Dissertations / Theses on the topic 'Minimum Mean Squared Error (MMSE)'

This paper investigates the effect of parameter selection for the decision feedback equalization (DFE) on communication performance through a dispersive underwater acoustic wireless channel (UAWC). A DFE based on minimum mean-square error (MMSE-DFE) criterion has been employed in the implementation for evaluation purposes. The output from the MMSE-DFE is input to the decoder to estimate the transmitted bit sequence. The main goal of this experimental simulation is to determine the best selection, such that the reduction in the computational overload is achieved without altering the performance of the system, where the computational complexity can be reduced by selecting an equalizer with a proper length. The system performance is tested for BPSK, QPSK, 8PSK and 16QAM modulation and a simulation for the system is carried out for Proakis channel A and real underwater wireless acoustic channel estimated during SPACE08 measurements to verify the selection.

The study of the spatial aspects of multipath in wireless communications environments is an increasingly important addition to the study of the temporal aspects in the search for ways to increase the utilization of the available wireless channel capacity. Traditionally, multipath has been viewed as an encumbrance in wireless communications, two of the major impairments being signal fading and intersymbol interference. However, recently the potential advantages of the diversity offered by multipath rich environments in multiuser communications have been recognised. Space time coding, for example, is a recent technique which relies on a rich scattering environment to create many practically uncorrelated signal transmission channels. Most often, statistical models have been used to describe the multipath environments in such applications. This approach has met with reasonable success but is limited when the statistical nature of a field is not easily determined or is not readily described by a known distribution.¶ Our primary aim in this thesis is to probe further into the nature of multipath environments in order to gain a greater understanding of their characteristics and diversity potential. We highlight the shortcomings of beamforming in a multipath multiuser access environment. We show that the ability of a beamformer to resolve two or more signals in angle directly limits its achievable capacity.¶ We test the probity of multipath as a source of spatial diversity, the limiting case of which is co-located users. We introduce the concept of separability to define the fundamental limits of a receiver to extract the signal of a desired user from interfering users’ signals and noise. We consider the separability performances of the minimum mean square error (MMSE), decorrelating (DEC) and matched filter (MF) detectors as we bring the positions of a desired and an interfering user closer together. We show that both the MMSE and DEC detectors are able to achieve acceptable levels of separability with the users as close as λ/10.¶ In seeking a better understanding of the nature of multipath fields themselves, we take two approaches. In the first we take a path oriented approach. The effects on the variation of the field power of the relative values of parameters such as amplitude and propagation direction are considered for a two path field. The results are applied to a theoretical analysis of the behaviour of linear detectors in multipath fields. This approach is insightful for fields with small numbers of multipaths, but quickly becomes mathematically complex.¶ In a more general approach, we take a field oriented view, seeking to quantify the complexity of arbitrary fields. We find that a multipath field has an intrinsic dimensionality of (πe)R/λ≈8.54R/λ, for a field in a two dimensional circular region, increasing only linearly with the radius R of the region. This result implies that there is no such thing as an arbitrarily complicated multipath field. That is, a field generated by any number of nearfield and farfield, specular and diffuse multipath reflections is no more complicated than a field generated by a limited number of plane waves. As such, there are limits on how rich multipath can be. This result has significant implications including means: i) to determine a parsimonious parameterization for arbitrary multipath fields and ii) of synthesizing arbitrary multipath fields with arbitrarily located nearfield or farfield, spatially discrete or continuous sources. The theoretical results are corroborated by examples of multipath field analysis and synthesis.

In this thesis, bit-by-bit power allocation in order to minimize mean-squared error (MSE) distortion of a basic communication system is studied. This communication system consists of a quantizer. There may or may not be a channel encoder and a Binary Phase Shift Keying (BPSK) modulator. In the quantizer, natural binary mapping is made. First, the case where there is no channel coding is considered. In the uncoded case, hard decision decoding is done at the receiver. It is seen that errors that occur in the more significant information bits contribute more to the distortion than less significant bits. For the uncoded case, the optimum power profile for each bit is determined analytically and through computer-based optimization methods like differential evolution. For low signal-to-noise ratio (SNR), the less significant bits are allocated negligible power compared to the more significant bits. For high SNRs, it is seen that the optimum bit-by-bit power allocation gives constant MSE gain in dB over the uniform power allocation. Second, the coded case is considered. Linear block codes like (3,2), (4,3) and (5,4) single parity check codes and (7,4) Hamming codes are used and soft-decision decoding is done at the receiver. Approximate expressions for the MSE are considered in order to find a near-optimum power profile for the coded case. The optimization is done through a computer-based optimization method (differential evolution). For a simple code like (7,4) Hamming code simulations show that up to 3 dB MSE gain can be obtained by changing the power allocation on the information and parity bits. A systematic method to find the power profile for linear block codes is also introduced given the knowledge of input-output weight enumerating function of the code. The information bits have the same power, and parity bits have the same power, and the two power levels can be different.

