We propose decoupling networks for the reconfigurable intelligent surface (RIS) array as a solution to benefit from the mutual coupling between the reflecting elements. In particular, we show that when incorporating these networks, the system model reduces to the same structure as if no mutual coupling is present. Hence, all algorithms and theoretical discussions neglecting mutual coupling can be directly applied when mutual coupling is present by utilizing our proposed decoupling networks. For example, by including decoupling networks, the channel gain maximization in RIS-aided single-input single-output (SISO) systems does not require an iterative algorithm but is given in closed form as opposed to using no decoupling network. In addition, this closed-form solution allows to analytically analyze scenarios under mutual coupling resulting in novel connections to the conventional transmit array gain. In particular, we show that super-quadratic (up to quartic) channel gains w.r.t. the number of RIS elements are possible and, therefore, the system with mutual coupling performs significantly better than the conventional uncoupled system in which only squared gains are possible. We consider diagonal as well as beyond diagonal (BD)-RISs and give various analytical and numerical results, including the inevitable losses at the RIS array. In addition, simulation results validate the superior performance of decoupling networks w.r.t. the channel gain compared to other state-of-the-art methods.

The use of 1-bit digital-to-analog converters (DACs) is a promising way to improve the energy efficiency of massive multiple-input-multiple-output (MIMO) systems. Many linear and nonlinear precoding techniques mitigating the severe quantization distortions have been introduced in order to enable the use of 1-bit DACs in the massive MIMO downlink. However, the vast majority of the proposed designs do not account for the channel strength imbalances between the users, which are highly likely to occur in the downlink scenarios. To achieve high sum rates in such scenarios, a user-specific power allocation scheme (USPA) is needed. In this paper, we propose a quantization aware USPA technique for the linear zero-forcing (ZF) precoder. Numerical results illustrate that higher achievable sum rates than the existing linear methods can be achieved with the proposed method, especially in the systems with low order modulated inputs. In the systems with higher modulation, the proposed method achieves similar performance to the existing methods, yet with a lower computational complexity.

Many linear and nonlinear precoding techniques have been proposed to tackle the quantization distortions in the multiple-input-multiple-output (MIMO) downlink system deployed with 1-bit digital-to-analog converters (DACs). Very few of these techniques can support adequate sum rates in a realistic scenario with channel strength imbalances between the users. Higher sum rates can be achieved through a power allocation scheme which takes the 1-bit quantization distortions into account. In this paper, we propose a quantization aware sum rate maximizing power allocation algorithm for the regularized zero-forcing (RZF) precoder based on the asymptotic analysis. Numerical results illustrate that the proposed method achieves higher sum rates than the other state-of-the-art quantization aware sum rate maximization schemes.

Millimeter wave (mmWave) massive multiple-input-multiple-output (mMIMO) is a key technology for high capacity mobile communications. The straightforward implementation of mMIMO with fully digital precoding (FDP) and high resolution digital-to-analog converters (DACs) ends up with poor energy efficiency (EE) at the transmitter. Use of 1-bit DACs is a promising approach to improve the EE, since the power amplifiers (PAs) are driven with the maximum efficiency when they are fed with constant envelope (CE) signals. In this paper, we evaluate spectral efficiency (SE) and EE performances of FDP and hybrid precoding (HP) in mmWave mMIMO downlink to identify the more compatible option with the 1-bit quantization. Numerical results illustrate that FDP in general achieves higher SE and has higher EE at high signal-to-noise ratio (SNR), whereas HP has higher EE and achieves higher maximum EE at low SNR.

Hybrid analog/digital (A/D) beamforming architectures are low complexity alternatives to fully digital designs in large antenna setups. Recent research efforts have been set out to find low-complexity algorithms, which have low implementation complexity in the analog domain. In this paper, we propose a two-stage hybrid precoding design assuming hardware constraints that avoid the use of adaptive phase-shifters (PSs) by introducing a novel combination of Butler matrices (BMs) to feed a uniform planar array (UPA). With several fixed beams, we propose an algorithm to design the analog precoding matrix in the first stage, while in the second stage, hybrid-beamforming (HBF)-weighted minimum mean square error (WMMSE) is proposed for baseband precoding by taking into account the analog precoding matrix designed in the first stage. The proposed algorithm shows fast convergence, thanks to the analog precoder design, which helps the HBF-WMMSE algorithm to converge within a few iterations. Compared with the classical matched filter (MF) and minimum mean square error (MMSE) solutions, HBF-WMMSE outperforms the latter in terms of sum-rate, especially in the low signal-to-noise-ratio (SNR) regime. Simulation results further show that the partially connected Butler matrices (PCBMS) approach implemented with fixed-PSs exhibit superior energy-efficiency while maintaining higher spectral-efficiency as compared to the partially connected analog phase shifting (PCAPS) network implemented with variable-PSs.

Employing 1-bit quantization in massive multiple-input-multiple-output (MIMO) systems is a promising approach to improve the power efficiency. Various linear and nonlinear precoding techniques including the maximum safety margin (MSM) precoder have been presented to cope with severe 1-bit quantization distortions. In this paper, we show the reason for MSM precoder's success in reducing the quantization distortions and propose a low complexity extension with a greedy algorithm by exploiting this analytic result. Numerical results illustrate that the extended MSM is almost optimal in terms of maximizing the constructive interference (CI) between the received signals.

In this paper, a novel two-dimensional parameter estimation method is proposed for frequency-selective millimeter-wave (mmWave) channels by probing a limited number of Kronecker products of discrete Fourier transform (DFT) beams, which are efficiently implemented in the analog domain by a novel combination of Butler matrices. The proposed strategy firstly estimates the channel parameters by using a modified parameter estimation via interpolation based on a DFT grid (PREIDG) algorithm. In a second step, high-resolution channel parameter estimation is achieved even in the low signal-to-noise-ratio (SNR) using the space-alternating generalized expectation-maximization (SAGE) algorithm. The proposed modified PREIDG algorithm outperforms state-of-the-art methods, e.g., the auxiliary beam pair (ABP) method while the SAGE algorithm achieves the derived Cramér-Rao lower bound (CRLB). Numerical results demonstrate that excellent estimation performance can be achieved for angle of departure (AoD) azimuth and elevation with addition to delay and complex path gain of each path even in the low SNR regime.

In this paper, we consider the transmit and receive antenna array gain of massive MIMO systems. In particular, we look at their dependence on the number of antennas in the array, and the antenna spacing for uniform linear and uniform circular arrays. It is known that the transmit array gain saturates at a certain antenna spacing, but the receive array gain had not been considered. With our physically consistent analysis based on the Multiport Communication Theory, we show that the receive array gain does not saturate, but that there is a peak at a certain antenna spacing when there is no decoupling network at the receiver. As implementing a decoupling network for massive MIMO would be almost impossible, this is a reasonable assumption. Furthermore, we analyze how the array gain changes depending on the antenna spacing and the size of the antenna array and derive design recommendations.

To achieve a reliable communication with short data blocks, we propose a novel decoding strategy for Kronecker-structured constant modulus signals that provides low bit error ratios (BERs) especially in the low energy per bit to noise power spectral density ratio (Eb/No). The encoder exploits the fact that any M-PSK constellation can be factorized as Kronecker products of lower or equal order PSK constellation sets. A construction of two types of schemes is first derived. For such Kronecker-structured schemes, a conceptually simple decoding algorithm is proposed, referred to as Kronecker-RoD (rank-one detector). The decoder is based on a rank-one approximation of the “tensorized” received data block, has a built-in noise rejection capability and a smaller implementation complexity than state-of-the-art detectors. Compared with convolutional codes with hard and soft Viterbi decoding, Kronecker-RoD outperforms the latter in BER performance at same spectral efficiency.

We analyze the performance of multiple-input multiple-output (MIMO) links with one-bit output quantization in terms of achievable rates and characterize their performance loss compared to unquantized systems for general channel statistical models and general channel state information (CSI) at the receiver. One-bit ADCs are particularly suitable for large-scale millimeter wave MIMO Communications (massive MIMO) to reduce the hardware complexity. In such applications, the signal-to-noise ratio per antenna is rather low due to the propagation loss. Thus, it is crucial to analyze the performance of MIMO systems in this regime by means of information-theoretical methods. Since an exact and general information-theoretic analysis is not possible, we resort to the derivation of a general asymptotic expression for the mutual information in terms of a second-order expansion around zero SNR. We show that up to second order in the SNR, the mutual information of a system with two-level (sign) output signals incorporates only a power penalty factor of π/2 (1.96 dB) compared to systems with infinite resolution for all channels of practical interest with perfect or statistical CSI. An essential aspect of the derivation is that we do not rely on the common pseudo-quantization noise model.

We consider the reciprocity of the information-theoretic channel of Time Division Duplex (TDD) Multi-User-Multiple Input Multiple Output (MU-MIMO) systems in the up- and downlink. Specifically, we assume that the transmit and receive chains are reciprocal. We take the mutual coupling between the antenna elements at the base station and at the mobiles into account. Mutual coupling influences how to calculate transmit power and noise covariance. The analysis is based on the Multiport Communication Theory, which ensures that the information-theoretic model is consistent with physics. It also includes a detailed noise model. We show that due to the coupling, the information-theoretic up- and downlink channels do not fulfill the ordinary reciprocity relation, even if the input-output relation of the transmit voltage sources and the receive load voltages, i.e., the channel which is estimated with the help of pilot signals in the uplink, is reciprocal. This is a fundamental effect that is not considered otherwise. We show via Monte Carlo simulations that both, using the ordinary reciprocity relation, and not taking the coupling into account, significantly decreases the ergodic rates in single-user and the ergodic sum rates in multi-user systems.

In this work, we study optimal transmit strategies for minimizing the positioning error bound in a line-of-sight scenario, under different levels of prior knowledge of the channel parameters. For the case of perfect prior knowledge, we prove that two beams are optimal, and determine their beam directions and optimal power allocation. For the imperfect prior knowledge case, we compute the optimal power allocation among the beams of a codebook for two different robustness-related objectives, namely average or maximum squared position error bound minimization. Our numerical results show that our low-complexity approach can outperform existing methods that entail higher signaling and computational overhead.

Future cellular systems will likely employ massive bi‐dimensional arrays to improve performance by large array gain and more accurate spatial filtering, motivating the design of low‐complexity signal‐processing methods. The authors propose optimising a Kronecker‐separable beamforming filter that takes advantage of the bi‐dimensional array geometry to reduce computational costs. The Kronecker factors are obtained using two strategies: alternating optimisation and sub‐array minimum mean square error (MMSE) beamforming with Tikhonov regularisation. According to the simulation results, the proposed methods are computationally efficient but come with source recovery degradation, which becomes negligible when the sources are sufficiently separated in space.

In this contribution, we investigate a coarsely quantized multi-user multiple-input single-output downlink communication system, where we assume 1-bit digital-to-analog converters at the base station antennas. First, we analyze the achievable sum rate lower-bound using the Bussgang decomposition under new assumptions. In the presence of the non-linear quantization, our analysis indicates the potential merit of reconsidering traditional signal processing techniques in coarsely quantized systems, i.e., reconsidering transmit covariance matrices whose rank is equal to the rank of the channel. Furthermore, in the latter part of this paper, we propose a linear precoder design that achieves the predicted increase in performance compared with a state-of-the-art linear precoder design. Moreover, our linear signal processing algorithm allows for higher order modulation schemes to be employed.

The fundamental limits of single-anchor multi-antenna positioning are investigated. Exploiting the structure of the multiple input-multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) channel at millimeter-wave frequencies, we present geometrically intuitive asymptotic expressions for the Fisher information on position, orientation and velocity for large bandwidth and number of antennas. The effects of synchronization errors and mobility are studied and it is shown that non-line-of-sight (NLOS) paths can be used to estimate the synchronization error and drastically improve the positioning performance. We also show that, in the presence of line-of-sight (LOS), mobility has a small impact on the achievable positioning accuracy, but in the NLOS-only scenario it can significantly improve the achievable performance, depending on the variance of the synchronization error. Finally, considering a communication system with device-specific transmission and reception constraints, we compare the positioning accuracy between the downlink and the uplink and show that they are equivalent under the same received signal-to-noise ratio (SNR).

We study the performance bounds of vehicle-to-vehicle (V2V) relative positioning for vehicles with multiple antenna arrays. The Cramér-Rao bound for the estimtion of the relative position and the orientation of the Tx vehicle is derived, when angle of arrival (AOA) measurements with or without time-difference of arrival (TDOA) measurements are used. In addition, geometrically intuitive expressions for the corresponding Fisher information are provided. The derived bounds are numerically evaluated for different carrier frequencies, bandwidths and array configurations under different V2V scenarios, i.e. overtaking and platooning. The significance of the AOA and TDOA measurements for position estimation is investigated. The achievable positioning accuracy is then compared with the present requirements of the 3rd Generation Partnership Project (3GPP) 5G New Radio (NR) vehicle-to-everything (V2X) standardization.

