Doktorsavhandlingar vid Signaler och System

Abstract

Many advanced transmission techniques utilize channel state information (CSI) at the transmitter (CSIT) to improve throughput, spectral efficiency, power efficiency, and other performance metrics. Estimating CSI accurately is important to fully benefit from many of these techniques. In situations where users travel at high speed, the channel can change rapidly, especially in small-scale fading environments. In many systems, there is also a delay between measuring CSI and using it for transmission. If the channel changes significantly during this delay, CSI becomes outdated and the benefits of advanced transmission techniques are typically negatively affected. Long-range channel prediction can be used to counteract this delay and enable advanced transmission to vehicles that travel at high velocity. Conventional prediction methods use channel extrapolation and have a limited prediction horizon that does not support high vehicular velocities for the current size of these delays. The predictor antenna concept has been shown to increase the prediction horizon by at least an order-of-magnitude. It does so by placing an antenna array on the exterior of a vehicle, in the direction of travel. The first antenna can then measure the channel at positions that the following antennas will visit later.

This thesis uses channel measurements to investigate how practical aspects affect the prediction performance of predictions based on predictor antennas. It also develops a general framework that can be used to calculate the predictions in a real system. This includes addressing the causality of all the processing methods involved and adapting these methods to the design of the system and the radio environment. In a massive multiple-inputmultiple-output (MIMO) system, multi-user transmission is enabled by channel prediction and increases the sum capacity by 100% compared to 1 ms old channel estimates at a velocity of 150 km/h. This is achieved with relatively dense pilots in time. The prediction performance of the proposed framework is shown to degrade if pilots are spread further than 0.3–0.5 wavelengths in space, if spline interpolation is used to interpolate between the channel estimates.

Abstract

With the advent and widespread applications of high data-rate wireless services and devices, two of the fundamental resources in wireless communication have become extremely important and are scarce. These two resources are bandwidth and energy respectively. To tackle the problem of the ever-growing requirements of bandwidth, the paradigm of cognitive radio has been proposed in the literature where the users without any license are capable of utilizing the wireless radio spectrum allocated to the licensed user when it is idle. The performance of such a dynamic spectrum allocation policy depends heavily on the unlicensed users' ability to detect the vacancy in a licensed user's radio spectrum. Different types of detection algorithms have been investigated in the literature for this purpose. Some classic detection techniques like energy detection, matched filter detection, cyclo-stationary detector, generalized likelihood ratio test detector have found significant applications in sensing the licensed spectrum. These group of detection techniques focuses on collecting samples and performing the detection in a non-sequential fashion. The sequential counterpart of such techniques which are implemented sequentially at every time instant has also been studied extensively.

Tackling the evergrowing energy requirement has been the other major challenge for wireless communication. To address this issue, a significant amount of research has been dedicated to the idea of incorporating the capability of energy harvesting in wireless devices. Further research in this domain has also introduced the idea of wireless energy sharing, where individual users are additionally capable of sharing energy with each other. The problem with such systems is the inherently stochastic nature of the energy harvesting process. Furthermore, there are practical limitations of the size of the battery for each user, which limits the amount of energy that can be stored at a particular time instant.

Motivated by these two factors, the work presented in this thesis has its focus on cognitive radio networks with energy harvesting capability. In the aforementioned network, unlicensed users are concerned with achieving two fundamental goals. Firstly, they want to efficiently utilize the radio spectrum when the licensed user is not active, which results in the sum-throughput maximization problem with energy harvesting constraint. We have also investigated this problem where individual unlicensed users are capable of sharing energy with each other. Secondly, they want to detect the change in the activity in the licensed user spectrum as soon as possible. Motivated by this goal, we have investigated the problem of change point detection delay minimization in wireless sensor networks with energy harvesting constraints in a decentralized setting. Furthermore, we have explored the detection delay parameter for the decentralized settings with local decisions with similar constraints of energy availability due to energy harvesting.

Abstract

In this thesis work we develop a new algorithm for detecting joint changes in statistical behavior of multiple, simultaneously recorded, signals. Such signal analysis is commonly known as multivariate change point (CP) detection (CPD) and is of interest in many scientific and engineering applications.

