Binaural room auralization involves Binaural Room Impulse Responses (BRIRs). Dynamic binaural synthesis (i.e., head-tracked presentation) requires BRIRs for multiple head poses. Artificial heads can be used to measure BRIRs, but BRIR modeling from microphone array room impulse responses (RIRs) is becoming popular since personalized BRIRs can be obtained for any head pose with low extra effort. We present a novel framework for estimating a binaural signal from microphone array signals, using causal Wiener filtering and polynomial matrix formalism. The formulation places no explicit constraints on the geometry of the microphone array and enables directional weighting of the estimation error. A microphone noise model is used for regularization and to balance filter performance and noise gain. A complete procedure for BRIR modeling from microphone array RIRs is also presented, employing the proposed Wiener filtering framework. An application example illustrates the modeling procedure using a 19-channel spherical microphone array. Direct and reflected sound segments are modeled separately. The modeled BRIRs are compared to measured BRIRs and are shown to be waveform-accurate up to at least 1.5\,kHz. At higher frequencies, correct statistical properties of diffuse sound field components are aimed for. A listening test indicates small perceptual differences to measured BRIRs. The presented method facilitates fast BRIR data set acquisition for use in dynamic binaural synthesis and is a viable alternative to Ambisonics-based binaural room auralization.

One of the essential challenges for MIMO or massive MIMO and cooperative multipoint systems is obtaining accurate channel state information (CSI) at the base station side, as the related closed loop MIMO precoders are sensitive to channel aging effects. For that reason, channel prediction is often seen as one of the main enablers to maintain a large part of the performance gains achievable with ideal CSI. It has been claimed that Kalman filtering provides an upper bound for channel prediction performance. In line with this, for real world radio channels alternative channel prediction methods based on parameter estimation achieved comparably worse prediction results. Here, we propose a so called profiling solution as a novel parameter estimation method, which promises to improve the prediction horizon by a factor of two to three compared to Kalman filtering based on autoregressive models. This is indicated by first evaluations based on a real world radio channel measurement in the NOKIA campus in Munich.

Massive multiple input multiple output (MIMO) transmission and coordinated multipoint transmission are candidate technologies for increasing data throughput in evolving 5G standards. Frequency division duplex (FDD) is likely to remain predominant in large parts of the spectrum below 6 GHz for future 5G systems. Therefore, it is important to estimate the downlink FDD channels from a very large number of antennas, while avoiding an excessive downlink reference signal overhead. We here propose and investigate a three part solution. First, massive MIMO downlinks use a fixed grid of beams. For each user, only a subset of beams will then be relevant, and require estimation. Second, sets of coded reference signal sequences, with cyclic patterns over time, are used. Third, each terminal estimates its most relevant channels. We here propose and compare a linear mean square estimation and a Kalman estimation. Both utilize frequency and antenna correlation, and the later also utilizes temporal correlation. In extensive simulations, this scheme provides channel estimates that lead to an insignificant beamforming performance degradation as compared to full channel knowledge. The cyclic pattern of coded reference signals is found to be important for reliable channel estimation, without having to adjust the reference signals to specific users.

For future large-scale multi-antenna systems, channel orthogonal downlink pilots are not feasible due to extensive overhead requirements. Instead, channel reciprocity can be utilized in time division duplex (TDD) systems so that the downlink channel estimates can be based on pilots transmitted during the uplink. User mobility affects the reciprocity and makes the channel state information outdated for high velocities and/or long downlink subframe durations. Channel extrapolation, e.g. through Kalman prediction, can reduce the problem but is also limited by high velocities and long downlink subframes. An alternative solution has been proposed where channel predictions are made with the help of an extra antenna, e.g. on the roof of a car, so called predictor antenna, with the primary objective to measure the channel at a position that is later encountered by the rearward antenna(s). The predictor antenna is not directly limited by high velocities and allows the channel in the downlinks to be interpolated rather than extrapolated. One remaining challenge here is to obtain a good interpolation of the uplink channel estimate, since a sequence of uplink reference signals (pilots) will be interrupted by downlink subframes. We here evaluate a Kalman smoothing estimate of the downlink channels and compare it to a cubic spline interpolation. These results are also compared to results where uplink channels are estimated through Kalman filters and predictors. Results are based on measured channels and show that with Kalman smoothing, predictor antennas can enable accurate channel estimates for a longer downlink period at vehicular velocities. The gaps in the uplink pilot stream, due to downlink subframes, can have durations that correspond to a vehicle movement of up to 0.75 carrier wavelengths in space, for Rayleigh-like non-line-of-sight fading.

