14 research outputs found
Spatio-Temporal processing for Optimum Uplink-Downlink WCDMA Systems
The capacity of a cellular system is limited by two different phenomena, namely
multipath fading and multiple access interference (MAl). A Two Dimensional (2-D)
receiver combats both of these by processing the signal both in the spatial and temporal
domain. An ideal 2-D receiver would perform joint space-time processing, but at the
price of high computational complexity. In this research we investigate computationally
simpler technique termed as a Beamfom1er-Rake. In a Beamformer-Rake, the output of a
beamfom1er is fed into a succeeding temporal processor to take advantage of both the
beamformer and Rake receiver. Wireless service providers throughout the world are
working to introduce the third generation (3G) and beyond (3G) cellular service that will
provide higher data rates and better spectral efficiency. Wideband COMA (WCDMA)
has been widely accepted as one of the air interfaces for 3G. A Beamformer-Rake
receiver can be an effective solution to provide the receivers enhanced capabilities
needed to achieve the required performance of a WCDMA system.
We consider three different Pilot Symbol Assisted (PSA) beamforming techniques,
Direct Matrix Inversion (DMI), Least-Mean Square (LMS) and Recursive Least Square
(RLS) adaptive algorithms. Geometrically Based Single Bounce (GBSB) statistical
Circular channel model is considered, which is more suitable for array processing, and
conductive to RAKE combining. The performances of the Beam former-Rake receiver are
evaluated in this channel model as a function of the number of antenna elements and
RAKE fingers, in which are evaluated for the uplink WCDMA system. It is shown that,
the Beamformer-Rake receiver outperforms the conventional RAKE receiver and the
conventional beamformer by a significant margin. Also, we optimize and develop a
mathematical formulation for the output Signal to Interference plus Noise Ratio (SINR)
of a Beam former-Rake receiver.
In this research, also, we develop, simulate and evaluate the SINR and Signal to Noise
Ratio (Et!Nol performances of an adaptive beamforming technique in the WCDMA
system for downlink. The performance is then compared with an omnidirectional antenna
system. Simulation shows that the best perfom1ance can be achieved when all the mobiles
with same Angle-of-Arrival (AOA) and different distance from base station are formed in
one beam
Successive DF relaying: MS-DIS aided interference suppression and three-stage concatenated architecture design
Conventional single-relay aided two-phase cooperative networks employing coherent detection algorithms incur a significant 50% throughput loss. Furthermore, it is unrealistic to expect that in addition to the task of relaying, the relay-station would dedicate further precious resources to the estimation of the source-relay channel in support of coherent detection. In order to circumvent these problems, we propose decode and-forward (DF) based successive relaying employing noncoherent detection schemes. A crucial challenge in this context is that of suppressing the successive relaying induced interference, despite dispensing with any channel state information (CSI). We overcome this challenge by introducing a novel adaptive Newton algorithm based multiple-symbol differential interference suppression (MS-DIS) scheme. Correspondingly, a three-stage concatenated transceiver architecture is devised. We demonstrate that our proposed system is capable of near-error-free transmissions at low signal-to-noise ratios
Spatio-Temporal processing for Optimum Uplink-Downlink WCDMA Systems
The capacity of a cellular system is limited by two different phenomena, namely
multipath fading and multiple access interference (MAl). A Two Dimensional (2-D)
receiver combats both of these by processing the signal both in the spatial and temporal
domain. An ideal 2-D receiver would perform joint space-time processing, but at the
price of high computational complexity. In this research we investigate computationally
simpler technique termed as a Beamfom1er-Rake. In a Beamformer-Rake, the output of a
beamfom1er is fed into a succeeding temporal processor to take advantage of both the
beamformer and Rake receiver. Wireless service providers throughout the world are
working to introduce the third generation (3G) and beyond (3G) cellular service that will
provide higher data rates and better spectral efficiency. Wideband COMA (WCDMA)
has been widely accepted as one of the air interfaces for 3G. A Beamformer-Rake
receiver can be an effective solution to provide the receivers enhanced capabilities
needed to achieve the required performance of a WCDMA system.