First we review some of the existing CPD algorithms, where special attention is given to the Bayesian methods. Traditionally, many of the previous works on Bayesian CPD have focused on sampling based methods using Markov Chain Monte Carlo (MCMC). More recent work has shown that it is possible to avoid the computationally expensive MCMC methods by using a technique that is reminiscent of the forward-backward algorithm used for hidden Markov models. We revisit that technique and extend it to a multivariate CPD scenario where subsets of the monitored signals are affected at each CP. The extended algorithm has excellent CPD accuracy, but unfortunately, this fully Bayesian approach quickly becomes intractable when the size of the data set increases.

For large data sets, we propose a two-stage algorithm which, instead of considering all possible combinations of joint CPs as in the fully Bayesian approach, only computes an approximate solution to the most likely combination. In the first stage, the time series are processed in parallel with a univariate CPD algorithm. In the second stage, a dynamic program (DP) is used to search for the combination of joint CPs that best explains the CPs detected by the first stage. The computational efficiency of the second stage is improved by incorporating a pruning condition which reduces the search space of the DP. 

To motivate the algorithm, we apply it to measurements of radio channels in factory environments. The analysis shows that certain subsets of radio channels often experiences simultaneous changes in channel gain.

In addition, a detailed statistical study of the radio channel measurements is presented, including empirical evidence that radio channels exhibit statistical dependencies over long time horizons which implies that it is possible to design predictors of future channel conditions.

Abstract

This thesis is devoted to the study of synthesis of causal filters for generating personal sound zones. Personal sound zones, personal audio or personal sound is the theory and practice of steering sound in such a way that it is amplified in one region and suppressed in another. In lower frequencies, this is accomplished by designing filters that control the complex pattern of constructive and destructive interference that a set of sound sources gives rise to. There are many ways of designing such filters and the field of personal audio has been rather active in the last few decades. As the algorithms approach actual production line implementation, the aspects of realisability gain in importance. It has been noted in the literature that filters that are causal by design solve many of the problems associated with implementability and may increase the subjective sound quality in the bright zone. However, the problem of synthesis of causal filters for personal audio has received less attention than that of non-causal filters. In this thesis, synthesis of causal filters for personal audio with emphasis on implementability is explored. Several different approaches, with varying formulations of the personal audio problem, are investigated and discussed. The majority of these designs are also implemented and tested in real systems.Practical designs that are studied include a weighted sound field synthesis design, drawing from the previous sound field synthesis literature, and a design with a constraint on the acoustic power transmitted into the quiet region. A design with constrained power difference between the quiet and the non-quiet (bright) region is also proposed and investigated. As an auxiliary result, a method for incorporating quadratic power constraints in the rational matrix filter approach to synthesis of Infinite Impulse Response Wiener filters is proposed. An expansion to include robustness to uncertainties in the systems under investigation is also investigated. General guidelines for the use of the different proposed methods are sought, but the problems are very complex. Over all, a user-centric approach is developed, where emphasis is placed on practical design and analysis of the optimization problems at hand.

Abstract

Accurate channel state information (CSI) is important for many candidate techniques of future wireless communication systems. However, acquiring CSI can sometimes be difficult, especially if the user equipment is mobile in which case the future channel realisations must be estimated/predicted. In realistic settings the predictability of radio channels is limited due to measurement noise, limited model orders and since the fading statistics must be modelled based on a set of limited and noisy training data.

In this thesis, the limits of predictability for the radio channel are investigated. Results show that the predictability is limited primarily due to limitations in the training data, while the model order provides a second order limitation effect and the measurement noise comes in as a third order effect.

Then, a Kalman-based linear filter is studied for potential 5G technologies:

Coherent coordinated multipoint joint transmission, where channel predictions and the covariance matrix of the prediction error are used to design a robust linear precoder, evaluated in a three base station system. Results show that prediction improves the CSI for the pedestrian users such that system delays of 10 ms are acceptable. The use of the covariance matrix is important for difficult user groups, but of less importance with a simple user grouping system proposed.

