A framework for low-complexity iterative interference cancellation in communication systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 211-215).Communication over interference channels poses challenges not present for the more traditional additive white Gaussian noise (AWGN) channels. In order to approach the information limits of an interference channel, interference mitigation techniques need to be integrated with channel coding and decoding techniques. This thesis develops such practical schemes when the transmitter has no knowledge of the channel. The interference channel model we use is described by r = Hx + w, where r is the received vector, H is an interference matrix, x is the transmitted vector of data symbols chosen from a finite set, and w is a noise vector. The objective at the receiver is to detect the most likely vector x that was transmitted based on knowledge of r, H, and the statistics of w. Communication contexts in which this general integer programming problem appears include the equalization of intersymbol interference (ISI) channels, the cancellation of multiple-access interference (MAI) in code-division multiple-access (CDMA) systems, and the decoding of multiple-input multiple-output (MIMO) systems in fading environments. We begin by introducing mode-interleaved precoding, a transmitter preceding technique that conditions an interference channel so that the pairwise error probability of any two transmit vectors becomes asymptotically equal to the pairwise error probability of the same vectors over an AWGN channel at the same signal-to-noise ratio (SNR). While mode-interleaved precoding dramatically increases the complexity of exact ML detection, we develop iterated-decision detection to mitigate this complexity problem. Iterated-decision detectors use optimized multipass algorithms to successively cancel interference from r and generate symbol(cont.) decisions whose reliability increases monotonically with each iteration. When used in uncoded systems with mode-interleaved preceding, iterated-decision detectors asyrmptotically achieve the performance of ML detection (and thus the interference-free lower bound) with considerably lower complexity. We interpret these detectors as low-complexity approximations to message-passing algorithms. The integration of iterated-decision detectors into communication systems with coding is also developed to approach information rates close to theoretical limits. We present joint detection and decoding algorithms based on the iterated-decision detector with mode-interleaved precoding, and also develop analytic tools to predict the behavior of such systems. We discuss the use of binary codes for channels that support low information rates, and multilevel codes and lattice codes for channels that support higher information rates.by Albert M. Chan.Ph.D

A Framework for Low-Complexity Iterative Interference Cancellation in Communication Systems

Thesis Supervisor: Gregory W. Wornell Title: ProfessorCommunication over interference channels poses challenges not present for the more traditional additive white Gaussian noise (AWGN) channels. In order to approach the information limits of an interference channel, interference mitigation techniques need to be integrated with channel coding and decoding techniques. This thesis develops such practical schemes when the transmitter has no knowledge of the channel. The interference channel model we use is described by r = Hx + w, where r is the received vector, H is an interference matrix, x is the transmitted vector of data symbols chosen from a finite set, and w is a noise vector. The objective at the receiver is to detect the most likely vector x that was transmitted based on knowledge of r, H, and the statistics of w. Communication contexts in which this general integer programming problem appears include the equalization of intersymbol interference (ISI) channels, the cancellation of multiple-access interference (MAI) in code-division multiple-access (CDMA) systems, and the decoding of multiple-input multiple-output (MIMO) systems in fading environments. We begin by introducing mode-interleaved precoding, a transmitter precoding technique that conditions an interference channel so that the pairwise error probability of any two transmit vectors becomes asymptotically equal to the pairwise error probability of the same vectors over an AWGN channel at the same signal-to-noise ratio (SNR). While mode-interleaved precoding dramatically increases the complexity of exact ML detection, we develop iterated-decision detection to mitigate this complexity problem. Iterateddecision detectors use optimized multipass algorithms to successively cancel interference from r and generate symbol decisions whose reliability increases monotonically with each iteration. When used in uncoded systems with mode-interleaved precoding, iterated-decision detectors asymptotically achieve the performance ofML detection (and thus the interferencefree lower bound) with considerably lower complexity. We interpret these detectors as low-complexity approximations to message-passing algorithms. The integration of iterated-decision detectors into communication systems with coding is also developed to approach information rates close to theoretical limits. We present joint detection and decoding algorithms based on the iterated-decision detector with modeinterleaved precoding, and also develop analytic tools to predict the behavior of such systems. We discuss the use of binary codes for channels that support low information rates, and multilevel codes and lattice codes for channels that support higher information ratesHewlett-Packard under the MIT/HPAlliance, the National Science Foundation, the Semiconductor Research Corporation, Texas Instruments through the Leadership Universities Program, and the Natural Sciences and Engineering Research Council of Canada (NSERC) Postgraduate Scholarship Program

