Turbo equalization for multiple-input multiple-output (MIMO) wireless communication systems

This dissertation investigates both of the frequency domain and time domain turbo equalization with multiple-input multiple-output (MIMO) fading channels for radio frequency and underwater acoustic communications. First, a low complexity frequency domain turbo equalization (FDTE) is proposed for the MIMO systems with zero padding (ZP) or cyclic prefix (CP) inserted between the transmitted data blocks and its performance is tested on the real-world UWA communications experiments. Second, as high speed communication system requires efficient bandwidth usage and power consumption, CP or ZP is not transmitted as auxiliary information. An inter-block interference cancelation and CP reconstruction algorithm is developed to re-arrange the channel matrix into a block diagonal one. This improvement makes the FDTE effectively detects the continuous data stream from the high speed UWA communications and its performance has been verified by processing data collected from the UWA communications experiment. Finally, a low complexity soft interference cancelation (SIC) time domain turbo equalizer for MIMO systems with high level modulation is proposed. Compared with the conventional linear or nonlinear turbo equalizers, the proposed SIC turbo equalizer can theoretically reach the bound set up by the ideal match filter and its bit error rate (BER) performance from Monte Carlo simulation achieves a lower error floor as well as a more rapid convergence speed. --Abstract, page iv

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

Low-complexity soft-decision feedback turbo equalization for multilevel modulations

This dissertation proposes two new decision feedback equalization schemes suitable for multilevel modulation systems employing turbo equalization. One is soft-decision feedback equalization (SDFE) that takes into account the reliability of both soft a priori information and soft decisions of the data symbols. The proposed SDFE exhibits lower signal to noise ratio (SNR) threshold that is required for water fall bit error rate (BER) and much faster convergence than the near-optimal exact minimum mean square error linear equalizer (Exact-MMSE-LE) for high-order constellation modulations. The proposed SDFE also offers a low computational complexity compared to the Exact-MMSE-LE. The drawback of the SDFE is that its coefficients cannot reach the matched filter bound (MFB) and therefore after a large number of iterations (e.g. 10), its performance becomes inferior to that of the Exact-MMSE-LE. Therefore, soft feedback intersymbol interference (ISI) canceller-based (SIC) structure is investigated. The SIC structure not only exhibits the same low complexity, low SNR threshold and fast convergence as the SDFE but also reaches the MFB after a large number of iterations. Both theoretical analysis and numerical simulations demonstrate why the SIC achieves MFB while the SDFE cannot. These two turbo equalization structures are also extended from single-input single-output (SISO) systems to multiple-input multiple-output (MIMO) systems and applied in high data-rate underwater acoustic (UWA) communications --Abstract, page iv

Energy and Spectral Efficient Wireless Communications

Energy and spectrum are two precious commodities for wireless communications. How to improve the energy and spectrum efficiency has become two critical issues for the designs of wireless communication systems. This dissertation is devoted to the development of energy and spectral efficient wireless communications. The developed techniques can be applied to a wide range of wireless communication systems, such as wireless sensor network (WSN) designed for structure health monitoring (SHM), medium access control (MAC) for multi-user systems, and cooperative spectrum sensing in cognitive radio systems. First, to improve the energy efficiency in SHM WSN, a new ultra low power (ULP) WSN is proposed to monitor the vibration properties of structures such as buildings, bridges, and the wings and bodies of aircrafts. The new scheme integrates energy harvesting, data sensing, and wireless communication into a unified process, and it achieves significant energy savings compared to existing WSNs. Second, a cross-layer collision tolerant (CT) MAC scheme is proposed to improve energy and spectral efficiency in a multi-user system with shared medium. When two users transmit simultaneously over a shared medium, a collision happens at the receiver. Conventional MAC schemes will discard the collided signals, which result in a waste of the precious energy and spectrum resources. In our proposed CT-MAC scheme, each user transmits multiple weighted replicas of a packet at randomly selected data slots in a frame, and the indices of the selected slots are transmitted in a special collision-free position slot at the beginning of each frame. Collisions of the data slots in the MAC layer are resolved by using multiuser detection (MUD) in the PHY layer. Compared to existing schemes, the proposed CT-MAC scheme can support more simultaneous users with a higher throughput. Third, a new cooperative spectrum sensing scheme is proposed to improve the energy and spectral efficiency of a cognitive radio network. A new Slepian-Wolf coded cooperation scheme is proposed for a cognitive radio network with two secondary users (SUs) performing cooperative spectrum sensing through a fusion center (FC). The proposed scheme can achieve significant performance gains compared to existing schemes