Massive multiple-input multiple-output (MIMO) in frequency division duplex (FDD) systems were a reduced, suboptimal, Kalman filter is suggested to estimate channels based on non-orthogonal pilots. By introducing a fixed grid of beams, the system generates sparsity in the channel vectors seen by each user, which then estimates its most relevant channels based on unique pilot codes for each beam. Results show that there is a 5 dB loss compared to orthogonal pilots.

Downlink time division duplex (TDD) channels are estimated based on uplink pilots. By using a predictor antenna, which scouts the channel in advance, the desired downlink channel can be estimated using pilot-based estimates of the channels before and after it (in space). Results indicate that, with the help of Kalman smoothing, predictor antennas can enable accurate CSI for TDD downlinks at vehicular velocities of 80 km/h.

Abstract

This thesis concerns and combines multichannel sound reproduction, digital room correction, audio signal processing, and human sound perception. It investigates perceived sound quality and new methods to improve timbral and spatial fidelity of loudspeaker-based multichannel sound reproduction in reverberant environments.

In the first part of the thesis, the perceived sound quality of regular optimized stereo sound systems is investigated by means of a listening experiment based on subjective comparison judgments. It is shown that average listeners' preferences are in favor of the optimized version of the systems.

The second part of the thesis takes on with three novel equalization schemes, which are based on insights from human perception. First, a general filter design framework based on multiple-input multiple-output (MIMO) feedforward control is introduced. The main objective is to provide means to jointly equalize a single loudspeaker by utilizing all available loudspeakers in a multichannel sound system.

Well-known drawbacks of standard multichannel sound reproduction are (a) that symmetrical system setups are generally assumed, and (b) that high fidelity sound reproduction is limited to a tight region in space, the so-called sweet spot. In order to ease drawback (a), the MIMO similarity framework incorporating a pairwise channel similarity requirement is introduced, which is based on a mathematical description of the perception of virtual sound sources created in multichannel sound reproduction. The aim is to obtain similar (symmetrical) room transfer functions in the listening area of a given listening environment. In order to ease drawbacks (a) and (b), the personal audio framework is introduced. It aims at producing filters which improve spatial and timbral sound reproduction in multiple listening positions simultaneously.

Evaluations based on simulations and measurements acquired in representative listening environments strongly indicate that the proposed methods successfully treat several causes which are known to impair sound quality and thus yield improved sound reproduction.

Abstract

Wireless broadband systems based on Orthogonal Frequency Division Multiplexing (OFDM) are being introduced to meet demands for high data transfer rates. In multiple users systems, the available bandwidth has to be shared efficiently by several users. The radio channel quality will fluctuate, or fade, as users move. Fading complicates the resource allocation, but channel prediction may alleviate this problem. A flexible and computationally inexpensive state space representation of fading channels is here used in conjunction with a Kalman filter, operating on special-purpose reference signals, to track and predict fading OFDM channels. The thesis investigates key design and performance aspects of such estimators. Taking a probabilistic approach, we interpret the output of the Kalman filter as a full representation of a state of knowledge about the fading channels, given whatever information is at hand. For systems analysis, this permits conclusions to be drawn about channel estimation and prediction performance based on only vague information about the fading characteristics of the channel rather than on actual channel measurements. This is an alternative to conducting classic simulation studies. Various reference signal designs are studied and good design choices are recommended. Superimposed reference signal schemes are also proposed for and evaluated in cases where multiple signals are received, e.g. in multi-user (MU), multi-input multi-output (MIMO), or coordinated multi-point (CoMP) settings. By using time-varying reference signals, channel estimation and prediction performance is shown to be improved considerably in crowded frequency bands. The variation of prediction performance with prediction range and Doppler spectrum characteristics is investigated. For link adaptation, we derive the appropriate metric on which adaptation decisions should be used. The probability density function for this metric is derived for general MIMO channels. Link adaptation is studied for a single link system when channel prediction and estimation errors are present, both for uncoded systems and systems using large block codes with soft decoders. Various aspects of channel model acquisition are addressed by conducting studies on measured channels. Owing to the use of special matrix structures and fast convergence to time-invariant or periodic solutions, we find the Kalman filter complexity to be reasonable for future implementation. Finally, expressions for the impact of modelling errors are derived and used to study the impact of modelling errors on channel prediction performance in some example cases.