The use of low resolution Analog to Digital Converters (ADCs) can significantly reduce the power consumption for massive Multiple Input Multiple Output (MIMO) systems. The existing literature on quantized massive MIMO systems deals with Cyclic Prefix (CP) transmission schemes in frequency- selective fading channels. In this paper, we propose a block processing Frequency Domain Equalization (FDE) technique in CP-free transmission schemes for massive MIMO systems having low resolution ADCs. The optimal block length for FDE is found by minimizing a computational complexity cost function and taking quantization distortion, channel impulse response and the number of transmit and receiver antennas into account. Through numerical simulation, it is shown that the optimal block length also guarantees good performance in terms of the Mean Square Error (MSE) and Bit Error-Rate (BER) criterion.

Coarsely quantized massive multiple-input multiple-output (MIMO) systems are gaining more interest due to their power efficiency. We present a new precoding technique to mitigate the multi-user interference and the quantization distortions in a downlink multi-user MIMO system with coarsely quantized constant envelope (QCE) signals at the transmitter. The transmit signal vector is optimized for every desired received vector taking into account a relaxed version of the QCE constraint. The optimization is based on maximizing the safety margin to the decision thresholds of the receiver constellation modulation. Due to the linear property of the objective function and the constraints, the optimization problem is formulated as a linear programming problem. The simulation results show a significant gain in terms of the uncoded bit error rate compared to the existing precoding techniques.

In this contribution, we provide an information theoretical analysis of coarsely-quantized downlink Single-User (SU)- Multiple Input Single Output (MISO) communication systems. We address the question of whether traditional signal processing techniques, i.e., proper signaling and channel rank transmit covariance matrices, are still optimal with respect to maximizing the data rate. We investigate the mutual information lower bound based on the Bussgang theorem, in the SU-MISO downlink scenario, where we assume 1-bit quantized Digital-to-Analog Converters (DACs) in the transmit antennas at the Base Station (BS). We prove that at low Signal-to-Noise Ratio (SNR), existing signal processing techniques maximize the data rate. However, at higher SNR we show, using counter examples, that the data rates can be improved using different signal processing techniques. These results show the potential merit of reconsidering signal processing techniques in coarsely- quantized SU-MISO downlink scenarios.

We present a novel linear minimum-mean-squared-error (MMSE) joint precoding and equalization technique for a downlink (DL) massive multiple-input-multiple-output (MIMO) scenario. To lower the power consumption, the computational and the design complexity, 1-bit digital to analog converters (DACs) and analog to digital converters (ADCs) are used at the transmitter and at the receiver antennas. These economical and computational gains come at the cost of a performance loss which can be recovered by the large number of antennas deployed at the base station and by implementing appropriate precoders and equalizers which can mitigate the coarse quantization effects. The proposed technique implements a two-stage digital and an analog precoder with a digital equalizer, both of which take into account the effects of the 1-bit quantizers. The simulation results indicate the superiority of the novel joint precoder and equalizer design to the previously proposed linear precoders and equalizers not only in terms of the uncoded bit error rate (BER) performance but also in terms of the robustness to errors in the estimation of channel state information at the transmitter (CSIT).

For 5G, it will be important to leverage the available millimeter wave spectrum. To achieve an approximately omnidirectional coverage with a similar effective antenna aperture compared with the state-of-the-art cellular systems, an antenna array is required at both the mobile and base stations. Due to the large bandwidth, the analog front-end of the receiver with a large number of antennas becomes especially power hungry. Two main solutions exist to reduce the power consumption: Hybrid BeamForming (HBF) and Digital BeamForming (DBF) with low resolution Analog to Digital Converters (ADCs). An HBF system can also be combined with low resolution ADCs. This paper compares the spectral and energy efficiency based on the RF-frontend configuration. A channel with multipath propagation is used. In contrast to previous publication, we take the spatial correlation of the quantization noise into account. We show that the low resolution ADC DBF is robust to small Automatic Gain Control (AGC) imperfections. We showed that in the low SNR regime, the performance of DBF even with 1-2 bit resolution outperforms HBF. If we consider the relationship of spectral and energy efficiency, DBF with 3-5 bit resolution achieves the best ratio of spectral efficiency per power consumption of the RF receiver frontend over a wide SNR range. The power consumption model is based on components reported in the literature.

Parameter estimation using quantized observations is of importance in many practical applications. Under a symmetric 1-bit setup, consisting of a zero-threshold hard-limiter, it is well known that the large sample performance loss for low signal-to-noise ratios (SNRs) is moderate (2/Π or -1.96dB). This makes low-complexity analog-to-digital converters (ADCs) with 1-bit resolution a promising solution for future wireless communications and signal processing devices. However, hardware imperfections and external effects introduce the quantizer with an unknown hard-limiting level different from zero. In this paper, the performance loss associated with pilot-based channel estimation, subject to an asymmetric hard limiter with unknown offset, is studied under two setups. The analysis is carried out via the Cramér-Rao lower bound (CRLB) and an expected CRLB for a setup with random parameter. Our findings show that the unknown threshold leads to an additional information loss, which vanishes for low SNR values or when the offset is close to zero.

We propose a novel linear minimum-mean-squared-error (MMSE) precoder design for a downlink (DL) massive multiple-input-multiple-output (MIMO) scenario. For economical and computational efficiency reasons low resolution 1-bit digital-to-analog (DAC) and analog-to-digital (ADC) converters are used. This comes at the cost of performance gain that can be recovered by the large number of antennas deployed at the base station (BS) and an appropriate pre-coder design to mitigate the distortions due to the coarse quantization. The proposed precoder takes the quantization non-linearities into account and is split into a digital precoder and an analog precoder. We formulate the two-stage precoding problem such that the MSE of the users is minimized under the 1-bit constraint. In the simulations, we compare the new optimized precoding scheme with previously proposed linear precoders in terms of uncoded bit error ratio (BER).

Energy-efficient signal processing systems require estimation methods operating on data collected with low-complexity devices. Using analog-to-digital converters (ADC) with 1-bit amplitude resolution has been identified as a possible option in order to obtain low power consumption. The 1-bit performance loss, in comparison to an ideal receiver with ∞-bit ADC, is well-established and moderate for low SNR applications (2/π or -1.96 dB). Recently it has been shown that for parameter estimation with state-space models the 1-bit performance loss with Bayesian filtering can be significantly smaller (√2/π or -0.98 dB). Here we extend the analysis to Bayesian smoothing where additional measurements are used to reconstruct the current state of the system parameter. Our results show that a 1-bit receiver performing smoothing is able to outperform an ideal ∞-bit system carrying out filtering by the cost of an additional processing delay Δ.

The mitigation of nonlinear distortion caused by power amplifiers (PA) in Orthogonal Frequency Division Multiplexing (OFDM) systems is an essential issue to enable energy efficient operation. We propose a new algorithm for receiver-based clipping estimation in OFDM systems that combines the existing Iterative Hard Thresholding method with a novel Bayesian framework to estimate clipping parameters at the receiver. We avoid the use of pilots and formulate the recovery problem solely on reliably detected sub-carriers. We also develop a new criterion for selecting these reliable carriers that takes into account the channel code. Through simulations, we show that the proposed technique outperforms the existing methods both in terms of BER and speed.

For 5G it will be important to leverage the available millimeter wave spectrum. Communication at high carrier frequencies requires antenna arrays at both the base and mobile station. A 1-bit analog to digital converter can effectively reduce the complexity and power consumption of the analog frontend. This is especially interesting in the context of large antenna arrays with a sizable signal bandwidth. In this paper we investigate the cell search capabilities of a mobile station with an antenna array and an analog frontend with 1-bit quantization behind each antenna. We derive the Likelihood Ratio Test (LRT) for this problem with full channel knowledge. We also show the generalized Likelihood Ratio Test (GLRT) for a case without channel knowledge, and compare the performance to practical algorithm based on correlation. We show that the practical correlation achieves a similar performance compared to the theoretical optimal GLRT. Compared to a system with high resolution analog to digital conversion our results show the theoretical derived SNR gap of 2 dB.

In this contribution we propose two new designs of transmit and receive processing for multi-user multiple-input-multiple-output (MIMO) downlink systems that employ filter bank based multicarrier with offset quadrature amplitude modulation (FBMC/OQAM). Our goal is to overcome the limits on the channel frequency selectivity and/or the allowed number of receive antennas per user terminal that are imposed on the state-of-the-art solutions. In the first method the design of precoders and equalizers is iterative and minimum mean square error (MMSE) based. The second is a closed-form design based on the signal-to-leakage ratio (SLR). Via numerical simulations we evaluate the performance of both methods and demonstrate their superiority over two other approaches in the literature.

In the downlink (DL) of a multi-user multiple-input and multiple-output (MU-MIMO) system, the maximization of the weighted sum rate with dirty paper precoding (DPC) is treated under multiple linear and linear conic constraints. By network duality, the problem is transformed to a minimax uplink (UL) problem. In the UL, the minimization of the utility with respect to the noise covariance and the maximization of the utility with respect to the transmit covariances is solved either jointly or alternately with the gradient-projection algorithm. The proposed algorithms do not only allow to find the maximum weighted sum rate with respect to conic constraints, they are also efficient implementations with respect to multiple linear constraints.

In this contribution we compare different realizations of the polyphase networks employed in Offset QAM based Filter Bank Multicarrier (OQAM-FBMC). Our objective is to evaluate the structures' computational cost and robustness for very low complexity implementations of the prototype filter coefficients. We compare a direct form, a classical lattice realization, where in both the Canonical Signed Digit (CSD) representation of the coefficients is applied, and a Coordinate Rotation by Digital Computer (CORDIC)-based lattice structure. We evaluate the cost after coefficient quantization in terms of number of additions and shifts. By considering the multicarrier application, we show the effects of coefficient quantization on the subcarrier filters spectrum and on the multicarrier transmit signal Power Spectral Density (PSD).

This paper studies the physical layer performance of a lean carrier with reduced reference symbols and reduced control channel overhead as a possible candidate for a new 5G air interface. Specifically, we will show that, with minor modifications and optimizations, competitive link level performance can be achieved even in extreme use case scenarios using synchronization and channel estimation algorithms already employed in LTE-A. Further, we propose a set of enhancements to the broadcast channel and to the common search space configurations to fully dispense with cell specific reference symbols and legacy control channels.

We consider a very basic communication scenario: a transmitter sends data to a receiver over a single invariant link, where each side has a certain energy budget which is a common restriction for sensor nodes powered by batteries or energy harvesting components. Given the channel coefficient, the instantaneous data rate that can be achieved depends on the transmit power employed by the transmitter, and the resolution of the analog-to-digital converter (ADC) employed by the receiver. The power consumptions of the transmitter and the receiver also depend, respectively, on these two parameters. In this paper we seek to answer the question: what is the transmit and receive strategy that leads to the maximal throughput achieved on a finite time interval [0, T], given the individual energy constraints at the transmitter and the receiver? The underlying control problem, which is found non-convex in its original form, can be made convex by a reconstruction of the power-rate surface based on which the optimal solution can be conveniently determined.

We consider the downlink of a cellular network with multiple antenna base stations and single antenna user terminals. In cellular networks with and without cooperation, the problem of instationary intercell interference arises. Some base stations change their beamforming unpredictably and the signal to interference plus noise ratios of the served user terminals are unknown at the base station. Consequently, the precoding and link rate adaption are outdated and the transmission might fail. Hybrid automatic repeat request can be used to mitigate the risk of such a fail. We propose to optimize the precoders at the base stations based on the expectation of the rate, where we include the effects of soft combining in the optimization.

Given a set of observations, the estimation of covariance matrices is required in the analysis of many applications. To this end, any know structure of the covariance matrix can be taken into account. For instance, in case of separable processes, the covariance matrix is given by the Kronecker product of two factor matrices. Assuming the covariance matrix is full rank, the maximum likelihood (ML) estimate in this case leads to an iterative algorithm known as the flip-flop algorithm in the literature. In this work, we first generalize the flip-flop algorithm to the case when the covariance matrix is rank deficient, which happens to be the case in several situations. In addition, we propose a non-iterative estimation approach which incurs in a performance loss compared to the ML estimate, but at the expense of less complexity.

Sensor arrays with Vandermonde or centro-hermitian responses cannot always be constructed. However, such array response structure can be achieved by means of a mapping which transforms the real array response to an array response with the desired properties by applying array interpolation algorithms. In this work a low-complexity, multi-sector, signal adaptive array interpolation approach that achieves low transformation bias in the presence of highly correlated signals is presented. Estimation of Signal Parameters via Rotational Invariance (ESPRIT) algorithm with Forward Backward Average (FBA) and Spatial Smoothing (SPS) as well as model order estimation is applied after array interpolation in conjunction with the Vandermonde Invariance Transformation (VIT) to obtain precise high resolution estimates in closed form. A set of numerical simulations show that the proposed approach provides precise estimates for arbitrary array responses in highly correlated signal signal environments.

The problem of optimum analog receive filtering for digital signal detection and parameter estimation is considered. Here the case of a signal source with bandwidth Btand a receiver with fixed sampling rate fsis discussed under the assumption that 2Bt> fs. We investigate the impact of adjusting the receive bandwidth Brof the analog pre-filter, which is applied prior to the sampler, with respect to the deflection coefficient or the Fisher information measure. This reveals that the design rule 2Brs, known as the sampling theorem, does not necessarily lead to optimum system performance. Studying the two analytical information measures under a fix sampling rate fsand an arbitrary choice of Br, we provide an example where receive setups with 2Br> fsachieve higher detection and parameter estimation performance.

