In this paper, we link the turbo principle to unconstrained maximum likelihood (ML) sequence detection and joint ML parameter estimation. First, we demonstrate for memoryless channels with complete channel state information how the turbo decoder can be systematically derived starting from the ML sequence detection criterion. In particular, we show that a method to solve the ML sequence detection problem is to iteratively solve the corresponding critical point equations of an equivalent unconstrained estimation problem by means of fixed-point iterations. The turbo decoding algorithm is obtained by approximating the overall a posteriori probabilities. Subsequently, we show how this general approximative iterative maximum likelihood (AIML) framework can be applied to general iterative ML receiver design. We consider static memoryless channels with unknown channel parameters. The time-selective fading channels with partial channel state information is the subject of a companion paper.

We extend the methodology in to flat fading channels. The systematically derived receiver structure corresponds to an iterative solution of the joint maximum likelihood (ML) optimization problem. The heuristically derived BICM with iterative decoding is shown to be the outcome of this joint ML optimization problem.

Opportunistic beamforming (OBF) is a technique to jointly increase the throughput and quality of service (QoS) in a multiuser system with fair and channel-aware scheduling. With this technique, the channel dynamics are influenced such that fading peaks occur more often within a given period of time. Channel-aware scheduling requires reliable SNR information in the base station that is obtained via feedback information from the users. Since this feedback is not instantaneous, the problem caused by a feedback delay are exacerbated by OBF because of higher channel dynamics. In this paper, a SNR predictor is derived for OBF that is matched to the beamforming process.

In this paper we derive an enhanced power control algorithm, fitting into the up/down control scheme, as it is considered in the frequency division duplex (FDD) mode of the current 3GPP standard. Analysis of the classical up/down power control scheme unveils, that with increasing velocities the power control performance degrades, as the fixed step size power control is not able to track the channel fading properly. For the uplink we derive a nonlinear control algorithm generating the up/down power control commands accounting for the future of the channel fading process. Simulations show that this algorithm in combination with perfect future channel state information can partially mitigate the drawbacks of a fixed step-size up/down power control. A prerequisite for predictive power control is the acquisition of the future channel state information. In this paper we deduce a robust and adaptive structure for the prediction of the channel fading process in the context of a power controlled code division multiple access (CDMA) system based on least mean square (LMS) adaptation. Link level simulations show a signal to noise and interference ratio (SINR) gain in terms of the block error rate, enabling a decrease of the target SINR and thus leading to an enhanced spectral efficiency.

Two different power-scaled noncoherent detection schemes are analyzed and compared in terms of detection probability and mean detection time in the uplink of a wideband CDMA system, where the mobile terminal transmits a pilot signal in the form of bursts of modulated chips, which are transmitted periodically and separated by long silent intervals. Both detection schemes employ temporal noncoherent averaging but differ in the way of spatial processing. One of the schemes, which is well suited for spatially uncorrelated scenarios, employs spatial noncoherent averaging, whereas the other scheme, better suited for scenarios with a distinct spatial structure, employs fixed beamforming. The performance analysis is carried out for spatially correlated frequency-selective fading channels taking channel dynamics and initial frequency offsets into account. With the presented analysis it is possible to investigate the effects of spatial correlation on the detection performance and to examine the improvement of using multiple antenna elements at the base station depending on the respective detection scheme.

An analytical evaluation of the performance in terms of detection probability and mean detection time of a noncoherent detector at the base station is presented for a wideband CDMA system, where the mobile terminal transmits a pilot signal in the form of bursts of modulated chips, which are transmitted periodically and separated by long silent intervals. The performance analysis is carried out for a power-scaled detector employing temporal and spatial noncoherent averaging in frequency-selective fading channels taking channel dynamics and initial frequency offsets into account. With the presented analysis, which is verified by means of simulations, it is possible, and convenient, to trade off temporal noncoherent versus coherent averaging, depending on the length of the pilot bursts and channel dynamics and to examine the improvement of using multiple diversity antennas at the base station.

The problem of direction of arrival (DOA) estimation of multiple sources is addressed considering possible coherence loss along the impinging wavefronts. The loss results from wave propagation through a fluctuating medium and leads to a decreasing signal correlation from sensor to sensor. Two new algorithms are proposed that are significantly less computationally complex than the well known covariance matching (CM) approach. Furthermore a polynomial approximation of the coherence loss parameters is introduced, which pen-nits a decoupling of the DOA estimation from the estimation of all other parameters. The proposed algorithms and theoretical results are verified by numerical examples.