Abstract

This thesis is concerned with the design and analysis of robust discrete-time filters for audio equalization and sound field control in real reverberant environments. Inspired by methods in polynomial control theory, a unified framework for acoustic modeling and filter design is developed. The work on modeling is centered around three main themes: First, the acoustic channel between a loudspeaker and a point in space is studied in time, frequency and space, and a polynomial matrix fraction description with diagonal denominator is selected as a physically motivated channel model. As a means for representing channel uncertainties, a probabilistic design model is proposed. Second, the concept of sound field dimensionality, based on the Karhunen-Loève expansion of the sound field, is explored and integrated into the polynomial systems context. Third, a method for spatial interpolation of acoustic transfer functions is proposed and evaluated. Interpolation errors are accounted for by applying the probabilistic uncertainty model to the interpolated data. The work on filter design can be categorized into single- and multichannel methods. The single-channel problem concerns the improvement of the impulse and frequency responses of a single loudspeaker over a region in space, by means of a scalar prefilter. This problem is posed in a SIMO (single-input multiple-output) feedforward control setting, and is solved using polynomial methods. The solution offers several useful insights and results. In particular, new results are derived regarding the adverse pre-ringing problem associated with mixed phase filters. Based on the new results, a refined mixed phase method is proposed that is practically free from pre-ringing artifacts. In the multichannel problem, a desired spatio-temporal sound field is approximated by the joint use of several loudspeakers. This problem is initially formulated and solved by feedforward control over a continuous spatial domain, assuming full knowledge of the spatial field. To obtain a practically feasible design, the control criterion is then spatially discretized, resulting in a standard MIMO(multiple-input multiple-output) linear quadratic feedforward control problem. Since information is generally lost in the discretization process, a robust design based on spatial interpolation and probabilistic error modeling is proposed. The multichannel designs are assessed in an automotive setting, using practical measurements of a nine-channel sound system in a car.

Abstract

Non-destructive testing (NDT) techniques are critical to ensure integrity and safety of engineered structures. Structural health monitoring (SHM) is considered as the next step in the field enabling continuous monitoring of structures.

The first part of the thesis concerns NDT and SHM using guided waves in plates, or Lamb waves, to perform imaging of plate structures. The imaging is performed using a fixed active array setup covering a larger area of a plate. Current methods are based on conventional beamforming techniques that do not efficiently exploit the available data from the small arrays used for the purpose. In this thesis an adaptive signal processing approach based on the minimum variance distortionless response (MVDR) method is proposed to mitigate issues related to guided waves, such as dispersion and the presence of multiple propagating modes. Other benefits of the method include a significant increase in resolution. Simulation and experimental results show that the method outperforms current standard processing techniques.

The second part of the thesis addresses transducer design issues for resonant ultrasound inspections. Resonant ultrasound methods utilize the shape and frequency of the object's natural modes of vibration to detect anomalies. The method considered in the thesis uses transducers that are acoustically coupled to the inspected structures. Changes in the transducer's electrical impedance are used to detect defects. The sensitivity that can be expected from such a setup is shown to highly depend on the transducer resonance frequency, as well as the working frequency of the instrument. Through simulations and a theoretical argumentation, optimal conditions to achieve high sensitivity are given.

Abstract

This thesis presents solutions and studies related to the design of wideband antennas for wireless handheld terminal applications. A method of electrically shortening the terminal chassis length to obtain resonance at high frequencies has been proposed and evaluated, thereby increasing the antennas impedance bandwidth. No significant effect on the lower frequency band in a dual-band antenna prototype has been observed, making the method suitable for multi-band applications. The chassis has further been utilized as a zero-thickness 0.9 - 2.7 GHz high efficiency antenna by inserting a notch in the chassis center, and a feasibility study for typical phones has been performed. Additionally, the effect of talk position on the chassis wave-mode has been investigated, where the standard equivalent circuit model for terminal antennas has been modified to include the presence of the users head. The model has been used to explain measured and simulated effects concerning frequency detuning, efficiency reduction and bandwidth enhancements when the terminal is placed in talk position.