To communicate over a priori unknown channels, pilot sequences can be exploited to assist the receiver in obtaining the channel state information. The analog-to-digital converter (ADC) at the front end of the receiver samples and quantizes the input signal, including both pilot and data symbols. This results in a reduction of the receive signal-to-noise ratio (SNR) as well as deteriorated quality of channel estimation, which depends quantitatively on the bit resolution used by the ADC. In this work, we consider the point-to-point, training based communication between a single-antenna transmitter and a multi-antenna receiver over a Rayleigh block fading channel, and take into account the impact of the ADC for a joint optimization of the training length, the average receive SNR, the number of receive antennas, and the bit resolution of the ADC. Goal of the optimization is to minimize the energy per bit metric, where we include both transmit power and power dissipation of the ADC into the energy consumption model, and employ a capacity lower bound which depends on all aforementioned design parameters. Results from numerical simulations are demonstrated and analyzed, leading to a number of insightful observations and conclusions which are important for the energy efficient operation of the system.

The problem of parameter estimation from large noisy data is considered. If the observation size N is large, the calculation of efficient estimators is computationally expensive. Further, memory can be a limiting factor in technical systems where data is stored for later processing. Here we follow the idea of reducing the size of the observation by projecting the data onto a subspace of smaller dimension M ≪ N, but with the highest possible informative value regarding the estimation problem. Under the assumption that a prior distribution of the parameter is available and the output size is fixed to M, we derive a characterization of the Pareto-optimal set of linear transformations by using a weighted form of the Bayesian Cramér-Rao lower bound (BCRLB) which stands in relation to the expected value of the Fisher information measure. Satellite-based positioning is discussed as a possible application. Here N must be chosen large in order to compensate for low signal-to-noise ratios (SNR). For different values of M, we visualize the information-loss and show by simulation of the MAP estimator the potential accuracy when operating on the reduced data.

The problem how to approximately determine the value of the Fisher information measure for a general parametric probabilistic system is considered. Having available the first and second moment of the system output in a parametric form, it is shown that the information measure can be bounded from below through a replacement of the original system by a Gaussian system with equivalent moments. The presented technique is applied to a system of practical importance and the potential quality of the bound is demonstrated.

The available channel state information (CSI) in a limited feedback system like the frequency division duplex (FDD) downlink is not perfect since it is subject to estimation, quantization and feedback errors, and in addition, can be outdated. Despite the fact that the capacity of limited feedback systems is unknown in general, we derive a novel lower bound on the capacity of single user limited feedback systems with imperfect CSI. Based on this bound, we propose the design of an FDD system by finding the optimum training and number of feedback bits. To this end we also take the FDD uplink into account, since practical FDD systems represent two-way systems. We also provide closed-form approximations for the optimum downlink training, uplink training and number of feedback bits which basically maximize lower bounds on the FDD downlink and uplink capacity with imperfect CSI.

Using coarse resolution analog-to-digital conversion (ADC) offers the possibility to reduce the complexity of digital receive systems but introduces a loss in effective signal-to-noise ratio (SNR) when comparing to ideal receivers with infinite resolution ADC. Therefore, here the problem of signal parameter estimation from a coarsely quantized receive signal is considered. In order to increase the system performance, we propose to adjust the analog radio front-end to the quantization device in order to reduce the quantization-loss. By optimizing the bandwidth of the analog filter with respect to a weighted form of the Cramér-Rao lower bound (CRLB), we show that for low SNR and a 1-bit hard-limiting device it is possible to significantly reduce the quantizationloss of initially -1.96 dB. As application, joint carrier-phase and time-delay estimation for satellite-based positioning and synchronization is discussed. Simulations of the maximum-likelihood estimator (MLE) show that the optimum estimator achieves the same quantization-loss reduction as predicted by the performance bound of the optimized system.

We investigate in this work the throughput maximizing transmission strategies of an energy harvesting transmitter which communicates over a time-varying channel. Two system models are considered to this end, the generic model for which the energy harvesting node is treated as a continuous-time control system, and the MQAM model for which practical modulation schemes are employed for transmission. A generalized circuit power function dependent on the transmit power is formulated for the generic model. We apply optimal control theory with this model and propose a modified water-filling algorithm to obtain the optimal transmission policy. The circuit power modelling with MQAM transmissions, on the other hand, is based on studies on the power dissipation of various functional components of the transmitter circuitry. Energy-efficient modulation orders can be found and the throughput maximization becomes a linear optimization problem in this case. A heuristic algorithm with near-optimal performance is also developed to conquer the difficulty of problems with large number of variables. Besides, the dynamic programming (DP) technique is applied with both system models which not only verifies the optimality of our obtained power allocations, but also provides an online solution to the situation that the transmitter does not have in advance the full channel state information (CSI) of the entire time slot of interest.

Circuit theory is essentially a physically based theory of dynamical systems. To demonstrate basic phenomena example circuits have to be selected and designed. These circuits consist of a few essential elements, which makes it easier to explain the mathematics. But, the abstraction of these simplified circuits can be a hurdle for students. To give students with a strong interest in the application an access to the theory and to add more verve to the lectures, we gathered some circuit examples, which are easy to explain in detail and have an entertaining effect. In this paper, we focus on circuits with at least one reactive element.

Filter bank based multicarrier (FBMC) systems present an alternative solution to cyclic prefix based orthogonal frequency division multiplexing (CP-OFDM) in wireless environments with multipath propagation. In this contribution we propose a novel training sequences design method to be employed in a per-subcarrier maximum likelihood (ML) narrowband channel estimation scheme, recently developed by the authors. In the training design we take into account the knowledge of transmitter and receiver side prototype filters to calculate the pilot sequences. The method targets at the minimum mean squared error (MMSE) of the channel estimation and uses a gradient projection algorithm to find the optimum sequences. In the numerical results we show a gain of up to 4 dB for the same MMSE value in a low to medium Eb/N0regime.

We consider an energy harvesting node which transmits data using the energy it harvests from the environment. In the simple scenario of point-to-point communication, the performance of the node in terms of throughput is greatly influenced by the transmission strategy it employs and its knowledge about the energy arriving process. We assume in this work that the transmitting node does not have non-causal information about the energy to be harvested in future, but only has available the statistics of the energy arriving process which is stationary. The practical considerations that the energy storage capacity of the node is limited and there is additional energy consumption within the circuitry of the node are taken into account by our system model. Viewing the system as a finite-state Markov decision process, we optimize the transmission policy the node employs as a function of the energy storage state after an energy arrival, by using the policy-iteration algorithm. The asymptotic performance of the system in terms of average throughput is studied within the established theoretical and algorithmic framework, under the several transmission strategies we propose. Simulation results indicate the advantage of each strategy with respect to a certain range of values that the system parameters may take.

We consider the downlink of a wireless cellular network where the base stations are equipped with multiple antennas and operate in the same frequency band. Since scheduling changes the spatial transmit signal processing with each time slot, information from neighboring base stations is required for data encoding. This can, in theory, be accomplished by a high-capacity backhaul network through which the base stations exchange channel state information (CSI) and other control signals. In reality, however, the temporal granularity of the scheduler does not allow for timely distribution of CSI among base stations. We propose a two-phase scheduler which optimizes the precoding in the first phase and allows the users to feed back their instantaneous interference power in the second phase. For the single-user case, we present a practical scheme that combines two-phase scheduling with precoders that maximize the signal-to-leakage-plus-noise ratio. If the users feed back the interference power together with a supported rate, communication between base stations can be limited to integers. By comparing the performance to multi-user two-phase scheduling with dirty paper coding and to algorithms that share CSI among base stations we show that two-phase scheduling is a technically and practically feasible solution to deal with non-stationary intercell interference.

In this paper, we analyze the fundamental trade-off between information transfer and power gain by means of an information-theoretic framework in communications circuits. This analysis is of interest as many of today's applications require that maximum information and maximum signal power are extracted (or transferred) through the circuit at the same time for further processing so that a compromise concerning the signal spectral shape as well as the matching network has to be found. To this end, the optimization framework is applied to a two-port circuit, which is used as an abstraction for a broadband amplifier. Thereby, we characterize the involved Pareto bound by considering different optimization problems. The first one aims at optimizing the input power spectral density (PSD) as well as the source and load admittances, whereas the second approach assumes the PSD to be fixed and uniformly distributed within a fixed bandwidth and optimizes the source and load admittances only. Moreover, we will show that additional matching networks may help to improve the trade-off.

In this work a systematic methodology is presented to design optimum chip pulse shapes for DS-CDMA systems for timing synchronization. A nonlinear bi-objective problem with additional constraints is established. The derived optimum chip pulse shapes show significant improvement in timing synchronization performance.

When formulated as constrained optimizations of various kinds, resource allocation (RA) in wireless communication systems involves practically not only continuous variables such as transmit power, but also discrete variables such as modulation formats and coding rates. Multicarrier systems with frequency division multiple access (FDMA), e.g., OFDMA, require exclusive usage of their subcarriers (or subchannels) by a single user during a certain time unit, and therefore render the assignment of subcarriers a combinatorial problem which is a crucial part of RA. In this paper, we formulate the quality of service (QoS) constrained RA problems in multicarrier systems as optimizations over a finite set of available modes of operations (MOP) and employ integer programming techniques to solve them. Specifically, optimal algorithms are developed based on the branch and bound (BAB) and the branch and price (BAP) methods for three types of RA problems: the downlink transmit power minimization, the uplink sum transmit power minimization, and the downlink transmit power constrained energy minimization. Besides justifying the optimality of these algorithms, simulation results also provide performance comparisons on ARQ and HARQ protocols which serve as an application example of the algorithms proposed, i.e., providing comparison benchmark for different system, protocol, or suboptimal algorithm design.

For the Multiuser Multiple-Input Single-Output (MUMISO) system in a downlink (DL) scenario, we design a non-linear precoding scheme, namely the spatial Tomlinson-Harashima precoder (S-THP) based on the total mean squared error (MSE) criterion. The channel state information (CSI) available at the transmitter for the precoding is the quantized channel direction information (CDI) relayed back in a frequency division duplex (FDD) system from each receiver using B feedback bits received erroneously and quasi-instantaneous. The proposed MMSE-STHP with a suboptimum precoding order clearly outperforms the linear precoding scheme given in starting from a certain Signal-to-Noise Ratio (SNR) and a number of feedback bits in terms of Bit Error Ratio (BER).

In this contribution we propose a simple method of obtaining a perfect reconstruction (PR) FIR prototype filter starting from a given near-perfect reconstruction (NPR) prototype filter for complex modulated filter banks. Our method consists of two steps. First, we calculate the product of a pair of polyphase components that fulfill the PR conditions, and second, we search for spectral factors that maximize the correlation coefficients between the impulse response of the new polyphase components and of the original ones. Our preliminary numerical results show that the price to be payed to satisfy the PR conditions is some loss in the spectral characteristics.

We consider the minimization of the overall power consumption when communicating over a noisy single-input single-output channel while satisfying a certain throughput and error rate constraint. The total power dissipation includes the radiated power as well as the circuit power consumption. In the context of battery operated short range communication, where low power, low cost and small size are key requirements (e.g. standard IEEE 802.15.4), this circuit aware system optimization is crucial given the growing importance of “Green Communication”. In fact, the power dissipation of certain analog and digital components along the signal path reaches values in the order of or is even higher than the transmit power in such applications. Using an appropriate information-theoretic framework we derive the optimal bit-resolution of the analog-to-digital converter (ADC), the optimal choice of the noise figure for the low noise amplifier (LNA), the optimal operating input back-off (IBO) of the power amplifier (PA), as well as the optimum decoding (DEC) strategy as a function of the path-loss (i.e. the communication distance), that guarantee a certain net data rate R under a certain error probability Pe. This work is an extension to the work [1], where only the power of the analog components was considered.

In signal processing, system theory, an in information theory signals are either complex valued waveforms or sequences of dimensionless complex numbers. The »power« of such signals is usually said to be the squared magnitude of these complex numbers. In an electrical circuit, power is always the product of voltage and current, both present at a port, i.e., at a pair of terminals. Here we ask the question: what is the relation between signal power and physical power? There are special cases, where the two notions of power are strictly proportional and, therefore, no conflict occurs. But in general, especially when we deal with vector signals, the commonly used squared Euclidean norm of the signal vector may not be a measure for the physical power associated with the voltages and currents, which correspond to the signal vector. In addition to the physically consistent computation of signal power, physically consistent modeling of the noise is crucial in all information processing systems. Again circuit theory provides with a methodology to model how the noise comes into the system. Modeling is one the most important tasks in engineering and, therefore, physically consistent modeling is very important for the education of electrical engineers. Circuit Theory is the essential framework to ensure physical consistency across several levels of abstraction.

The availability of the Channel State Information (CSI) at the transmitter is crucial for the precoder design in Multi-user Multiple Input Single Output (MU-MISO) systems. In Frequency Division Duplex (FDD) systems, CSI can be just available at the transmitter through a limited feedback channel [1], where we assume that each user estimates, normalizes, and finally quantizes its channel direction with a finite number of quantization bits relayed back error-free and quasi-instantaneous. In this paper, we consider a simple sequential uniform scalar quantization (SQ) scheme of the individual components (real and imaginary parts) of the Channel Direction Information (CDI). Although vector quantization (VQ) schemes [2], [3] still outperform this scalar scheme in terms of quantization error and Bit Error Rate (BER), the former scheme suffers from an exponential search complexity and high storage requirements at the receiver for high number of feedback bits.