We consider three different Pilot Symbol Assisted (PSA) beamforming techniques,
Direct Matrix Inversion (DMI), Least-Mean Square (LMS) and Recursive Least Square
(RLS) adaptive algorithms. Geometrically Based Single Bounce (GBSB) statistical
Circular channel model is considered, which is more suitable for array processing, and
conductive to RAKE combining. The performances of the Beam former-Rake receiver are
evaluated in this channel model as a function of the number of antenna elements and
RAKE fingers, in which are evaluated for the uplink WCDMA system. It is shown that,
the Beamformer-Rake receiver outperforms the conventional RAKE receiver and the
conventional beamformer by a significant margin. Also, we optimize and develop a
mathematical formulation for the output Signal to Interference plus Noise Ratio (SINR)
of a Beam former-Rake receiver.
In this research, also, we develop, simulate and evaluate the SINR and Signal to Noise
Ratio (Et!Nol performances of an adaptive beamforming technique in the WCDMA
system for downlink. The performance is then compared with an omnidirectional antenna
system. Simulation shows that the best perfom1ance can be achieved when all the mobiles
with same Angle-of-Arrival (AOA) and different distance from base station are formed in
one beam
Unbiased MMSE vs. Biased MMSE Equalizers
[[abstract]]We systematically analyze the biased and unbiased minimum mean square error (MMSE) equalizers of finite as well as infinite length, with and without decision feedback sections. New closed-form expressions of optimum equalizer weights, the MMSE, and symbol error probabilities (SEP), solely in terms of channel response parameters and noise power, are derived for the above receivers. These new expressions have not appeared in the literature and should be included for completeness. We also prove analytically that the biased and unbiased MMSE equalizers have the same optimum weights and that an infinitely long unbiased MMSE equalizer approaches the optimum minimum error probability equalizer. Performance curves are presented and compared for all the receivers discussed. Moreover, for all the infinite length equalizers presented, alternative error probability expressions are provided to best suit computer simulations.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙
Efficient Robust Adaptive Beamforming Algorithms for Sensor Arrays
Sensor array processing techniques have been an important research area in recent years.
By using a sensor array of a certain configuration, we can improve the parameter estimation
accuracy from the observation data in the presence of interference and noise. In this
thesis, we focus on sensor array processing techniques that use antenna arrays for beamforming,
which is the key task in wireless communications, radar and sonar systems.
Firstly, we propose a low-complexity robust adaptive beamforming (RAB) technique
which estimates the steering vector using a Low-Complexity Shrinkage-Based Mismatch
Estimation (LOCSME) algorithm. The proposed LOCSME algorithm estimates the covariance
matrix of the input data and the interference-plus-noise covariance (INC) matrix
by using the Oracle Approximating Shrinkage (OAS) method. Secondly, we present
cost-effective low-rank techniques for designing robust adaptive beamforming (RAB) algorithms.
The proposed algorithms are based on the exploitation of the cross-correlation
between the array observation data and the output of the beamformer. Thirdly, we propose
distributed beamforming techniques that are based on wireless relay systems. Algorithms
that combine relay selections and SINR maximization or Minimum Mean-Square-
Error (MMSE) consensus are developed, assuming the relay systems are under total relay
transmit power constraint. Lastly, we look into the research area of robust distributed
beamforming (RDB) and develop a novel RDB approach based on the exploitation of
the cross-correlation between the received data at the relays and the destination and a
subspace projection method to estimate the channel errors, namely, the cross-correlation
and subspace projection (CCSP) RDB technique, which efficiently maximizes the output
SINR and minimizes the channel errors. Simulation results show that the proposed
techniques outperform existing techniques in various performance metrics
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
Adaptive antenna array beamforming using a concatenation of recursive least square and least mean square algorithms