Use of imaging systems for performing various situational awareness tasks in militaryand commercial settings has a long history. There is increasing recognition,however, that a much better job can be done by developing non-traditional opticalsystems that exploit the task-specific system aspects within the imager itself. Insome cases, a direct consequence of this approach can be real-time data compressionalong with increased measurement fidelity of the task-specific features. In others,compression can potentially allow us to perform high-level tasks such as direct trackingusing the compressed measurements without reconstructing the scene of interest.In this dissertation we present novel advancements in feature-specific (FS) imagersfor large field-of-view surveillence, and estimation of temporal object-scene changesutilizing the compressive imaging paradigm. We develop these two ideas in parallel.In the first case we show a feature-specific (FS) imager that optically multiplexesmultiple, encoded sub-fields of view onto a common focal plane. Sub-field encodingenables target tracking by creating a unique connection between target characteristicsin superposition space and the target's true position in real space. This isaccomplished without reconstructing a conventional image of the large field of view.System performance is evaluated in terms of two criteria: average decoding time andprobability of decoding error. We study these performance criteria as a functionof resolution in the encoding scheme and signal-to-noise ratio. We also includesimulation and experimental results demonstrating our novel tracking method. Inthe second case we present a FS imager for estimating temporal changes in the objectscene over time by quantifying these changes through a sequence of differenceimages. The difference images are estimated by taking compressive measurementsof the scene. Our goals are twofold. First, to design the optimal sensing matrixfor taking compressive measurements. In scenarios where such sensing matrices arenot tractable, we consider plausible candidate sensing matrices that either use theavailable a priori information or are non-adaptive. Second, we develop closed-form and iterative techniques for estimating the difference images. We present results to show the efficacy of these techniques and discuss the advantages of each.

This thesis presents a novel method to solve the problem of estimating the carrier frequency set in an Orthogonal Frequency Division Multiplexing (OFDM) system. The approach is based on the minimization of the probability of symbol error. Hence, this approach is called the Minimum Symbol Error Rate (MSER) approach. An existing approach based on Maximum Likelihood (ML) is chosen to benchmark the performance of the MSER-based algorithm. The MSER approach is computationally intensive. The thesis evaluates the approximations that can be made to the MSER-based objective function to make the computation tractable. A modified gradient function based on the MSER objective is developed which provides better performance characteristics than the ML-based estimator. The estimates produced by the MSER approach exhibit lower Mean Squared Error compared to the ML benchmark. The performance of MSER-based estimator is simulated with Quaternary Phase Shift Keying (QPSK) symbols, but the algorithm presented is applicable to all complex symbol constellations.

Wireless communication systems have exploded in popularity over the past few decades. Due to their popularity, the demand for higher data rates by the users, and the high cost of wireless spectrum, wireless providers are actively seeking ways to improve the spectral efficiency of their networks. One promising technique to improve spectral efficiency is to equip the wireless devices with multiple antennas. If both the transmitter and receiver of a link are equipped with multiple antennas, they form a multiple-input multiple-output (MIMO) link. The multiple antennas at the nodes provide degrees-of-freedom that can be used for either sending multiple streams of data simultaneously (a technique known as spatial multiplexing), or for suppressing interference through linear combining, but not both. Due to this trade-off, careful allocation of how many streams each link should carry is important to ensure that each node has enough degrees-of-freedom available to suppress the interference and support its desired streams. How the streams are sent and received and how interference is suppressed is ultimately determined by the beamforming weights at the transmitters and the combining weights at the receivers. Determining these weights is, however, made difficult by their inherent interdependency. Our focus is on unplanned and/or dense single-hop networks, such as WLANs and femtocells, where each single-hop network is composed of an access point serving several associated clients. The objective of this research is to design algorithms for maximizing the performance of dense single-hop wireless networks of MIMO links. We address the problems of determining which links to schedule together at each time slot, how many streams to allocate to each link (if any), and the beamforming and combining weights that support those streams. This dissertation describes four key contributions as follows: - We classify any interference suppression technique as either unilateral interference suppression or bilateral interference suppression. We show that a simple bilateral interference suppression approach outperforms all known unilateral interference suppression approaches, even after searching for the best unilateral solution. - We propose an algorithm based on bilateral interference suppression whose goal is to maximize the sum rate of a set of interfering MIMO links by jointly optimizing which subset of transmitters should transmit, the number of streams for each transmitter (if any), and the beamforming and combining weights that support those streams. - We propose a framework for optimizing dense single-hop wireless networks. The framework implements techniques to address several practical issues that arise when implementing interference suppression, such as the overhead of performing channel measurements and communicating channel state information, the overhead of computing the beamforming and combining weights, and the overhead of cooperation between the access points. - We derive the optimal scheduler that maximizes the sum rate subject to proportional fairness. Simulations in ns-3 show that the framework, using the optimal scheduler, increases the proportionally fair aggregate goodput by up to 165% as compared to the aggregate goodput of 802.11n for the case of four interfering single-hop wireless networks with two clients each.