The study of circuits and systems requires some basic reasoning with various equivalent interconnections of components like resistors, sources, ideal opamps, ideal transistors, short and open circuits.... Students should be able to handle a rich variety of such interconnections correctly and appropriately. Special components like nullators and norators bring in some valuable insights, flexibility and design methods based thereupon. From the practical experience of teaching nullators and norators in basic circuits courses for electrical engineering students, the authors stress a number of didactical goals and experiences. First, it is crucial that the students understand the notions of nullator and norator and are able to distinguish these from other components, such as short circuits, open circuits, resistors, and sources. Second, they should be able to reason correctly with interconnections that involve nullators and norators. Third, they should be able to find equivalents for circuits having nullators and norators. Analysis and design of circuits with nullators and norators will be presented. A particular case study of the use of nullators and norators for the implicit inversion of a 2 × 2 matrix will be used as an illustration of some unexpected results, which we will hardly And without nullators and norators. This will lead to an evaluation of the didactical processes of using nullators and norators.

We report on our investigation of the capacity region of a Gaussian multiple access channel with two independent transmitters for the case where the receiver's antenna spacing is very small (compared to the wavelength). In contrast to common belief, we find that a rectangular capacity region can be maintained even as the distance of the antennas approaches zero. Moreover, the size of the capacity region even increases as the antenna separation is reduced. Therefore, high-performance multiple access is perfectly possible in confined space situations such as in hand-held mobile devices using standard frequencies for mobile communications.

This paper provides a systematic characterization of Zero-Forcing (ZF) detectors over multiple-input multiple-output (MIMO) channels that experience both small and large-scale fading. In particular, we consider the generic K distribution (Rayleigh/gamma distribution) to model the composite fading fluctuations and also assume the general case of semi-correlated small-scale fading. In the following, novel exact analytical expressions for the achievable sum rate are derived, followed by asymptotic expressions in the high and low Signal-to-Noise ratio (SNR) regimes. In these limiting cases, two common and insightful affine expansions are studied followed by new, closed-form upper and lower bounds on the sum rate that remain tight for all SNRs. In the second part of the paper, we present exact tractable expressions along with first-order expansions for the symbol error rate (SER) and outage probability; we also quantify the performance of ZF detectors in terms of diversity order and array (or coding) gain. The implications of the model parameters on the ZF detector performance are investigated via Monte-Carlo simulations which also validate the theoretical analysis.

We consider the downlink of a cellular wireless network where the base stations are equipped with multiple transmit antennas and operate in the same frequency band. Each base station serves one user per orthogonal frequency-division multiplexing (OFDM) sub-carrier by means of spatial precoding. Due to temporal scheduling, the active users change rapidly in time and consequently, intercell interference becomes non-stationary limiting the achievable network sum-rate. Under the assumption of perfect channel knowledge at each base station, we optimize both precoding and scheduling in order to maximize the achievable network sum-rate. The solution, however, unveils that both precoding and scheduling can be performed locally at each base station without the need for CSI to be exchanged between base stations if they are solely based on average channel measurements.

It is well known that the availability of M transmit antennas at the base station enables to serve K single-antenna users (K ≤ M), under the assumption of perfect channel state information (CSI) at the base station. However, in practical frequency division duplex (FDD) systems, the channel knowledge available at the base station is not perfect since it is obtained through a limited feedback in the uplink consisting of B bits per user. Part of the B feedback bits can be employed to quantize the channel direction information (CDI), while the rest can be used to quantize the channel magnitude information (CMI) of each user. In this work we address the transceiver design of a multiuser MISO system based on the minimum mean square error criterion and with transmit CSI obtained through a limited feedback composed of quantized CDI and CMI. We treat the case of K ≤ M users with different average channel gains and based on our findings we conclude that for uncorrelated channels it is optimum to employ all the B bits for quantizing the CDI under the assumption that the base station knows the statistics of the CMI of the users.

In this paper, we present a general analytical framework for the exact mutual information (MI) cumulants of multiple-input multiple-output (MIMO) systems with perfect receiver channel state information (CSI) and no transmitter CSI. Our derivation is based on a recent parameterization of the joint ordered eigenvalue probability density function (PDF), that encompasses both uncorrelated/semi-correlated Rayleigh channels as well as uncorrelated Rician channels. In addition, we extend our framework to account for the cumulants of doubly-correlated Rayleigh channels and also to deduce tractable expressions in the high Signal-to-Noise ratio (SNR) regime. The cumulants are particularly useful to study all high-order statistics (HOS) of the MI; in fact, they can be used to express the MI mean and variance as a finite sum of determinants. Our analytical expressions are then validated via Monte-Carlo simulations with the attained accuracy being excellent in all cases.

We present a channel-matched coding method for the compensation of dispersion in coarsely quantized coherent optical communication systems. The approach consists in optimizing a subset of equiprobable input symbols for each channel state, which is robust against dispersion and coarse quantization. This codebook optimization is solved efficiently using an iterative algorithm. The performance of the channel-matched codes is simulated for a 42.8Gbit/s dual-polarization coherent optical fiber system with QPSK modulation and single-bit analog-to-digital (A/D) conversion. Apart from the optimization, the proposed coding method does not increase the encoding/decoding complexity, but yields a considerable coding gain.

The standard condition number (SCN) is a fundamental metric in the context of multiple-input multiple-output communication systems, linear detection and classical linear algebra. Hence, in this paper we propose a novel generic framework for the SCN distribution of three different classes of Wishart matrices which leads to new results and insights. In particular, our analysis covers both central and non-central Wishart distributions of arbitrary dimension and therefore is applicable to uncorrelated/semi-correlated Rayleigh fading and Ricean fading scenarios. For the special case of dual semi-correlated central Wishart matrices, we derive exact and asymptotic polynomial expressions for the SCN distributions. All analytical results are validated via Monte-Carlo simulations with the attained accuracy being excellent in all cases. The impact of the model parameters on channel conditioning is also investigated in detail.

The presence of line-of-sight (LoS) components is typically considered as a hindrance for multiple-input multiple-output (MIMO) communications due to the limited amount of multipath scattering which, in turn, results in low spatial multiplexing gains. However, some recent investigations have questioned this common belief and demonstrated that by employing specifically designed antenna arrays at both the transmitter (Tx) and receiver (Rx), the mean channel matrix can become full-rank and, consequently, we can obtain high channel capacities even at high Ricean K-factors. In this paper, using the joint ordered eigenvalue probability density function (PDF) as a starting point, we derive analytical exact and asymptotic expressions for the mutual information (MI) statistics of these optimized LoS MIMO configurations. The proposed analytical formulae are given in a tractable determinant form and thus can be easily evaluated and efficiently programmed. The implications of the model parameters on MI statistics are also assessed with the match between the analytical curves and Monte-Carlo simulations being excellent.

We consider the maximization of the energy efficiency when communicating over a noisy single-input single-output channel, taking into account the transmit power as well as the power consumption of the analog-to-digital converter (ADC). This analysis is of interest in the context of energy constrained short range communication where low power, low cost and small size are key requirements (e.g. standard IEEE 802.15.4). In fact, the transmit power in such applications become smaller and reaches values in the order of the conversion or the processing power. Using an appropriate information-theoretic framework we show that the analog-to-digital converters (ADCs) for short range communication should be low-resolution, in order to reduce the overall power consumption. In addition we derive the optimal operating bit-resolution and signal-to-noise ratio (SNR) as function of the path-loss (i.e. the communication distance).

In multiple-antenna communications, as bandwidth and modulation order increase, system components must work with demanding tolerances. In particular, high resolution and high sampling rate analog-to-digital converters (ADCs) are often prohibitively challenging to design. Therefore ADCs for such applications should be low-resolution. This paper provides new insights into the problem of optimal signal detection based on quantized received signals for multiple-input multiple-output (MIMO) channels. It capitalizes on previous works which extensively analyzed the unquantized linear vector channel using graphical inference methods. In particular, a “loopy” belief propagation-like (BP) MIMO detection algorithm, operating on quantized data with low complexity, is proposed. In addition, we study the impact of finite receiver resolution in fading channels in the large-system limit by means of a state evolution analysis of the BP algorithm, which refers to the limit where the number of transmit and receive antennas go to infinity with a fixed ratio. Simulations show that the theoretical findings might give accurate results even with moderate number of antennas.

This paper provides a systematic mutual information (MI) and multichannel beamforming (MBF) characterization of optimized multiple-input multiple-output (MIMO) communication systems operating in Ricean fading. These optimized configurations are of high practical importance since, contrary to the common belief, benefit from the presence of direct Line-of-Sight (LoS) components and deliver maximum multiplexing gains, by deploying specifically designed antenna arrays at both ends. In the following, using elements from random matrix theory, novel analytical expressions are derived for the exact and asymptotic MI statistics while the prevalent Gaussian approximation is examined. Moreover, new explicit expressions for the marginal eigenvalues are deduced which are thereafter used to analyze the BF performance of the associated eigenmodes in terms of Signal-to-Noise ratio (SNR) outage probability. We note that all derived formulas are given in tractable determinant form and therefore allow for fast and efficient computation and also yield an excellent match with Monte-Carlo simulations, under different fading scenarios and model parameters.

This paper investigates the distribution of the condition number of complex Wishart matrices. Two closely related measures are considered: the standard condition number (SCN) and the Demmel condition number (DCN), both of which have important applications in the context of multiple-input multiple-output (MIMO) communication systems, as well as in various branches of mathematics. We first present a novel generic framework for the SCN distribution which accounts for both central and non-central Wishart matrices of arbitrary dimension. This result is a simple unified expression which involves only a single scalar integral, and therefore allows for fast and efficient computation. For the case of dual Wishart matrices, we derive new exact polynomial expressions for both the SCN and DCN distributions. We also formulate a new closed-form expression for the tail SCN distribution which applies for correlated central Wishart matrices of arbitrary dimension and demonstrates an interesting connection to the maximum eigenvalue moments of Wishart matrices of smaller dimension. Based on our analytical results, we gain valuable insights into the statistical behavior of the channel conditioning for various MIMO fading scenarios, such as uncorrelated/semi-correlated Rayleigh fading and Ricean fading.

In this paper, we explore the concept of inner and outer functions to come up with two novel parametric families of Nyquist pulses. Aside from requiring only two design parameters, the proposed pulses yield an enhanced performance compared to the sophisticated flipped-inverse hyperbolic secant (asech) pulse, that was recently presented in the literature. While the construction of parametric families originates from the work of Beaulieu and Damen, the usage of inner and outer functions guarantees a higher flexibility in the choice of the composite family members. The proposed pulses may have a slower decay rate than the well-known raised-cosine (RC) pulse, but exhibit a more pronounced decrease in the amplitudes of the two largest sidelobes and this accounts for their improved robustness to error probabilities. In the following, it is clearly demonstrated that a lower bit error rate (BER), compared to the existing pulses, can be achieved for different values of the roll-off factor and timing jitter. Moreover, a smaller maximum distortion along with a more open eye diagram are attained as well.

Both fast scheduling and spatial signal processing have proven to be capacity-increasing methods in wireless communication systems. However, when applied in the downlink of a cellular network, the combination of both leads to non- stationary intercell interference. If the base stations do not cooperate, either they have to encode the data very conservatively to gain robustness or the non-stationary fluctuations of the interference powers lead to frequent outages, both of which strongly impair the average achievable throughput. On the other hand, base station cooperation increases complexity and delays, contradicting the desire for fast scheduling algorithms. In this paper, we propose a scheme that makes average channel state information available to all base stations via low-rate back haul communication, whereas high-rate inter-base-station communication is limited to B lceillog2Krceil-bit integers, K being the number of users in each of the B cells. Simulations show that for slow fading channels, the proposed algorithm preserves most of the per cell sum-rate of other beamforming and dirty- paper coding approaches that have unlimited-capacity back haul links. Furthermore, when out-of-cell information is outdated the proposed algorithm even outperforms those.

We comment on the famous work GtOpportunistic beamforming using dumb antennasLt by P. Viswanath, D.N.C. Tse, and R. Laroia, from 2002. In that paper, it is argued that in an independent and fast fading environment, the opportunistic beamforming technique provides no performance gain. This assessment is based on the argument that the fading distribution of the equivalent channel, which is obtained through opportunistic beamforming, does not depend on the number of transmit antennas in independent and fast fading scenarios. In this paper, we show that this argument of the original work is based on a non-physical model of Rayleigh fading, and as a consequence, results in a wrong conclusion. In contrast, we show that there is beamforming gain to be obtained by the opportunistic beamforming technique even for independent and fast fading environments. For the case of uncoupled antennas, the amount of obtainable beamforming gain approaches - with probability one - the number of transmit antennas from below as the number of users approaches infinity.