In recent years, adaptive or smart antennas have become a key component for various wireless applications, such as radar, sonar and cellular mobile communications including worldwide interoperability for microwave access (WiMAX). They lead to an increase in the detection range of radar and sonar systems, and the capacity of mobile radio communication systems. These antennas are used as spatial filters for receiving the desired signals coming from a specific direction or directions, while minimizing the reception of unwanted signals emanating from other directions.Because of its simplicity and robustness, the LMS algorithm has become one of the most popular adaptive signal processing techniques adopted in many applications, including antenna array beamforming. Over the last three decades, several improvements have been proposed to speed up the convergence of the LMS algorithm. These include the normalized-LMS (NLMS), variable-length LMS algorithm, transform domain algorithms, and more recently the constrained-stability LMS (CSLMS) algorithm and modified robust variable step size LMS (MRVSS) algorithm. Yet another approach for attempting to speed up the convergence of the LMS algorithm without having to sacrifice too much of its error floor performance, is through the use of a variable step size LMS (VSSLMS) algorithm. All the published VSSLMS algorithms make use of an initial large adaptation step size to speed up the convergence. Upon approaching the steady state, smaller step sizes are then introduced to decrease the level of adjustment, hence maintaining a lower error floor. This convergence improvement of the LMS algorithm increases its complexity from 2N in the case of LMS algorithm to 9N in the case of the MRVSS algorithm, where N is the number of array elements.An alternative to the LMS algorithm is the RLS algorithm. Although higher complexity is required for the RLS algorithm compared to the LMS algorithm, it can achieve faster convergence, thus, better performance compared to the LMS algorithm. There are also improvements that have been made to the RLS algorithm families to enhance tracking ability as well as stability. Examples are, the adaptive forgetting factor RLS algorithm (AFF-RLS), variable forgetting factor RLS (VFFRLS) and the extended recursive least squares (EX-KRLS) algorithm. The multiplication complexity of VFFRLS, AFF-RLS and EX-KRLS algorithms are 2.5N2 + 3N + 20 , 9N2 + 7N , and 15N3 + 7N2 + 2N + 4 respectively, while the RLS algorithm requires 2.5N2 + 3N .All the above well known algorithms require an accurate reference signal for their proper operation. In some cases, several additional operating parameters should be specified. For example, MRVSS needs twelve predefined parameters. As a result, its performance highly depends on the input signal.In this study, two adaptive beamforming algorithms have been proposed. They are called recursive least square - least mean square (RLMS) algorithm, and least mean square - least mean square (LLMS) algorithm. These algorithms have been proposed for meeting future beamforming requirements, such as very high convergence rate, robust to noise and flexible modes of operation. The RLMS algorithm makes use of two individual algorithm stages, based on the RLS and LMS algorithms, connected in tandem via an array image vector. On the other hand, the LLMS algorithm is a simpler version of the RLMS algorithm. It makes use of two LMS algorithm stages instead of the RLS – LMS combination as used in the RLMS algorithm.Unlike other adaptive beamforming algorithms, for both of these algorithms, the error signal of the second algorithm stage is fed back and combined with the error signal of the first algorithm stage to form an overall error signal for use update the tap weights of the first algorithm stage.Upon convergence, usually after few iterations, the proposed algorithms can be switched to the self-referencing mode. In this mode, the entire algorithm outputs are swapped, replacing their reference signals. In moving target applications, the array image vector, F, should also be updated to the new position. This scenario is also studied for both proposed algorithms. A simple and effective method for calculate the required array image vector is also proposed. Moreover, since the RLMS and the LLMS algorithms employ the array image vector in their operation, they can be used to generate fixed beams by pre-setting the values of the array image vector to the specified direction.The convergence of RLMS and LLMS algorithms is analyzed for two different operation modes; namely with external reference or self-referencing. Array image vector calculations, ranges of step sizes values for stable operation, fixed beam generation, and fixed-point arithmetic have also been studied in this thesis. All of these analyses have been confirmed by computer simulations for different signal conditions. Computer simulation results show that both proposed algorithms are superior in convergence performances to the algorithms, such as the CSLMS, MRVSS, LMS, VFFRLS and RLS algorithms, and are quite insensitive to variations in input SNR and the actual step size values used. Furthermore, RLMS and LLMS algorithms remain stable even when their reference signals are corrupted by additive white Gaussian noise (AWGN). In addition, they are robust when operating in the presence of Rayleigh fading. Finally, the fidelity of the signal at the output of the proposed algorithms beamformers is demonstrated by means of the resultant values of error vector magnitude (EVM), and scatter plots. It is also shown that, the implementation of an eight element uniform linear array using the proposed algorithms with a wordlength of nine bits is sufficient to achieve performance close to that provided by full precision