Multidimensional harmonic retrieval problems are encountered in a variety of signal processing applications including radar, sonar, communications, medical imaging, and the estimation of the parameters of the dominant multipath components from MIMO channel measurements. R-dimensional subspace-based methods, such as R-D Unitary ESPRIT, R-D RARE, or R-D MUSIC, are frequently used for this task. Since the measurement data is multidimensional, current approaches require stacking the dimensions into one highly structured matrix. However, in the conventional subspace estimation step, e.g., via an SVD of the latter matrix, this structure is not exploited. In this paper, we define a measurement tensor and estimate the signal subspace through a higher-order SVD. This allows us to exploit the structure inherent in the measurement data already in the first step of the algorithm which leads to better estimates of the signal subspace. We show how the concepts of forward-backward averaging and the mapping of centro-Hermitian matrices to real-valued matrices of the same size can be extended to tensors. As examples, we develop the R-D standard Tensor-ESPRIT and the R-D Unitary Tensor-ESPRIT algorithms. However, these new concepts can be applied to any multidimensional subspace-based parameter estimation scheme. Significant improvements of the resulting parameter estimation accuracy are achieved if there is at least one of the R dimensions, which possesses a number of sensors that is larger than the number of sources. This can already be observed in the two-dimensional case.

In this paper we present a generic framework for the asymptotic performance analysis of subspace-based parameter estimation schemes. It is based on earlier results on an explicit first-order expansion of the estimation error in the signal subspace obtained via an SVD of the noisy observation matrix. We extend these results in a number of aspects. Firstly, we demonstrate that an explicit first-order expansion of the Higher-Order SVD (HOSVD)-based subspace estimate can be derived. Secondly, we show how to obtain explicit first-order expansions of the estimation error of arbitrary ESPRIT-type algorithms and provide the expressions for R-D Standard ESPRIT, R-D Unitary ESPRIT, R-D Standard Tensor-ESPRIT, as well as R-D Unitary Tensor-ESPRIT. Thirdly, we derive closed-form expressions for the mean square error (MSE) and show that they only depend on the second-order moments of the noise. Hence, to apply this framework we only need the noise to be zero mean and possess finite second order moments. Additional assumptions such as Gaussianity or circular symmetry are not needed.

Measuring the degree of diffuseness of a sound field is crucial in many modern parametric spatial audio techniques. In these applications, intensity-based diffuseness estimators are particularly convenient, as the sound intensity can also be used to obtain, e.g., the direction of arrival of the sound. This contribution reviews different diffuseness estimators comparing them under the conditions found in practice, i.e., with arrays of noisy microphones and with the expectation operators substituted by finite temporal averages. The estimators show a similar performance, however, each with specific advantages and disadvantages depending on the scenario. Furthermore, the paper derives an estimator and highlights the possibility of using spatial averaging to improve the temporal resolution of the estimates.

In this paper, we propose a low-complexity fair scheduling algorithm for wireless multiuser MIMO communication systems in which users are multiplexed via time-, frequency-, and space-division multiple access (SDMA) schemes. In such systems, the transmission quality considerably degrades if users with spatially correlated channels are to be served at the same time and frequency. The approach presented here works with both zero- and nonzero-forcing SDMA precoding schemes by deciding, for each time and frequency slot, which users are to be served in order to maximize the precoding performance. The number of users is not a fixed parameter of the algorithm (as often assumed for other schedulers present in the literature), but it is also adjusted in accordance to the channel conditions. While smaller SDMA groups allow us to transmit with a higher average power per user, larger groups lead to higher multiplexing gains. Our algorithm ProSched is based on a novel interpretation of the precoding process using orthogonal projections which permit us to estimate the precoding results of all user combinations of interest with significantly reduced complexity. In addition, the possible user combinations are efficiently treated with the help of a tree-based sorting algorithm. The ProSched takes advantage of a perfect channel state information, when available, or, alternatively, of second-order channel statistics. The individual-user quality-of-service requirements can be considered in the decision-making process. The effectiveness of the algorithm is illustrated with simulations based on the IlmProp channel model, which features realistic correlation in space, time, and frequency.

We consider two approaches for real-time end-to-end over-the-air testing for intelligent traffic systems using virtual radio environments. One approach is based on coherent wavefield synthesis, while the other aims at emulating the essential features of a realistic radio channel in vehicular ad-hoc networks. Due to finite hardware resources, the former is limited to electrically small systems-under-test, while the latter is suited for typical vehicle-to-vehicle scenarios. Three major aspects of the emulation of radio environments in our facilities in Ilmenau, Germany, are described: 1. Emulation hardware, 2. Channel sounding, modeling, and feature extraction, 3. Emulation methods.