In integrated sensing and communication (ISAC) systems, the radar and communication functionality share the same infrastructure and radio resources. The flexible access scheme in mobile communication systems allows for variable and efficient radio resource allocation for multiple users and services. In this paper, we present a resource allocation strategy for Orthogonal Frequency Division Multiple (OFDM) based radar sensing. Furthermore, we propose a Deep Reinforcement Learning (DRL) method combined with classic OFDM radar signal processing techniques to optimize the radio resources for radar sensing. Two agents are trained with the DRL method on simulated data to predict the radio resources for the subsequent signal, aiming to achieve the required radar performance while improving the resource efficiency. One agent optimizes the transmission power, while the other optimizes the signal bandwidth/duration. The investigated scenario is a highway with traffic. We evaluate the performance of the agents based on detection loss and radio resource efficiency the metrics. Further, we compare the results against random and maximum resource-selecting agents.

Integrated Sensing and Communications (ISAC) aims at incorporating radar and communications functionalities into a single system, achieving higher spectral efficiency through their joint operation. This paper proposes a methodology for reducing resource elements and scanning beams used in the target detection stage of ISAC. Building upon the well-known symbol domain Orthogonal Frequency Division Multiplexing (OFDM) ISAC processing algorithm, its relevant characteristics and limits are considered to minimize the periodicity of sensing resource elements. Moreover, a methodology for finding the required beams to sense a scenario using the radar range equation in a link-budget analysis is proposed and later illustrated via simulations. The simulations show that it is possible to cover a road scenario using a limited number of beams and resource elements from a New Radio (NR) OFDM frame.

In this paper, we present the ray tracing (RT) simulation in the 3D model of one highly dense clutter industrial hall, which is scanned by laser scanner and reconstructed based on accurate point cloud. The whole processing chain from the scanning of the physical environment to running the simulation is presented in detail. To validate the simulation results, the synthetic channel characteristics and large-scale parameters, including delay spread (DS), angular spread (AS) and path loss (PL), are compared with those obtained from channel sounding measurement in both LOS and NLOS cases, at 6.75 GHz, 30 GHz and 60 GHz. The simulation results show that some scatters are significant in all bands and may be well identified and tracked. This indicates that our target to generate a deterministic channel model or a hybrid channel model at multi-band for industrial scenario may be possible.

A parallel receiver architecture for multiple input multiple output (MIMO) channel sounding application is presented with a software-defined radio (SDR)-based field-programmable gate array (FPGA) implementation. The receiver covers phase coherent reception via shared local oscillator (LO) and reference clock, a timing scheme synchronous to the antenna switching at the transmitter, and an integrated automatic gain control (AGC) in all receive channels. It is built with SDRs (NI USRP-2955, X310 series with TwinRx daughterboards). The use of these off-the-shelf hardware components reduces the costs of the sounding system. The FPGA implementation together with the system parameters of the chosen hardware allows a minimum AGC update interval of approx. 44.38$\mu \mathrm{s}$. Our setup demonstrates the applicability of state-of-the-art SDRs as a sounding system for continuous acquisition of the time variant, space, and frequency selective radio propagation channel.

With the fast development of wireless devices, over-the-air (OTA) testing is becoming the preferred method among developers and manufacturers of wireless equipment. The ability to recreate a scenario under controllable and repeatable conditions keeps the method under constant development, providing new features that increase the realism during the tests. A recent proof of that is the integration of 3D wave field synthesis (3DWFS) to OTA testing, which becomes a significant step to accurately emulate wireless scenarios within a controlled environment.In this context, this contribution improves the OTA system calibration for 3DWFS; efficiently increasing the emulation quality of electromagnetic plane waves impinging from any angular position within an anechoic chamber. In fact, this enhancement implicitly delivers a new method for accurate estimation of the antenna radiation pattern in 3D. This is not only a highly demanded application among antenna manufacturers but in this case also proves the validity of the results and consolidates the integration of 3DWFS to OTA testing.

The lower THz bands are foreseen as candidates to achieve the data-rates demanded in the sixth generation (6G) of wireless communications. Hence, multiple measurements for characterization of propagation at these frequencies in different scenarios are being conducted all around the world. This also impulses activities on standardization of measurement equipment and methodologies to define common practices and make measurements from different actors comparable. Therefore, in the present paper we investigate the accuracy of a dual-polarized ultra-wideband channel sounder in the time, angular, and polarization domains by means of a verification methodology based on interferometry.

