High capacity high spectral efficiency transmission techniques in wireless broadband systems

Ph.DDOCTOR OF PHILOSOPH

Wireless multiuser communication systems: diversity receiver performance analysis, GSMuD design, and fading channel simulator

Multipath fading phenomenon is central to the design and analysis of wireless communication systems including multiuser systems. If untreated, the fading will corrupt the transmitted signal and often cause performance degradations such as increased communication error and decreased data rate, as compared to wireline channels with little or no multipath fading. On the other hand, this multipath fading phenomenon, if fully utilized, can actually lead to system designs that provide additional gains in system performance as compared to systems that experience non-fading channels.;The central question this thesis tries to answer is how to design and analyze a wireless multiuser system that takes advantage of the benefits the diversity multipath fading channel provides. Two particular techniques are discussed and analyzed in the first part of the thesis: quadrature amplitude modulation (QAM) and diversity receivers, including maximal ratio combining (MRC) and generalized selection combining (GSC). We consider the practical case of imperfect channel estimation (ICE) and develop a new decision variable (DV) of MRC receiver output for M-QAM. By deriving its moment generating function (MGF), we obtain the exact bit error rate (BER) performance under arbitrary correlated Rayleigh and Rician channels, with ICE. GSC provides a tradeoff between receiver complexity and performance. We study the effect of ICE on the GSC output effective SNR under generalized fading channels and obtain the exact BER results for M-QAM systems. The significance of this part lies in that these results provide system designers means to evaluate how different practical channel estimators and their parameters can affect the system\u27s performance and help them distribute system resources that can most effectively improve performance.;In the second part of the thesis, we look at a new diversity technique unique to multiuser systems under multipath fading channels: the multiuser diversity. We devise a generalized selection multiuser diversity (GSMuD) scheme for the practical CDMA downlink systems, where users are selected for transmission based on their respective channel qualities. We include the effect of ICE in the design and analysis of GSMuD. Based on the marginal distribution of the ranked user signal-noise ratios (SNRs), we develop a practical adaptive modulation and coding (AMC) scheme and equal power allocation scheme and statistical optimal 1-D and 2-D power allocation schemes, to fully exploit the available multiuser diversity. We use the convex optimization procedures to obtain the 1-D and 2-D power allocation algorithms, which distribute the total system power in the waterfilling fashion alone the user (1-D) or both user and time (2-D) for the power-limited and energy-limited system respectively. We also propose a normalized SNR based GSMuD scheme where user access fairness issues are explicitly addressed. We address various fairness-related performance metrics such as the user\u27s average access probability (AAP), average access time (AAT), and average wait time (AWT) in the absolute- and normalized-SNR based GSMuD. These metrics are useful for system designers to determine parameters such as optimal packet size and delay constraints.;We observe that Nakakagami-m fading channel model is widely applied to model the real world multipath fading channels of different severity. In the last part of the thesis, we propose a Nakagami-m channel simulator that can generate accurate channel coefficients that follow the Nakagami-m model, with independent quadrature parts, accurate phase distribution and arbitrary auto-correlation property. We demonstrate that the proposed simulator can be extremely useful in simulations involving Nakagami-m fading channel models, evident from the numerous simulation results obtained in earlier parts of the thesis where the fading channel coefficients are generated using this proposed simulator

A Continuous-Time Recurrent Neural Network for Joint Equalization and Decoding – Analog Hardware Implementation Aspects

Equalization and channel decoding are “traditionally” two cascade processes at the receiver side of a digital transmission. They aim to achieve a reliable and efficient transmission. For high data rates, the energy consumption of their corresponding algorithms is expected to become a limiting factor. For mobile devices with limited battery’s size, the energy consumption, mirrored in the lifetime of the battery, becomes even more crucial. Therefore, an energy-efficient implementation of equalization and decoding algorithms is desirable. The prevailing way is by increasing the energy efficiency of the underlying digital circuits. However, we address here promising alternatives offered by mixed (analog/digital) circuits. We are concerned with modeling joint equalization and decoding as a whole in a continuous-time framework. In doing so, continuous-time recurrent neural networks play an essential role because of their nonlinear characteristic and special suitability for analog very-large-scale integration (VLSI). Based on the proposed model, we show that the superiority of joint equalization and decoding (a well-known fact from the discrete-time case) preserves in analog. Additionally, analog circuit design related aspects such as adaptivity, connectivity and accuracy are discussed and linked to theoretical aspects of recurrent neural networks such as Lyapunov stability and simulated annealing