Single Carrier Frequency Division Multiple Access (SC-FDMA) is a multiple access transmission scheme that has been adopted in the 4th generation 3GPP Long Term Evolution (LTE) of cellular systems. In fact, its relatively low peak-to-average power ratio (PAPR) makes it ideal for the uplink transmission where the transmit power efficiency is of paramount importance. Multiple access among users is made possible by assigning different users to different sets of non-overlapping subcarriers. With the current LTE specifications, if an SC-FDMA system is operating at its full capacity and a new user requests channel access, the system redistributes the subcarriers in such a way that it can accommodate all of the users. Having less subcarriers for transmission, every user has to increase its modulation order (for example from QPSK to 16QAM) in order to keep the same transmission rate. However, increasing the modulation order is not always possible in practice and may introduce considerable complexity to the system. The technique presented in this thesis report describes a new way of adding more users to an SC-FDMA system by assigning the same sets of subcarriers to different users. The main advantage of this technique is that it allows the system to accommodate more users than conventional SC-FDMA and this corresponds to increasing the spectral efficiency without requiring a higher modulation order or using more bandwidth. During this work, special attentions wee paid to the cases where two and three source signals are being transmitted on the same set of subcarriers, which leads respectively to doubling and tripling the spectral efficiency. Simulation results show that by using the proposed technique, it is possible to add more users to any SC-FDMA system without increasing the bandwidth or the modulation order while keeping the same performance in terms of bit error rate (BER) as the conventional SC-FDMA. This is realized by slightly increasing the energy per bit to noise power spectral density ratio (Eb/N0) at the transmitters.

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.

No
In this work we present the work on the equalization algorithms to be used in future orthogonally multiplexed wavelets based multi signaling communication systems. The performance of ZF and MMSE algorithms has been analyzed using SISO and MIMO communication models. The transmitted electromagnetic waves were subjected through Rayleigh multipath fading channel with AWGN. The results showed that the performance of both of the above mentioned algorithms is the same in SISO channel but in MIMO environment MMSE has better performance.

No
We have studied the performance of multidimensional signaling techniques using wavelets based modulation within an orthogonally multiplexed communication system. The discrete wavelets transform and wavelet packet modulation techniques have been studied using Daubechies 2 and 8, Biothogonal1.5 and 3.1 and reverse Biorthognal 1.5 and 3.1 wavelets in the presence of Rayleigh multipath fading channels with AWGN. Results showed that DWT based systems outperform WPM systems both in terms of BER vs. SNR performance as well as processing. The performances of two different equalizations techniques, namely zero forcing (ZF) and minimum mean square error (MMSE), were also compared using DWT. When the channel is modeled using Rayleigh multipath fading, AWGN and ISI both techniques yield similar performance.

No
Study is presented into the performance of Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) and MIMO-DWT with transmit beamforming. Feedback loop has been used between the equalizer at the transmitter to the receiver which provided the channel state information which was then used to construct a steering matrix for the transmission sequence such that the received signals at the transmitter can be combined constructively in order to provide a reliable and improved system for next generation wireless systems. As convolution in time domain equals multiplication in frequency domain no such counterpart exist for the symbols in space, means linear convolution and Intersymbol Interference (ISI) generation so both zero forcing (ZF) and minimum mean squared error (MMSE) equalizations have been employed. The results show superior performance improvement and in addition allow keeping the processing, power and implementation cost at the transmitter which has less constraints and the results also show that both equalization algorithms perform alike in wavelets and the ISI is spread equally between different wavelet domains.

Parameter estimation and signal detection are the two key components of a wireless communication system. They directly impact the bit-error-ratio (BER) performance of the system. Several criteria have been successfully applied for parameter estimation and signal detection. They include maximum likelihood (ML), maximum a-posteriori probability (MAP), least square (LS) and minimum mean square error (MMSE) etc. In the linear detection framework, linear MMSE (LMMSE) and LS are the most popular ones. Nevertheless, these criteria do not necessarily minimize the BER, which is one of the key aspect of any communication receiver design. Thus, minimization of BER is tantamount to an important design criterion for a wireless receiver, the minimum bit/symbol error ratio (MBER/MSER). We term this design criterion as the minimum-error-probability (MEP). In this thesis, parameter estimation and signal detection have been extensively studied based on the MEP framework for various unexplored scenar-ios of a wireless communication system. Thus, this thesis has two broad categories of explorations, first parameter estimation and then signal detection. Traditionally, the MEP criterion has been well studied in the context of the discrete signal detection in the last one decade, albeit we explore this framework for the continuous parameter es-timation. We first use this framework for channel estimation in a frequency flat fading single-input single-output (SISO) system and then extend this framework to the carrier frequency offset (CFO) estimation of multi-user MIMO OFDM system. We observe a reasonably good SNR improvement to the tune of 1 to 2.5 dB at a fixed BER (tentatively at 10−3). In this context, it is extended to the scenario of multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) or MIMO-OFDM with pa-rameter estimation error statistics obtained from LMMSE only and checked its effect at the equalizer design using MEP and LMMSE criteria. In the second exploration of the MEP criterion, it is explored for signal detection in the context of MIMO-relay and MIMO systems. Various low complexity solutions are proposed to alleviate the effect of high computational complexity for the MIMO-relay. We also consider various configurations of relay like cognitive, parallel and multi-hop relaying. We also propose a data trans-mission scheme with a rate of 1/Ns (Ns is the number of antennas at the transmitter) with the help of the MEP criterion to design various components. In all these cases, we obtain considerable BER improvement compared to the existing solutions.

