G. Del Galdo

A novel tree-based scheduling algorithm for the downlink of multi-user MIMO systems with ZF beamforming

Spatial multiplexing in the downlink of wireless multiple antenna communications promises high gains in system throughput. However, spatially correlated users and a limited number of antennas at the base station motivates the need for a scheduling algorithm which efficiently arranges users into groups to be served in different time or frequency slots. In this paper we propose a novel tree-based scheduling algorithm which successfully solves this problem achieving a close to optimum grouping strategy. The algorithm has been tested with zero forcing beamforming techniques and is based on a new metric for the user performance considering the effect of other users present in the same group analyzing their spatial features.

Low-Complexity Space–Time–Frequency Scheduling for MIMO Systems With SDMA

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.

Enhanced Model Order Estimation using Higher-Order Arrays

Frequently, R-dimensional subspace-based methods are used to estimate the parameters in multi-dimensional harmonic retrieval problems in a variety of signal processing applications. Since the measured data is multi-dimensional, traditional approaches require stacking the dimensions into one highly structured matrix. Recently, we have shown how an HOSVD based low-rank approximation of the measurement tensor leads to an improved signal subspace estimate, which can be exploited in any multi-dimensional subspace-based parameter estimation scheme. To achieve this goal, it is required to estimate the model order of the multi-dimensional data. In this paper, we show how the HOSVD of the measurement tensor also enables us to improve the model order estimation step. This is due to the fact that only one set of eigenvalues is available in the matrix case. Applying the HOSVD, we obtain R + 1 sets of n-mode singular values of the measurement tensor that are used jointly to improve the accuracy of the model order selection significantly.

A Novel Automatic Cluster Tracking Algorithm

On the way to answer the controversial question What is a cluster?, we introduce a novel cluster tracking mechanism which is based on the multi-path component distance (MCD). We perform a temporal tracking of cluster centroids in the multidimensional parameter domain, starting from cluster estimates obtained by a recently introduced framework which automatically clusters parametric MIMO channel data. To validate our algorithm, we use both synthetic and measured MIMO channels. We generate the synthetic channel using the IlmProp simulation tool. For the real-world channels we use data from an outdoor measurement campaign in a rural area. Simulation results with synthetic channels validate the tracking algorithm, while its application on measurement data shows the occurrence of several clusters with different lifetimes

Comparison of zero-forcing methods for downlink spatial multiplexing in realistic multi-user MIMO channels

The use of space-division multiple access (SDMA) in the downlink of a multi-user MIMO wireless communications system can offer a substantial gain in system throughput. We compare the throughput achieved by employing recently developed algorithms for transmit beamforming in multi-user MIMO systems. The block-diagonalization algorithm is a generalization of the channel inversion scheme which suppresses completely all interference among users, although allowing interference among different data streams. The successive optimization algorithm, on the other hand, solves the power minimization problem one user at a time, so that the n-th user produces no interference with respect to the m-th user, where 1/spl les/m<n. The paper tests these algorithms on several realistic multi-user MIMO channels generated by IlmProp, a flexible geometry based propagation model for wireless communications developed at Ilmenau University of Technology. This analysis reveals the potentials and limitations of these algorithms. In order to overcome the drawbacks of these beamforming schemes, a subspace-based grouping algorithm is proposed. Its performance is evaluated in a realistic multi-user synthetic scenario, especially taking into account how the algorithms adapt to the different channel conditions as time progresses.

Tensor-based framework for the prediction of frequency-selective time-variant MIMO channels

In this contribution we propose a tensor-based framework for the prediction of time-variant frequency-selective multiple-input multiple-output (MIMO) channels from noisy channel estimates. This method performs the prediction in a transformed domain obtained via the higher order singular value decomposition (HOSVD), namely on the transformed tensor elements. This is followed by the inverse transformation of the predicted transformed tensor elements onto a basis corresponding to the signal subspace. To verify our strategy, we compare the results in terms of the normalized mean square error using a known prediction method, e.g., a Wiener filter, applied to the transformed tensor elements with the identical method applied directly to the channel coefficients. The results of our investigation show that the tensor-based prediction method outperforms the direct prediction method. Although we concentrate in this contribution on the prediction in the time domain, this framework can also be used for the estimation in other domains.

Increasing the throughput in multi-hop wireless networks by using spatial multiplexing

Raising the carrier frequencies of future mobile communication systems results in a higher path loss which would lead to a dense mesh of access points (base stations) to guarantee coverage. To avoid the high number of access points, multi hop concepts such as the wireless media system (WMS) are proposed to extend the range of the access points. In a typical multi-hop scenario, the data is routed from an access point (AP) over one or more relays to the mobile station. The relays do not require a connection to the core network. Therefore, the infrastructure costs are lower. These concepts might use fixed or mobile wireless relays (FWRs) to extend the range of the AP as this guarantees a certain coverage. If the WMS uses relaying in the time domain, there is a reduction of the throughput for each hop due to idle times. This reduction in throughput is avoided by the use of spatial multiplexing via smart antennas. This is due to the fact that a relaying scheme based on spatial multiplexing (RSM) can handle several connections to spatially separated locations at the same time.

Sparsity order estimation for single snapshot compressed sensing

In this paper we discuss the estimation of the spar-sity order for a Compressed Sensing scenario where only a single snapshot is available. We demonstrate that a specific design of the sensing matrix based on Khatri-Rao products enables us to transform this problem into the estimation of a matrix rank in the presence of additive noise. Thereby, we can apply existing model order selection algorithms to determine the sparsity order. The matrix is a rearranged version of the observation vector which can be constructed by concatenating a series of non-overlapping or overlapping blocks of the original observation vector. In both cases, a Khatri-Rao structured measurement matrix is required with the main difference that in the latter case, one of the factors must be a Vandermonde matrix. We discuss the choice of the parameters and show that an increasing amount of block overlap improves the sparsity order estimation but it increases the coherence of the sensing matrix. We also explain briefly that the proposed measurement matrix design introduces certain multilinear structures into the observations which enables us to apply tensor-based signal processing, e.g., for enhanced denoising or improved sparsity order estimation.

Spatio-Temporal Availability in Satellite-to-Indoor Broadcasting

This contribution studies the spatio-temporal availability of satellite links inside typical indoor environments. The spatio-temporal satellite-to-indoor channels are obtained by a 3D ray tracing engine and by a geometry-based channel modeling tool. In this paper the temporal fluctuations of the channels have been modeled based on satellite-to-indoor measurements. Here the performance of single as well as multiple receive antennas with different polarimetric radiation patterns are compared for different satellite elevation angles. The results show that additional antennas placed at the receiver reduce both the spatial and temporal variability of the received power, leading to a significant reduction in transmit power necessary for the same target availability.

Spatially resolved sub-Nyquist sensing of multiband signals with arbitrary antenna arrays

In recent years it has been shown that wideband analog signals can be sampled significantly below the Nyquist rate without loss of information, provided that the unknown frequency support occupies only a small fraction of the overall bandwidth. The modulated wideband converter (MWC) is a particular architecture that implements this idea. In this paper we discuss how the use of antenna arrays allows to extend this concept towards spatially resolved wideband spectrum sensing by leveraging the sparsity in the angular-frequency domain. In our system each antenna element of the array is sampled at a sub-Nyquist rate by an individual MWC block. This results in a trade-off between the number of antennas and MWC channels per antenna. We derive bounds on the minimal total number of channels and minimal sampling rate required for perfect recovery of the 2D angular-frequency spectrum of the incoming signal and present a concrete reconstruction approach. The proposed system is applicable to arbitrary antenna arrays, provided that the array manifold is ambiguity-free.