Master of Engineering (Research) / Multiple antenna systems are an appealing candidate for emerging fourth-generation wireless networks due to its potential to exploit space diversity for increasing conveyed throughput without wasting bandwidth and power resources. Particularly, layered space-time architecture (LST) proposed by Foschini, is a technique to achieve a significant fraction of the theoretical capacity with a reasonable implementation complexity. There has been a great deal of challenges in the detection of space-time signal; especially to design a low-complexity detector, which can efficiently remove multi-layer interference and approach the interference free bound. The application of iterative principle to joint detection and decoding has been a promising approach. It has been shown that, the iterative receiver with parallel interference canceller (PIC) has a low linear complexity and near interference free performance. Furthermore, it is widely accepted that the performance of digital communication systems can be considerably improved once the channel state information (CSI) is used to optimize the transmit signal. In this thesis, the problem of the design of a power allocation strategy in LST architecture to simultaneously optimize coding, diversity and weighting gains is addressed. A more practical scenario is also considered by assuming imperfect CSI at the receiver. The effect of channel estimation errors in LST architecture with an iterative PIC receiver is investigated. It is shown that imperfect channel estimation at an LST receiver results in erroneous decision statistics at the very first iteration and this error propagates to the subsequent iterations, which ultimately leads to severe degradation of the overall performance. We design a transmit power allocation policy to take into account the imperfection in the channel estimation process. The transmit power of various layers is optimized through minimization of the average bit error rate (BER) of the LST architecture with a low complexity iterative PIC detector. At the receiver, the PIC detector performs both interference regeneration and cancellation simultaneously for all layers. A convolutional code is used as the constituent code. The iterative decoding principle is applied to pass the a posteriori probability estimates between the detector and decoders. The decoder is based on the maximum a posteriori (MAP) algorithms. A closed-form optimal solution for power allocation in terms of the minimum BER is obtained. In order to validate the effectiveness of the proposed schemes, substantial simulation results are provided.

Master of Engineering (Research) / Multiple antenna systems are an appealing candidate for emerging fourth-generation wireless networks due to its potential to exploit space diversity for increasing conveyed throughput without wasting bandwidth and power resources. Particularly, layered space-time architecture (LST) proposed by Foschini, is a technique to achieve a significant fraction of the theoretical capacity with a reasonable implementation complexity. There has been a great deal of challenges in the detection of space-time signal; especially to design a low-complexity detector, which can efficiently remove multi-layer interference and approach the interference free bound. The application of iterative principle to joint detection and decoding has been a promising approach. It has been shown that, the iterative receiver with parallel interference canceller (PIC) has a low linear complexity and near interference free performance. Furthermore, it is widely accepted that the performance of digital communication systems can be considerably improved once the channel state information (CSI) is used to optimize the transmit signal. In this thesis, the problem of the design of a power allocation strategy in LST architecture to simultaneously optimize coding, diversity and weighting gains is addressed. A more practical scenario is also considered by assuming imperfect CSI at the receiver. The effect of channel estimation errors in LST architecture with an iterative PIC receiver is investigated. It is shown that imperfect channel estimation at an LST receiver results in erroneous decision statistics at the very first iteration and this error propagates to the subsequent iterations, which ultimately leads to severe degradation of the overall performance. We design a transmit power allocation policy to take into account the imperfection in the channel estimation process. The transmit power of various layers is optimized through minimization of the average bit error rate (BER) of the LST architecture with a low complexity iterative PIC detector. At the receiver, the PIC detector performs both interference regeneration and cancellation simultaneously for all layers. A convolutional code is used as the constituent code. The iterative decoding principle is applied to pass the a posteriori probability estimates between the detector and decoders. The decoder is based on the maximum a posteriori (MAP) algorithms. A closed-form optimal solution for power allocation in terms of the minimum BER is obtained. In order to validate the effectiveness of the proposed schemes, substantial simulation results are provided.