The receive antenna gain of an antenna array critically depends on the receiver noise covariance. Receiver noise essentially originates from two sources: the receive low-noise amplifiers (LNA), and background radiation that is received by the array. In case that the LNAs are the sole origin of noise, it is state of the art to argue that the receiver noise is spatially uncorrelated, for noise originates in components of physically separate LNAs. However, this argument ignores coupling between antennas due to their spatial proximity, and moreover the coupling introduced by the impedance matching network which is located between the antenna outputs and the LNA inputs. Because of these coupling effects, the receiver noise is usually spatially correlated even when independent LNAs are the sole source of noise in the system. The noise covariance, depends on the following four factors: 1) antenna spacing of the array, 2) properties of the impedance matching network, 3) noise-resistance of the LNAs, 4) intensity of received background noise with respect to noise generated by the LNAs. Taking these issues into account, we derive, in this paper, the receive antenna gain of a uniform linear antenna array of isotrops in closed form. It turns out that the receive antenna gain can become much larger than the number of antennas. For the case of low noise-resistance, and low relative intensity of received background noise, we show that the receive antenna gain can even grow exponentially with the number of antennas. These results are exciting, because they imply that the performance of communication systems which use multiple receive antennas can be much better than previously reported.

In this work we present a per-subchannel nonlinear equalizer for a class of filter bank based multicarrier (FBMC) systems. We consider the class of exponentially modulated FBMCs with offset quadrature amplitude modulated (OQAM) input symbols. We design a fractionally spaced decision feedback equalizer (DFE) that minimizes the mean squared error (MMSE) and takes into account the inter-(sub)channel interference (ICI). The input of each equalizer comprises only the output of each subchannel. In the simulation results we show that, despite its increased computational complexity, the performance and the higher bandwidth efficiency of OQAM FBMC systems makes them a competitive alternative to conventional multicarrier systems like cyclic prefix based orthogonal frequency division multiplexing (CP-OFDM).

In this work we present a per-subchannel nonlinear adaptive equalizer for a class of filter bank based multicarrier (FBMC) systems. We consider the class of exponentially modulated FBMCs with offset quadrature amplitude modulated (OQAM) input symbols. We present a fractionally spaced adaptive decision feedback equalizer (DFE) based on the least-mean-square (LMS) algorithm. The input of each equalizer comprises only the output of each subchannel. In the simulation results we show that, despite its increased computational complexity, the performance and higher bandwidth efficiency of OQAM FBMC systems makes them a competitive alternative to conventional multicarrier systems like cyclic prefix based orthogonal frequency division multiplexing (CP-OFDM).

It is common in information theoretic channel models to rely on the average squared Euclidean norm of the channel input as being proportional to transmit power. Likewise, it is common to assume noise that is additive, Gaussian, and white. It is a legitimate question to ask, whether such a modeling approach has enough degrees of freedom to capture the physical constraints that are imposed on implementations of a communication system. In this paper, we show that in many, though not all, situations it is indeed possible to obtain a complete physical model, while nevertheless sticking with average squared Euclidean norm as power, and white Gaussian noise. Our systematic approach works in two steps. First, all channel inputs and outputs are replaced by ports, which are de.ned by two conjugated variables (like voltage and current). By this multi-port modeling approach, we can obtain a complete physical model. Secondly, we introduce linear transformations between the inputs and outputs of the information theoretic channel model on the one hand, and the physical inputs and physical outputs of the communication system, on the other. This approach gives us enough degrees of freedom to obtain a complete information theoretic model, which correctly reflects the physical constraints that are imposed upon the communication system by its environment. We apply the proposed approach to a multi-antenna communication system, and show that it is indeed possible that the channel capacity of multi-antenna systems can grow super-linearly with the number of antennas for large signal to noise ratios.

We consider general multi-antenna fading channels with coarsely quantized outputs, where the channel is unknown to the transmitter and receiver. This analysis is of interest in the context of sensor network communication where low power and low cost are key requirements (e.g. standard IEEE 802.15.4 applications). This is also motivated by highly energy constrained communications devices where sampling the signal may be more energy consuming than processing or transmitting it. Therefore the analog-to-digital converters (ADCs) for such applications should be low-resolution, in order to reduce their cost and power consumption. In this paper, we consider the extreme case of only 1-bit ADC for each receive signal component. We derive asymptotics of the mutual information up to the second order in the signal-to-noise ratio (SNR) under average and peak power constraints and study the impact of quantization. We show that up to second order in SNR, the mutual information of a system with two-level (sign) output signals incorporates only a power penalty factor of almost ¿/2 (1.96 dB) compared to the system with infinite resolution for all channels of practical interest. This generalizes a recent result for the coherent case.

In this paper the quality of service (QoS) constrained radio resource allocation problem at the downlink of a multiuser multicarrier system is investigated. We demonstrate and analyze the trade-off between energy consumption and transmit power within a cross physical and link layer system model, which jointly considers power allocation, adaptive modulation and coding and ARQ/HARQ retransmission protocols. A novel transmit power constrained energy minimization problem is formulated based on the competing nature of the two resources. Due to the combinatorial property of the problem, a suboptimal heuristic resource allocation algorithm is proposed, the accuracy of which is compared with the dual optimal value obtained as a byproduct of the algorithm. Simulation results also provide performance comparisons on ARQ and HARQ protocols, and their different impacts on the choice of the optimal modes of operations.

Recently, the capacity region of the Gaussian broadcast channel has been characterized. For a given transmit power constraint, those points on the boundary of the capacity region can be regarded as the set of optimal operational points. The present work addresses the problem of selecting the point within this set that satisfies given constraints on the ratios between rates achieved by the different users in the network. This problem is usually known as rate balancing. To this end, the optimum iterative approach for general MIMO channels is revisited and adapted to an OFDM transmission scheme. Specifically, an algorithm is proposed that exploits the structure of the OFDM channel and whose convergence speed is essentially insensitive to the number of subcarriers. This is in contrast to a straightforward extension of the general MIMO algorithm to an OFDM scheme. Still, relatively high complexity and the need of a time-sharing policy to reach certain rates are at least two obstacles for a practical implementation of the optimum solution. Based on a novel decomposition technique for broadcast channels a suboptimum non-iterative algorithm is introduced that does not require time-sharing and very closely approaches the optimum solution.

This paper deals with the joint estimation of temporal (time- delay, Doppler frequency) and spatial (direction-of-arrival, DOA) parameters of several replicas of a known signal in an unknown spatially correlated field. Unstructured and structured models have been proposed in the literature. The former suffers from a severe performance degradation in some scenarios, whereas the latter involves huge complexity. It is shown how the extended invariance principle (EXIP) can be applied to obtain estimates with the quality of those of the structured model, but with the complexity of the unstructured one. We present a method to improve the quality of the time- delay and Doppler estimates obtained with an unstructured spatial model when an estimate of the DOAs is available. Exemplarily, simulation results for time-delay estimation for GPS (global positioning system) are included and confirm that our proposal approaches the Cramer-Rao lower bound (CRLB) of the structured model even when suboptimal DOA estimates obtained by ESPRIT are introduced.

In orthogonal frequency division multiplexing (OFDM), carrier frequency offset (CFO) must be mitigated since it generates interference between received symbols transmitted through different sub-carriers. This paper presents a new algorithm for CFO estimation with reduced computational complexity. The new approach is based on the segmentation of the input-signal autocorrelation matrix into the noise and signal subspaces, the latter being employed to estimate the desired CFO. Simulations validate the effectiveness of the new algorithm in comparison to the traditional parametric estimation technique ESPRIT.

We study the joint optimization of the quantizer and the spatial decision feedback equalizer (DFE) for the flat multi-input multi-output (MIMO) channel with quantized outputs. Our design is based on a minimum mean square error (MMSE) approach, taking into account the effects of quantization. Our derivation does not make use of the assumption of uncorrelated white quantization errors and considers the correlations of the quantization error with the other signals of the system. Through simulation, we compare the new DFE to the conventional spatial DFE operating on quantized data in terms of uncoded BER.

The major difference between a non-cellular system, i.e. a single isolated cell, and a cellular system is the intercell interference (ICI). In the uplink, the base station can measure the users' signal to noise and interference ratio (SINR) and through a feedforward channel it can inform the users which coding and modulation scheme to apply in order to perform link adaptation (LA). However, a user's SINR observed in the uplink by the base station when determining the link adaptation decision might no longer be the same when the link adaptation is effected by the user due to fluctuations of the intercell interference. By then, the intercell interference could have greatly changed even if the users are static, due to changes in the scheduling decisions in the interfering cells. Hence, the uplink transmission would no longer have the correct link adaptation for the current SINR, since we are to some extent blind with respect to the ICI. In this work, we quantify this degree of ICI blindness by the correlation between the measured ICI and the actual experienced ICI. Furthermore, we analyze the degradation in throughput for different degrees of ICI correlation which depends on the scheduling in the interfering cells. Additionally, we show how much benefit correct link adaptation provides in a cellular environment over outdated link adaptation. In this work, we assume that there is no intracell interference as a consequence of an orthogonal multiple access scheme.

In many practical applications besides a small generalization error also the interpretability of classification systems is of great importance. There is always a tradeoff among these two properties of classifiers. The similarity measure in the input space as defined by one of the most powerful classifiers, the Random Forest (RF) algorithm, is used in this paper as basis for the construction of Generalized Radial Basis Function (GRBF) classifiers. Hereby, interpretability and a low generalization error can be achieved. The main idea is to approximate the RF kernel by Gaussian functions in a GRBF network. This way the GRBF network can be constructed to approximate the conditional probability of each class given a query input. Since each center in the GRBF is used for the representation of the distribution of a single target class in a localized area of the classifiers input space, interpretability can be achieved by taking account for the membership of a query input to the different localized areas. Whereas in most algorithms the pruning technique is used only to improve the generalization property, here a method is proposed how pruning can be applied to additionally improve the interpretability. Another benefit that comes along with the resulting GRBF classifier is the possibility to detect outliers and to reject decisions that have a low confidence. Experimental results underline the advantages of the classification system.

Filter bank based multicarrier systems (FBMC) offer a number of benefits over conventional orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP). One benefit is the improved spectral efficiency by not using a redundant CP and by having much better control of out-of-band emission. Another advantage is the ease of accomodating multiple users in an FDMA fashion especially in the uplink, i. e. the multiple access channel (MAC). On the other hand, more elaborate equalization concepts are needed compared to the single-tap per-subcarrier equalizer sufficient in the OFDM with CP case. Therefore, we will present a least-mean-square (LMS) algorithm which is adapted to the principle of orthogonally multiplexed QAM filter banks (OQAM-FBMC). This leads to an adaptive equalizer solution with low complexity. The initialization of the LMS equalizer results from a pilot based channel estimation. The results will be compared with a classical OFDM system, where the loss in data rate is compensated with a higher modulation scheme.

We consider multi-input multi-output (MIMO) Rayleigh-fading channels with coarsely quantized outputs, where the channel is unknown to the transmitter and receiver. This analysis is of interest in the context of sensor network communication with low cost devices. The key point is that the analog-to-digital converters (ADCs) for such applications should be low-resolution, in order to reduce their cost and power consumption. In this paper, we consider the extreme case of only 1-bit ADC for each receive signal component. We elaborate on some properties of the mutual information compared to the unquantized case. For the SISO case, we show that on-off QPSK signaling is the capacity achieving distribution. To our knowledge, the block-wise Rayleigh-fading channel with mono-bit detection was not studied in the literature.

We present a model for multiuser multi-input multi-output (MIMO) high speed downlink packet access (HSDPA) with covariance based linear precoders. The linear procoders, which are designed for single stream detection with rake receivers, are independent of the equalizers employed in multiple antenna receivers to support multistream detection. These symbol based equalizers are designed to minimize the mean square error (MSE) of the signal estimates. Both theoretical analysis and numerical results show the applicability of the proposed approach in HSDPA. There is no limitation on the number of users as long as enough code channels are available. Also, all the users have free choice on how many antennas they can deploy. This approach extends the covariance based precoding concept from MISO to MIMO systems.

Finding the matrix with the maximum singular value amongst a set of matrices is a common problem occurring in transmit signal processing algorithms for multiuser multiple-input multiple-output (MIMO) systems. However, computing the principal singular value of a matrix is a rather numerically complex task. Furthermore, in many practical scenarios, the number of users is large and for each user this task has to be conducted. In this paper we therefore propose a novel user preselection method which reduces the computational complexity at no performance loss. This is achieved by deselecting some users based on a simple criterion and thus avoiding explicit computations of the singular values of those users. This criterion is based on easily computable bounds for the principal singular values. Finally, a statistical analysis is provided and the application to the Successive Encoding Successive Allocation Method (SESAM) is shown.

In machine learning the preprocessing of the observations and the resulting features are one of the most important factors for the performance of the final system. In this paper a method to perform feature selection for change detection in multivariate time-series is presented. Feature selection aims to determine a small subset which is representative for the change detection task from a given set of features. We are dealing with time-series where the classification has to be done on time-stamp level, although the smallest independent entity is a scenario consisting of one or more time-series. Despite this difficulty we will show how feature selection based on the generalization ability of a classifier can be realized by defining a cost function on scenario level. For the classification step in the feature selection process a modified random forest (RF) algorithm - which we will call scenario based random forest (SBRF) - is used due to its intrinsic possibility to estimate the generalization error. The excellent performance of the proposed feature selection algorithm will be shown in a car crash detection application

Intercell interference (ICI) is the substantial difference between a cellular and a non-cellular communication system. Effective modeling of the cellular downlink as a Gaussian broadcast channel requires that the ICI powers at the user positions are known to the basestation (BS), as it otherwise, cannot attempt to approach (or even know) its capacity region. However, ICI depends on the transmit processing of the BSs in the neighboring cells, which furthermore is subject to change quickly due to temporal scheduling. The BS therefore only has limited knowledge about the true ICI powers. In this paper, the implications of this lack of knowledge about ICI power on the achievable sum- rate of a Gaussian MISO (multi-input, single-output) broadcast channel is examined. Four different approaches on dealing with unpredictable ICI are discussed and their performance compared to the single-cell (or non-cellular) Gaussian broadcast channel.

