MIMO-aided near-capacity turbo transceivers: taxonomy and performance versus complexity

In this treatise, we firstly review the associated Multiple-Input Multiple-Output (MIMO) system theory and review the family of hard-decision and soft-decision based detection algorithms in the context of Spatial Division Multiplexing (SDM) systems. Our discussions culminate in the introduction of a range of powerful novel MIMO detectors, such as for example Markov Chain assisted Minimum Bit-Error Rate (MC-MBER) detectors, which are capable of reliably operating in the challenging high-importance rank-deficient scenarios, where there are more transmitters than receivers and hence the resultant channel-matrix becomes non-invertible. As a result, conventional detectors would exhibit a high residual error floor. We then invoke the Soft-Input Soft-Output (SISO) MIMO detectors for creating turbo-detected two- or three-stage concatenated SDM schemes and investigate their attainable performance in the light of their computational complexity. Finally, we introduce the powerful design tools of EXtrinsic Information Transfer (EXIT)-charts and characterize the achievable performance of the diverse near- capacity SISO detectors with the aid of EXIT charts

Approaching universal frequency reuse through base station cooperation

Base Station (BS) architectures are a promising cellular wireless solution to mitigate the interference issues and to avoid the high frequency reuse factors implemented in conventional systems. Combined with block transmission techniques, such as Orthogonal Frequency-Division Multiplexing (OFDM) for the downlink and Single-Carrier with Frequency-Domain Equalization (SC-FDE) for the uplink, these systems provide a significant performance improvement to the overall system. Block transmission techniques are suitable for broadband wireless communication systems, which have to deal with strongly frequency-selective fading channels and are able to provide high bit rates despite the channel adversities. In BS cooperation schemes users in adjacent cells share the same physical channel and the signals received by each BS are sent to a Central Processing Unit (CPU) that combines the different signals and performs the user detections and/or separation, which can be regarded as a Multi-User Detection (MUD) technique. The work presented in this thesis is focused on the study of uplink transmissions in BS cooperations systems, considering single carrier block transmission schemes and iterative receivers based on the Iterative-Block Decision Feedback Equalization (IB-DFE) concept, which combined with the employment of Cyclic Prefix (CP)-assisted block transmission techniques are appropriate to scenarios with strongly time-dispersive channels. Furthermore, the impact of the sampling and quantization applied to the received signals from each Mobile Terminal (MT) to the corresponding BS is studied, with the achievement of the spectral characterization of the quantization noise. This thesis also provides a conventional analytical model for the BER (Bit Error Rate) performance complemented with an approach to improve its results. Finally, this thesis addresses the contextualization of BS cooperation schemes in clustered C-RAN (Centralized-Radio Access Network)-type solutions.As arquitecturas BS cooperation são uma solução promissora de redes celulares sem fios para atenuar o problema da interferência e evitar os factores de reuso elevados, que se encontram implementados nos sistemas convencionais. Combinadas com técnicas de transmissão por blocos, como o OFDM para o downlink e o SC-FDE no uplink, estes sistemas fornecem uma melhoria significativa no desempenho geral do sistema. Técnicas de transmissão por blocos são adequadas para sistemas de comunicações de banda larga sem fios, que têm que lidar com canais que possuem um forte desvanescimento selectivo na frequência e são capazes de fornecer ligações com taxas de transmissão altas apesar das adversidades do canal. Em esquemas BS cooperation os terminais móveis situados em células adjacentes partilham o mesmo canal físico e os sinais recebidos em cada estação de base são enviados para uma Unidade Central de Processamento (CPU) que combina os diferentes sinais recebidos associados a um dado utilizador e realiza a detecção e/ou separação do mesmo, sendo esta considerada uma técnica de Detecção Multi-Utilizador (MUD). O trabalho apresentado nesta tese concentra o seu estudo no uplink de transmissões em sistemas BS cooperation, considerando transmissões em bloco de esquemas monoportadoras e receptores iterativos baseados no conceito B-DFE, em que quando combinados com a implementação de técnicas de transmissao por blocos assistidas por prefixos cíclicos (CP) são apropriados a cenários com canais fortemente dispersivos no tempo. Além disso, é estudado o impacto do processo de amostragem e quantização aplicados aos sinais recebidos de cada terminal móvel para a estação de base, com a obtenção da caracterização espectral do ruído de quantização. Esta tese também fornece um modelo analítico convencional para a computação do desempenho da taxa de erros de bit (BER), com um método melhorado para o mesmo. Por último, esta tese visa a contextualização dos sistemas BS cooperation em soluções do tipo C-RAN

Estimation and detection techniques for doubly-selective channels in wireless communications

A fundamental problem in communications is the estimation of the channel. The signal transmitted through a communications channel undergoes distortions so that it is often received in an unrecognizable form at the receiver. The receiver must expend significant signal processing effort in order to be able to decode the transmit signal from this received signal. This signal processing requires knowledge of how the channel distorts the transmit signal, i.e. channel knowledge. To maintain a reliable link, the channel must be estimated and tracked by the receiver. The estimation of the channel at the receiver often proceeds by transmission of a signal called the 'pilot' which is known a priori to the receiver. The receiver forms its estimate of the transmitted signal based on how this known signal is distorted by the channel, i.e. it estimates the channel from the received signal and the pilot. This design of the pilot is a function of the modulation, the type of training and the channel. [Continues.