Parallel Interference Cancellation Based Turbo Space-Time Equalization in the SDMA Uplink

A novel Parallel Interference Cancellation (PIC) based turbo Space Time Equalizer (STE) structure designed for multiple antenna assisted uplink receivers is introduced. The proposed receiver structure allows the employment of non-linear type of detectors such as the Bayesian Decision Feedback (DF) assisted turbo STE or the Maximum Aposteriori (MAP) STE, while operating at a moderate computational cost. Receivers based on the proposed structure outperform the linear turbo detector benchmarker based on the Minimum Mean-Squared Error (MMSE) criterion, even if the latter aims for jointly detecting all transmitters’ signals. Additionally the PIC based receiver is capable of equalizing non-linear binary pre-coded channels. The performance difference between the presented algorithms is discussed using Extrinsic Information Transferfunction (EXIT) charts. Index Terms—PIC, EXIT chart, precoding, Bayesian, STE

A compressed sensing approach to block-iterative equalization: connections and applications to radar imaging reconstruction

The widespread of underdetermined systems has brought forth a variety of new algorithmic solutions, which capitalize on the Compressed Sensing (CS) of sparse data. While well known greedy or iterative threshold type of CS recursions take the form of an adaptive filter followed by a proximal operator, this is no different in spirit from the role of block iterative decision-feedback equalizers (BI-DFE), where structure is roughly exploited by the signal constellation slicer. By taking advantage of the intrinsic sparsity of signal modulations in a communications scenario, the concept of interblock interference (IBI) can be approached more cunningly in light of CS concepts, whereby the optimal feedback of detected symbols is devised adaptively. The new DFE takes the form of a more efficient re-estimation scheme, proposed under recursive-least-squares based adaptations. Whenever suitable, these recursions are derived under a reduced-complexity, widely-linear formulation, which further reduces the minimum-mean-square-error (MMSE) in comparison with traditional strictly-linear approaches. Besides maximizing system throughput, the new algorithms exhibit significantly higher performance when compared to existing methods. Our reasoning will also show that a properly formulated BI-DFE turns out to be a powerful CS algorithm itself. A new algorithm, referred to as CS-Block DFE (CS-BDFE) exhibits improved convergence and detection when compared to first order methods, thus outperforming the state-of-the-art Complex Approximate Message Passing (CAMP) recursions. The merits of the new recursions are illustrated under a novel 3D MIMO Radar formulation, where the CAMP algorithm is shown to fail with respect to important performance measures.A proliferação de sistemas sub-determinados trouxe a tona uma gama de novas soluções algorítmicas, baseadas no sensoriamento compressivo (CS) de dados esparsos. As recursões do tipo greedy e de limitação iterativa para CS se apresentam comumente como um filtro adaptativo seguido de um operador proximal, não muito diferente dos equalizadores de realimentação de decisão iterativos em blocos (BI-DFE), em que um decisor explora a estrutura do sinal de constelação. A partir da esparsidade intrínseca presente na modulação de sinais no contexto de comunicações, a interferência entre blocos (IBI) pode ser abordada utilizando-se o conceito de CS, onde a realimentação ótima de símbolos detectados é realizada de forma adaptativa. O novo DFE se apresenta como um esquema mais eficiente de reestimação, baseado na atualização por mínimos quadrados recursivos (RLS). Sempre que possível estas recursões são propostas via formulação linear no sentido amplo, o que reduz ainda mais o erro médio quadrático mínimo (MMSE) em comparação com abordagens tradicionais. Além de maximizar a taxa de transferência de informação, o novo algoritmo exibe um desempenho significativamente superior quando comparado aos métodos existentes. Também mostraremos que um equalizador BI-DFE formulado adequadamente se torna um poderoso algoritmo de CS. O novo algoritmo CS-BDFE apresenta convergência e detecção aprimoradas, quando comparado a métodos de primeira ordem, superando as recursões de Passagem de Mensagem Aproximada para Complexos (CAMP). Os méritos das novas recursões são ilustrados através de um modelo tridimensional para radares MIMO recentemente proposto, onde o algoritmo CAMP falha em aspectos importantes de medidas de desempenho

Efficient Radio Resource Allocation Schemes and Code Optimizations for High Speed Downlink Packet Access Transmission

