The well-known code division multiple access (CDMA) decorrelating detector (DD) and minimum mean-square error (MMSE) detector use a bank of correlators, followed by the inverse of the matrix operation to eliminate the multiple access interference (MAI). However, the operation for the inverse of the matrix involves a great deal of computation, especially when the users¡¦ number is large. Therefore, in this thesis, we propose some recursive methods, the least-mean-square (LMS) algorithm and the recursive least-squares (RLS) algorithm, to detect users¡¦ signals adaptively. We make use of the analogy between a traditional Winner filter and the decorrelating detector to construct adaptive implementation schemes of the decorrelating detector and MMSE detector, called decorrelating transversal filter and MMSE transversal filter, respectively. We applied both LMS algorithm and RLS algorithm to the decorrelating transversal filter and MMSE transversal filter, just as the ways to apply the LMS algorithm and RLS algorithm to the Winner filters. With the proposed schemes, we can greatly reduce the computational complexity of a CDMA multi-user detector while maintaining an acceptable performance.

Recent research has shown that multiple-input multiple-output (MIMO) systems provide high spectral efficiencies and error performance gains. However, the use of multiple antennas in mobile terminals may not be very practical. Certainly there is limited space and other implementation issues which make this a challenging problem. Therefore, to harness the diversity gains afforded by MIMO transmitter diversity techniques, while maintaining a minimal number of antennas on each handset, cooperative diversity techniques have been proposed. In addition, attention has also been given to combining wireless relaying systems with MIMO techniques to improve capacity, coverage, and obtain better diversity at the expense of increased node complexity. This thesis considers the design and analysis of cooperative diversity systems and MIMO amplify-and-forward relaying systems. In particular, we investigate adaptive time- and frequency-domain equalization techniques for cooperative diversity systems using space-time block codes (STBC). For MIMO relaying systems, we analyze the ergodic capacity of various systems and compare different amplify-and-forward methods in terms of system capacity performance. We propose a new block time-domain adaptive equalization structure for time reversal-space time block coding (TR-STBC) systems, which eliminates the separate decoder and also the need for explicit channel state information (CSI) estimation at the receiver. Our simulation results show that the time-domain adaptive block equalizer performs better than the frequency-domain counterpart but at the cost of increased complexity. Then, we extend this time-domain adaptive equalization scheme to distributed TR-STBC systems. We also develop a frequency-domain counterpart for the distributed systems. Our simulation results show that the adaptive algorithms work well for Protocols I and III proposed by Nabar et al. The time-domain adaptive algorithms perform better than the frequency-domain algorithms, and overall the Protocol I receivers outperform the Protocol III receivers. We also show that, if only the Protocol III receiver is used, it can be susceptible to noise amplification due to a weaker source-to-relay link compared to the relay-to-destination link. This problem can be mitigated by using the Protocol I receivers with some extra complexity but much superior diversity performance. We also present an ergodic capacity analysis of an amplify-and-forward (AF) MIMO two-hop system including the direct link and validate the analysis with simulations. We show that having the direct link improves the capacity due to diversity and quantify this improvement. We also present an ergodic capacity analysis of an AF MIMO two-hop, two relay system. Our results verify the capacity gain of relaying systems with two relays due to the extra diversity compared to a single relaying system. However, the results also show that when one of the source-to-relay links has a markedly higher SNR compared to the other, a single relay system has better capacity than a two relay system. Finally, we compare three types of relay amplification methods: a) average amplification, b) instantaneous channel amplification, and c) instantaneous power amplification. The instantaneous power amplification method has a higher mean capacity but with a higher variance. Also, it requires additional information at the destination and would create enormous overheads compared to the other methods. We also find that the instantaneous channel amplification method has almost no advantage in terms of the mean capacity but its capacity is less variable than the average amplification method. On the other hand, the average amplification method is simpler to implement as it does not require channel estimation at the relaying terminal.

