Microscopic traffic simulation is useful tool in analysing traffic and estimating the capacity and level of service of road networks. In this thesis, the four legged Idrottsparken roundabout in the city of Norrkoping in Sweden is analysed by using the microscopic traffic simulation package AIMSUN. For this purpose, data regarding traffic flow counts, travel times and queue lengths were collected for three consecutive weekdays during both the morning and afternoon peak periods. The data were then used in model building for simulation of traffic of the roundabout. The Root Mean Square Error (RMSE) method is used to get the optimal parameter value between queue length and travel time data and validation of travel time data are carried out to obtain the basic model which represents the existing condition of the system. Afterward, the results of the new models were evaluated and compared to the results of a SUMO model for the same scenario model. Based on calibrated and validated model, three alternative scenarios were simulated and analysed to improve efficiency of traffic network in the roundabout. The three scenarios includes: (1) add one free right turn in the north and east sections; (2) add one free right turn in the east and south sections; and (3) addition of one lane in roundabout. The analysis of these scenarios shows that the first and second scenario are only able to reduce the queue length and travel time in two or three legs, while the third scenario is not able to improve the performance of the roundabout. In this research, it can be concluded that the first scenario is considered as the best scenario compared to the second scenario and the third scenario. The comparison between AIMSUN and SUMO for the same scenario shows that the results have no significance differences. In calibration process, to get the optimal parameter values between the model measurements and the field measurements, both of AIMSUN and SUMO uses two significantly influencing parametersfor queue and travel time. AIMSUN package uses parameter of driver reaction time and the maximum acceleration, while SUMO package uses parameter of driver imperfection and also the driver rection time.

In this thesis, we investigate the problem of channel estimation in the amplify-and-forward cooperative communication systems when the networks could be in the presence of selfish relays. The information received at the destination will be detected and then used to estimate the channel. In previous studies, the relays will deliver the information under the prerequisite for cooperation and the destination can receive the information sent from the source without any possible selfish relay. Therefore, the channel will be estimated under this over idealistic assumption. Unfortunately, the assumption does not make sense in real applications. Currently, we don¡¦t have a mechanism to guarantee the relays will always be cooperative. The performance of channel estimation will be significantly degraded when the selfish relays present in the network. Therefore, this thesis considers an amplify-and-forward cooperative communication system with direct transmission and proposes a detection mechanism to overcome the misbehaving relay problem. The detection mechanism employed estimation is based on likelihood ratio test using both direct transmission and relayed information. The detection result will then be used to reconstruct the codeword used for estimating product channel gain of the source-to-relay and relay- to-destination links. The mathematical derivation for the considered problem is developed and numerical simulations for illustration is also carried out in the thesis. The numerical simulation results verify that the proposed method is indeed able to achieve robust channel estimation.

Empowered by linear precoding and decoding, a spatially multiplexed multiple-input multiple-output (MIMO) system becomes a convenient framework to offer high data rate, diversity and interference management. While most of the current precoding/decoding designs have assumed perfect channel state information (CSI) at the receiver, and sometimes even at the transmitter, in this thesis we design the precoder and decoder with imperfect CSI at both the transmit and the receive sides, and investigate the joint impact of channel estimation errors and channel correlation on system structure and performance. The mean-square error (MSE) related performance metrics are used as the design criteria. We begin with the minimum total MSE precoding/decoding design for a single-user MIMO system assuming imperfect CSI at both ends. Here the CSI includes the channel estimate and channel correlation information. The structures of the optimum precoder and decoder are determined. Compared to the perfect CSI case, linear filters are added to the transceiver structure to improve system robustness against imperfect CSI. The effects of channel estimation error and channel correlation are quantified by simulations. With imperfect CSI at both ends, the exact capacity expression for a single-user MIMO channel is difficult to obtain. Instead, a tight capacity lower-bound is used for system design. The optimum structure of the transmit covariance matrix for the lower-bound has not been found in the existing literature. By transforming the transmitter design into a joint precoding/decoding design problem, we derive the expression of the optimum transmit covariance matrix. The close relationship between the maximum mutual information design and the minimum total MSE design is also discovered assuming imperfect CSI. For robust multiuser MIMO communications, minimum average sum MSE transceiver (precoder-decoder pairs) design problems are formulated for both the uplink and the downlink, assuming imperfect channel estimation and channel correlation at the base station (BS). We propose improved iterative algorithms based on the associated Karush-Kuhn-Tucker (KKT) conditions. Under the assumption of imperfect CSI, an uplink--downlink duality in average sum MSE is proved. As an alternative for the uplink optimization, a sequential semidefinite programming (SDP) method is proposed. Simulation results are provided to corroborate the analysis. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2008-07-25 10:53:45.175

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.

ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / This work explores aeronautical telemetry communication performance with the SOQPSK- TG ARTM waveforms when frequency-selective multipath corrupts received information symbols. A multi-antenna equalization scheme is presented where each antenna's unique multipath channel is equalized using a pilot-aided optimal linear minimum mean-square error filter. Following independent channel equalization, a maximal ratio combining technique is used to generate a single receiver output for detection. This multi-antenna equalization process is shown to improve detection performance over maximal ratio combining alone.

Approved for public release; distribution is unlimited / Computer simulation studies of a frequency domain adaptive beamforming algorithm are presented. These simulation studies were conducted to determine the multiple broadband target localization capability and the full angular coverage capability of the algorithm. The algorithm was evaluated at several signal-to-noise ratios with varying sampling rates. The number of iterations that the adaptive algorithm took to reach a minimum estimation error was determined. Results of the simulation studies indicate that the algorithm can localize multiple broadband targets and has full angular coverage capability. / http://archive.org/details/computersimulati00behr / Lieutenant, United States Navy

This thesis explores forecasting for intermittent demand requirements. Intermittent demand occurs at random, with some time periods showing no demand. In addition, demand, when it occurs, may not be for a single unit or a constant size. Consequently, intermittent demand creates significant problems in the supply and manufacturing environment as far as forecasting and inventory control are concerned. A certain confusion is shared amongst academics and practitioners about how intermittent demand (or indeed any other demand pattern that cannot be reasonably represented by the normal distribution) is defined. As such, we first construct a framework that aims at facilitating the conceptual categorisation of what is termed, for the purposes of this research, “non-normal” demand patterns. Croston (1972) proposed a method according to which intermittent demand estimates can be built from constituent elements, namely the demand size and inter-demand interval. The method has been claimed to provide unbiased estimates and it is regarded as the “standard” approach to dealing with intermittence. In this thesis we show that Croston’s method is biased. The bias is quantified and two new estimation procedures are developed based on Croston’s concept of considering both demand sizes and inter-demand intervals. Consequently the issue of variability of the intermittent demand estimates is explored and finally Mean Square Error (MSE) expressions are derived for all the methods discussed in the thesis. The issue of categorisation of the demand patterns has not received sufficient academic attention thus far, even though, from the practitioner’s standpoint it is appealing to switch from one estimator to the other according to the characteristics of the demand series under concern. Algebraic comparisons of MSE expressions result in universally applicable (and theoretically coherent) categorisation rules, based on which, “non-normal” demand patterns can be defined and estimators be selected. All theoretical findings are checked via simulation on theoretically generated demand data. The data is generated upon the same assumptions considered in the theoretical part of the thesis. Finally, results are generated using a large sample of empirical data. Appropriate accuracy measures are selected to assess the forecasting accuracy performance of the estimation procedures discussed in the thesis. Moreover, it is recognised that improvements in forecasting accuracy are of little practical value unless they are translated to an increased customer service level and/or reduced inventory cost. In consequence, an inventory control system is specified and the inventory control performance of the estimators is also assessed on the real data. The system is of the periodic order-up-to-level nature. The empirical results confirm the practical validity and utility of all our theoretical claims and demonstrate the benefits gained when Croston’s method is replaced by an estimator developed during this research, the Approximation method.

This thesis describes the use of scattering functions to characterize time-varying multipath radio channels. Channel Impulse responses were measured at Edwards Air Force Base (EAFB) and scattering functions generated from the impulse response data. From the scattering functions we compute the corresponding Doppler power spectrum and multipath intensity profile. These functions completely characterize the signal delay and the time varying nature of the channel in question and are used by systems engineers to design reliable communications links. We observe from our results that flight paths with ample reflectors exhibit significant multipath events. We also examine the bit error rate (BER) performance of a reduced-complexity equalizer for a truncated version of the pulse amplitude modulation (PAM) representation of SOQPSK-TG in a multipath channel. Since this reduced-complexity equalizer is based on the maximum likelihood (ML) principle, we expect it to perform optimally than any of the filter-based equalizers used in estimating received SOQPSK-TG symbols. As such we present a comparison between this ML detector and a minimum mean square error (MMSE) equalizer for the same example channel. The example channel used was motivated by the statistical channel characterizations described in thisthesis. Our analysis shows that the ML equalizer outperforms the MMSE equalizer in estimating received SOQPSK-TG symbols.