Sparse nonlinear optimization for signal processing and communications

This dissertation proposes three classes of new sparse nonlinear optimization algorithms for network echo cancellation (NEC), 3-D synthetic aperture radar (SAR) image reconstruction, and adaptive turbo equalization in multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications, respectively. For NEC, the proposed two proportionate affine projection sign algorithms (APSAs) utilize the sparse nature of the network impulse response (NIR). Benefiting from the characteristics of l₁-norm optimization, affine projection, and proportionate matrix, the new algorithms are more robust to impulsive interferences and colored input than the conventional adaptive algorithms. For 3-D SAR image reconstruction, the proposed two compressed sensing (CS) approaches exploit the sparse nature of the SAR holographic image. Combining CS with the range migration algorithms (RMAs), these approaches can decrease the load of data acquisition while recovering satisfactory 3-D SAR image through l₁-norm optimization. For MIMO UWA communications, a robust iterative channel estimation based minimum mean-square-error (MMSE) turbo equalizer is proposed for large MIMO detection. The MIMO channel estimation is performed jointly with the MMSE equalizer and the maximum a posteriori probability (MAP) decoder. The proposed MIMO detection scheme has been tested by experimental data and proved to be robust against tough MIMO channels. --Abstract, page iv

Iterative receivers and multichannel equalisation for time division multiple access systems

The thesis introduces receiver algorithms improving the performance of TDMA mobile radio systems. Particularly, we consider receivers utilising side information, which can be obtained from the error control coding or by having a priori knowledge of interference sources. Iterative methods can be applied in the former case and interference suppression techniques in the latter. Convolutional coding adds redundant information into the signal and thereby protects messages transmitted over a radio channel. In the coded systems the receiver is usually comprised of separate channel estimation, detection and channel decoding tasks due to complexity restrictions. This suboptimal solution suffers from performance degradation compared to the optimal solution achieved by optimising the joint probability of information bits, transmitted symbols and channel impulse response. Conventional receiver utilises estimated channel state information in the detection and detected symbols in the channel decoding to finally obtain information bits. However, the channel decoder provides also extrinsic information on the bit probabilities, which is independent of the received information at the equaliser input. Therefore it is beneficial to re-perform channel estimation and detection using this new extrinsic information together with the original input signal. We apply iterative receiver techniques mainly to Enhanced General Packet Radio System (EGPRS) using GMSK modulation for iterative channel estimation and 8-PSK modulation for iterative detection scheme. Typical gain for iterative detection is around 2 dB and for iterative channel estimation around 1 dB. Furthermore, we suggest two iteration rounds as a reasonable complexity/performance trade-off. To obtain further complexity reduction we introduce the soft trellis decoding technique that reduces the decoder complexity significantly in the iterative schemes. Cochannel interference (CCI) originates from the nearby cells that are reusing the same transmission frequency. In this thesis we consider CCI suppression by joint detection (JD) technique, which detects simultaneously desired and interfering signals. Because of the complexity limitations we only consider JD for two binary modulated signals. Therefore it is important to find the dominant interfering signal (DI) to achieve the best performance. In the presence of one strong DI, the JD provides major improvement in the receiver performance. The JD requires joint channel estimation (JCE) for the two signals. However, the JCE makes the implementation of the JD more difficult, since it requires synchronised network and unique training sequences with low cross-correlation for the two signals.reviewe

Polynomial matrix decomposition techniques for frequency selective MIMO channels

For a narrowband, instantaneous mixing multi-input, multi-output (MIMO) communications system, the channel is represented as a scalar matrix. In this scenario, singular value decomposition (SVD) provides a number of independent spatial subchannels which can be used to enhance data rates or to increase diversity. Alternatively, a QR decomposition can be used to reduce the MIMO channel equalization problem to a set of single channel equalization problems. In the case of a frequency selective MIMO system, the multipath channel is represented as a polynomial matrix. Thus conventional matrix decomposition techniques can no longer be applied. The traditional solution to this broadband problem is to reduce it to narrowband form by using a discrete Fourier transform (DFT) to split the broadband channel into N narrow uniformly spaced frequency bands and applying scalar decomposition techniques within each band. This describes an orthogonal frequency division multiplexing (OFDM) based system. However, a novel algorithm has been developed for calculating the eigenvalue decomposition of a para-Hermitian polynomial matrix, known as the sequential best rotation (SBR2) algorithm. SBR2 and its QR based derivatives allow a true polynomial singular value and QR decomposition to be formulated. The application of these algorithms within frequency selective MIMO systems results in a fundamentally new approach to exploiting spatial diversity. Polynomial matrix decomposition and OFDM based solutions are compared for a wide variety of broadband MIMO communication systems. SVD is used to create a robust, high gain communications channel for ultra low signal-to-noise ratio (SNR) environments. Due to the frequency selective nature of the channels produced by polynomial matrix decomposition, additional processing is required at the receiver resulting in two distinct equalization techniques based around turbo and Viterbi equalization. The proposed approach is found to provide identical performance to that of an existing OFDM scheme while supporting a wider range of access schemes. This work is then extended to QR decomposition based communications systems, where the proposed polynomial approach is found to not only provide superior bit-error-rate (BER) performance but significantly reduce the complexity of transmitter design. Finally both techniques are combined to create a nulti-user MIMO system that provides superior BER performance over an OFDM based scheme. Throughout the work the robustness of the proposed scheme to channel state information (CSI) error is considered, resulting in a rigorous demonstration of the capabilities of the polynomial approach