Energy efficiency has recently drawn significant attention in diverse applications, such as vehicle-to-everything (V2X) communications. This paper investigates a joint energy efficiency and sum-rate maximization problem in autonomous resource selection in the 5G new radio (NR) vehicular communication while catering for reliability and latency requirements. An autonomous resource allocation problem is formulated as a ratio of sum-rate to energy consumption, where the objective is to maximize the total energy efficiency of power-saving users subject to the reliability and latency requirements. The energy efficiency problem is a mixed-integer programming optimization problem that is computationally complex when the number of users increases. Therefore, a heuristic algorithm, density of traffic-based resource allocation (DeTRA), is proposed to solve the problem. The traffic density per traffic type is considered a metric to split the radio resources and trigger an appropriate resource selection strategy. The simulation results show that the proposed algorithm outperforms the existing schemes in terms of energy efficiency.

Single side mMIMO channel measurements over 100 MHz bandwidth at 3.75 GHz were performed in two German cities. In dense urban as well as urban/suburban environments, a total track length of about 20km has been measured. Multipath parameters by means of the high-resolution parameter estimator RIMAX are estimated and subsequently used to derive the statistics of large scale parameters and to allow a comparison to the statistics from standard channel model (3GPP, WINNER). While most of the parameters match quite well, for the angular spread at the base station (BS) partly larger deviations are observed. The correlation properties between the 32 dual polarized antenna patches of the BS antenna are investigated with respect to the antenna patch separation as well as to the distance between the BS and user equipment (UE).

Research on non-line-of-sight emitter localization relies almost exclusively on simulation data for the evaluation of proposed algorithms. This paper aims to encourage the use of measurements by introducing a setup based on off-the-shelf components that enables 1.5 ns synchronization accuracy and 2 cm ground truth accuracy for multiple distributed receivers and a mobile transmitter. Results from a measurement campaign in a 16 km2urban area are used to validate the system's performance.

We study the problem of direction of arrival estimation for arbitrary antenna arrays. We formulate it as a continuous line spectral estimation problem and solve it under a sparsity prior without any gridding assumptions. Moreover, we incorporate the array's beampattern in form of the Effective Aperture Distribution Function (EADF), which allows to use arbitrary (synthetic as well as measured) antenna arrays. This generalizes known atomic norm based grid-free DOA estimation methods (that have so far been limited to uniformly spaced arrays) to arbitrary antenna arrays. In addition, our formulation allows to incorporate compressed sensing in form of special linear combinations of the antennas' output ports. We provide conditions for the successful reconstruction of a certain number of targets depending on the amount of compression and the EADF of the antenna array. Our results are applicable to measurement matrices from any sub-Gaussian distribution.

We perform simultaneous vehicle-to-vehicle (V2V) multi-band double-directional measurements at 6.75 GHz, 30 GHz, and 60 GHz in a street canyon environment. We analyse both line-of-sight channels and those obstructed by vehicle blocker. For the different bands, we analyse delay spread, angular spread, vehicle blockage loss, and path loss. Finally, we discuss how the system aspects influence the radio channel.

Reliable and accurate multipath clustering results are essential to design and parameterize geometry based (stochastic) channel models proposed by organizations such as 3GPP, COST, WINNER and others. Clustering within wireless channel models assumes that multipath components arrive/depart with similar properties in the considered parameter domains. We propose a new approach combining tracking and initialization of the classic K-Means clustering algorithm. The results show a significantly improved consistency for cluster estimation, which subsequently allows for analyzing the lifetime of these clusters. Finally, we apply the proposed approach on two urban macro channel sounding datasets and compare them to the current 3GPP standard. We found that the angular parameters are rather similar to the standard whereas the cluster delay spreads and the number of rays per cluster differ significantly.

In this paper we propose a method to improve the detection of shadowed flaws in ultrasonic non-destructive testing (NDT). The B-scans are expressed in the context of Sparse Signal Recovery (SSR), where the shadowing effect is incorporated during the reconstruction: Whenever a new defect is found, its shadow on all other dictionary atoms is determined and the dictionary is updated accordingly. We develop models for determining the affected dictionary entries as well as for the intensity of the attenuation due to the shadow. Using Orthogonal Matching Pursuit (OMP), we demonstrate that the proposed method significantly improves the reconstruction amplitudes, i.e., the detection reliability, compared to conventional detection without incorporation of shadowing.