Connected vehicles in large numbers will be expensive in terms of power and bandwidth unless advanced transmit schemes are employed. These would rely on channel state information at transmitter (CSIT), which rapidly becomes outdated for fading vehicular channels. We here evaluate the predictor antenna concept, that solves this problem by using antennas on the outside of vehicles, with one extra antenna in front of the others. Its estimated channel is a scaled prediction for the channels encountered by rearward antennas when they reach that position. We evaluate this concept on a large set of channel sounding measurements from an urban environment. Recent investigations of the correlations of these measurements indicate that the average normalized mean squared errors (NMSEs) of the complex valued channel predictions should be around -10 dB for prediction horizons in space of up to 3 wavelengths. This represents an extension of the attainable prediction horizon by an order of magnitude, as compared to Kalman or Wiener extrapolation of past channel measurements. It represents an accuracy that would enable e.g. accurate massive multiple input multiple output (MIMO) downlink beamforming to vehicles. We here perform predictions on a subset of the measurements with good channel-to-estimation error power ratio (SNR). The approximate true channels are here available and we evaluate the performance on a validation data set. The results confirm that the distribution of the NMSE, over all investigated propagation environments, is close to that obtained by correlation-based models and outperforms the use of outdated channel measurements.

The ongoing standardization within 3GPP for the so called new radio (NR) system has identified massive multiple-input multiple output (MIMO) transmission, also called full dimension MIMO, as one of the main contributors to higher spectral efficiency for the mobile broadband case. In particular for radio frequencies below 6 GHz, channel estimation has to be supported in frequency division duplex (FDD) as well as time division duplex (TDD) operation. In TDD we may obtain downlink channels by estimating uplink channels, assuming reciprocity. For FDD, codebook based design as well as some type of explicit feedback is under discussion. Separately, there are also ongoing discussions of the question if massive MIMO in combination with FDD is a reasonable choice at all. Here we highlight some of our recent results obtained within several 5G research projects. To our understanding they overcome some of the inherent limitations of massive MIMO for FDD. As indicated by simulations, the resulting concept enables a grid of beam (GoB) and reference signal design with a reasonable downlink reference signal overhead of around 10 percent, together with reasonable feedback overhead of several hundred kbit/s per UE. Such a design attains around 90 percent of the massive MIMO system performance with ideal channel state information.

A method for sound field control applied to active noise control is presented and evaluated. The method uses Linear Quadratic Gaussian (LQG) feedforward control to find a Minimal Mean Square Error (MMSE)-optimal linear sound field controller under a causality constraint. It is obtained by solving a polynomial matrix spectral factorization and a linear (Diophantine) polynomial matrix equation. An important component in the design is the control signal penalty term of the criterion. Its use and influence is here discussed and evaluated using measured room impulse responses. The results indicate that the use of a relatively simple, frequency-weighted penalty on individual control signals provides most of the benefits obtainable by the considered more advanced alternative. We also introduce and illustrate several tools for performance analysis. An analytical expression for the attainable performance clearly reveals the performance loss generated by having to use a causal controller instead of the ideal noncausal controller. This loss is largest at low frequencies. Furthermore, we introduce a measure of the reproducibility of the target noise sound field with given control loudspeaker setups and room transfer functions. It describes how well a controller that uses an input subspace of dimension equal to the effective rank of the system is able to reproduce a target sound field. This performance measure can e.g. be used to support the selection of good combinations of placements of control loudspeakers.