Parameter estimation and signal detection are the two key components of a wireless communication system. They directly impact the bit-error-ratio (BER) performance of the system. Several criteria have been successfully applied for parameter estimation and signal detection. They include maximum likelihood (ML), maximum a-posteriori probability (MAP), least square (LS) and minimum mean square error (MMSE) etc. In the linear detection framework, linear MMSE (LMMSE) and LS are the most popular ones. Nevertheless, these criteria do not necessarily minimize the BER, which is one of the key aspect of any communication receiver design. Thus, minimization of BER is tantamount to an important design criterion for a wireless receiver, the minimum bit/symbol error ratio (MBER/MSER). We term this design criterion as the minimum-error-probability (MEP). In this thesis, parameter estimation and signal detection have been extensively studied based on the MEP framework for various unexplored scenar-ios of a wireless communication system. Thus, this thesis has two broad categories of explorations, first parameter estimation and then signal detection. Traditionally, the MEP criterion has been well studied in the context of the discrete signal detection in the last one decade, albeit we explore this framework for the continuous parameter es-timation. We first use this framework for channel estimation in a frequency flat fading single-input single-output (SISO) system and then extend this framework to the carrier frequency offset (CFO) estimation of multi-user MIMO OFDM system. We observe a reasonably good SNR improvement to the tune of 1 to 2.5 dB at a fixed BER (tentatively at 10−3). In this context, it is extended to the scenario of multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) or MIMO-OFDM with pa-rameter estimation error statistics obtained from LMMSE only and checked its effect at the equalizer design using MEP and LMMSE criteria. In the second exploration of the MEP criterion, it is explored for signal detection in the context of MIMO-relay and MIMO systems. Various low complexity solutions are proposed to alleviate the effect of high computational complexity for the MIMO-relay. We also consider various configurations of relay like cognitive, parallel and multi-hop relaying. We also propose a data trans-mission scheme with a rate of 1/Ns (Ns is the number of antennas at the transmitter) with the help of the MEP criterion to design various components. In all these cases, we obtain considerable BER improvement compared to the existing solutions.

The study of the spatial aspects of multipath in wireless communications environments is an increasingly important addition to the study of the temporal aspects in the search for ways to increase the utilization of the available wireless channel capacity. Traditionally, multipath has been viewed as an encumbrance in wireless communications, two of the major impairments being signal fading and intersymbol interference. However, recently the potential advantages of the diversity offered by multipath rich environments in multiuser communications have been recognised. Space time coding, for example, is a recent technique which relies on a rich scattering environment to create many practically uncorrelated signal transmission channels. Most often, statistical models have been used to describe the multipath environments in such applications. This approach has met with reasonable success but is limited when the statistical nature of a field is not easily determined or is not readily described by a known distribution. ¶ ...

In this thesis, our focus is on low-complexity, high-performance detection algorithms for multi-antenna communication receivers. A key contribution in this thesis is the demonstration that efficient algorithms from metaheuristics and machine learning can be gainfully adapted for signal detection in multi- antenna communication receivers. We first investigate a popular metaheuristic known as the reactive tabu search (RTS), a combinatorial optimization technique, to decode the transmitted signals in large-dimensional communication systems. A basic version of the RTS algorithm is shown to achieve near-optimal performance for 4-QAM in large dimensions. We then propose a method to obtain a lower bound on the BER performance of the optimal detector. This lower bound is tight at moderate to high SNRs and is useful in situations where the performance of optimal detector is needed for comparison, but cannot be obtained due to very high computational complexity. To improve the performance of the basic RTS algorithm for higher-order modulations, we propose variants of the basic RTS algorithm using layering and multiple explorations. These variants are shown to achieve near-optimal performance in higher-order QAM as well. Next, we propose a new receiver called linear regression of minimum mean square error (MMSE) residual receiver (referred to as LRR receiver). The proposed LRR receiver improves the MMSE receiver by learning a linear regression model for the error of the MMSE receiver. The LRR receiver uses pilot data to estimate the channel, and then uses locally generated training data (not transmitted over the channel) to find the linear regression parameters. The LRR receiver is suitable for applications where the channel remains constant for a long period (slow-fading channels) and performs well. Finally, we propose a receiver that uses a committee of linear receivers, whose parameters are estimated from training data using a variant of the AdaBoost algorithm, a celebrated supervised classification algorithm in ma- chine learning. We call our receiver boosted MMSE (B-MMSE) receiver. We demonstrate that the performance and complexity of the proposed B-MMSE receiver are quite attractive for multi-antenna communication receivers.