We consider amulti-usermultiple-inputsingle-output(MU-MISO) scenario, where the decentralized users are served by a centralized transmitter with multiple channel inputs via frequency selective vector channels. For this broadcast setup, we jointly design the transmitter and receivers based on a minimum summeansquareerror(MSE) criterion. Contrary to previous work in this field, we do not restrict the receivers to be scalar weights but employfiniteimpulseresponse(FIR) receive filters. Since the sum MSE minimization has no closed-form solution neither for linear preceding nor forTomlinson-Harashimaprecoding(THP), we propose to use an alternating optimization and prove the convergence of the resulting iterative algorithm. The simulations show that the obtained linear and nonlinear preceding solutions with FIR receivers clearly outperform the state-of-the-art precoders with scalar receivers.

We study the performance of multi-input multi-output (MIMO) channels with coarsely quantized outputs in the low signal-to-noise ratio (SNR) regime, where the channel is perfectly known at the receiver. This analysis is of interest in the context of ultra-wideband (UWB) communications from two aspects. First the available power is spread over such a large frequency band, that the power spectral density is extremely low and thus the SNR is low. Second the analog-to-digital converters (ADCs) for such high bandwidth signals should be low-resolution, in order to reduce their cost and power consumption. In this paper we consider the extreme case of only 1-bit ADC for each receive signal component. We compute the mutual information up to second order in the SNR and study the impact of quantization. We show that, up to first order in SNR, the mutual information of the 1-bit quantized system degrades only by a factor of 2/pi compared to the system with infinite resolution independent of the actual MIMO channel realization. With channel state information (CSI) only at receiver, we show that QPSK is, up to the second order, the best among all distributions with independent components. We also elaborate on the ergodic capacity under this scheme in a Rayleigh flat-fading environment.

We consider an innovative downlink multiuser MIMO scheme. Several users compete for the available resources in time, frequency and space. The proposed scheme exploits multiuser diversity and uses interference cancellation at the transmitter. We evaluate it for indoor, hot spot and multihop scenarios. Significant gains in terms of sum capacity can be achieved even under line of sight conditions. The theoretical limit can be approximately achieved in most scenarios. A nice feature of the proposed scheme is that it inherently provides some fairness regarding rate distribution among users even though fairness is not explicitly taken into account by the scheduler.

The main difference between a non-cellular and a cellular communication system is the intercell interference. Therefore, modelling the intercell interference and analyzing its effects is of particular interest for cellular communication systems. On the one hand, the intercell interference can be modeled by system level simulations. On the other hand, it is also meaningful to assess the intercell interference without performing exhaustive simulations which nonetheless at the same time still capture the major effects that determine the interference. To this end, we consider the intercell interference as a random variable composed of many other random variables. In this paper, we present a semi-analytical method to analyze the downlink intercell interference in a cellular system. Through such an approach a quick and reliable assessment of the downlink inter-cell interference can be obtained. Our focus is on the methodology and we consider basic access schemes: WCDMA, TDMA, FDMA, and random OFDMA. However, our model can be extended to include other access schemes and other features.

The exploitation of multiuser diversity and the application of multiple antennas at transmitter and receiver are considered to be key technologies for future highly bandwidth-efficient wireless systems. We combine both ideas in a downlink multicarrier transmission scheme where multiple users compete for the available resources in time, frequency and space. The instantaneous channel impulse responses for all users are assumed to be perfectly known at the transmitter. Our proposed algorithm allocates each spatial dimension on a subcarrier to the user which has the highest channel tap gain on the respective spatial dimension. The scheduling strategy is optimized for sum capacity maximization. In this paper, we restrict ourselves to a more illustrative description of the idea rather then providing mathematical details. We demonstrate the potential of the proposed scheme by capacity results for measured real world channels in a large office environment. Finally, video streaming is used as a potential application with high data rate and low latency demands. It is shown that the proposed method has the potential to exploit multiuser diversity while still providing stable video streams even though QoS constraints are not explicitly taken into account by the scheduler.

We consider a multiuser MIMO-OFDMA scheme which exploits multiuser diversity in all dimensions: time, frequency and space. The main contribution of this paper is the evaluation and explanation of multiuser MIMO in a real world scenario, i.e. a large office room, based on measured channels. We report interesting results of a measurement campaign which suggest that significant MIMO gains are possible in an indoor environment even under strong line-of-sight condition as long as the distance of the users from the base station is larger than a reverberation distance which only depends on room surface and material. We show results on the achievable multiuser MIMO data rates for the given scenario compare to theoretical limits and discuss the results in the light of the insights gained from the measurement campaign. We also introduce restrictions on the rate distribution between users, i.e. QoS constraints. It is shown that the theoretical limits can be approximately achieved provided that the users which compete for the spatial resources are carefully chosen.

In the work at hand a general procedure to analyze the impact of channel estimation errors on the performance of decomposition techniques for multiuser MIMO channels is presented. In particular, this procedure is applied to a decomposition technique called cooperative zero-forcing with successive encoding and successive allocation method (CZF-SESAM). Based on the resulting analytical expressions the transmitter is able to adjust bit and power loading so that in spite of estimation errors transmission quality requirements can still be met

In the work at hand relevant issues concerning implementation of optimal and nearly optimal transmission approaches for the MIMO OFDM broadcast channel are discussed. In particular, algorithms proposed to compute optimum covariance matrices are efficiently extended to the multicarrier setting. Furthermore, a method is proposed to transform the resulting vector channels into a set of scalar subchannels over which information can be independently transmitted without incurring any capacity loss. This effective diagonalization of the broadcast channel is most convenient for practical purposes as, so far, existing techniques for coding with side information have exclusively been conceived for scalar subchannels. Finally, we discuss the practical advantages of a suboptimum technique such as the cooperative zero-forcing with successive encoding and successive allocation method (CZF-SESAM). This technique exhibits a nearly optimum performance and significantly simplifies both computation of transmit covariance matrices and downlink signaling.

A new semi-analytical method to analyze intercell interference in a cellular system is introduced. A sound comprehension of the properties of intercell interference is the key to a meaningful assessment of communication systems without exhaustive simulation campaigns. We consider the basic access schemes TDMA, WCDMA and random OFDMA for uplink transmission. We will also give hints, how the methods can be extended to capture other access schemes, and/or additional features. The focus of this work is more on the introduction of the new methodology. In principle, we describe the intercell interference as a random variable which is composed of many other random variables. The distributions, and in particular the mutual dependencies of those have to be carefully studied. Applications and extensions of the ideas will be addressed in future work, only first examples are given here.

In this paper, we compare the performance of the diversity approach at the application layer and that at the physical layer for H.264/AVC encoded streaming video transmission in a wireless system with two independent channels. The application layer diversity is achieved by using multiple description source coding, while the physical layer diversity is obtained by channel coding over the two channels. Numerical results show that PHY layer diversity always outperforms the application layer diversity.

A significant part of the energy consumed by a flat panel display is dissipated in the controlling circuitry. The column line flat panel display driver proposed in (Saas et al., 2004) has shown the high potential of stepwise charging to reduce the power dissipation while driving the column lines of the display. The driver has been extended to full dynamic resolution in (Saas et al., 2005), thus allowing market relevant image quality. In this work a method for high efficient recharging of the temporary supply sources in the driver is presented. In the proposed approach the charging current is switched to avoid static bias currents.

Multicarrier systems, used in many of today's communications standards like 802.11b, are very prominent candidates for future mobile communication systems. Complex modulated transmultiplexer filter banks (Tzannes et al., 1994) providing improved frequency responses in comparison to orthogonal frequency division multiplex (OFDM) are proposed without a cyclic prefix. For the sake of increased bandwidth efficiency, they require an equalizer which mitigates the distortion of the frequency selective transmission channel. An efficient implementation of these transmultiplexer filter banks leads to discrete Fourier transformation (DFT) transmultiplexer filter banks. The advantages of such modifications with respect to OFDM are discussed and the results for the design of the required channel equalizer are presented

This paper provides analysis of binary space-time block codes with optimum decoding for multi-input multi-output (MIMO) systems with one-bit receive signal quantization. Exhaustive investigation of short binary space-time block codes with unity code rate shows some remarkable results. For instance, almost all codes perform poorly, except one optimum channel-dependent code which can achieve large coding gain. There are no binary 2×2 space-time block codes (including the Alamouti codes) which can achieve transmit diversity without some channel knowledge. However, there exist a few semi-universal codes which are optimum for almost all MIMO channels and can be selected by feedback from the receiver. Design of longer and more powerful codes remains a challenge and requires further research activity.

In this paper, the effects of receive signal quantization on the channel capacity and the performance of error control coding in multi-input multi-output (MIMO) systems are investigated. The receive antennas of a MIMO system experience a channel-dependent superposition of modulated signals originating from all transmit antennas. A fine-granular analog to digital conversion of the resulting irregular constellation is difficult to obtain in practice. The quantization is therefore likely to be rather coarse. It turns out, however, that the loss in channel capacity due to coarse quantization is surprisingly small. On the other hand, existing coding schemes do not seem to tolerate coarse quantization gracefully, producing rather high error floors. Closing the huge gap between information theoretic opportunities offered by coarse quantized MIMO systems on the one hand, and the actual poor performance of coding schemes which are designed without signal quantization in mind, remains a challenging task.

This paper extends the self-contained theory of linear precoding to the field of covariance based spatio-temporal downlink processing for direct-sequence code-division multiple-access (CDMA) systems and shows the applicability to the release 6 of high-speed downlink packet access (HSDPA). To this end, a unifying theory is developed to formulate the three known linear filters, namely, the transmit matched filter, the transmit zero-forcing filter, and the transmit Wiener filter, as optimization problems even in systems, where only covariance knowledge is available at the transmitter. Second, the solutions of these transmit filters are given for such systems with partial channel state information (CSI). Finally, it is shown how covariance-based linear precoding can be employed in the new generation CDMA system HSDPA, i.e., how channel estimation on the secondary common pilot channel allows for optimum full rank linear precoding employing only partial CSI.

Assuming perfect channel state information at the transmitter of a Gaussian broadcast channel, strategies are investigated on how to assign subchannels in frequency and space domain to each receiver aiming at a maximization of the sum rate transmitted over the channel. For the general sum capacity maximizing solution, which has recently been found, a method is proposed that transforms each of the resulting vector channels into a set of scalar channels. This makes possible to achieve capacity by simply using scalar coding and detection techniques. The high complexity involved in the computation of this optimum solution motivates the introduction of a novel suboptimum zero-forcing allocation strategy that directly results in a set of virtually decoupled scalar channels. Simulation results show that this technique tightly approaches the performance of the optimum solution, i.e., complexity reduction comes at almost no cost in terms of sum capacity. As the optimum solution, the zero-forcing allocation strategy applies to any number of transmit antennas, receive antennas and users

In this paper, we provide an accurate and fully analytical model for the distortion due to lost frames in wireless video transmission. Our analysis combines the properties of the video sequence and those of the wireless transmission link. Built on the long-term average properties of the video source, we first develop a model for the average distortion due to the loss of frames, and then derive a mathematical model for analysis of transmission errors occurring during wireless transmission. Experimental results show a surprisingly accurate behaviour of the proposed model.

In this paper, two empirical models for the sequence-level distortion-rate performance of predictive video source encoding are proposed. They require very limited amount of empirical data, namely three pairs of rate and distortion, in order to set up the model parameters. The advantages of these proposed models are the robustness towards measurement noise, low number of model parameters with a closed form setup solution, and high accuracy. Experimental validation using H.264/AVC encoded video test sequences reports high accuracy of these two proposed models.

In the work at hand, decomposition approaches are investigated for the downlink of a multiuser MIMO setting. The focus is on approaches that use successive encoding to eliminate part of the interference between users or information streams. We start reviewing the well known zero-forcing with successive encoding (ZF-SE) approach and generalize the basic idea behind this technique to arrive at a block ZF-SE that can be applied to the case of users with multiple antennas exploiting their cooperation capability. Aiming at a maximization of the achievable sum-rate we elaborate on the ZF-SE and block ZF-SE approaches to come up with the ZF-SE with successive allocation method (ZF-SESAM) and the cooperative ZF-SE with successive allocation method (CZF-SESAM), respectively. This two approaches proceed successively selecting at each step a user to which the next spatial dimension is assigned. Specifically, we propose a largest gain criterion for this selection and provide some rationale for that. Using an OFDM transmission scheme, Tomlinson-Harashima precoding and a common bit loading algorithm we compare the sum-rate achieved by the different approaches. Finally, some simulation results show that in a large MIMO-OFDM system with a moderate number of users even the weakest user can profit from the increase in sum-rate if compared to a maximally fair system, where the same number of dimensions is assigned to every user.