Coordinated multipoint (CoMP) transmission and reception techniques have been proposed to combat inter-cell interference in cellular systems and, hence, to increase the data rates. Due to the overhead introduced, not all users may be served with CoMP. In this paper, we focus on how to select users for joint processing CoMP under limited backhaul capacity. The evaluated user selection schemes take user experience into account, quantified by the utility of Internet application types. We propose a heuristic algorithm utility based user selection that has low computational complexity. Utility based user selection and maximize utility, which maximizes the sum utilities, are compared to maximize rate, which maximizes the sum data rates. A range of traffic mixes and user locations are evaluated. The simulation results indicate that resources required for CoMP are more efficiently used if the user selection is based on utility rather than on maximizing the total data rate. If utility is taken into account a higher total utility can in many cases be achieved for the same limited backhaul capacity.

Active noise control is of increasing interest in e.g. cars, but the zone of noise damping becomes limited in reverberant environments. We investigate the possibility of extending this spatial zone significantly, by using multiple control loudspeakers. MIMO feedforward controllers designed by linear quadratic control theory are here shown to increase the limiting frequency for uniform damping in a 0.3×0.3 m test area, from 200 Hz to around 600 Hz.

Coordinated multi-point (CoMP) transmission is considered as an efficient technique to improve cell-edge performance as well as system spectrum efficiency. In CoMP-enabled systems, a cluster of coordinated base stations (BSs) are typically assumed to be connected to a control unit (CU) via backhaul links, and the provided performance gain relies heavily on the quality of the channel state information (CSI) available at the CU side. In this paper, we consider the downlink of a CoMP cluster and compare three different CoMP transmission schemes: zero-forcing coherent joint transmission, non-coherent joint transmission and coordinated scheduling. Moreover, for each of the analyzed schemes, the performance in terms of average sum rate of the CoMP cluster is studied with predicted CSI, considering the effects of the feedback and backhaul latency, as well as the user mobility. Compared to zero-forcing coherent joint transmission, we show that non-coherent joint transmission and coordinated scheduling are more robust to channel uncertainty. In addition, depending on the latency, user mobility and user locations, different schemes would achieve the highest average sum rate performance. Hence, a system could switch between the transmission schemes to improve the sum rate.

The next generation wireless systems based on Orthogonal Frequency Division Multiple Access (OFDMA) need to operate in widely different deployment and usage scenarios. Thus, support for flexible resource allocation is important. In this paper we investigate the performance of different memory- less and memory-based channel estimators for different OFDMA subcarrier allocation schemes and different pilot patterns. We evaluate the performance in various fading environments and for different user terminal velocities. The results show that channel estimation can perform well enough for time-frequency localized resources as small as 22 channel symbols with two pilots in many important scenarios. The results provided can be used to identify appropriate subcarrier allocations for the next generation OFDMA based wireless systems.

The EU WINNER projects have studied OFDM-based packet data systems beyond 3G that use adaptivity on all time- scales to obtain high flexibility and performance. The adaptive transmission in both downlink and uplink is scheduled and controlled at base stations and relay nodes and requires frequent transmission of control information over the downlink. The use of scheduling, adaptive modulation and coding, with fine granularity in both time and space, could potentially result in unrealistic bandwidth demands for such downlink control signaling. The present paper describes how this problem has been handled within WINNER in two cases: frequency-adaptive transmission, which allows individual link adaptation within time-frequency resource units and non-frequency adaptive transmission, which averages over the channel variations in the frequency domain. An important tool for limiting the associated control information is to broadcast only a small essential set of control data to all user terminals, using a safe but therefore bandwidth-demanding code rate. The remaining control information is multicast to groups of users with different signal to interference and noise ratios (SINRs). The modulation and code rates of these transmissions are adjusted to the SINRs of these groups. The over-all coded data rate of the control transmission can thereby be reduced to acceptable levels.