Novel Complex Adaptive Signal Processing Techniques Employing Optimally Derived Time-varying Convergence Factors With Applicatio

In digital signal processing in general, and wireless communications in particular, the increased usage of complex signal representations, and spectrally efficient complex modulation schemes such as QPSK and QAM has necessitated the need for efficient and fast-converging complex digital signal processing techniques. In this research, novel complex adaptive digital signal processing techniques are presented, which derive optimal convergence factors or step sizes for adjusting the adaptive system coefficients at each iteration. In addition, the real and imaginary components of the complex signal and complex adaptive filter coefficients are treated as separate entities, and are independently updated. As a result, the developed methods efficiently utilize the degrees of freedom of the adaptive system, thereby exhibiting improved convergence characteristics, even in dynamic environments. In wireless communications, acceptable co-channel, adjacent channel, and image interference rejection is often one of the most critical requirements for a receiver. In this regard, the fixed-point complex Independent Component Analysis (ICA) algorithm, called Complex FastICA, has been previously applied to realize digital blind interference suppression in stationary or slow fading environments. However, under dynamic flat fading channel conditions frequently encountered in practice, the performance of the Complex FastICA is significantly degraded. In this dissertation, novel complex block adaptive ICA algorithms employing optimal convergence factors are presented, which exhibit superior convergence speed and accuracy in time-varying flat fading channels, as compared to the Complex FastICA algorithm. The proposed algorithms are called Complex IA-ICA, Complex OBA-ICA, and Complex CBC-ICA. For adaptive filtering applications, the Complex Least Mean Square algorithm (Complex LMS) has been widely used in both block and sequential form, due to its computational simplicity. However, the main drawback of the Complex LMS algorithm is its slow convergence and dependence on the choice of the convergence factor. In this research, novel block and sequential based algorithms for complex adaptive digital filtering are presented, which overcome the inherent limitations of the existing Complex LMS. The block adaptive algorithms are called Complex OBA-LMS and Complex OBAI-LMS, and their sequential versions are named Complex HA-LMS and Complex IA-LMS, respectively. The performance of the developed techniques is tested in various adaptive filtering applications, such as channel estimation, and adaptive beamforming. The combination of Orthogonal Frequency Division Multiplexing (OFDM) and the Multiple-Input-Multiple-Output (MIMO) technique is being increasingly employed for broadband wireless systems operating in frequency selective channels. However, MIMO-OFDM systems are extremely sensitive to Intercarrier Interference (ICI), caused by Carrier Frequency Offset (CFO) between local oscillators in the transmitter and the receiver. This results in crosstalk between the various OFDM subcarriers resulting in severe deterioration in performance. In order to mitigate this problem, the previously proposed Complex OBA-ICA algorithm is employed to recover user signals in the presence of ICI and channel induced mixing. The effectiveness of the Complex OBA-ICA method in performing ICI mitigation and signal separation is tested for various values of CFO, rate of channel variation, and Signal to Noise Ratio (SNR)

Blind Receiver Design for OFDM Systems Over Doubly Selective Channels

We develop blind data detectors for orthogonal frequency-division multiplexing (OFDM) systems over doubly selective channels by exploiting both frequency-domain and time-domain correlations of the received signal. We thus derive two blind data detectors: a time-domain data detector and a frequency-domain data detector. We also contribute a reduced complexity, suboptimal version of a time-domain data detector that performs robustly when the normalized Doppler rate is less than 3%. Our frequency-domain data detector and suboptimal time-domain data detector both result in integer least-squares (LS) problems. We propose the use of the V-BLAST detector and the sphere decoder. The time-domain data detector is not limited to the Doppler rates less than 3%, but cannot be posed as an integer LS problem. Our solution is to develop an iterative algorithm that starts from the suboptimal time-domain data detector output. We also propose channel estimation and prediction algorithms using a polynomial expansion model, and these estimators work with data detectors (decision-directed mode) to reduce the complexity. The estimators for the channel statistics and the noise variance are derived using the likelihood function for the data. Our blind data detectors are fairly robust against the parameter mismatch