Like numerous other applications, novel cross-layer optimization schemes require the determination of the outage probability of fading channels. Relying on the assumption of frequency selective Rayleigh fading channels we develop a closed form expression for the probability density function of the sum of the square channel coefficients at the output of maximum ratio combining (MRC) receivers. This allows for the formulation of the channel outage probability directly in terms of the channel energy and thus enables the analytic derivation of the outage probability in multi-user CDMA systems. Moreover, low complexity lower and upper bounds are derived, which provide the means for a well fitting approximation of the exact result directly through the cumulative distribution of the channel energy as obtained at the MRC output

Orthogonal space-time block codes (STBC) constitute a simple way of exploiting transmit diversity. If no channel knowledge is available at the transmitter the use of diversity can increase performance significantly. However, if some partial channel state information (CSI) is available, such as knowledge of the transmit correlation matrix, adapting transmission to this knowledge provides additional performance gains. In such case, adaptivity can be introduced by using a unitary eigenbeamformer with beams pointing along the directions of the eigenvectors of the transmit correlation matrix and applying a convenient power loading along the resulting beams. While for Rayleigh-fading channel eigenbeamforming has been shown to be optimum in terms of ergodic capacity, so far, no closed solution for the optimum power loading has been found. In the work at hand, orthogonal STBC are combined with eigenbeamforming in an orthogonal frequency-division multiplexing (OFDM) context and an optimum power loading is found, where optimality refers to an upperbound of pairwise error probability (PEP). The resulting signaling scheme can be viewed as an extension of STBC to OFDM with partial channel knowledge. The solution represents a very interesting trade-off between transmit diversity and antenna gain.

Compared to wired channels, time dispersive radio channels possess more frequent nulls in their spectral characteristics. Thus, the performance of linear filters to compensate intersymbol interference degrades dramatically. The well-known nonlinear decision feedback equalizer (DFE) is one approach to improve this behavior. However, in systems with observations of high dimensionality, the optimum DFE structure is computational intensive. In this paper, we apply the method of the multi-stage Wiener filter (MSWF) to a conventional minimum mean square error (MMSE) DFE in order to reduce computational complexity. The application of the new algorithm to an Enhanced Data rates for GSM Evolution (EDGE) system demonstrates the ability to outperform the even more computational intensive linear Wiener filter (WF).

Two methods for designing almost linear phase wave digital filters are compared. First of them is based on designing a minimum phase filter and equalizing its phase response by some all-pass equalizer. The second one utilizes previously published method of designing almost linear phase filters without the equalizer. Several improvements to both approaches are introduced and a comparison of the two methods is given.

We investigate the effects of fading correlations on wireless communication systems employing multiple antennas at both the receiver and the transmitter side of the link, so called multiple-input multiple-output (MIMO) systems. It turns out that the amount of transmitter sided channel knowledge plays an important part when dealing with fading correlations. Furthermore, the possible availability of time diversity in a time-selective channel can have essential influence on performance. To study the influence of time-selectivity, the concept of sample-mean outage is introduced and applied to information theoretic measures, like capacity or cutoff rate. It will be shown, that in some cases correlated fading may offer better performance than uncorrelated fading permits, which is due to exploitable antenna gain, that will also be defined in a general form for MIMO systems.

In a wireless communication link with one transmit and multiple receive antennas we optimally reduce the spatio-temporal channel rank at the receiver based on long-term (average) channel properties. The channel is estimated and symbols are detected using this reduced rank approximation of the received signal. Examples for this receiver architecture are the temporal/spatio-temporal RAKE, space-time eigenRAKE, or beamspace processing. We discuss the fundamental trade-offs in reduced rank channel estimation and detection and propose an appropriate model for describing the spatio-temporal channel structure. Based on these insights and definitions, we derive an analytical expression for the minimum receiver SNR, which is necessary for channel estimation. Furthermore, we state under which conditions reduced rank channel estimation is asymptotically optimum.

We present space-time transmit filters for FDD DS-CDMA systems based on partial channel state information, i.e. we do not take into account the channel coefficients. Although the FDD transmit zero-forcing filter seems to be the most intuitive approach, we focus on the FDD transmit matched filter and the FDD transmit Wiener filter. Similarly to the respective receive filters and TDD transmit filters, the FDD matched filter maximizes the desired signal portion at the receiver and is optimum for low signal-to-noise-ratio scenarios, whereas the FDD transmit Wiener filter takes into account the noise power at the receiver and is therefore able to find an optimum trade-off between signal maximization and interference suppression. Additionally, we show that the FDD transmit matched filter is a type of eigenbeamforming. The simulation results reveal the excellent performance of the two FDD transmit filters. The FDD transmit Wiener filter even outperforms the TDD transmit matched filter for high signal-to-noise-ratio which is based on the instantaneous channel properties.

In this article we introduce a novel multiple-input-multiple-output (MIMO) spatial filter (SF) which can be applied as a preprocessing scheme to uniform linear arrays in mobile communications, preserving the Vandermonde structure of the steering vectors while changing the amplitude and the phase gradient of the steering vector in a nonlinear fashion. The new scheme is therefore titled Vandermonde invariance transformation. The introduced degrees of freedom due to this preprocessing transformation can be used to beneficially influence the properties of the channel to achieve an enhanced performance of the subsequent signal processing algorithm.

For WCDMA base stations using adaptive antennas, we present a new space-time RAKE structure for uplink reception. Our approach combines short-term and long-term spatial and temporal channel properties using an eigenanalysis. By choosing dominant eigenbeams in time and space, the algorithm enhances interference suppression as well as spatial and temporal receive diversity. In contrast to previously introduced well-known receiver structures, the space-time eigenRAKE inherently adapts to different propagation environments and achieves higher spectral efficiency than other receivers, e.g., the beamformer RAKE. This is illustrated by Monte-Carlo simulations. Finally, we extend the proposed concept to the downlink, improving closed-loop downlink diversity compared to other proposals in standardization. Even though the feedback rate remains unchanged, additional antenna elements can be included to increase the antenna and diversity gains.

Datapath architectures exhibit a large amount of undesired switching (glitches) which does not contribute to the functionality but leads to increased power consumption. While glitch propagation can be effectively reduced by pipelining circuits with acyclic SFGs, this technique is not directly applicable if the circuit contains loops. The paper addresses this problem and discusses a methodology for reducing switching activity in recursive circuits. Simulation results of a few example circuits are given.

In this paper a new approach for minimizing glitches in the combinational parts of static CMOS circuits is presented. Delay balancing is applied in order to guarantee synchronously arriving signal slopes at the inputs of the logic gates. Thus, glitching can be avoided. The delay of a logic gate depends directly on the transistor sizes, i.e. the channel-widths and -lengths (W and L). Specific variation of the transistor sizes allows one to equalize different path delays without influencing the total propagation delay of the circuit. Besides the delay, the total capacitance and the short-circuit power consumption of a circuit also depend on the transistor sizes. In order to take this fact into account when sizing transistors for delay balancing, the method is formulated as a multiobjective optimization problem, where the path delay differences and the power consumption are the design objectives. A program GliMATS for automated circuit optimization has been implemented. Experimental results show that significant power savings can be achieved with this method.

Utilizing adaptive antenna arrays at the base stations of next generation mobile communication systems has been proposed as a promising approach to meet future requirements, e.g. spatio-temporal filtering techniques benefit from the detailed knowledge of the directional channel parameters. Unfortunately, in parameter estimation the computation of the signal subspace often turns out to be the most time-consuming part. However, the computational complexity of the subspace estimation can be significantly reduced by means of tracking. To this end, we propose a new technique which is merely based on the projector representation of the subspace. The presented results are based on the DSP implementation of two algorithms for tracking the azimuthal and elevation angles of impinging wavefronts in an alternating mobile communications system.

Adaptive space-frequency RAKE receivers use maximum ratio combining and multi-user interference suppression to obtain a considerable increase in performance in DS-CDMA systems such as WCDMA. To this end, the signal-plus-interference-and-noise and the interference-plus-noise space-time covariance matrices are estimated. The computational complexity is reduced significantly by transforming the covariance matrices into the space-frequency domain and by omitting noisy space-frequency bins. The optimum weight vector for symbol decisions is the "largest" generalized eigenvector of the resulting matrix pencil. By iteratively updating the optimum weight vector slot by slot, real-time applicability becomes feasible while the fast fading is still tracked. The performance and the computational complexity depend on the number of space-frequency bins, antenna elements, and iterations. Therefore, the performance can easily be scaled with respect to the available computational power.

On the downlink of an SDMA mobile radio system, different users who share the same frequency band and time slot have to be spatially separated by beamforming. In this paper we analyze an approach to jointly calculate weights in such a way that all users receive their signal with a given signal-to-noise-and-interference ratio. The computations are based on spatial covariance matrices which describe the user-specific channels averaged over the fast fading. The presented algorithm succeeds in minimizing the array transmit power while spatially separating the users, no matter which rank their spatial covariance matrices have. But since it needs to solve a complex nonlinear constrained optimization problem, several approximations of this algorithm are presented and compared to each other on the basis of array power, complexity, and convergence.

In this paper the efficient implementation of different types of orthogonal wavelet transforms with respect to practical applications is discussed. Orthogonal single-wavelet transforms being based on one scaling function and one wavelet function are used for denosing of signals. Orthogonal multiwavelets are based on several scaling functions and several wavelets. Since they allow properties like regularity, orthogonality and symmetry being impossible in the single-wavelet case, multiwavelets are well suited bases for image compression applications. With respect to an efficient implementation of these orthogonal wavelet transforms approximating the exact rotation angles of the corresponding orthogonal wavelet lattice filters by using very few CORDIC-based elementary rotations reduces the number of shift and add operations significantly. The performance of the resulting, computationally cheap, approximated wavelet transforms with respect to practical applications is discussed in this paper.

In this paper a methodology for reducing the power consumption of shift-and-add operations in general and especially of CORDIC stages is presented. The proposed method uses the fact of simultaneous carry generation in redundant carry-save and signed digit structures to predict the minimum necessary hardware effort for shift-and-add operations. As a carry once generated in a certain bit position cannot "ripple" through the adder if using redundant number representation, hardware parts can be switched on or off depending on the shift constant. Simulations have shown, that shift dependent hardware utilization of parallel implementations leads to monotonically decreasing power consumption for increasing shift constants. A CORDIC processor element for 16 digit SDNR has been implemented as a layout and simulated with PowerMill in terms of power consumption.

It is essential for an efficient frequency and time slot allocation procedure in future mobile communication systems using space division multiple access (SDMA) to determine the mobiles that are spatially well separated from one another. Thus, once a mobile desires to initiate a call, precise knowledge of the 2-D arrival angles of its dominant wavefronts is required. In this application, 3-D Unitary ESPRIT for joint 2-D angle and carrier estimation offers an efficient way to handle such mobile access requests since it provides efficient high-resolution measurements of the spatial characteristics of the wireless channel, even if only a small number of antennas is available at the base station. Automatic pairing of the 3-D estimates is achieved via a new simultaneous Schur decomposition (SSD) of three real-valued, non-symmetric matrices. In general, the SSD enables an R-dimensional extension of Unitary ESPRIT (R/spl ges/3) to estimate several undamped R-dimensional modes or frequencies along with their correct pairing in multidimensional harmonic retrieval problems. We present a Jacobi-type method to calculate the SSD. For each of the R dimensions, the corresponding frequency estimates are obtained from the real eigenvalues of a real valued matrix. The SSD jointly estimates the eigenvalues of all R-matrices and, thereby, achieves automatic pairing of the estimated R-dimensional modes via a closed-form procedure that neither requires any search nor any other heuristic pairing strategy.

Numerical algorithms for signal processing and control are quite often constructed by intuition. When the system to be designed contains algebraic or other invariants, then these constraints can be exploited to find appropriate transformations. The transformations in system theory are usually Lie groups. One has to find Lie groups which are consistent with the invariants. We show how this point of view can be applied to construct pole placement algorithms for symmetric and skew-symmetric realizations. However, Lie group theory only reveals the appropriate transformations but is not able to reduce the design process to a trivial task. The problem discussed here also shows this limitation.

An extension of a feedforward neural network is presented. Although utilizing linear threshold functions and a Boolean function in the second layer, signal processing within the neural network is real. After mapping input vectors onto a discretization of the input space, real valued features of the internal representation of the pattern are extracted. A vectorquantizer assigns a class hypothesis to a pattern based on its extracted features and adequate reference vectors of all classes in the decision space of the output layer. Training consists of a combination of combinatorial and convex optimization. This work has been applied to a standard optical character recognition task. Results and comparison to alternative approaches are presented.

The central theme of this paper is to overcome the inability of feed forward neural networks with hard limiting units to provide confidence evaluation. We consider a Madaline architecture for a 2-group classification problem and concentrate on the probability density function for the neural activation of the first-layer units. As the following layers perform a Boolean table, the expectation value of the output is determined, utilizing the probability of a pattern to perform a definite binary input for the Boolean table. The Madaline architecture can be modified to the introduced /spl Sigma/-/spl Pi/-/spl Sigma/ network, which evaluates the expectation value. Several assumptions on the distribution of the neural activation lead to a clear and simple architecture, which is applied to an OCR problem.