Advanced receivers for distributed cooperation in mobile ad hoc networks

Mobile ad hoc networks (MANETs) are rapidly deployable wireless communications systems, operating with minimal coordination in order to avoid spectral efficiency losses caused by overhead. Cooperative transmission schemes are attractive for MANETs, but the distributed nature of such protocols comes with an increased level of interference, whose impact is further amplified by the need to push the limits of energy and spectral efficiency. Hence, the impact of interference has to be mitigated through with the use PHY layer signal processing algorithms with reasonable computational complexity. Recent advances in iterative digital receiver design techniques exploit approximate Bayesian inference and derivative message passing techniques to improve the capabilities of well-established turbo detectors. In particular, expectation propagation (EP) is a flexible technique which offers attractive complexity-performance trade-offs in situations where conventional belief propagation is limited by computational complexity. Moreover, thanks to emerging techniques in deep learning, such iterative structures are cast into deep detection networks, where learning the algorithmic hyper-parameters further improves receiver performance. In this thesis, EP-based finite-impulse response decision feedback equalizers are designed, and they achieve significant improvements, especially in high spectral efficiency applications, over more conventional turbo-equalization techniques, while having the advantage of being asymptotically predictable. A framework for designing frequency-domain EP-based receivers is proposed, in order to obtain detection architectures with low computational complexity. This framework is theoretically and numerically analysed with a focus on channel equalization, and then it is also extended to handle detection for time-varying channels and multiple-antenna systems. The design of multiple-user detectors and the impact of channel estimation are also explored to understand the capabilities and limits of this framework. Finally, a finite-length performance prediction method is presented for carrying out link abstraction for the EP-based frequency domain equalizer. The impact of accurate physical layer modelling is evaluated in the context of cooperative broadcasting in tactical MANETs, thanks to a flexible MAC-level simulato

Estudio de la aplicación de técnicas de modulación OFDM para comunicaciones ópticas guiadas en el canal infrarrojo

En los últimos años, las redes guiadas de datos han experimentado una profunda transformación, alcanzándose tasas de transferencia de centenas de Megabits por segundo. Sin embargo, la filosofía de las comunicaciones personales ha cambiado y, hoy en día, el usuario final demanda conexiones de tipo inalámbrico que puedan brindarle una mayor flexibilidad y autonomía, pero que a su vez no presenten ningún tipo de limitación en cuanto a la máxima velocidad de transmisión, de tal forma que puedan desarrollarse sobre la misma aplicaciones tan exigentes como puede ser la transmisión de video de alta calidad. En este sentido, existen dos tecnologías que compiten para que lo anterior pueda llegar a hacerse realidad. Por un lado, encontramos la tecnología de radiofrecuencia, la cual ha recibido un gran apoyo por parte de las empresas y de los centros de investigación, lo que se ha traducido en el importante desarrollo que presentan actualmente las redes locales no guiadas IEEE 802.11. Por otro lado, encontramos la tecnología que hace uso del enlace infrarrojo, cuyo máximo exponente es el estándar IrDA para comunicaciones personales punto a punto de media velocidad. Esta no es, a día de hoy, una alternativa real dentro de las redes locales no guiadas, pero la tecnología infrarroja presenta una serie de características que hacen de ella una candidata muy atractiva para varios entornos de aplicación de redes de alta velocidad. En este contexto, el presente trabajo profundiza en la caracterización del canal infrarrojo con la idea de determinar sus límites prácticos de aplicación

Improved BDFE Using A Priori Information for Turbo Equalization

Turbo equalization improves communication system performance by iteratively exchanging information between soft-input soft-output (SISO) equalizer and SISO channel decoder. The trellis-based maximum a posteriori probability (MAP) algorithm serves as the optimum SISO equalizer for turbo equalization. However, MAP algorithm is unsuitable for systems with large modulation constellation size and severe inter-symbol interference (ISI) due to its prohibitively high computational complexity. In this paper, an improved SISO block decision feedback equalizer (BDFE) is proposed for low complexity turbo equalization. Unlike other sub-optimum equalizers which perform symbol by symbol detection, the proposed equalizer generates the soft output for each data bit by collecting information from a sequence of samples as in MAP algorithm. The sequence-based equalization is enabled by using not only soft a priori input from channel decoder, but also hard a priori information obtained from BDFE in previous iteration. The combination of soft a priori information and hard a priori information renders better performance with less iterations compared to other suboptimum algorithms. In addition, the computational complexity of the proposed algorithm is on the same order as conventional SISO BDFE algorithm, and is much lower compared to the trellis-based MAP algorithm