Soft detection and decoding in wideband CDMA systems

A major shift is taking place in the world of telecommunications towards a communications environment where a range of new data services will be available for mobile users. This shift is already visible in several areas of wireless communications, including cellular systems, wireless LANs, and satellite systems. The provision of flexible high-quality wireless data services requires a new approach on both the radio interface specification and the design and the implementation of the various transceiver algorithms. On the other hand, when the processing power available in the receivers increases, more complex receiver algorithms become feasible. The general problem addressed in this thesis is the application of soft detection and decoding algorithms in the wideband code division multiple access (WCDMA) receivers, both in the base stations and in the mobile terminals, so that good performance is achieved but that the computational complexity remains acceptable. In particular, two applications of soft detection and soft decoding are studied: coded multiuser detection in the CDMA base station and improved RAKE-based reception employing soft detection in the mobile terminal. For coded multiuser detection, we propose a novel receiver structure that utilizes the decoding information for multiuser detection. We analyze the performance and derive lower bounds for the capacity of interference cancellation CDMA receivers when using channel coding to improve the reliability of tentative decisions. For soft decision and decoding techniques in the CDMA downlink, we propose a modified maximal ratio combining (MRC) scheme that is more suitable for RAKE receivers in WCDMA mobile terminals than the conventional MRC scheme. We also introduce an improved soft-output RAKE detector that is especially suitable for low spreading gains and high-order modulation schemes. Finally we analyze the gain obtained through the use of Brennan's MRC scheme and our modified MRC scheme. Throughout this thesis Bayesian networks are utilized to develop algorithms for soft detection and decoding problems. This approach originates from the initial stages of this research, where Bayesian networks and algorithms using such graphical models (e.g. the so-called sum-product algorithm) were used to identify new receiver algorithms. In the end, this viewpoint may not be easily noticeable in the final form of the algorithms, mainly because the practical efficiency considerations forced us to select simplified variants of the algorithms. However, this viewpoint is important to emphasize the underlying connection between the apparently different soft detection and decision algorithms described in this thesis.reviewe

Spectrally efficient multicarrier communication systems: signal detection, mathematical modelling and optimisation

This thesis considers theoretical, analytical and engineering design issues relating to non-orthogonal Spectrally Efficient Frequency Division Multiplexing (SEFDM) communication systems that exhibit significant spectral merits when compared to Orthogonal FDM (OFDM) schemes. Alas, the practical implementation of such systems raises significant challenges, with the receivers being the bottleneck. This research explores detection of SEFDM signals. The mathematical foundations of such signals lead to proposals of different orthonormalisation techniques as required at the receivers of non-orthogonal FDM systems. To address SEFDM detection, two approaches are considered: either attempt to solve the problem optimally by taking advantage of special cases properties or to apply sub-optimal techniques that offer reduced complexities at the expense of error rates degradation. Initially, the application of sub-optimal linear detection techniques, such as Zero Forcing (ZF) and Minimum Mean Squared Error (MMSE), is examined analytically and by detailed modelling. To improve error performance a heuristic algorithm, based on a local search around an MMSE estimate, is designed by combining MMSE with Maximum Likelihood (ML) detection. Yet, this new method appears to be efficient for BPSK signals only. Hence, various variants of the sphere decoder (SD) are investigated. A Tikhonov regularised SD variant achieves an optimal solution for the detection of medium size signals in low noise regimes. Detailed modelling shows the SD detector to be well suited to the SEFDM detection, however, with complexity increasing with system interference and noise. A new design of a detector that offers a good compromise between computational complexity and error rate performance is proposed and tested through modelling and simulation. Standard reformulation techniques are used to relax the original optimal detection problem to a convex Semi-Definite Program (SDP) that can be solved in polynomial time. Although SDP performs better than other linear relaxations, such as ZF and MMSE, its deviation from optimality also increases with the deterioration of the system inherent interference. To improve its performance a heuristic algorithm based on a local search around the SDP estimate is further proposed. Finally, a modified SD is designed to implement faster than the local search SDP concept. The new method/algorithm, termed the pruned or constrained SD, achieves the detection of realistic SEFDM signals in noisy environments

MIMO Systems

In recent years, it was realized that the MIMO communication systems seems to be inevitable in accelerated evolution of high data rates applications due to their potential to dramatically increase the spectral efficiency and simultaneously sending individual information to the corresponding users in wireless systems. This book, intends to provide highlights of the current research topics in the field of MIMO system, to offer a snapshot of the recent advances and major issues faced today by the researchers in the MIMO related areas. The book is written by specialists working in universities and research centers all over the world to cover the fundamental principles and main advanced topics on high data rates wireless communications systems over MIMO channels. Moreover, the book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Low-Complexity Algorithms for Channel Estimation in Optimised Pilot-Assisted Wireless OFDM Systems

