6 research outputs found
Full-Diversity QO-STBC Technique for Large-Antenna MIMO Systems
YesThe need to achieve high data rates in modern telecommunication systems, such as 5G standard, motivates the study and development of large antenna and multiple-input multiple-output (MIMO) systems. This study introduces a large antenna-order design of MIMO quasi-orthogonal space-time block code (QO-STBC) system that achieves better signal-to-noise ratio (SNR) and bit-error ratio (BER) performances than the conventional QO-STBCs with the potential for massive MIMO (mMIMO) configurations. Although some earlier MIMO standards were built on orthogonal space-time block codes (O-STBCs), which are limited to two transmit antennas and data rates, the need for higher data rates motivates the exploration of higher antenna configurations using different QO-STBC schemes. The standard QO-STBC offers a higher number of antennas than the O-STBC with the full spatial rate. Unfortunately, also, the standard QO-STBCs are not able to achieve full diversity due to self-interference within their detection matrices; this diminishes the BER performance of the QO-STBC scheme. The detection also involves nonlinear processing, which further complicates the system. To solve these problems, we propose a linear processing design technique (which eliminates the system complexity) for constructing interference-free QO-STBCs and that also achieves full diversity using Hadamard modal matrices with the potential for mMIMO design. Since the modal matrices that orthogonalize QO-STBC are not sparse, our proposal also supports O-STBCs with a well-behaved peak-to-average power ratio (PAPR) and better BER. The results of the proposed QO-STBC outperform other full diversity techniques including Givens-rotation and the eigenvalue decomposition (EVD) techniques by 15 dB for both MIMO and multiple-input single-output (MISO) antenna configurations at 10−3 BER. The proposed interference-free QO-STBC is also implemented for 16×NR and 32×NR MIMO systems, where NR≤2. We demonstrate 8 x 16 and 32 transmit antenna-enabled MIMO systems with the potential for mMIMO design applications with attractive BER and PAPR performance characteristics
Digital signal processing techniques for peak-to-average power ratio mitigation in MIMO–OFDM systems
The focus of this thesis is to mitigate the very large peak-to-average
transmit power ratios (PAPRs) inherent to conventional orthogonal
frequency division multiplexing (OFDM) systems, particularly in the
context of transmission over multi-input multi-output (MIMO) wireless
broadband channels. This problem is important as a large PAPR
generally needs an expensive radio frequency (RF) power amplifier at
the transmitter due to the requirement for linear operation over a wide
amplitude range and such a cost would be compounded when multiple
transmit antennas are used. Advanced signal processing techniques
which can reduce PAPR whilst retain the integrity of digital transmission
therefore have considerable potential for application in emergent
MIMO–OFDM wireless systems and form the technical contributions
of this study. [Continues.
Técnicas de pré-codificação para sistemas multicelulares coordenados
Doutoramento em TelecomunicaçõesCoordenação Multicélula é um tópico de investigação em rápido
crescimento e uma solução promissora para controlar a interferência entre
células em sistemas celulares, melhorando a equidade do sistema e
aumentando a sua capacidade. Esta tecnologia já está em estudo no LTEAdvanced
sob o conceito de coordenação multiponto (COMP). Existem
várias abordagens sobre coordenação multicélula, dependendo da
quantidade e do tipo de informação partilhada pelas estações base, através
da rede de suporte (backhaul network), e do local onde essa informação é
processada, i.e., numa unidade de processamento central ou de uma forma
distribuída em cada estação base.
Nesta tese, são propostas técnicas de pré-codificação e alocação de
potência considerando várias estratégias: centralizada, todo o
processamento é feito na unidade de processamento central; semidistribuída,
neste caso apenas parte do processamento é executado na
unidade de processamento central, nomeadamente a potência alocada a
cada utilizador servido por cada estação base; e distribuída em que o
processamento é feito localmente em cada estação base. Os esquemas
propostos são projectados em duas fases: primeiro são propostas soluções
de pré-codificação para mitigar ou eliminar a interferência entre células,
de seguida o sistema é melhorado através do desenvolvimento de vários
esquemas de alocação de potência. São propostas três esquemas de
alocação de potência centralizada condicionada a cada estação base e com
diferentes relações entre desempenho e complexidade. São também
derivados esquemas de alocação distribuídos, assumindo que um sistema
multicelular pode ser visto como a sobreposição de vários sistemas com
uma única célula. Com base neste conceito foi definido uma taxa de erro
média virtual para cada um desses sistemas de célula única que compõem
o sistema multicelular, permitindo assim projectar esquemas de alocação
de potência completamente distribuídos.