Frequency-adaptive multiuser scheduling in OFDM utilizes the frequency-selective small-scale fading to allocate subcarriers with advantageous signal-to-noise ratio to each user. Due to channel time-variability and delays of the transmission control loop, this will in general require channel prediction. FDD (Frequency Division Duplex) uplinks pose the most challenging prediction problem: All sub-bands that may potentially be allocated must here be predicted for all involved user terminals, based on pilots transmitted from all terminals. This poses challenges with respect to prediction accuracy, estimator complexity and pilot overhead. This paper explores the design, performance and complexity of Kalman predictors used for uplink prediction, in the context of the EU WINNER project baseline design system. One conclusion is that uplink prediction that is useful at vehicular velocities in 4G systems operating at 3-5 GHz is indeed feasible. However, the channel predictability depends crucially on the local fading environment, so predictors should be based on models of the Doppler spectrum for each terminal.

High data rates, high spectral efficiency, flexibility, and low delays over the air interface will be important features in next-generation wireless systems. The overall challenge will be packet scheduling and adaptive radio transmission for multiple users, via multiple antennas and over frequency-selective wideband channels. This problem needs to be structured to obtain feasible solutions. The basic simplifying assumptions used here are clustering of antennas into cells, orthogonal transmission by use of cyclic-prefix orthogonal frequency-division multiplexing (OFDM) and a time-scale separation view of the total link adaptation, scheduling and intercell coordination problem. Based on these assumptions, we survey techniques that adapt the transmission to the temporal, frequency, and spatial channel properties. We provide a systematic overview of the design problems, such as the dimensioning of the allocated time-frequency resources, the influence of duplexing schemes, adaptation control issues for downlinks and uplinks, timing issues, and their relation to the required performance of channel predictors. Specific design choices are illustrated by recent research within the Swedish Wireless IP program and the EU IST-WINNER project. The presented results indicate that high-performance adaptive OFDM transmission systems are indeed feasible, also for challenging scenarios that involve vehicular velocities, high carrier frequencies, and high bandwidths.

In this paper we propose a spatial multiplexing technique for the downlink of a multiple-input-multiple-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) system. For outdoor environments with a limited angular spread at the base station, the proposed technique is able to separate the users' streams through a joint spatial processing at both the transmitter and the receiver requiring only a limited feedback from each user. Adaptive transmission is adopted on each stream to set a fixed probability of error. Numerical simulations show that the proposed technique is able to provide significant throughput gains compared to fixed-beams based approaches proposed in the literature.

Numerous investigations of spatial processing algorithms are available in literature showing the benefit of MIMO systems in particular situations. From a system perspective, however, spatial processing has to provide adaptivity and scalability to a wide range of scenarios and has also to be seen in the context of its impact on other system services and functions, its enabling requirements, overhead, and robustness. This paper summarizes recent work towards integrated spatial processing services and functions for an OFDM-based B3G air interface, developed in the European IST research project WINNER. The current status of the air interface and the multi-user spatial domain link adaptation concept is summarized. A baseline spatial scheme selection process is developed as part of the overall MAC radio resource control

The European IST research projects WINNER and WINNER II aim at developing a single new ubiquitous radio access system concept that can be adapted for use in a wide variety of mobile communication scenarios, from wide area coverage to hot spots. This cellular packet data system uses a multicarrier-based air interface. Its medium access control (MAC) system layer is designed for either FDD or TDD cellular transmission. It supports adaptive transmission by tight interaction with the physical layer, and provides efficient resource allocation and scheduling within relay-enhanced cells. It also supports separate allocation of antenna resources per packet flow and furthermore provides support for spectrum sharing and flexible spectrum use. The paper outlines the resource allocation within a super-frame and the use of either opportunistic scheduling or diversity-based transmission within frames.

Within the EU FP6 Integrated Project WINNER, adaptive transmission is investigated as a key technology for boosting the spectral efficiency of a new radio interface for 4G systems. Adaptive allocation and link adaptation of time-frequency chunks based on channel prediction in an OFDM-based system offers a significant potential to design a spectrally efficient system. The chunk size is typically defined based on the minimum coherence time and coherence bandwidth of the targeted channels. It is important to allow efficient channel coding and link retransmission schemes without restricting the resource scheduler, even for systems using small chunk sizes, to achieve multi-user diversity gains. In this paper we introduce some possible approaches to implement FEC coding and hybrid ARQ and analyze their interplay with resource scheduling in packet oriented adaptive TDMA/OFDMA