An important enhancement on the Wideband Code Division Multiple Access (WCDMA) air interface of the 3G mobile communications, High Speed Downlink Packet Access (HSDPA) standard has been launched to realize higher spectral utilization efficiency. It introduces the features of multicode CDMA transmission and Adaptive Modulation and Coding (AMC) technique, which makes radio resource allocation feasible and essential. This thesis studies channel-aware resource allocation schemes, coupled with fast power adjustment and spreading code optimization techniques, for the HSDPA standard operating over frequency selective channel. A two-group resource allocation scheme is developed in order to achieve a promising balance between performance enhancement and time efficiency. It only requires calculating two parameters to specify the allocations of discrete bit rates and transmitted symbol energies in all channels. The thesis develops the calculation methods of the two parameters for interference-free and interference-present channels, respectively. For the interference-present channels, the performance of two-group allocation can be further enhanced by applying a clustering-based channel removal scheme. In order to make the two-group approach more time-efficient, reduction in matrix inversions in optimum energy calculation is then discussed. When the Minimum Mean Square Error (MMSE) equalizer is applied, optimum energy allocation can be calculated by iterating a set of eigenvalues and eigenvectors. By using the MMSE Successive Interference Cancellation (SIC) receiver, the optimum energies are calculated recursively combined with an optimum channel ordering scheme for enhancement in both system performance and time efficiency. This thesis then studies the signature optimization methods with multipath channel and examines their system performances when combined with different resource allocation methods. Two multipath-aware signature optimization methods are developed by applying iterative optimization techniques, for the system using MMSE equalizer and MMSE precoder respectively. A PAM system using complex signature sequences is also examined for improving resource utilization efficiency, where two receiving schemes are proposed to fully take advantage of PAM features. In addition by applying a short chip sampling window, a Singular Value Decomposition (SVD) based interference-free signature design method is presented

Investigation of coding and equalization for the digital HDTV terrestrial broadcast channel

Includes bibliographical references (p. 241-248).Supported by the Advanced Telecommunications Research Program.Julien J. Nicolas

Space-time-frequency methods for interference-limited communication systems

textTraditionally, noise in communication systems has been modeled as an additive, white Gaussian noise process with independent, identically distributed samples. Although this model accurately reflects thermal noise present in communication system electronics, it fails to capture the statistics of interference and other sources of noise, e.g. in unlicensed communication bands. Modern communication system designers must take into account interference and non-Gaussian noise to maximize efficiencies and capacities of current and future communication networks. In this work, I develop new multi-dimensional signal processing methods to improve performance of communication systems in three applications areas: (i) underwater acoustic, (ii) powerline, and (iii) multi-antenna cellular. In underwater acoustic communications, I address impairments caused by strong, time-varying and Doppler-spread reverberations (self-interference) using adaptive space-time signal processing methods. I apply these methods to array receivers with a large number of elements. In powerline communications, I address impairments caused by non-Gaussian noise arising from devices sharing the powerline. I develop and apply a cyclic adaptive modulation and coding scheme and a factor-graph-based impulsive noise mitigation method to improve signal quality and boost link throughput and robustness. In cellular communications, I develop a low-latency, high-throughput space-time-frequency processing framework used for large scale (up to 128 antenna) MIMO. This framework is used in the world's first 100-antenna MIMO system and processes up to 492 Gbps raw baseband samples in the uplink and downlink directions. My methods prove that multi-dimensional processing methods can be applied to increase communication system performance without sacrificing real-time requirements.Electrical and Computer Engineerin

Applications of Lattices over Wireless Channels

In wireless networks, reliable communication is a challenging issue due to many attenuation factors such as receiver noise, channel fading, interference and asynchronous delays. Lattice coding and decoding provide efficient solutions to many problems in wireless communications and multiuser information theory. The capability in achieving the fundamental limits, together with simple and efficient transmitter and receiver structures, make the lattice strategy a promising approach. This work deals with problems of lattice detection over fading channels and time asynchronism over the lattice-based compute-and-forward protocol. In multiple-input multiple-output (MIMO) systems, the use of lattice reduction significantly improves the performance of approximate detection techniques. In the first part of this thesis, by taking advantage of the temporal correlation of a Rayleigh fading channel, low complexity lattice reduction methods are investigated. We show that updating the reduced lattice basis adaptively with a careful use of previous channel realizations yields a significant saving in complexity with a minimal degradation in performance. Considering high data rate MIMO systems, we then investigate soft-output detection methods. Using the list sphere decoder (LSD) algorithm, an adaptive method is proposed to reduce the complexity of generating the list for evaluating the log-likelihood ratio (LLR) values. In the second part, by applying the lattice coding and decoding schemes over asynchronous networks, we study the impact of asynchronism on the compute-and-forward strategy. While the key idea in compute-and-forward is to decode a linear synchronous combination of transmitted codewords, the distributed relays receive random asynchronous versions of the combinations. Assuming different asynchronous models, we design the receiver structure prior to the decoder of compute-and-forward so that the achievable rates are maximized at any signal-to-noise-ratio (SNR). Finally, we consider symbol-asynchronous X networks with single antenna nodes over time-invariant channels. We exploit the asynchronism among the received signals in order to design the interference alignment scheme. It is shown that the asynchronism provides correlated channel variations which are proved to be sufficient to implement the vector interference alignment over the constant X network