This dissertation focuses on three major advances on multiple-input multiple-output (MIMO) systems. The first studies and compares decoding algorithms for multi-layered space time coded (MLSTC) systems. These are single user systems that combine spatial multiplexing and transmit diversity. Each layer consists of a space time code. The detection algorithms are based on multi-user detection theory. We consider joint, interference nulling and cancellation, and spatial sequence estimation algorithms. As part of joint detection algorithms, the sphere decoder is studied and its complexity is evaluated over MIMO channels. The second part contributes to the field of space frequency time (SFT) coding for MIMO-OFDM systems. It proposes a full spatial and frequency diversity codes at much lower number of trellis states. The third part proposes and compares uplink scheduling algorithms for multiuser systems with spatial multiplexing. Several scheduling criteria are examined and compared. The capacity and error rate study of MLSTBC reveals the performance of the detection algorithms and their advantage over other open loop MIMO schemes. The results show that the nulling and cancellation operations limit the diversity of the system to the first detected layer in serial algorithms. For parallel algorithms, the diversity of the system is dominated by the performance after parallel nulling. Theoretically, parallel cancellation should provide full receive diversity per layer but error propagations as a result of cancellation prevent the system from reaching this goal. However, parallel cancellation provides some gains but it doesn't increase the diversity. On the other hand, joint detection provides full receive diversity per layer. It could be practically implemented with sphere decoding which has a cubic complexity at high SNR. The results of the SFT coding show the superiority of the IQ-SFT codes over other codes at the same number of sates. The IQ-SFT codes achieve full spatial and frequency diversity at much lower number of trellis states compared to conventional codes. For V-BLAST scheduling, we propose V-BLAST capacity maximizing scheduler and we show that scheduling based on optimal MIMO capacity doesn't work well for V-BLAST. The results also show that maximum minimum singularvalue (MaxMinSV) scheduling performs very close to the V-BLAST capacity maximizing scheduler since it takes into account both the channel power and the orthogonality of the channel. / Ph. D.

Technological progress in the field of wireless communications over the past few years has only been matched by the increasing demand for sophisticated services at lower costs. A significant breakthrough was achieved in the design of efficient wireless communication systems with the advent of the diversity concept. Spatial diversity exploits the availability of multiple spatial paths between the transmitter and receiver by placing antenna arrays at either end. In addition to improving the reliability of communication by creating redundant copies of the transmitted information at the receiver, wireless transceivers with multiple antennas exploit the spatial degrees of freedom to multiplex multiple streams of data and achieve significant gains in spectral efficiencies. In this thesis, we design spatial diversity techniques for slow-fading wireless channels. There are two parts to this thesis: In Part I we propose spatial diversity techniques for point-to-point single-user wireless systems, while in Part II we propose multiuser cooperative diversity techniques for multiuser wireless communication systems. In the first part, we propose a set of new wireless communication techniques for multiple-input, multiple-output (MIMO) channels over Rayleigh slow-fading wireless channels. We introduce MIMO transceivers that achieve high data rates and low error rates using a class of MIMO systems known as layered space-time (ST) architectures, which use low complexity, suboptimal decoders such as successive cancellation (SC) decoders. We propose a set of improved layered space-time architectures and show that it is possible to achieve near-optimal error performance over MIMO channels while requiring just SC decoding at the receiver. We show that these architectures achieve high rate and diversity gains. We also show that some of the proposed layered space-time architectures could find applications in multiple-access communications as low-complexity solutions for achieving near-optimum performance. In the second part of this thesis, we propose novel techniques for cooperative communication between terminals in multiuser wireless communication systems. Cooperative communication is a concept where neighboring terminals share their antennas and signal processing resources to create a virtual transmit array . In addition to transmitting their own information, users in a cooperative communication system listen to transmission from other users and relay this information to the destination, thus creating multiple paths between transmitter and receiver. This form of diversity, known as cooperative diversity, helps improve the overall reliability of all the users in a network. We start with a simple three node multiple-access system where two users are communicating with a common destination. We propose new high-rate cooperation strategies which achieve the full diversity gain offered by the cooperative channel for this simple system. We propose a new framework to address the tradeoff between cooperation and independent transmission over a multiple access channel and determine the conditions under which each idea is better than the other. Finally, we propose a high rate cooperation protocol which achieves the maximum diversity over a multiple access system with an arbitrary number of users and achieves high rates which scale favorably as the number of users increases.

Soft iterative detection and decoding techniques have been shown to be able to achieve near-capacity performance in multiple-antenna systems. To obtain the optimal soft information by marginalization over the entire observation space is intractable; and the current literature is unable to guide us towards the best way to obtain the suboptimal soft information. In this thesis, several existing soft-input soft-output (SISO) detectors, including minimum mean-square error-successive interference cancellation (MMSE-SIC), list sphere decoding (LSD), and Fincke-Pohst maximum-a-posteriori (FPMAP), are examined. Prior research has demonstrated that LSD and FPMAP outperform soft-equalization methods (i.e., MMSE-SIC); however, it is unclear which of the two scheme is superior in terms of performance-complexity trade-off. A comparison is conducted to resolve the matter. In addition, an improved scheme is proposed to modify LSD and FPMAP, providing error performance improvement and a reduction in computational complexity simultaneously. Although list-type detectors such as LSD and FPMAP provide outstanding error performance, issues such as the optimal initial sphere radius, optimal radius update strategy, and their highly variable computational complexity are still unresolved. A new detection scheme is proposed to address the above issues with fixed detection complexity, making the scheme suitable for practical implementation. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2008-07-08 19:29:17.66