The use of a hands-free earpiece cord is currently mandatory for FM radio reception as the cord is utilized as antenna. However, there is currently a market driven demand for removing the cord requirement since many modern phones are equipped with speakers and Bluetooth headsets. In this thesis, an active ferrite loop antenna is proposed as an internal replacement/complement with a performance of -23 dB (G/T degradation) compared to a full-size lossless dipole in urban environments. Also, a modification to the cord is suggested for DVB H reception.

Complex matching networks have been investigated to increase the bandwidth of dual band PIFA antennas, and a printed dual band dipole has been integrated with a modified Marchand balun for dual resonance at two separate frequency bands, thus covering the commercial cellular bands 824-960 and 1710-2170 MHz with a single antenna.

Abstract

Revenue maximization for network operators is considered as a criterion for resource allocation in wireless cellular networks. A business model encompassing service level agreements between network operators and service providers is presented. Admission control, through price model aware admission policing and service level control, is critical for the provisioning of useful services over a general purpose wireless network. A technical solution consisting of a fast resource scheduler taking into account service requirements and wireless channel properties, a service level controller that provides the scheduler with a reasonable load, and an admission policy to uphold the service level agreements and maximize revenue, is presented.

Two different types of service level controllers are presented and implemented. One is based on a scalar PID controller, that adjusts the admitted data rates for all active clients. The other one is obtained with linear programming methods, that optimally assign data rates to clients, given their channel qualities and price models.

Two new scheduling criteria, and algorithms based on them, are presented and evaluated in a simulated wireless environment. One is based on a quadratic criterion, and is implemented through approximative algorithms, encompassing a search based algorithm and two different linearizations of the criterion. The second one is based on statistical measures of the service rates and channel states, and is implemented as an approximation of the joint probability of achieving the delay limits while utilizing the available resources efficiently.

Two scheduling algorithms, one based on each criterion, are tested in combination with each of the service level controllers, and evaluated in terms of throughput, delay, and computational complexity, using a target test system. Results show that both schedulers can, when feasible, meet explicit throughput and delay requirements, while at the same time allowing the service level controller to maximize revenue by allocating the surplus resources to less demanding services.

Abstract

This thesis is concerned with scheduling the use of resources, or allocating resources, so as to meet future demands for the entities produced by the resources. We consider applications in mobile communications such as scheduling users' transmissions so that the amount of transmitted information is maximized, and scenarios in the manufacturing industry where the task is to distribute work among production units so as to minimize the number of missed orders.

The allocation decisions are complicated by a lack of information concerning the future demand and possibly also about the capacities of the available resources. We therefore resort to using probability theory and the maximum entropy principle as a means for making rational decisions under uncertainty.

By using probabilities interpreted as a reasonable degree of belief, we find optimum decision rules for the manufacturing problem, bidding under uncertainty in a certain type of auctions, scheduling users in communications with uncertain channel qualities and uncertain arrival rates, quantization of channel information, partitioning bandwidth between interfering and non-interfering areas in cellular networks, hand-overs and admission control. Moreover, a new method for making optimum approximate Bayesian inference is introduced.

We further discuss reasonable optimization criteria for the mentioned applications, and provide an introduction to the topic of probability theory as an extension to two-valued logic. It is argued that this view unifies a wide range of resource-allocation problems, and we discuss various directions for further research.

Abstract

Using an array synthesis tool based on a modified differential evolution algorithm, it is shown that the position-phase synthesis exhibits improved pattern characteristics compared to both the phase only and position only synthesis of uniform amplitude antenna arrays. The design of an unequally spaced planar reflect-array and an active power combining reflect-array are presented. The unit cell of the active reflect-array consists of an amplifying active reflect-antenna designed using a novel dual polarized microstrip-T coupled patch antenna. Two modelling approaches are proposed for the active reflect-antenna and the modelling methods are compared with the experiments.

