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f(x) Probability density function(pdf) of x
F(x) Cumulative distribution function (cdf) of x
I Identity matrix
N Number of antenna at the transmitter
t
N Number of antenna at the receiver
r
H Channel from transmitter j to receiver k
jk
G Cross - Channel from transmitter j (out of cell) to receiver k
jk
ˆ
H Erroneous channel from transmitter j to receiver k
jk
ˆ
G Erroneous cross-channel from transmitter j to receiver k
jk
v Precoding vector (Precoder)
c The ith codebook.
i
¯
P Secondary precoding matrix (Secondary precoder)
vˆ Erroneous precoding vector
u Postcoding vector (Postcoder)
uˆ Erroneous postcoding vector
P Transmit signal power
t
P Received signal power
r
d Distance between transmitter and receiver
h Height of the base station
BS
h Height of the mobile station
MS
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f Carrier frequency
c
σ Shadowing standard deviation
SF
γ The ratio of remaining over dominant interference
γ˜ The ratio of remaining over dominant interference for [1]
σ2 Noise variance
n Additive white Gaussian noise
n[1] Additive white Gaussian noise in the first hop of [2]
n[2] Additive white Gaussian noise in the second hop of [2]
η Refence vector for message i
i
ηICI Intercell Interference subspace for receiver i
i
ς The parameter that controls the CSI imperfection
ξ Antenna correlation coefficient
S Number of streams
K Number of users
L Antenna correlation matrix
R Achievable rate for user i
i
˜
R Ergodic mean sum rate for BS α
α
κ Colouring parameter
κ˜ Optimized colouring parameter
ρ Received signal power for user i of cell α considering path loss
α,i
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ρ˜ Received signal power for user i of cell a for the system of [1]
a,i
Φ Interference covariance matrix
α,k
¯
Φ Expected interference covariance matrix
α,k
˜
ξ Design parameter of orthogonality between channels
ψ Power adaptation coefficient
α,k
M Number of users selected
ST
Ψ Total transmit power of a BS
α,k
Λ Cooperation threshold
INR Dominant interference to noise ratio
dom
INR Remaining interference to noise ratio
rem
R Choosen rate threshold
τ
diag{a,b,c} Diagonal matrix with diagonal elements of a, b and c