Pré-codificação e equalização para sistemas SC-FDMA heterogéneos

Mestrado em Engenharia Electrónica e TelecomunicaçõesMobile traffic in cellular networks is increasing exponentially. Small-cells are considered as a key solution to meet these requirements. Under the same spectrum the small-cells and the associated macro-cell (forming the so called heterogeneous systems) must cooperate so that one system can adapt to the other. If no cooperation is considered then the small-cells will generate harmful interference at the macro-cell. Interference alignment (IA) is a precoding technique that is able to achieve the maximum degrees of freedom of the interference channel, and can efficiently deal with inter-systems interference. Single carrier frequency division multiple access (SC-FDMA) is a promising solution technique for high data rate uplink communications in future cellular systems. Conventional linear equalizers are not efficient to remove the residual inter-carrier interference of the SC-FDMA systems. For this reason, there has been significant interest in the design of nonlinear frequency domain equalizers in general and decision feedback equalizers in particular, with the iterative block decision feedback equalizer (IB-DFE) being the most promising nonlinear equalizer. In this dissertation we propose and evaluate joint interference alignment precoding at the small cell user terminals with iterative non-linear frequency domain equalizer at the receivers (macro base station and central unit) for SC-FDMA based heterogeneous networks. The small-cell precoders are designed by enforcing that all generated interference at the macro-cell is aligned in an orthogonal subspace to the macro-cell received signal subspace. This enforces that no performance degradation is observed at the macro cell. Then, we design an iterative nonlinear frequency domain equalizer at the macro-cell receiver that is able to recover the macro-cell spatial streams, in the presence of both small-cell and inter-carrier interferences. The results show that the proposed transmitter and receiver structures are robust to the inter-system interferences and at the same time are able to efficient separate the macro and small cells spatial streams.O trafego móvel nas redes celulares tem aumentado exponencialmente. As pico- células são consideradas como a solução chave para cumprir estes requisitos. Dentro do mesmo espectro, as pico-células e as macro-células (formando os chamados sistemas heterogéneos) precisam de colaborar de modo a que um sistema possa adaptar-se ao outro. Se não for considerada a cooperação, então as pico-células irão gerar interferência prejudicial na macro-célula. Interference alignment (IA) é uma técnica de précodificação que é capaz de atingir o grau máximo de liberdade do canal de interferência, e consegue lidar eficazmente com interferência entre sistemas. Single carrier frequency division multiple access (SC-FDMA) é uma solução técnica promissora para transmissão de dados em uplink, para sistemas celulares futuros. Equalizadores lineares convencionais não são eficientes a remover a interferência residual entre portadoras dos sistemas SC-FDMA. Por este motivo, tem havido interesse significativo no desenho de equalizadores não lineares no domínio da frequência em geral e em equalizadores baseados em decisão por feedback em particular, tendo o iterative block decision feedback equalizer (IB-DFE) como o equalizador não linear mais promissor. Nesta dissertação propomos e avaliamos précodificação de alinhamento de interferência nos terminais das pico-células em conjunto com equalizadores não lineares no domínio da frequência nos recetores (estação base da macro-célula e unidade central de processamento) para redes heterogéneas baseadas em SC-FDMA. Os précodificadores das pico-células são desenhados de maneira a obrigar a que toda a interferência gerada na macro-célula esteja alinhada num subespaço ortogonal em relação ao subespaço do sinal recebido na macro- célula. Isto obriga a que não seja observada degradação de desempenho na macro-célula. Em seguida, desenhamos um equalizador não linear no domínio da frequência no recetor da macro-célula capaz de recuperar os fluxos de dados da macro-célula, na presença de interferência tanto entre portadoras como das pico-células. Os resultados mostram que as estruturas de transmissão e receção propostas são robustas contra a interferência entre sistemas e ao mesmo tempo capaz de separar eficientemente os dados da macro e das pico células