<p>Linear time-invariant approximations of nonlinear systems are used in many applications and can be obtained in several ways. For example, using system identification and the prediction-error method, it is always possible to estimate a linear model without considering the fact that the input and output measurements in many cases come from a nonlinear system. One of the main objectives of this thesis is to explain some properties of such approximate models.</p><p>More specifically, linear time-invariant models that are optimal approximations in the sense that they minimize a mean-square error criterion are considered. Linear models, both with and without a noise description, are studied. Some interesting, but in applications usually undesirable, properties of such optimal models are pointed out. It is shown that the optimal linear model can be very sensitive to small nonlinearities. Hence, the linear approximation of an almost linear system can be useless for some applications, such as robust control design. Furthermore, it is shown that standard validation methods, designed for identification of linear systems, cannot always be used to validate an optimal linear approximation of a nonlinear system.</p><p>In order to improve the models, conditions on the input signal that imply various useful properties of the linear approximations are given. It is shown, for instance, that minimum phase filtered white noise in many senses is a good choice of input signal. Furthermore, the class of separable signals is studied in detail. This class contains Gaussian signals and it turns out that these signals are especially useful for obtaining approximations of generalized Wiener-Hammerstein systems. It is also shown that some random multisine signals are separable. In addition, some theoretical results about almost linear systems are presented.</p><p>In standard methods for robust control design, the size of the model error is assumed to be known for all input signals. However, in many situations, this is not a realistic assumption when a nonlinear system is approximated with a linear model. In this thesis, it is described how robust control design of some nonlinear systems can be performed based on a discrete-time linear model and a model error model valid only for bounded inputs.</p><p>It is sometimes undesirable that small nonlinearities in a system influence the linear approximation of it. In some cases, this influence can be reduced if a small nonlinearity is included in the model. In this thesis, an identification method with this option is presented for nonlinear autoregressive systems with external inputs. Using this method, models with a parametric linear part and a nonparametric Lipschitz continuous nonlinear part can be estimated by solving a convex optimization problem.</p> / <p>Linjära tidsinvarianta approximationer av olinjära system har många användningsområden och kan tas fram på flera sätt. Om man har mätningar av in- och utsignalerna från ett olinjärt system kan man till exempel använda systemidentifiering och prediktionsfelsmetoden för att skatta en linjär modell utan att ta hänsyn till att systemet egentligen är olinjärt. Ett av huvudmålen med den här avhandlingen är att beskriva egenskaper för sådana approximativa modeller.</p><p>Framförallt studeras linjära tidsinvarianta modeller som är optimala approximationer i meningen att de minimerar ett kriterium baserat på medelkvadratfelet. Brusmodeller kan inkluderas i dessa modelltyper och både fallet med och utan brusmodell studeras här. Modeller som är optimala i medelkvadratfelsmening visar sig kunna uppvisa ett antal intressanta, men ibland oönskade, egenskaper. Bland annat visas det att en optimal linjär modell kan vara mycket känslig för små olinjäriteter. Denna känslighet är inte önskvärd i de flesta tillämpningar och innebär att en linjär approximation av ett nästan linjärt system kan vara oanvändbar för till exempel robust reglerdesign. Vidare visas det att en del valideringsmetoder som är framtagna för linjära system inte alltid kan användas för validering av linjära approximationer av olinjära system.</p><p>Man kan dock göra de optimala linjära modellerna mer användbara genom att välja lämpliga insignaler. Bland annat visas det att minfasfiltrerat vitt brus i många avseenden är ett bra val av insignal. Klassen av separabla signaler detaljstuderas också. Denna klass innehåller till exempel alla gaussiska signaler och just dessa signaler visar sig vara speciellt användbara för att ta fram approximationer av generaliserade wiener-hammerstein-system. Dessutom visas det att en viss typ av slumpmässiga multisinussignaler är separabel. Några teoretiska resultat om nästan linjära system presenteras också.</p><p>De flesta metoder för robust reglerdesign kan bara användas om storleken på modellfelet är känd för alla tänkbara insignaler. Detta är emellertid ofta inte realistiskt när ett olinjärt system approximeras med en linjär modell. I denna avhandling beskrivs därför ett alternativt sätt att göra en robust reglerdesign baserat på en tidsdiskret modell och en modellfelsmodell som bara är giltig för begränsade insignaler.</p><p>Ibland skulle det vara önskvärt om en linjär modell av ett system inte påverkades av förekomsten av små olinjäriteter i systemet. Denna oönskade påverkan kan i vissa fall reduceras om en liten olinjär term tas med i modellen. En identifieringsmetod för olinjära autoregressiva system med externa insignaler där denna möjlighet finns beskrivs här. Med hjälp av denna metod kan modeller som består av en parametrisk linjär del och en ickeparametrisk lipschitzkontinuerlig olinjär del skattas genom att man löser ett konvext optimeringsproblem.</p>