Orthogonal frequency division multiplexing (OFDM) has recently become a dominant transmission technology considered for the next generation fixed and mobile broadband wireless communication systems. OFDM has an advantage of lessening the severe effects of the frequency-selective (multipath) fading due to the band splitting into relatively flat fading subchannels, and allows for low-complexity transceiver implementation based on the fast Fourier transform algorithms. Combining OFDM modulation with multilevel frequency-domain symbol mapping (e.g., QAM) and spatial multiplexing (SM) over the multiple-input multiple-output (MIMO) channels, can theoretically achieve near Shannon capacity of the communication link. However, the high-rate and spectrumefficient system implementation requires coherent detection at the receiving end that is possible only when accurate channel state information (CSI) is available. Since in practice, the response of the wireless channel is unknown and is subject to random variation with time, the receiver typically employs a channel estimator for CSI acquisition. The channel response information retrieved by the estimator is then used by the data detector and can also be fed back to the transmitter by means of in-band or out-of-band signalling, so the latter could adapt power loading, modulation and coding parameters according to the channel conditions. Thus, design of an accurate and robust channel estimator is a crucial requirement for reliable communication through the channel, which is selective in time and frequency. In a MIMO configuration, a separate channel estimator has to be associated with each transmit/receive antenna pair, making the estimation algorithm complexity a primary concern. Pilot-assisted methods, relying on the insertion of reference symbols in certain frequencies and time slots, have been found attractive for identification of the doubly-selective radio channels from both the complexity and performance standpoint. In this dissertation, a family of the reduced-complexity estimators for the single and multiple-antenna OFDM systems is developed. The estimators are based on the transform-domain processing and have the same order of computational complexity, irrespective of the number of pilot subcarriers and their positioning. The common estimator structure represents a cascade of successive small-dimension filtering modules. The number of modules, as well as their order inside the cascade, is determined by the class of the estimator (one or two-dimensional) and availability of the channel statistics (correlation and signal-to-noise power ratio). For fine precision estimation in the multipath channels with statistics not known a priori, we propose recursive design of the filtering modules. Simulation results show that in the steady state, performance of the recursive estimators approaches that of their theoretical counterparts, which are optimal in the minimum mean square error (MMSE) sense. In contrast to the majority of the channel estimators developed so far, our modular-type architectures are suitable for the reconfigurable OFDM transceivers where the actual channel conditions influence the decision of what class of filtering algorithm to use, and how to allot pilot subcarrier positions in the band. In the pilot-assisted transmissions, channel estimation and detection are performed separately from each other over the distinct subcarrier sets. The estimator output is used only to construct the detector transform, but not as the detector input. Since performance of both channel estimation and detection depends on the signal-to-noise power vi ratio (SNR) at the corresponding subcarriers, there is a dilemma of the optimal power allocation between the data and the pilot symbols as these are conflicting requirements under the total transmit power constraint. The problem is exacerbated by the variety of channel estimators. Each kind of estimation algorithm is characterised by its own SNR gain, which in general can vary depending on the channel correlation. In this dissertation, we optimise pilot-data power allocation for the case of developed low-complexity one and two-dimensional MMSE channel estimators. The resultant contribution is manifested by the closed-form analytical expressions of the upper bound (suboptimal approximate value) on the optimal pilot-to-data power ratio (PDR) as a function of a number of design parameters (number of subcarriers, number of pilots, number of transmit antennas, effective order of the channel model, maximum Doppler shift, SNR, etc.). The resultant PDR equations can be applied to the MIMO-OFDM systems with arbitrary arrangement of the pilot subcarriers, operating in an arbitrary multipath fading channel. These properties and relatively simple functional representation of the derived analytical PDR expressions are designated to alleviate the challenging task of on-the-fly optimisation of the adaptive SM-MIMO-OFDM system, which is capable of adjusting transmit signal configuration (e.g., block length, number of pilot subcarriers or antennas) according to the established channel conditions

QoS-driven adaptive resource allocation for mobile wireless communications and networks

Quality-of-service (QoS) guarantees will play a critically important role in future mobile wireless networks. In this dissertation, we study a set of QoS-driven resource allocation problems for mobile wireless communications and networks. In the first part of this dissertation, we investigate resource allocation schemes for statistical QoS provisioning. The schemes aim at maximizing the system/network throughput subject to a given queuing delay constraint. To achieve this goal, we integrate the information theory with the concept of effective capacity and develop a unified framework for resource allocation. Applying the above framework, we con-sider a number of system infrastructures, including single channel, parallel channel, cellular, and cooperative relay systems and networks, respectively. In addition, we also investigate the impact of imperfect channel-state information (CSI) on QoS pro-visioning. The resource allocation problems can be solved e±ciently by the convex optimization approach, where closed-form allocation policies are obtained for different application scenarios. Our analyses reveal an important fact that there exists a fundamental tradeoff between throughput and QoS provisioning. In particular, when the delay constraint becomes loose, the optimal resource allocation policy converges to the water-filling scheme, where ergodic capacity can be achieved. On the other hand, when the QoS constraint gets stringent, the optimal policy converges to the channel inversion scheme under which the system operates at a constant rate and the zero-outage capacity can be achieved. In the second part of this dissertation, we study adaptive antenna selection for multiple-input-multiple-output (MIMO) communication systems. System resources such as subcarriers, antennas and power are allocated dynamically to minimize the symbol-error rate (SER), which is the key QoS metric at the physical layer. We propose a selection diversity scheme for MIMO multicarrier direct-sequence code- division-multiple-access (MC DS-CDMA) systems and analyze the error performance of the system when considering CSI feedback delay and feedback errors. Moreover, we propose a joint antenna selection and power allocation scheme for space-time block code (STBC) systems. The error performance is derived when taking the CSI feedback delay into account. Our numerical results show that when feedback delay comes into play, a tradeoff between performance and robustness can be achieved by dynamically allocating power across transmit antennas

Interleaved frequency Division Multiple Access with Multiple Antennas and Block spreading for Mobile Broadband Communications

Ph.DDOCTOR OF PHILOSOPH