Since its more general introduction, adiabatic charging [1] has been considered to have a more or less unlimited potential to reduce the power consumption of a CMOS circuit. The prediction was that when reducing operation speed the power consumption could be decreased unlimited, in extreme down to zero. However, if static losses are considered, too, a limit for the achievable minimum power consumption occurs, stating an optimum charging time with minimal power consumption, that is different from infinity, in opposition to the well known considerations used up to now. A linear network model giving the reason for such losses is introduced in this article, together with a closed formula for the prediction of the power consumption of such a circuit. From this formula the optimum charging time and minimal power consumption can be derived, in closed form, too. Further on it is shown, that for such circuits with static losses the well-known linear charging ramps do no longer form the optimal ...

ESPRIT type high-resolution (spatial) frequency estimation techniques, like standard ESPRIT, state space methods, matrix pencil methods, or Unitary ESPRIT, obtain their (spatial) frequency estimates from the solution of a highly-structured, overdetermined system of equations. Here, the structure is defined in terms of two selection matrices applied to a matrix spanning the estimated signal subspace. Structured least squares (SLS) is a new algorithm to solve this overdetermined system, the so called invariance equation, by preserving its structure. Simulations confirm that SLS outperforms the least squares (LS) and total least squares (TLS) solutions of this invariance equation, since the accuracy of the resulting (spatial) frequency estimates and the accuracy of the underlying signal subspace are improved significantly. Furthermore, SLS can be used to improve the accuracy of adaptive frequency estimating schemes that are based on fast adaptive subspace tracking techniques. Moreover, SLS has been extended to the two-dimensional (2-D) case to be used in conjunction with 2-D Unitary ESPRIT, an efficient ESPRIT-type algorithm that provides automatically paired 2-D (spatial) frequency estimates.

A method is presented that parameterizes orthogonal wavelet transforms with respect to their properties (i.e. compact support, vanishing moments, regularity, symmetry) and also takes into consideration a simple implementation of the transform. The parameter space is given by the rotation angles of the orthogonal 2/spl times/2 rotations used in the lattice filters that realize the different stages of the wavelet transform. The different properties of an orthogonal wavelet transform can be expressed in this parameter space. Then, restricting the parameter space to the rotation angles of simple CORDIC-based approximate rotations leads to a reduced parameter space. The wavelet transforms in this reduced parameter space are amenable to a very simple implementation (only a small number of shift and add operations).

We propose a new strategy for a constructive training of feedforward neural networks to classify linearly nonseparable patterns. The algorithm results in a configuration of the first layer of the network, which is able to give a faithful internal representation of the input patterns. The weights of the network are obtained by the CadaTron algorithm introduced, which is able to separate clusters of data in a robust way. Iteratively, further neurons are added to the neural net in order to decrease the training error. Unnecessary neurons are removed, so this algorithm leads to a network with low complexity and excellent generalization properties. The results of this work are based on the classification of handwritten characters.

In this paper a statistical point of view of feedforward neural networks is presented. The hidden layer of a multilayer perceptron neural network is identified of representing the mapping of random vectors. Utilizing hard limiter activation functions, the second and all further layers of the multilayer perceptron, including the output layer represent the mapping of a Boolean function. Boolean type of neural networks are naturally appropriate for categorization of input data. Training is exclusively carried out on the first layer of the neural network, whereas the definition of the Boolean function generally remains a matter of experience or due to considerations of symmetry. In this work a method is introduced, how to adapt the Boolean function of the network, utilizing statistical knowledge of the internal representation of input data. Applied to the classification problem of greylevel bitmaps of handwritten characters the misclassification rate of the neural network is approximately reduced by 20%.

In SDMA mobile radio systems where only base stations, but no mobiles are equipped with antenna arrays, co-channel interference on the downlink has to be kept down by beamforming. The performance of the beamformer is determined by the directions of arrival (DOAs) of the users operating in the same frequency/time/code slot. Thus the full capacity of an SDMA cell can only be made use of by an effective channel allocation scheme. In this paper, a real time concept for channel allocation is presented. Furthermore, several criteria to quickly evaluate the spatial separability of subsets of users, were defined and then compared to each other by means of simulations.

This paper presents efficient Schur-type algorithms for estimating the column space (signal subspace) of a low rank data matrix corrupted by additive noise. Its computational structure and complexity are similar to that of an LQ-decomposition, except for the fact that plane and hyperbolic rotations are used. Therefore, they are well suited for a parallel (systolic) implementation. The required rank decision, i.e., an estimate of the number of signals, is automatic, and updating as well as downdating are straightforward. The new scheme computes a matrix of minimal rank which is /spl gamma/-close to the data matrix in the matrix 2-norm, where /spl gamma/ is a threshold that can be determined from the noise level. Since the resulting approximation error is not minimized, critical scenarios lead to a certain loss of accuracy compared to SVD-based methods. This loss of accuracy is compensated by using unitary ESPRIT in conjunction with the Schur-type subspace estimation scheme. Unitary ESPRIT represents a simple way to constrain the estimated phase factors to the unit circle and provides a new reliability test. Due to the special algebraic structure of the problem, all required factorizations can be transformed into decompositions of real-valued matrices of the same size. The advantages of unitary ESPRIT dramatically improve the resulting subspace estimates, such that the performance of unitary Schur ESPRIT is comparable to that of SVD-based methods, at a fraction of the computational cost. Compared to the original Schur method, unitary Schur ESPRIT yields improved subspace estimates with a reduced computational load, since it is formulated in terms of real-valued computations throughout.

Considers multiple narrowband signals that are incident upon a planar sensor array. 2D unitary ESPRIT is a new closed-form high resolution algorithm to provide automatically paired source azimuth and elevation angle estimates along with an efficient way to reconstruct the impinging signals. In the final stage of the algorithm, the real and imaginary parts of the ith eigenvalue of a matrix are one-to-one related to the respective direction cosines of the ith source relative to the two array axes. 2D unitary ESPRIT offers several advantages over other ESPRIT based closed-form 2D angle estimation techniques. First, except for the final eigenvalue decomposition of dimension equal to the number of sources, it is efficiently formulated in terms of real-valued computation throughout. Second, it is amenable to an efficient DFT beamspace implementation. Third, it is also applicable to array configurations that do not exhibit three identical subarrays, as long as the array is centro-symmetric and possesses invariances in two distinct directions. Finally, 2D unitary ESPRIT easily handles sources having one member of the spatial frequency coordinate pair in common.

Feature extraction is a crucial part of classification procedures. In this paper we present an approach to utilize feature extraction criteria to predict the potential efficiency of a neural network classifier. Statistical and geometrical criteria are introduced for analysis. The complete system of our research consists of a class of generalized Hough-transformations for feature extraction and a subsequent neural network. The neural network performs the classification based on respective features. For an example we concentrated on a pattern recognition problem-the classification of handwritten numerals. As a result of our work we assign two feature extraction criteria to the employed network for a significant estimation of its efficiency.

On the downlink of an SDMA mobile radio system, co-channel interference has to be kept down by beamforming. In this paper two DOA-based beamforming approaches are presented minimizing array signal power while maintaining given signal-to-noise-and-interference ratios for all users. The linear approach is computationally cheap, so that it is suited for channel allocation as well, quickly evaluating the spatial separability of a specific mobile radio scenario. The nonlinear algorithm yields optimum results but only converges quickly, if provided with a good starting point. Therefore, its basic application is burst-to-burst updating of the beamformer.

An algebraic approach to the design of different kinds of discrete wavelet transforms (orthogonal and biorthogonal single-/multiwavelet transforms, multiwavelet-like transforms) is taken. The different transforms are analysed with respect to computational efforts, approximation properties and symmetry. The design of the orthogonal and biorthogonal single-/multiwavelets requires the solution of a system of linear and nonlinear equations. Only the biorthogonal case enables symmetric coefficients. The basis matrix of the multiwavelet-like transform is easy to compute, orthogonal and ultimately symmetric. Modifications of this multiwavelet-like transform are given with respect to practical applications.>

The paper describes an analog current mode realization of Discrete-Time Cellular Neural Networks (DTCNNs) with high cell density, which have local analog and local logic memory. Hence, some important parts of the CNN Universal Machine concept are implemented. The computation speed can be adjusted simply to the application by changing the clock rate. The circuit components are described in detail and SPICE level 2 simulation results are given for the ORBIT 2.0 /spl mu/m process. A layout has been designed for a chip with 12 by 12 cells on a square grid realizing a one-neighborhood with 9 feedback and 9 control coefficients. The cell size is 619 /spl mu/m by 425 /spl mu/m and the simulated speed is between 1 MHz and 10 MHz depending on the minimum value of the state current. For the latter this leads to a simulated performance of 25.9 10/sup 9/ XPS for a single chip operation with an effective area of 0.379 cm/sup 2/ and a worst case power consumption of 0.86 W. Another important feature of the chip is its capability for a spatial cascaded connection.>

The various special features of cellular neural networks are presented. It is stated what has been achieved so far and what future work is still needed in order to obtain a truly universal building block for information processing systems.>

In the past, there have been several attempts to relate Hamiltonian equations, which describe conservative systems, to electrical networks. In control theory the ideas of Hamiltonian systems were expanded to affine Hamiltonian systems with feedback that are input/output systems with Hamiltonian vectorfields. In this paper the principles of affine Hamiltonian systems are applied to a class of electrical networks. It is shown that voltage and current coordinates admit a Poisson structure and how input/output Poisson systems with feedback are related to electrical networks.>

This paper deals with retiming, a register reconfiguration technique, introduced by Leiserson and Saxe (1983 and 1991), to speed up VLSI circuits. Retiming is generally formulated as an optimization problem which is solvable applying linear-programming algorithms. This work presents a different way of considering retiming. A loop analysis, related to network theory, is developed to evaluate all possible retiming solutions. Based on the circuit model used in Leiserson's paper, the incidence matrix of the circuit is formulated in order to find the linearly independent loops which are needed to represent all register configurations. Finally, the set of all retiming solutions is efficiently reduced to those, which fulfil design and timing constraints and thus, the designer is able to choose an appropriate one for implementation.>

An approach is proposed to introduce a space division multiple access (SDMA) component into a frequency/time division multiple access (FTDMA) mobile radio system like the European GSM system providing higher frequency reuse and spectral efficiency. Except from signalling aspects the mobiles remain uneffected by the presented SDMA method without any need for antenna diversity or more sophisticated equalizers. Additional hard- and software-demands due to the new SDMA features are only restricted to base stations as they have to be equipped with an adaptive antenna array. This array is used to separate wavefronts in coherent multipath environments by means of a new algorithm. The number of antenna elements needed is significantly lower than the number of wavefronts impinging on the array.

Partitioning of a class of algorithms with global data dependencies, called multistage algorithms, is investigated. Partitioning requires intermediate results of computations of a specific block of the partition to be stored in an intermediate memory. Furthermore a decomposition of the global interconnection structure of the algorithm is necessary. The authors outline a design methodology for the intermediate memories which perform the data rearrangements according to the interconnection relation and that consist of locally connected synchronous modules. Additionally procedures for deriving control signals for the intermediate memory are presented, which can serve as a basis for control minimization.>

The basic nonlinear operation in median filtering is a sorting of the input data vector. An analog structure for this filtering operation is presented. The sorting operation is embedded in a matrix-valued ordinary differential equation (ODE). The data to be sorted serve as initial values for the ODE. To induce the dynamics of the system a small perturbation is necessary. The circuit structure for the analog median filter consists of basic processing cells containing multipliers, integrators, and adders, arranged in an one-dimensional array. All connections are local only and no external control is necessary.>

An analog circuit structure for the realization of discrete-time cellular neural networks (DTCNNs) is introduced. The computation is done by a balanced clocked circuit based on the idea of conductance multipliers and operational transconductance amplifiers. The circuit is proposed for a one-neighborhood on a hexagonal grid, but can also be modified to larger neighborhoods and/or other grid topologies. A layout was designed for a standard CMOS process, and the corresponding HSPICE simulation results are given. A test chip containing 16 cells was fabricated, and measurements of the transfer characteristics are provided. The functional behavior is demonstrated for a simple example.

Digital radio systems employing multilevel QAM are at least optionally equipped with adaptive time- and/or frequency-domain equalizers. Their purpose is to reduce the vulnerability of these systems to linear distortion caused by multipath propagation. Linear transversal filters are prominent candidates for the realization of time-domain equalizers, especially for high-capacity applications. They are well known for their good performance and their relatively easy implementation at a high data rate. On the other hand, decision feedback equalizers are known to be very capable of eliminating linear distortion, especially of the so-called minimum-phase type. But realization problems are likely to occur in a high-speed application. A solution is proposed which merges the relative advantages of both the linear transversal and the decision feedback approaches. The goal of a frequency-domain equalizer, which is the restoration of the shape of the power density spectrum of the received signal without any recovered carrier and timing signals, can also be achieved with the aid of a transversal filter. The performance obtained with the joint utilization of the novel timeand frequency-domain equalizers is described.

A wave digital filter (WDF) two-port has two input and two output terminals. By using these to appropriately connect a WDF to its transpose, the filtering effect is fully compensated except for an all-pass phase, and this independently of any selectivity requirement. This remains true if at the points of interconnection every second sample is reduced to zero, provided that a certain condition, easily to be satisfied exactly, is fulfilled. Very efficient solutions are then possible. The method is of interest e.g. for subband coding.