Compressed sensing (CS) is a sampling paradigm that allows us to simultaneously measure and compress signals that are sparse or compressible in some domain. The choice of a sensing matrix that carries out the measurement has a defining impact on the system performance and it is often advocated to draw its elements randomly. It has been noted that in the presence of input (signal) noise, the application of the sensing matrix causes signal-to-noise ratio (SNR) degradation due to the noise folding effect. In fact, it might also result in the variations of the output SNR in compressive measurements over the support of the input signal, potentially resulting in unexpected nonuniform system performance. In this letter, we study the impact of a distribution from which the elements of a sensing matrix are drawn on the spread of the output SNR. We derive analytic expressions for several common types of sensing matrices and show that the SNR spread grows with the decrease of the number of measurements. This makes its negative effect especially pronounced for high compression rates that are often of interest in CS.

Audio authentication is a critical task in multimedia forensics demanding robust methods to detect and identify tampered audio recordings. In this paper, a new technique to detect adulterations in audio recordings is proposed by exploiting abnormal variations in the electrical network frequency (ENF) signal eventually embedded in a questioned audio recording. These abnormal variations are caused by abrupt phase discontinuities due to insertions and suppressions of audio snippets during the tampering task. First, we propose an ESPRIT-Hilbert ENF estimator in conjunction with an outlier detector based on the sample kurtosis of the estimated ENF. Next, we use the computed kurtosis as an input for a support vector machine classifier to indicate the presence of tampering. The proposed scheme, herein designated as SPHINS, significantly outperforms related previous tampering detection approaches in the conducted tests. We validate our results using the Carioca 1 corpus with 100 unedited authorized audio recordings of phone calls.

Technological advances in information and communications systems are enabling access to education for millions of students, regardless of their geographical location. In this sense, distance learning schools take advantage of technological tools, such as course management systems (CMS) and their plugins. This work proposes a plugin for the e-learning platform Moodle that automatically sends e-mail messages to the students in order to motivate them to take part in the distance learning courses and, consequently, to reduce the dropout rates. This plugin has been tested in courses promoted by the General Coordination of Distance Education (CGEAD) of the Brazilian National School of Public Administration (ENAP). Based on data from its operation within the real ENAP distance learning environment, the proposed plugin is particularly successful to tackle the enrollment locking cases that compose the student dropout rate and also to motivate course enrolled students to access the virtual learning system more frequently.

Considered is OTA testing of future generation communications systems, especially of Devices-under-Test of large electrical size and with highly directive antenna patterns. The channel is assumed to be dominated by a single continuous cluster. Two methods of stochastic cluster emulation by discrete incident fields, modelled as plane waves, are investigated. With both methods, a potential problem with calibrating OTA set-ups was noticed. Random variations in received power of more than 15 dB were observed in cases of angular symmetry around the DuT heading. With omnidirectional antennas, these variations can be reduced to less than 0.1 dB by selecting the initial phases. On DuT antennas with different angular phase responses, this has limited effect, still up to 6 dB variations were seen. Only for the MPAC emulation method, introducing angular asymmetry in the array reduced random variations to smaller than 0.15 dB. Additional deterministic power variations, depending on the method either ripple or bias, were observed too. On the highly directive pattern of a car, the ripple could be stronger than 10 dB (peak-to-peak) for a 12-antenna OTA ring, the bias up to 5 dB. The more OTA antennas, the smaller these deterministic power variations. Recommendations for calibration of MPAC set-ups and designing OTA antenna arrays are given.