In this thesis, our focus is on low-complexity, high-performance detection algorithms for multi-antenna communication receivers. A key contribution in this thesis is the demonstration that efficient algorithms from metaheuristics and machine learning can be gainfully adapted for signal detection in multi- antenna communication receivers. We first investigate a popular metaheuristic known as the reactive tabu search (RTS), a combinatorial optimization technique, to decode the transmitted signals in large-dimensional communication systems. A basic version of the RTS algorithm is shown to achieve near-optimal performance for 4-QAM in large dimensions. We then propose a method to obtain a lower bound on the BER performance of the optimal detector. This lower bound is tight at moderate to high SNRs and is useful in situations where the performance of optimal detector is needed for comparison, but cannot be obtained due to very high computational complexity. To improve the performance of the basic RTS algorithm for higher-order modulations, we propose variants of the basic RTS algorithm using layering and multiple explorations. These variants are shown to achieve near-optimal performance in higher-order QAM as well. Next, we propose a new receiver called linear regression of minimum mean square error (MMSE) residual receiver (referred to as LRR receiver). The proposed LRR receiver improves the MMSE receiver by learning a linear regression model for the error of the MMSE receiver. The LRR receiver uses pilot data to estimate the channel, and then uses locally generated training data (not transmitted over the channel) to find the linear regression parameters. The LRR receiver is suitable for applications where the channel remains constant for a long period (slow-fading channels) and performs well. Finally, we propose a receiver that uses a committee of linear receivers, whose parameters are estimated from training data using a variant of the AdaBoost algorithm, a celebrated supervised classification algorithm in ma- chine learning. We call our receiver boosted MMSE (B-MMSE) receiver. We demonstrate that the performance and complexity of the proposed B-MMSE receiver are quite attractive for multi-antenna communication receivers.

It is well known that communication using multiple antennas provides high data rate and reliability. Coding across space and time is necessary to fully exploit the gains offered by multiple input multiple output (MIMO) systems. One such popular method of coding for MIMO systems is space-time block coding. In applications where the terminals do not have enough physical space to mount multiple antennas, relaying or cooperation between multiple single antenna terminals can help achieve spatial diversity in such scenarios as well. Relaying techniques can also help improve the range and reliability of communication. Recently it has been shown that certain space-time block codes (STBCs) can be employed in a distributed fashion in single antenna relay networks to extract the same benefits as in point to point MIMO systems. Such STBCs are called distributed STBCs. However an important practical issue with STBCs and DSTBCs is its associated high maximum likelihood (ML) decoding complexity. The central theme of this thesis is to systematically construct STBCs and DSTBCs applicable for various scenarios such that are amenable for low decoding complexity. The first part of this thesis provides constructions of high rate STBCs from crossed product algebras that are minimum mean squared error (MMSE) optimal, i.e., achieves the least symbol error rate under MMSE reception. Moreover several previous constructions of MMSE optimal STBCs are found to be special cases of the constructions in this thesis. It is well known that STBCs from orthogonal designs offer single symbol ML decoding along with full diversity but the rate of orthogonal designs fall exponentially with the number of transmit antennas. Thus it is evident that there exists a tradeoff between rate and ML decoding complexity of full diversity STBCs. In the second part of the thesis, a definition of rate of a STBC is proposed and the problem of optimal tradeoff between rate and ML decoding complexity is posed. An algebraic framework based on extended Clifford algebras is introduced to study the optimal tradeoff for a class of multi-symbol ML decodable STBCs called ‘Clifford unitary weight (CUW) STBCs’ which include orthogonal designs as a special case. Code constructions optimally meeting this tradeoff are also obtained using extended Clifford algebras. All CUW-STBCs achieve full diversity as well. The third part of this thesis focusses on constructing DSTBCs with low ML decoding complexity for two hop, amplify and forward based relay networks under various scenarios. The symbol synchronous, coherent case is first considered and conditions for a DSTBC to be multi-group ML decodable are first obtained. Then three new classes of four-group ML decodable full diversity DSTBCs are systematically constructed for arbitrary number of relays. Next the symbol synchronous non-coherent case is considered and full diversity, four group decodable distributed differential STBCs (DDSTBCs) are constructed for power of two number of relays. These DDSTBCs have the best error performance compared to all previous works along with low ML decoding complexity. For the symbol asynchronous, coherent case, a transmission scheme based on orthogonal frequency division multiplexing (OFDM) is proposed to mitigate the effects of timing errors at the relay nodes and sufficient conditions for a DSTBC to be applicable in this new transmission scheme are given. Many of the existing DSTBCs including the ones in this thesis are found to satisfy these sufficient conditions. As a further extension, differential encoding is combined with the proposed transmission scheme to arrive at a new transmission scheme that can achieve full diversity in symbol asynchronous, non-coherent relay networks with no knowledge of the timing errors at the relay nodes. The DDSTBCs in this thesis are proposed for application in the proposed transmission scheme for symbol asynchronous, non-coherent relay networks. As a parallel to the non-coherent schemes based on differential encoding, we also propose non-coherent schemes for symbol synchronous and symbol asynchronous relay networks that are based on training. This training based transmission scheme leverages existing coherent DSTBCs for non-coherent communication in relay networks. Simulations show that this training scheme when used along with the coherent DSTBCs in this thesis outperform the best known DDSTBCs in the literature. Finally, in the last part of the thesis, connections between multi-group ML decodable unitary weight (UW) STBCs and groups with real elements are established for the first time. Using this connection, we translate the necessary and sufficient conditions for multi-group ML decoding of UW-STBCs entirely in group theoretic terms. We discuss various examples of multi-group decodable UW-STBCs together with their associated groups and list the real elements involved. These examples include orthogonal designs, quasi-orthogonal designs among many others.