This master thesis has been performed at SAAB AB in Järfälla, Sweden.A radar warning receiver must alert the user when someone highlights it with radarsignals. Radar signals used today varies and has a wide frequency band. In order todetect all possible radar signals the radar warning receiver must have a widebandwidth. This results in that the noise power will be high in the radar warningreceiver and weak radar signals will be hard to detect or even undetected.The aim of the thesis work was to investigate the possibility to suppress the noise inthe received radar signals. Unfortunately we do not know the frequency of thereceived radar signals, since the frequency has been decided by the threat radar. Wehave used adaptive filters, which adapts it band-pass to the received radar signal. Theadaptive filters must converge quickly to the state it reduces the noise and passes theradar signals since radar pulses can be very short in the time domain. We also wantto achieve a high SNR gain that is a measurement of how well the adaptive filterreduces the noise.We have investigated two adaptive algorithms, the recursive least square (RLS)algorithm and the least mean square (LMS) algorithm. We found out that the LMSalgorithm was more suitable for noise cancellation in radar applications due to its lowcomplexity and stability compared to RLS algorithm. The LMS algorithm gave SNRgains in the span 14-20 dB for different radar pulses.

International Telemetering Conference Proceedings / October 21, 2002 / Town & Country Hotel and Conference Center, San Diego, California / This paper presents the development of two distributed terrestrial radiolocation algorithms that use range estimates derived from DS-CDMA waveforms. The first algorithm, which is RLS-based, is derived as the solution of an approximate least-squares positioning problem. This algorithm has the advantage of reduced computational complexity, compared with the EKF-based algorithm that is presented. It is shown via simulations that both positioning algorithms perform well, with the performance of the EKF-based algorithm being superior.

<p>The main goal of this thesis is to create a continuous phase modulated radio system with a recursive least square equalizer. The two tested channel models are typical urban and rural area. The result of the performance of this radio system is displayed in Matlab plots as the bit error rate. Three error rates are displayed; with error correction, without error correction and the rate of received incorrect message bursts. Conclusions are also drawn of the performance of the radio system in kbit/sec of bandwidth when the different channel models are used. The performance is also divided into how the equalizer handles inter symbol interference or a fading channel without inter symbol interference.</p> / <p>I detta examensarbete har ett fasmodulerat radiosystem simulerats, fokusering ligger på kanalutjämnare som är av typen recursive least square (RLS). RLS utjämnaren har testats med två olika gsm kanalmodeler, dels typical urban som simulerar radioförbindelser i stadsmiljö den andra modellen är rural area där sändare och mottagare kan se varandra. Tre olika resultat presenteras; med felrättande koder, utan felrättande koder och mängden icke korrekta datapaket. Slutsatser dras om radiosystemets bandbredd när de olika kanalmodellerna används vid olika brusmängd. Även utjämnarens förmåga att hantera inter-symbol interference och fading utvärderas också.</p>

The analysis of this thesis concerns various problems associated with adaptive CDMA multi-user detection in asynchronous multi-path channels. Starting with some simple concept of Wiener filtering and correlating detector, we construct a novel adaptive decorrelating transversal filter suitable for CDMA multi-user detection in uplink channels. Then, we make use of the LMS and RLS algorithms to replace the traditional decorrelating transversal filter (which is also called inverse matrix based decorrelating detector) to make the scheme work fully adaptively. In this way, a great advantage in terms of computation load reduction is made possible. To further improve the detection efficiency, we will also make use of RAKE receiver to enhance the overall decision reliability in the proposed adaptive CDMA MUD scheme. Although the focus of this thesis is put on analysis, we will also use computer simulations to counter-check the results obtained from theoretical analysis, showing a very good match between the two. In the last part of the thesis, we will also discuss the various issues on fully blind implementation of the proposed adaptive CDMA MUD scheme with some useful multipath channel delay and amplitude estimation algorithms.

The technique of direct-sequence (DS) code division multiple access (CDMA) cellular systems has been the focus of increased attention. In this thesis, the problem of narrow-band interference (NBI) cancellation for the DS-CDMA communication systems is considered. It has been shown that the performance of single NBI cancellation for CDMA systems by using the non-linear filtering approach, the so-called DDK filter or the MDK filter, is superior to the one using the linear filtering approach. The main concern of this thesis is to deal with the multiple NBI cancellation. This may occur in some practical application, for instance, in the 2.4GHz CDMA system, the bluetooth and wireless LAN may exist in the same frequency band with different power ratio. In this thesis, the nonlinear filtering with fast convergence least square (LS) algorithms, viz., the modified inverse QRD-RLS (IQRD-RLS) and the interior point (IP) LS algorithms, are devised for multiple NBI cancellation in the multi-user CDMA system. In fact, the IQRD-RLS and the IP LS algorithms are known to have better numerical stability and convergence property in the RLS family. Since in the non-linear MDK filter with the IQRD-RLS algorithm, the prediction error £`k,k-1 used in the conventional IQRD-RLS is replaced by the nonlinear function of £l(£`k,k-1), and is defined to as the modified IQRD-RLS algorithm. The merits of the proposed algorithms are verified via computer simulation. We showed that the performance of our proposed algorithms outperformed the one using the conventional nonlinear filtering approach with LMS algorithm, in terms of convergence property and the signal-to-noise ratio improvement (SNRI).