A computationally efficient analysis of an H-slot in the ground plane of a microstripline is carried out using a transmission line model. To improve the accuracy in the resonant region of the H-slot and retaining the computational efficiency, an artificial neural network is combined with an efficient spectral domain method. An efficient analysis tool for a silicon micromachined H-slot coupled antenna is developed by combining the transmission line models of the H-slot and an aperture coupled antenna. The experimental results are compared with the theory showing good agreement.

The analysis and design of a microwave amplifier based on non-resonant slot matching is carried out. It is seen that the designed slot matched amplifier has decreased layout size, improved gain and noise figure characteristics compared to a stub matched amplifier. An efficient method for the analysis of non-resonant slots is compared with other approaches showing good agreement. This points to the fact that non-resonant slot matched circuits can be designed with the same speed and efficiency as we design the traditional stub based matching circuits.

To address the problem of bandwidth and performance of reflect-arrays we propose a dielectric resonator antenna with slotline stubs. As a preliminary step we design a dielectric resonator antenna with slotline feed and the experimental results are compared with those of a commercial CAD tool. Design and analysis of 3D interconnects based on non-radiative dielectric waveguides is carried out. At millimeterwave, these interconnects are useful for hybrid and multilayer integration techniques.

Abstract

Control of linear systems with saturating actuators are considered and anti-windup compensators for multiple-input multiple-output systems, and robust, almost time-optimal controllers for double integrators with input amplitude saturations, are proposed.

Windup effects are defined and anti-windup compensators aiming at minimizing the windup effects are proposed. The design is based on 1) linear quadratic (LQ) optimization techniques and 2) heuristic design using Nyquist-like techniques and pole-placement techniques.

A root-locus like technique that can, approximately, foretell possible directional problems that may be present in MIMO systems with input saturations, and that can be used for design of anti-windup compensators and for selection of appropriate static directional compensators, is proposed.

The problem of control of double integrators via saturating inputs is addressed and a robust piece-wise linear controller that gives almost time-optimal performance is suggested. It is shown that time optimal control of a double integrator via an input amplitude limiter, is equivalent to time-optimal control of a single integrator having a rate limiter at the input. One such application, concerning control of hydraulic cylinders in container crane systems, is presented. An extension of the controller, allowing synchronous control of two integrators with input rate limitations, is proposed.

Abstract

This thesis is concerned with detection of transient signal families and detectors in nonlinear static sensor systems. The detection problems are treated within the framework of likelihood ratio based binary hypothesis testing.

An analytical solution to the noncoherent detection problem is derived, which in contrast to the classical noncoherent detector, is optimal for wideband signals. An optimal detector for multiple transient signals with unknown arrival times is also derived and shown to yield higher detection performance compared to the classical approach based on the generalized likelihood ratio test.

An application that is treated in some detail is that of ultrasonic nondestructive testing, particularly pulse-echo detection of defects in elastic solids. The defect detection problem is cast as a composite hypothesis test and a methodology, based on physical models, for designing statistically optimal detectors for cracks in elastic solids is presented. Detectors for defects with low computational complexity are also formulated based on a simple phenomenological model of the defect echoes. The performance of these detectors are compared with the physical model-based optimal detector and is shown to yield moderate performance degradation.

Various aspects of optimal detection in static nonlinear sensor systems are also treated, in particular the stochastic resonance (SR) phenomenon which, in this context, implies noise enhanced detectability. Traditionally, SR has been quantified by means of the signal-to-noise ratio (SNR) and interpreted as an increase of a system's information processing capability. Instead of the SNR, rigorous information theoretic distance measures, which truly can support the claim of noise enhanced information processing capability, are proposed as quantifiers for SR. Optimal detectors are formulated for two static nonlinear sensor systems and shown to exhibit noise enhanced detectability.