It is well known that communication using multiple antennas provides high data rate and reliability. Coding across space and time is necessary to fully exploit the gains offered by multiple input multiple output (MIMO) systems. One such popular method of coding for MIMO systems is space-time block coding. In applications where the terminals do not have enough physical space to mount multiple antennas, relaying or cooperation between multiple single antenna terminals can help achieve spatial diversity in such scenarios as well. Relaying techniques can also help improve the range and reliability of communication. Recently it has been shown that certain space-time block codes (STBCs) can be employed in a distributed fashion in single antenna relay networks to extract the same benefits as in point to point MIMO systems. Such STBCs are called distributed STBCs. However an important practical issue with STBCs and DSTBCs is its associated high maximum likelihood (ML) decoding complexity. The central theme of this thesis is to systematically construct STBCs and DSTBCs applicable for various scenarios such that are amenable for low decoding complexity. The first part of this thesis provides constructions of high rate STBCs from crossed product algebras that are minimum mean squared error (MMSE) optimal, i.e., achieves the least symbol error rate under MMSE reception. Moreover several previous constructions of MMSE optimal STBCs are found to be special cases of the constructions in this thesis. It is well known that STBCs from orthogonal designs offer single symbol ML decoding along with full diversity but the rate of orthogonal designs fall exponentially with the number of transmit antennas. Thus it is evident that there exists a tradeoff between rate and ML decoding complexity of full diversity STBCs. In the second part of the thesis, a definition of rate of a STBC is proposed and the problem of optimal tradeoff between rate and ML decoding complexity is posed. An algebraic framework based on extended Clifford algebras is introduced to study the optimal tradeoff for a class of multi-symbol ML decodable STBCs called ‘Clifford unitary weight (CUW) STBCs’ which include orthogonal designs as a special case. Code constructions optimally meeting this tradeoff are also obtained using extended Clifford algebras. All CUW-STBCs achieve full diversity as well. The third part of this thesis focusses on constructing DSTBCs with low ML decoding complexity for two hop, amplify and forward based relay networks under various scenarios. The symbol synchronous, coherent case is first considered and conditions for a DSTBC to be multi-group ML decodable are first obtained. Then three new classes of four-group ML decodable full diversity DSTBCs are systematically constructed for arbitrary number of relays. Next the symbol synchronous non-coherent case is considered and full diversity, four group decodable distributed differential STBCs (DDSTBCs) are constructed for power of two number of relays. These DDSTBCs have the best error performance compared to all previous works along with low ML decoding complexity. For the symbol asynchronous, coherent case, a transmission scheme based on orthogonal frequency division multiplexing (OFDM) is proposed to mitigate the effects of timing errors at the relay nodes and sufficient conditions for a DSTBC to be applicable in this new transmission scheme are given. Many of the existing DSTBCs including the ones in this thesis are found to satisfy these sufficient conditions. As a further extension, differential encoding is combined with the proposed transmission scheme to arrive at a new transmission scheme that can achieve full diversity in symbol asynchronous, non-coherent relay networks with no knowledge of the timing errors at the relay nodes. The DDSTBCs in this thesis are proposed for application in the proposed transmission scheme for symbol asynchronous, non-coherent relay networks. As a parallel to the non-coherent schemes based on differential encoding, we also propose non-coherent schemes for symbol synchronous and symbol asynchronous relay networks that are based on training. This training based transmission scheme leverages existing coherent DSTBCs for non-coherent communication in relay networks. Simulations show that this training scheme when used along with the coherent DSTBCs in this thesis outperform the best known DDSTBCs in the literature. Finally, in the last part of the thesis, connections between multi-group ML decodable unitary weight (UW) STBCs and groups with real elements are established for the first time. Using this connection, we translate the necessary and sufficient conditions for multi-group ML decoding of UW-STBCs entirely in group theoretic terms. We discuss various examples of multi-group decodable UW-STBCs together with their associated groups and list the real elements involved. These examples include orthogonal designs, quasi-orthogonal designs among many others.