Linear time-invariant approximations of nonlinear systems are used in many applications and can be obtained in several ways. For example, using system identification and the prediction-error method, it is always possible to estimate a linear model without considering the fact that the input and output measurements in many cases come from a nonlinear system. One of the main objectives of this thesis is to explain some properties of such approximate models. More specifically, linear time-invariant models that are optimal approximations in the sense that they minimize a mean-square error criterion are considered. Linear models, both with and without a noise description, are studied. Some interesting, but in applications usually undesirable, properties of such optimal models are pointed out. It is shown that the optimal linear model can be very sensitive to small nonlinearities. Hence, the linear approximation of an almost linear system can be useless for some applications, such as robust control design. Furthermore, it is shown that standard validation methods, designed for identification of linear systems, cannot always be used to validate an optimal linear approximation of a nonlinear system. In order to improve the models, conditions on the input signal that imply various useful properties of the linear approximations are given. It is shown, for instance, that minimum phase filtered white noise in many senses is a good choice of input signal. Furthermore, the class of separable signals is studied in detail. This class contains Gaussian signals and it turns out that these signals are especially useful for obtaining approximations of generalized Wiener-Hammerstein systems. It is also shown that some random multisine signals are separable. In addition, some theoretical results about almost linear systems are presented. In standard methods for robust control design, the size of the model error is assumed to be known for all input signals. However, in many situations, this is not a realistic assumption when a nonlinear system is approximated with a linear model. In this thesis, it is described how robust control design of some nonlinear systems can be performed based on a discrete-time linear model and a model error model valid only for bounded inputs. It is sometimes undesirable that small nonlinearities in a system influence the linear approximation of it. In some cases, this influence can be reduced if a small nonlinearity is included in the model. In this thesis, an identification method with this option is presented for nonlinear autoregressive systems with external inputs. Using this method, models with a parametric linear part and a nonparametric Lipschitz continuous nonlinear part can be estimated by solving a convex optimization problem. / Linjära tidsinvarianta approximationer av olinjära system har många användningsområden och kan tas fram på flera sätt. Om man har mätningar av in- och utsignalerna från ett olinjärt system kan man till exempel använda systemidentifiering och prediktionsfelsmetoden för att skatta en linjär modell utan att ta hänsyn till att systemet egentligen är olinjärt. Ett av huvudmålen med den här avhandlingen är att beskriva egenskaper för sådana approximativa modeller. Framförallt studeras linjära tidsinvarianta modeller som är optimala approximationer i meningen att de minimerar ett kriterium baserat på medelkvadratfelet. Brusmodeller kan inkluderas i dessa modelltyper och både fallet med och utan brusmodell studeras här. Modeller som är optimala i medelkvadratfelsmening visar sig kunna uppvisa ett antal intressanta, men ibland oönskade, egenskaper. Bland annat visas det att en optimal linjär modell kan vara mycket känslig för små olinjäriteter. Denna känslighet är inte önskvärd i de flesta tillämpningar och innebär att en linjär approximation av ett nästan linjärt system kan vara oanvändbar för till exempel robust reglerdesign. Vidare visas det att en del valideringsmetoder som är framtagna för linjära system inte alltid kan användas för validering av linjära approximationer av olinjära system. Man kan dock göra de optimala linjära modellerna mer användbara genom att välja lämpliga insignaler. Bland annat visas det att minfasfiltrerat vitt brus i många avseenden är ett bra val av insignal. Klassen av separabla signaler detaljstuderas också. Denna klass innehåller till exempel alla gaussiska signaler och just dessa signaler visar sig vara speciellt användbara för att ta fram approximationer av generaliserade wiener-hammerstein-system. Dessutom visas det att en viss typ av slumpmässiga multisinussignaler är separabel. Några teoretiska resultat om nästan linjära system presenteras också. De flesta metoder för robust reglerdesign kan bara användas om storleken på modellfelet är känd för alla tänkbara insignaler. Detta är emellertid ofta inte realistiskt när ett olinjärt system approximeras med en linjär modell. I denna avhandling beskrivs därför ett alternativt sätt att göra en robust reglerdesign baserat på en tidsdiskret modell och en modellfelsmodell som bara är giltig för begränsade insignaler. Ibland skulle det vara önskvärt om en linjär modell av ett system inte påverkades av förekomsten av små olinjäriteter i systemet. Denna oönskade påverkan kan i vissa fall reduceras om en liten olinjär term tas med i modellen. En identifieringsmetod för olinjära autoregressiva system med externa insignaler där denna möjlighet finns beskrivs här. Med hjälp av denna metod kan modeller som består av en parametrisk linjär del och en ickeparametrisk lipschitzkontinuerlig olinjär del skattas genom att man löser ett konvext optimeringsproblem.