This paper focuses on the design of linear baseband precoding and combining techniques for millimeter wave (mmWave) multiple input multiple output (MIMO) backhaul networks. We consider a scenario known as MIMO multi-X channel with L transmitter base stations and K receiver base stations, each equipped with multiple antennas, where each transmitter base station communicates simultaneously with all receiver base stations. The extremely short wavelength of mmWave requires the use of large antenna arrays at the base stations (BS) to combat the high path loss to achieve sufficiently high link gains, enabled by directional beamforming. As signal processing with large antenna arrays lead to implementations that are associated with high power consumption and high hardware complexity, hybrid (analog/digital) precoding/combining has been proposed recently to overcome these limitations. The design of such hybrid precoders/combiners is typically performed separately for the analog and the digital domains. In this work, we propose several novel low-complexity schemes suited for the digital part of such hybrid precoders/combiners. Our solutions are based on block-diagonalization and minimum mean squared error (MMSE) techniques and assume the availability of partial knowledge of CSI at each BS in the network. Finally the simulation results demonstrate the potential gains of the proposed algorithms.

This paper considers the design of precoding and decoding techniques for backhaul networks at Ka band. In this frequency band, large antenna arrays must be employed with appropriate beamforming and precoding techniques to combat the high path loss. Traditional multi-antenna systems use digital baseband beamforming and precoding that is not economical for large antenna arrays due to its cost and power consumption. Recently, hybrid analog-digital solutions were suggested for millimeter wave multiple-input-multiple-output systems. They rely on two steps. The first step consists of an analog only beamforming which aims at the exploitation of the high antenna gain offered by the large-scale antenna array. The second step mitigates the multiuser interference by means of digital precoding. In this paper, we focus on the second step of the hybrid precoding. We propose a solution for interference mitigation in multi-base station scenarios. Our solution is based on a block diagonalization technique and requires full channel state information at each base station of a backhaul network. The performance of the algorithm is compared to a partial block diagonalization which was originally proposed for the single base station downlink scenario.

In this paper we investigate the design of the measurement matrix for applying Compressed Sensing (CS) to the problem of Direction Of Arrival (DOA) estimation with antenna arrays. So far, it has been suggested to choose the coefficients randomly since this choice satisfies the restricted isometry property (RIP) with a high probability. We demonstrate that this choice may be sub-optimal since it can result in an effective array with significant sidelobes and blind spots. The sidelobes are especially problematic when we use correlation-based greedy algorithms for the sparse recovery stage as they can lead to detecting spurious peaks. To address the problem, we introduce a design methodology for constructing a measurement matrix that mitigates these unwanted effects to achieve a better DOA estimation performance. Numerical results demonstrate the usefulness of our design.

The application of the Compressed Sensing (CS) paradigm to the sampling of sparse wireless signals allows a significant reduction of the sampling rate compared to the one dictated by the Nyquist sampling theorem. The majority of the theoretical results derived within CS are expressed in terms of the known sparsity order of the signal. In this work we address the problem of sparsity order estimation of multiband signals with unknown sparse spectral supports. We show that it can be estimated directly in the compressed domain as the dimension of the signal subspace of the observations' covariance matrix. We analyze how the results of the sparsity estimation can be utilized during the reconstruction step and which requirements it imposes on the performance of the subspace estimation algorithms. The results of the numerical study demonstrate that the reconstruction step is particularly sensitive to type II errors. This in turn indicates that the classical non-parametric model order selection algorithms might be unfavorable for this application since they tend to underestimate model order in the low SNR regime. As a remedy we propose to apply parametric approaches that allow to compromise resulting probabilities of over- and underestimation.

Compressed Sensing (CS) has been recently applied to direction of arrival (DOA) estimation, leveraging the fact that a superposition of planar wavefronts corresponds to a sparse angular power spectrum. However, to apply the CS framework we need to construct a finite dictionary by sampling the angular domain with a predefined sampling grid. Therefore, the target locations are almost surely not located exactly on a subset of these grid points. This leads to a model mismatch which deteriorates the performance of the estimators. In this paper we take an analytical approach to investigate the effect of such grid offsets on the recovered spectra. We show that each off-grid source can be well approximated by the closest two neighboring points on the grid. We propose a simple and efficient scheme to estimate the grid offset for a single source or multiple well-separated sources. We also discuss a numerical procedure for the joint estimation of the grid offsets of closer sources. Simulation results demonstrate the effectiveness of the proposed methods.