The search for the closest lattice point arises in many communication problems, and is known to be NP-hard. The Maximum Likelihood (ML) Detector is the optimal detector which yields an optimal solution to this problem, but at the expense of high computational complexity. Existing near-optimal methods used to solve the problem are based on the Sphere Decoder (SD), which searches for lattice points confined in a hyper-sphere around the received point. The SD has emerged as a powerful means of finding the solution to the ML detection problem for MIMO systems. However the bottleneck lies in the determination of the initial radius. This thesis is concerned with the detection of transmitted wireless signals in Multiple-Input Multiple-Output (MIMO) digital communication systems as efficiently and effectively as possible. The main objective of this thesis is to design efficient ML detection algorithms for MIMO systems based on the depth-first search (DFS) algorithms whilst taking into account complexity and bit error rate performance requirements for advanced digital communication systems. The increased capacity and improved link reliability of MIMO systems without sacrificing bandwidth efficiency and transmit power will serve as the key motivation behind the study of MIMO detection schemes. The fundamental principles behind MIMO systems are explored in Chapter 2. A generic framework for linear and non-linear tree search based detection schemes is then presented Chapter 3. This paves way for different methods of improving the achievable performance-complexity trade-off for all SD-based detection algorithms. The suboptimal detection schemes, in particular the Minimum Mean Squared Error-Successive Interference Cancellation (MMSE-SIC), will also serve as pre-processing as well as comparison techniques whilst channel capacity approaching Low Density Parity Check (LDPC) codes will be employed to evaluate the performance of the proposed SD. Numerical and simulation results show that non-linear detection schemes yield better performance compared to linear detection schemes, however, at the expense of a slight increase in complexity. The first contribution in this thesis is the design of a near ML-achieving SD algorithm for MIMO digital communication systems that reduces the number of search operations within the sphere-constrained search space at reduced detection complexity in Chapter 4. In this design, the distance between the ML estimate and the received signal is used to control the lower and upper bound radii of the proposed SD to prevent NP-complete problems. The detection method is based on the DFS algorithm and the Successive Interference Cancellation (SIC). The SIC ensures that the effects of dominant signals are effectively removed. Simulation results presented in this thesis show that by employing pre-processing detection schemes, the complexity of the proposed SD can be significantly reduced, though at marginal performance penalty. The second contribution is the determination of the initial sphere radius in Chapter 5. The new initial radius proposed in this thesis is based on the variable parameter α which is commonly based on experience and is chosen to ensure that at least a lattice point exists inside the sphere with high probability. Using the variable parameter α, a new noise covariance matrix which incorporates the number of transmit antennas, the energy of the transmitted symbols and the channel matrix is defined. The new covariance matrix is then incorporated into the EMMSE model to generate an improved EMMSE estimate. The EMMSE radius is finally found by computing the distance between the sphere centre and the improved EMMSE estimate. This distance can be fine-tuned by varying the variable parameter α. The beauty of the proposed method is that it reduces the complexity of the preprocessing step of the EMMSE to that of the Zero-Forcing (ZF) detector without significant performance degradation of the SD, particularly at low Signal-to-Noise Ratios (SNR). More specifically, it will be shown through simulation results that using the EMMSE preprocessing step will substantially improve performance whenever the complexity of the tree search is fixed or upper bounded. The final contribution is the design of the LRAD-MMSE-SIC based SD detection scheme which introduces a trade-off between performance and increased computational complexity in Chapter 6. The Lenstra-Lenstra-Lovasz (LLL) algorithm will be utilised to orthogonalise the channel matrix H to a new near orthogonal channel matrix H ̅.The increased computational complexity introduced by the LLL algorithm will be significantly decreased by employing sorted QR decomposition of the transformed channel H ̅ into a unitary matrix and an upper triangular matrix which retains the property of the channel matrix. The SIC algorithm will ensure that the interference due to dominant signals will be minimised while the LDPC will effectively stop the propagation of errors within the entire system. Through simulations, it will be demonstrated that the proposed detector still approaches the ML performance while requiring much lower complexity compared to the conventional SD.