The main goal of this thesis is to create a continuous phase modulated radio system with a recursive least square equalizer. The two tested channel models are typical urban and rural area. The result of the performance of this radio system is displayed in Matlab plots as the bit error rate. Three error rates are displayed; with error correction, without error correction and the rate of received incorrect message bursts. Conclusions are also drawn of the performance of the radio system in kbit/sec of bandwidth when the different channel models are used. The performance is also divided into how the equalizer handles inter symbol interference or a fading channel without inter symbol interference. / I detta examensarbete har ett fasmodulerat radiosystem simulerats, fokusering ligger på kanalutjämnare som är av typen recursive least square (RLS). RLS utjämnaren har testats med två olika gsm kanalmodeler, dels typical urban som simulerar radioförbindelser i stadsmiljö den andra modellen är rural area där sändare och mottagare kan se varandra. Tre olika resultat presenteras; med felrättande koder, utan felrättande koder och mängden icke korrekta datapaket. Slutsatser dras om radiosystemets bandbredd när de olika kanalmodellerna används vid olika brusmängd. Även utjämnarens förmåga att hantera inter-symbol interference och fading utvärderas också.

The conventional temporal filtering approach cannot be used to separate signal from interference which occupies the same temporal frequency band as signal. Using a spatial filtering at the receiver can separate signals from interference that originates from different spatial location. Many adaptive array beamforming algorithms, based on linear constraints, have been proposed for suppressing undesired interference and being applied to wireless communication systems for multiuser detection. The adaptive array system can be employed to automatically adjust its directional to achieve the purpose that nulls the interferences or jammers and thus, enhances the reception of the desired signal. Inverse QR Decomposition Recursive Least-square (IQRD-RLS) algorithm has many advantages such as where the LS weight vector be computed without back substitution, a well known numerical stable algorithm and offering better convergence rate, steady-state means-square error, and parameter tracking capability over the adaptive least mean square (LMS) based algorithms. In this thesis, a new application, GSC-IQRD-RLS combining Generalized Sidelobe Canceller (GSC) and IQRD-RLS algorithm, is developed. It preserves the advantages of GSC such as simple structure, less computations, and converts a linearly constrained optimization problem into a standard optimum filtering problem. But the performance is equivalent between GSC-IQRD-RLS and LC-IQRD-RLS algorithms.

A wide range of modern large-scale systems relies on recovering a signal from noisy linear measurements. In many applications, the useful signal has inherent properties, such as sparsity, low-rankness, or boundedness, and making use of these properties and structures allow a more efficient recovery. Hence, a significant amount of work has been dedicated to developing and analyzing algorithms that can take advantage of the signal structure. Especially, since the advent of Compressed Sensing (CS) there has been significant progress towards this direction. Generally speaking, the signal structure can be harnessed by solving an appropriate regularized or constrained M-estimator. In modern Multi-input Multi-output (MIMO) communication systems, all transmitted signals are drawn from finite constellations and are thus bounded. Besides, most recent modulation schemes such as Generalized Space Shift Keying (GSSK) or Generalized Spatial Modulation (GSM) yield signals that are inherently sparse. In the recovery procedure, boundedness and sparsity can be promoted by using the ℓ1 norm regularization and by imposing an ℓ∞ norm constraint respectively. In this thesis, we propose novel optimization algorithms to recover certain classes of structured signals with emphasis on MIMO communication systems. The exact analysis permits a clear characterization of how well these systems perform. Also, it allows an automatic tuning of the parameters. In each context, we define the appropriate performance metrics and we analyze them exactly in the High Dimentional Regime (HDR). The framework we use for the analysis is based on Gaussian process inequalities; in particular, on a new strong and tight version of a classical comparison inequality (due to Gordon, 1988) in the presence of additional convexity assumptions. The new framework that emerged from this inequality is coined as Convex Gaussian Min-max Theorem (CGMT).