Στην παρούσα εργασία επιχειρείται η εφαρμογή αλγορίθμων αποθορυβοποίησης σε σήματα μουσικής και η εξαγωγή συμπερασμάτων σχετικά με την απόδοση αυτών ανά μουσικό είδος. Η κύρια επιδίωξη είναι να αποσαφηνιστούν τα βασικά προβλήματα της ενίσχυσης ήχων και να παρουσιαστούν οι διάφοροι αλγόριθμοι που έχουν αναπτυχθεί για την επίλυση των προβλημάτων αυτών. Αρχικά γίνεται μία σύντομη εισαγωγή στις βασικές έννοιες πάνω στις οποίες δομείται η τεχνολογία ενίσχυσης ομιλίας. Στην συνέχεια εξετάζονται και αναλύονται αντιπροσωπευτικοί αλγόριθμοι από κάθε κατηγορία τεχνικών αποθορυβοποίησης, την κατηγορία φασματικής αφαίρεσης, την κατηγορία στατιστικών μοντέλων και αυτήν του υποχώρου. Για να μπορέσουμε να αξιολογήσουμε την απόδοση των παραπάνω αλγορίθμων χρησιμοποιούμε αντικειμενικές μετρήσεις ποιότητας, τα αποτελέσματα των οποίων μας δίνουν την δυνατότητα να συγκρίνουμε την απόδοση του κάθε αλγορίθμου. Με την χρήση τεσσάρων διαφορετικών μεθόδων αντικειμενικών μετρήσεων διεξάγουμε τα πειράματα εξάγοντας μια σειρά ενδεικτικών τιμών που μας δίνουν την ευχέρεια να συγκρίνουμε είτε τυχόν διαφοροποιήσεις στην απόδοση των αλγορίθμων της ίδιας κατηγορίας είτε διαφοροποιήσεις στο σύνολο των αλγορίθμων. Από την σύγκριση αυτή γίνεται εξαγωγή χρήσιμων συμπερασμάτων σχετικά με τον προσδιορισμό των παραμέτρων κάθε αλγορίθμου αλλά και με την καταλληλότητα του κάθε αλγορίθμου για συγκεκριμένες συνθήκες θορύβου και για συγκεκριμένο μουσικό είδος.
This thesis attempts to apply Noise Reduction algorithms to signals of music and draw conclusions concerning the performance of each algorithm for every musical genre. The main aims are to clarify the basic problems of sound enhancement and present the various algorithms developed for solving these problems. After a brief introduction to basic concepts on sound enhancement we examine and analyze various algorithms that have been proposed at times in the literature for speech enhancement. These algorithms can be divided into three main classes: spectral subtractive algorithms, statistical-model-based algorithms and subspace algorithms. In order to evaluate the performance of the above algorithms we use objective measures of quality, the results of which give us the opportunity to compare the performance of each algorithm. By using four different methods of objective measures to conduct the experiments we draw a set of values that facilitate us to make within-class algorithm comparisons and across-class algorithm comparisons. From these comparisons we can draw conclusions on the determination of parameters for each algorithm and the appropriateness of algorithms for specific noise conditions and music genre.

In this dissertation the aim was to investigate the usage of algorithms found in computer science and apply suitable algorithms to the problem of decoding multiple-input multipleoutput (MIMO) space-time-frequency block coded signals. It was found that the sphere decoder is a specific implementation of the A* tree search algorithm that is well known in computer science. Based on this knowledge, the sphere decoder was extended to include a priori information in the maximum a posteriori probability (MAP) joint decoding of the STFC block coded MIMO signals. The added complexity the addition of a priori information has on the sphere decoder was investigated and compared to the sphere decoder without a priori information. To mitigate the potential additional complexity several algorithms that determine the order in which the symbols are decoded were investigated. Three new algorithms incorporating a priori information were developed and compared with two existing algorithms. The existing algorithms compared against are sorting based on the norms of the channel matrix columns and the sorted QR decomposition. Additionally, the zero forcing (ZF) and minimum mean squared error (MMSE) decoderswith and without decision feedback (DF) were also extended to include a priori information. The developed method of incorporating a priori information was compared to an existing algorithm based on receive vector translation (RVT). The limitation of RVT to quadrature phase shift keying (QPSK) and binary shift keying (BPSK) constellations was also shown in its derivation. The impact of the various symbol sorting algorithms initially developed for the sphere decoder on these decoders was also investigated. The developed a priori decoders operate in the log domain and as such accept a priori information in log-likelihood ratios (LLRs). In order to output LLRs to the forward error correcting (FEC) code, use of the max-log approximation, occasionally referred to as hard-to-soft decoding, was made. In order to test the developed decoders, an iterative turbo decoder structure was used together with an LDPC decoder to decode threaded algebraic space-time (TAST) codes in a Rayleigh faded MIMO channel. Two variables that have the greatest impact on the performance of the turbo decoder were identified: the hard limit value of the LLRs to the LDPC decoder and the number of independently faded bits in the LDPC code.
Dissertation (MEng)--University of Pretoria, 2013.
gm2014
Electrical, Electronic and Computer Engineering
unrestricted

Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia
As comunicações sem fios são, sem sombra de dúvida, o segmento de mais rápido crescimento da indústria de comunicações. Não só os telemóveis, que se tornaram uma ferramenta fulcral no mundo dos negócios e parte da vida quotidiana em todo o mundo, mas também os computadores e outros dispositivos de consumo de dados têm vindo a experienciar um crescimento exponencial na última década, trazendo novos desafios para a próxima geração de sistemas sem fios. As redes sem fio de quinta geração como próximo standard deverão ser capazes de satisfazer os requisitos impostos pela crescente procura de maior capacidade, ao mesmo tempo que garantem robustez, fiabilidade e maiores taxas de transferência.Uma das alternativas mais promissoras consiste no aumento do número de antenas tanto no transmissorcomo no recetor, isto é sistemas MIMO, que beneficiam de técnicas de processamento de sinal explorando uma diversidade adicional permitindo uma maior eficiência espectral ou uma transmissão robusta. No que diz respeito à obtenção de taxas de transferência de dados maiores e uma capacidade aumentada, empregando multiplexagem espacial combinada com sistemas OFDM ou que derivem dos mesmos é vista como uma das soluções mais poderosas. Particularmente, quando novas técnicas, como o TIBWB-OFDM, são adotados é possível obter um sistema de comunicação sem fios com uma melhor eficiência espectral e energética, robusto aos desvanecimentos profundos do canal selectivo na frequência.No entanto, existe alguma complexidade computacional inerente aos sistemas MIMO, que aumenta com onúmero de antenas no sistema, tornando o recetor muito mais complexo, nomeadamente na fase de equalização onde equalizadores presentes no estado de arte, como o MMSE e ZF, necessitam de inversões de matrizes de altas dimensões. Para superar este problema, é crucial considerar recetores iterativos, tais como EGC e MRC, que não requerem inversões de matrizes de canal de altas dimensões e, como tal, o recetor pode ser mantido a uma complexidade razoável.Portanto, o objetivo principal deste trabalho é conseguir um sistema com alta eficiência tanto a nívelespectral como a nível energético, capaz de lidar com as deficiências do canal MIMO, enquanto a complexidade do recetor se mantém reduzida através do uso de técnicas que não necessitem de inverter as matrizes de canal. Os resultados obtidos mostraram que empregar equalizadores lineares ou não lineares, tais como o EGC and MRC, permite ganhos substanciais relativamente ao sistema conventional MIMO usando o prefixo cíclico como intervalo de guarda nas mesmas condições. Além disso, os métodos iterativos de baixa complexidade mostraram um melhor desempenho quando usados em esquemas do tipo MIMO TIBWB-OFDM, sendo conseguido um desempenho excelente capaz de aproximar o match filter bound com apenas algumas iterações.
Wireless communications are, by any measure, the fastest growing segment of the communications industry. Not only the cellular phones, which have become a critical business tool and part of everyday lifeworldwide, but also computers and other data consuming devices have experienced exponential growth over the last decade, bringing some new challenges to the next generation wireless systems. Fifth generation wireless networks as the next standard must be able to meet the requirements imposed by the ever increasing demand in capacity, while guaranteeing robustness, reliability and higher data rates.One of the most promising alternatives is the increase in the number of antennas in both transmitterand receiver, i.e. multiple-input multiple-output (MIMO) systems, which leveraged on signal processingtechniques exploring added diversity may allow for higher spectral efficiency or improved robustness transmission. Regarding to achieve higher data rates and an increased capacity, employing spatial multiplexing combined with orthogonal frequency division multiplexing (OFDM) type systems is seen as one of most potential solutions. Particularly, when new techniques, such as the time-interleaved block-windowed burst OFDM (TIBWB-OFDM), are adopted is possible to achieve a highly spectral and power efficient wireless communication system, robust to the deep fades of the selective-frequency channel.However, there is some computational complexity inherent to the MIMO systems, that grows with thenumber of antennas elements, making the receiver much more complex, namely the equalisation stage where state-of-the art equalisers, such as minimum mean squared error (MMSE) and zero forcing (ZF), require for the inversion of the channel’s high dimension matrix. To overcome this problem, it is crucial to consider low complexity frequency-domain iterative receivers, such as equal gain combiner (EGC) and maximum ratio combiner (MRC), which do not require high dimension channel matrices inversions and as so, the receiver can be kept at an affordable complexity.Therefore, the main goal of this work is to achieve a spectral and power efficient system able to handle withthe impairments of the frequency-selective MIMO channel, while keeping the receiver complexity reducedthrough the use of techniques that does not require channel matrix inversions. Performance results shownthat employing linear equalisers or nonlinear equalisers, such as EGC and MRC, allows substantial gainsover the conventional MIMO employing cyclic prefix technique, in the same conditions. Furthermore, lowcomplexity iterative methods have their best performances when employed in the multiple-input multiple- output TIBWB-OFDM (MIMO TIBWB-OFDM) scheme, achieving excellent performance and approachingthe matched filter bound (MFB) with just a few iterations.

This dissertation designs linear transceivers for the multiuser downlink in multiple-input multiple-output (MIMO) systems. The designs rely on an uplink/downlink duality for the mean squared error (MSE) of each individual data stream. We first consider the design of transceivers assuming channel state information (CSI) at the transmitter. We consider minimization of the sum-MSE over all users subject to a sum power constraint on each transmission. Using MSE duality, we solve a computationally simpler convex problem in a virtual uplink. The transformation back to the downlink is simplified by our demonstrating the equality of the optimal power allocations in the uplink and downlink. Our second set of designs maximize the sum throughput for all users. We establish a series of relationships linking MSE to the signal-to-interference-plus-noise ratios of individual data streams and the information theoretic channel capacity under linear minimum MSE decoding. We show that minimizing the product of MSE matrix determinants is equivalent to sum-rate maximization, but we demonstrate that this problem does not admit a computationally efficient solution. We simplify the problem by minimizing the product of mean squared errors (PMSE) and propose an iterative algorithm based on alternating optimization with near-optimal performance. The remainder of the thesis considers the more practical case of imperfections in CSI. First, we consider the impact of delay and limited-rate feedback. We propose a system which employs Kalman prediction to mitigate delay; feedback rate is limited by employing adaptive delta modulation. Next, we consider the robust design of the sum-MSE and PMSE minimizing precoders with delay-free but imperfect estimates of the CSI. We extend the MSE duality to the case of imperfect CSI, and consider a new optimization problem which jointly optimizes the energy allocations for training and data stages along with the sum-MSE/PMSE minimizing transceivers. We prove the separability of these two problems when all users have equal estimation error variances, and propose several techniques to address the more challenging case of unequal estimation errors.