Abstract

Prediction of the rapidly fading envelope of a mobile radio channel enables a number of capacity improving techniques like fast resource allocation and fast link adaptation. This thesis deals with linear prediction of the complex impulse response of a channel and unbiased quadratic prediction of the power. The design and performance of these predictors depend heavily on the correlation properties of the channel. Models for a channelwhere the multipath is caused by clusters of scatterers are studied. The correlation for the contribution from a cluster can be approximated as a damped complex sinusoid. A suitable model for the dynamics of the channel is an ARMA-process. This motivates the use of linear predictors.

A limiting factor in the prediction are the estimation errors on the observed channels. This estimation error, caused by measurement noise and time variation, is analyzed for a block based least squares algorithm which operates on a Jakes channel model. Efficient noise reduction on the estimated channel impulse responses can be obtained with Wienersmoothers that are based on simple models for the dynamics of the channel combined with estimates of the variance of the estimation error.

Power prediction that is based on the squared magnitude of linear prediction of the taps will be biased. Hence, a bias compensated power predictor is proposed and the optimal prediction coefficients are derived for the Rayleigh fading channel. The corresponding probability density functions for the predicted power are also derived. A performance evaluation of the prediction algorithm is carried out on measured broadband mobile radio channels. The performance is highly dependent on the variance of the estimation error and the dynamics of the individual taps.

Abstract

This thesis is concerned with the use of multiple antenna elements in wireless communication over frequency non-selective radio channels. Both measurement results and theoretical analysis are presented. New transmit strategies are derived and compared to existing transmit strategies, such as beamforming and space-time block coding (STBC). It is found that the best transmission algorithm is largely dependent on the channel characteristics, such as the number of transmit and receive antennas and the existence of a line of sight component. Rayleigh fading multiple input multiple output (MIMO) channels are studied using an eigenvalue analysis and exact expressions for the bit error rates and outage capacities for beamforming and STBC is found. In general are MIMO fading channels correlated and there exists a mutual coupling between antenna elements. These findings are supported by indoor MIMO measurements. It is found that the mutual coupling can, in some scenarios, increase the outage capacity. An adaptive antenna testbed is used to obtain measurement results for the single input multiple output (SIMO) channel. The results are analyzed and design guidelines are obtained for how a beamformer implemented in hardware shall be constructed. The effects of nonlinear transmit amplifiers in array antennas are also analyzed, and it is shown that an array reduces the effective intermodulation distortion (IMD) transmitted by the array antenna by a spatial filtering of the IMD. A novel frequency allocation algorithm is proposed that reduces IMD even further. The use of a low cost antenna with switchable directional properties, the switched parasitic antenna, is studied in a MIMO context and compared to array techniques. It is found that it has comparable performance, at a fraction of the cost for an array antenna.

Abstract

Signal processing within the neurophysiological field is challenging and requires short processing time and reliable results. In this thesis, three main problems are considered.

First, a modified line source model for simulation of muscle action potentials (APs) is presented. It is formulated in continuous-time as a convolution of a muscle-fiber dependent transmembrane current and an electrode dependent weighting (impedance) function. In the discretization of the model, the Nyquist criterion is addressed. By applying anti-aliasing filtering, it is possible to decrease the discretization frequency while retaining the accuracy. Finite length muscle fibers are incorporated in the model through a simple transformation of the weighting function. The presented model is suitable for modeling large motor units.

Second, the possibility of discerning the individual AP components of the concentric needle electromyogram (EMG) is explored. Simulated motor unit APs (MUAPs) are prefiltered using Wiener filtering. The mean fiber concentration (MFC) and jitter are estimated from the prefiltered MUAPs. The results indicate that the assessment of the MFC may well benefit from the presented approach and that the jitter may be estimated from the concentric needle EMG with an accuracy comparable with traditional single fiber EMG.

Third, automatic, rather than manual, detection and discrimination of recorded C-fiber APs is addressed. The algorithm, detects the Aps reliably using a matched filter. Then, the detected APs are discriminated using multiple hypothesis tracking combined with Kalman filtering which identifies the APs originating from the same C-fiber. To improve the performance, an amplitude estimate is incorporated into the tracking algorithm. Several years of use show that the performance of the algorithm is excellent with minimal need for audit.