Todos os esquemas propostos foram avaliados em cenários realistas,
bastante próximos dos considerados no LTE. Os resultados mostram que
os esquemas propostos são eficientes a remover a interferência entre
células e que o desempenho das técnicas de alocação de potência
propostas é claramente superior ao caso de não alocação de potência. O
desempenho dos sistemas completamente distribuídos é inferior aos
baseados num processamento centralizado, mas em contrapartida podem
ser usados em sistemas em que a rede de suporte não permita a troca de
grandes quantidades de informação.Multicell coordination is a promising solution for cellular wireless systems
to mitigate inter-cell interference, improving system fairness and
increasing capacity and thus is already under study in LTE-A under the
coordinated multipoint (CoMP) concept. There are several coordinated
transmission approaches depending on the amount of information shared
by the transmitters through the backhaul network and where the
processing takes place i.e. in a central processing unit or in a distributed
way on each base station.
In this thesis, we propose joint precoding and power allocation techniques
considering different strategies: Full-centralized, where all the processing
takes place at the central unit; Semi-distributed, in this case only some
process related with power allocation is done at the central unit; and Fulldistributed,
where all the processing is done locally at each base station.
The methods are designed in two phases: first the inter-cell interference is
removed by applying a set of centralized or distributed precoding vectors;
then the system is further optimized by centralized or distributed power
allocation schemes. Three centralized power allocation algorithms with
per-BS power constraint and different complexity tradeoffs are proposed.
Also distributed power allocation schemes are proposed by considering
the multicell system as superposition of single cell systems, where we
define the average virtual bit error rate (BER) of interference-free single
cell system, allowing us to compute the power allocation coefficients in a
distributed manner at each BS.
All proposed schemes are evaluated in realistic scenarios considering LTE
specifications. The numerical evaluations show that the proposed schemes
are efficient in removing inter-cell interference and improve system
performance comparing to equal power allocation. Furthermore, fulldistributed
schemes can be used when the amounts of information to be
exchanged over the backhaul is restricted, although system performance is
slightly degraded from semi-distributed and full-centralized schemes, but
the complexity is considerably lower. Besides that for high degrees of
freedom distributed schemes show similar behaviour to centralized ones
Physical layer model design for wireless networks
Wireless network analysis and simulations rely on accurate physical layer models.
The increased interest in wireless network design and cross-layer design require an
accurate and efficient physical layer model especially when a large number of nodes
are to be studied and building the real network is not possible. For analysis of upper
layer characteristics, a simplified physical layer model has to be chosen to model the
physical layer.
In this dissertation, the widely used two-state Markov model is examined and
shown to be deficient for low to moderate signal-to-noise ratios. The physical layer
statistics are investigated, and the run length distributions of the good and bad
frames are demonstrated to be the key statistics for accurate physical layer modeling. A four-state Markov model is proposed for the flat Rayleigh fading channel by
approximating the run length distributions with a mixture of exponential distributions. The transition probabilities in the four-state Markov model can be established
analytically without having to run extensive physical layer simulations, which are
required for the two-state Markov model. Physical layer good and bad run length
distributions are compared and it is shown that the four-state Markov model reasonably approximates the run length distributions. Ns2 simulations are performed and
the four-state Markov model provides a much more realistic approximation compared
to the popular two-state Markov model. Achieving good results with the flat Rayleigh fading channel, the proposed four-state Markov model is applied to a few diversity channels. A coded orthogonal fre-
quency division multiplexing (OFDM) system with a frequency selective channel and
the Alamouti multiple-input multiple-output system are chosen to verify the accuracy of the four-state Markov model. The network simulation results show that the
four-state Markov model approximates the physical layer with diversity channel well
whereas the traditional two-state Markov model estimates the network throughput
poorly. The success of adapting the four-state Markov model to the diversity channel
also shows the flexibility of adapting the four-state Markov model to various channel
conditions