In adaptive wireless packet transmission for multiple users, resources are allocated based on measurement and feedback of the channel qualities. Uplink channels of adaptive OFDM systems that use FDD have to be estimated and predicted based on uplink pilots transmitted by all active users. To prevent a prohibitive pilot overhead, the use of overlapping (simultaneously transmitted) pilots is considered. Kalman estimators and predictors can efficiently utilize the channel correlation in time and frequency to obtain estimates. The estimates have higher error than in the downlink case where overlapping pilots are not needed. However, the estimation and prediction MSE increases rather slowly with the number of simultaneous users. The results indicate that the accuracy is adequate for control of an adaptive transmission loop.

In this paper, we formulate and solve a problem of resource allocation over a given time horizon with uncertain demands and uncertain capacities of the available resources. In particular, we consider a number of data sources with uncertain bit rates, sharing a set of parallel channels with time-varying and possibly uncertain transmission capacities. We present a method for allocating the channels so as to maximize the expected system throughput. The framework encompasses quality-of-service (QoS) requirements, e.g., minimum-rate constraints, as well as priorities represented by a user-specific cost per transmitted bit. We assume only limited statistical knowledge of the source rates and channel capacities. Optimal solutions are found by using the maximum entropy principle and elementary probability theory. The suggested framework explains how to make use of multiuser diversity in various settings, a field of recently growing interest in communication theory. It admits scheduling over multiple base stations and includes transmission buffers to obtain a method for optimal resource allocation in rather general multiuser communication systems.

Uncoded adaptive M-QAM transmission over flat Rayleigh fading channels is here optimized in a novel way in the presence of channel prediction errors. The modulation rate is determined based on the predicted channel state. The modulation rate limits are adjusted by maximizing the throughput in error-free link-level frames, averaged over the pdf of the true channel state. No bit error rate constraint is imposed. This approach is appropriate when fast link-level retransmissions can be used to attain required error levels. The resulting scheme is evaluated analytically in a multiuser environment where predictive link adaptation is used in combination with a scheduling strategy that provides multiuser diversity gain. Prediction errors typical of prediction 1/3 wavelength ahead in space will then result in only 8%-12% reduction in the spectral efficiency, as compared to a case with perfectly known channels. The resulting performance is very robust with respect to the prediction error variance assumed when optimizing the rate adaptation scheme.

When adaptive modulation is used to counter short-term fading in mobile radio channels, signaling delays create problems with outdated channel state information. The use of channel power prediction will improve the performance of the link adaptation. It is then of interest to take the quality of these predictions into account explicitly when designing an adaptive modulation scheme. We study the optimum design of an adaptive modulation scheme based on uncoded M-quadrature amplitude modulation, assisted by channel prediction for the flat Rayleigh fading channel. The data rate, and in some variants the transmit power, are adapted to maximize the spectral efficiency, subject to average power and bit-error rate constraints. The key issues studied here are how a known prediction error variance will affect the optimized transmission properties, such as the signal-to-noise ratio (SNR) boundaries that determine when to apply different modulation rates, and to what extent it affects the spectral efficiency. This investigation is performed by analytical optimization of the link adaptation, using the statistical properties of a particular, but efficient, channel power predictor. Optimum solutions for the rate and transmit power are derived, based on the predicted SNR and the prediction error variance.

When adaptive modulation is used to counter short-term fading in mobile radio channels, signaling delays create problems with outdated channel state information. The use of channel power prediction improves the performance of the link adaptation. It is then of interest to take the quality of these predictions into account explicitly when designing an adaptive modulation scheme. We study the optimum design of an adaptive modulation scheme based on uncoded M-QAM modulation assisted by channel prediction for the flat Rayleigh fading channel. The data rate, and in some variants the transmit power, are adapted to maximize the spectral efficiency subject to average power and bit error rate constraints. The key issues studied here are how a known prediction error variance affects the optimized transmission properties such as the SNR boundaries that determine when to apply different modulation rates, and to what extent it affects the spectral efficiency. This investigation is performed by analytical optimization of the link adaptation, using the statistical properties of a particular but efficient channel power predictor. Optimum solutions for the rate and transmit power are derived based on the predicted SNR and the prediction error variance.

Adaptation laws with constant gains, that adjust parameters of linear regression models, are investigated. The class of algorithms includes LMS as its simplest member, while other algorithms such as Wiener LMS may improve performance by including linear filters. Expressions in closed form for the tracking MSE are obtained for rapidly varying parameters of FIR systems with white inputs. This situation may occur in e.g. the tracking of fading communication channels. Stability and convergence in MSE are ascertained by the stability of a transfer function, without assuming independent regressor vectors. A key technique for obtaining these results is a transformation of these adaptation algorithms into linear time-invariant filters, called learning filters, that operate in open loop for slow parameter variations.

This paper investigates the estimation and tracking of time varying propagation channels in the uplink of a time slotted CDMA system. An antenna array is adopted at the base station. Both the estimation and tracking are performed by exploiting the low-rank nature of the channel matrix. The accuracy of the estimate is improved by using a multi-slot approach: the slowly varying component of the low-rank channel is estimated from the observation of successive midambles (inter-slot tracking), while the fast varying component is updated over the burst interval in decision-directed mode (intra-slot tracking).

For pt.I see ibid., vol.49, p.2207-17 (2001). Low-complexity Wiener LMS (WLMS) adaptation algorithms, of use for channel estimation, have been derived in Lindbom et al. (2001). They are here evaluated on the fast fading radio channels encountered in IS-136 TDMA systems, with the aim of clarifying several issues: How much can channel estimation performance be improved with these tools, as compared to LMS adaptation? When can an improved tracking MSE be expected to result in a meaningful reduction of the bit error rate? Will optimal prediction of future channel estimates significantly improve the equalization? Can one single tracker with fixed gain be used for all encountered Doppler frequencies and SNRs, or must a more elaborate scheme be adopted? These questions are here investigated both analytically and by simulation. An exact analytical expression for the tracking MSE on two-tap FIR channels is presented and utilized. With this tool, the MSE performance and robustness of WLMS algorithms based on different statistical models can be investigated. A simulation study then compares the uncoded bit error rate of detectors, where channel trackers are used in decision directed mode in conjunction with Viterbi algorithms. A Viterbi detector combined with WLMS, based on second order autoregressive fading models possibly combined with integration, provides good performance and robustness at a reasonable complexity.

We present a method for optimizing adaptation laws that are generalizations of the LMS algorithm. Time-varying parameters of linear regression models are estimated in situations where the regressors are stationary or have slowly time-varying properties. The parameter variations are modeled as ARIMA processes and the aim is to use such prior information to provide high-performance filtering, prediction or fixed lag smoothing estimates for arbitrary lags. The method is based on a novel signal transformation that recasts the algorithm design problem into a Wiener design.

Adaptation laws with constant gains, that adjust parameters of linear regression models, are investigated. The class of algorithms includes LMS as its simplest member. Closed-form expressions for the tracking MSE are obtained for parameters described by ARIMA processes. A key element of the analysis is that adaptation algorithms are expressed as linear time-invariant filters, here called learning filters, that work in open loop for slow parameter variations. Performance analysis can then easily be performed for slow variations, and stability is assured by stability of these learning filters.

Adaptation algorithms with constant gains are designed for tracking smoothly time-varying parameters of linear regression models, in particular channel models occurring in mobile radio communications. In a companion paper, an application to channel tracking in the IS-136 TDMA system is discussed. The proposed algorithms are based on two key concepts. First, the design is transformed into a Wiener filtering problem. Second, the parameters are modeled as correlated ARIMA processes with known dynamics. This leads to a new framework for systematic and optimal design of simple adaptation laws based on prior information. The algorithms can be realized as Wiener filters, called learning filters, or as "LMS/Newton" updates complemented by filters that provide predictions or smoothing estimates. The simplest algorithm, named the Wiener LMS, is presented. All parameters are here assumed governed by the same dynamics and the covariance matrix of the regressors is assumed known. The computational complexity is of the same order of magnitude as that of LMS for regressors which are either white or have autoregressive statistics. The tracking performance is, however, substantially improved.

A general MMSE decision feedback equalizer (DFE) for multiple input-multiple output channels is presented. It is derived under the constraint of realizability, requiring finite smoothing lag and causal filters both in the forward and feedback link. The proposed DFE, which provides an optimal structure as well as optimal filter degrees, is obtained as an explicit solution in terms of the channel and noise description. A generalization of the scalar zero-forcing DFE is also presented. Conditions for the existence of such an equalizer are discussed. We argue that when no zero-forcing equalizer exists, poor near-far resistance of the corresponding optimal minimum mean square error DFE can be expected. A numerical example indicates the potential advantages of using a decision feedback equalizer with appropriate structure. The main improvements are obtained at moderate to high signal-to-noise ratios.

We investigate the use of an antenna array at the receiver in frequency-division multiple-access/time-division multiple-access systems to let several users share one communication channel within a cell. A decision-feedback equalizer (DFE) which simultaneously detects all incoming signals is compared to a set of DFEs, each detecting one signal and rejecting the remaining as interference. We also introduce the existence of a zero-forcing solution to the equalization problem as an indicator of near-far resistance of different detector structures. Near-far resistance guarantees good performance if the noise level is low. Simulations show that with an increased number of users in the cell, the incremental performance degradation is small for the multiuser detector. We have also applied the proposed algorithms to experimental measurements from a DCS-1800 antenna array testbed. The results from these confirm that reuse within a cell is indeed possible using either an eight-element array antenna or a two-branch diversity sector antenna. Multiuser detection will, in general, provide better performance than interference rejection, especially when the power levels of the users differ substantially. The difference in performance is of crucial importance when the available training sequences are short.

The purpose of this paper is to investigate the use of combined spatial and temporal equalization, in particular for short training sequences. The motivation for combining spatial and temporal equalization is the existence of multipath propagation and co-channel interference. Our main concern is to obtain good performance yet low complexity. We will suggest a low complexity algorithm utilizing a circular antenna array. Although it has inferior performance in an asymptotic sense, it turns out to be superior to the general solution for short training sequences. This conclusion is supported by simulations where a number of algorithms are evaluated for different scenarios involving co-channel interference.

During the last few years, the second generation mobile cellular telephone systems have become operational. One can foresee a dramatic increase in traffic demands, both concerning transmission quality and system capacity. We present a multivariable minimum mean square error (MMSE) DFE, which has the ability to operate on channels with different number of inputs and outputs. It is derived under the constraint of realizability and is calculated by solving a system of linear equations. The suggested DFE can be used for combined temporal and spatial equalization, as well as for narrowband, multiuser detection in systems with multiple antenna elements. The DFE may also serve as a multiuser detector in DS-CDMA systems. Simulations indicate that the DFE performs well as a multiuser detector. In a system with multiple antennas, it outperforms a corresponding detector for a single user in terms of the BER while doubling the capacity. If utilized for multiuser detection in CDMA, its performance is comparable to the best achievable performance of the decorrelating detector, and is attained at a substantially lower computational cost.

A simple algorithm for optimizing decision feedback equalizers (DFEs) by minimizing the mean-square error (MSE) is presented. A complex baseband channel and correct past decisions are assumed. The dispersive channel may have infinite impulse response, and the noise may be colored. Consideration is given to optimal realizable (stable and finite-lag smoothing) forward and feedback filters in discrete time. They are parameterized as recursive filters. In the special case of transmission channels with finite impulse response and autoregressive noise, the minimum MSE can be attained with transversal feedback and forward filters. In general, the forward part should include a noise-whitening filter (the inverse noise model). The finite realizations of the filters are calculated using a polynomial equation approach to the linear quadratic optimization problem. The equalizer is optimized essentially by solving a system of linear equations Ax=B, where A contains transfer function coefficients from the channel and noise model. No calculation of correlations is required with this method. A simple expression for the minimal MSE is presented. The DFE is compared to MSE-optimal linear recursive equalizers. Expressions for the equalizer in the limiting case of infinite smoothing lags are also discussed.>