Compressive Sensing (CS) allows to acquire signals at sampling rates significantly lower than the Nyquist rate, provided that the signals possess a sparse representation in an appropriate basis. However, in some applications of CS, the dictionary providing the sparse description is partially or entirely unknown. It has been shown that dictionary learning algorithms are able to estimate the basis vectors from a set of training samples. In some applications the dictionary is multidimensional, e.g., when estimating jointly azimuth and elevation in a 2-D direction of arrival (DOA) estimation context. In this paper we show that existing dictionary learning algorithms can be extended to exploit this structure, thereby providing a more accurate estimate of the dictionary. As examples we choose two prominent dictionary learning algorithms, the method of optimal directions (MOD) and the K-SVD algorithm. We propose tensor-based multidimensional extensions for both algorithms and show their improved performances numerically.

This paper provides an overview of over-the-air (OTA) testing for next generation communication devices with special consideration of Car-to-Car (C2C) and Car-to-Infrastructure (C2I) communication. Existing state-of-the-art techniques, their merits, and limitations are discussed. We identify the requirements for the OTA test in virtual electromagnetic environment (OTAinVEE) for the C2C/C2I communication scenario, where interference emulation becomes a highly challenging task. We propose systematic test procedure and closed-loop OTA set-up applicable for such scenario. Since the main test challenge in C2C/C2I communication is the modeling of highly time variant channel, we discuss channel modeling concepts and propose a framework appropriate for such communication. The ongoing activities in Ilmenau towards simulation and testing for C2C/C2I communication is also briefly discussed.

The ubiquitous problem of radio spectrum scarcity caused by ever-increasing traffic, e.g. in mobile communications, demands measures. One approach would be secondary use of spectrum by radio devices with cognitive abilities employing dynamic spectrum access. In order to avoid interference with the primary use of the spectrum, such cognitive devices must be thoroughly tested. In this paper, we present a high time-resolution model of spectrum occupancy, stemming from the activity of primary users, for testing purposes. The proposed model is based on a few statistical parameters and includes a novel parameter adaptation procedure that improves the accuracy of current models significantly. The intended use of the model is over-the-air testing of cognitive devices, for instance for prototyping purposes, e.g. evaluation of the achievable performance, or for conformance testing. The model parameters are extracted from measurement data in the GSM 900 downlink band obtained at the Ilmenau University of Technology campus. However, the modeling approach is not restricted to GSM 900 but lends itself to other bands and radio technologies, too.

The Vehicle-Mounted Earth Stations (VMES) operation requirements defined by the regulatory authorities are bounding for terminal manufacturers. Testing the VMES for these requirements (e.g., pointing accuracy and polarization alignment) is therefore a necessity. The disadvantages of involving operational satellites and having fixed separation between them in traditional test methods are overcome in the proposed Facility for Over-the-air Research and TEsting (FORTE). FORTE comprises an antenna tower and a laboratory building. A sensor array is mounted on the antenna tower with the center sensor emulating the satellite. In the laboratory building, the VMES is mounted on a motion emulator which can replay realistic motion profiles. In this contribution, the main components of FORTE are introduced and the performance of the de-pointing measurement system is verified. The tracking unit of the VMES may steer the antenna either mechanically or electronically to keep the pointing direction towards the target satellite. Measurements with antennas that have a fixed pattern (independent of the steering angle) as well as that have a variable pattern (dependent on the steering angle) are considered in this contribution.

In this paper we discuss the impact of cooperative satellite diversity on the availability of the land mobile satellite (LMS) channel. By the use of multiple terminals the overall probability to encounter conditions where all terminals are in a bad channel state is reduced. This reduction depends on the number of terminals and the terminal distance. The results presented in this paper are completely based on the analysis of measurements. These were conducted for two geostationary satellites and four different environments using a single mobile satellite terminal. Thus, in order to virtually increase the number of terminals we rely on a convoy scenario using shifted versions of the data. Analyzing up to four cooperative terminals and different terminal distances a significant bad state reduction is already achieved for rather small distances.

Traditional spatial sound acquisition aims at capturing a sound field with multiple microphones such that at the reproduction side a listener can perceive the sound image as it was at the recording location. Standard techniques for spatial sound acquisition usually use spaced omnidirectional microphones or coincident directional microphones. Alternatively, microphone arrays and spatial filters can be used to capture the sound field. From a geometric point of view, the perspective of the sound field is fixed when using such techniques. In this paper, a geometry-based spatial sound acquisition technique is proposed to compute virtual microphone signals that manifest a different perspective of the sound field. The proposed technique uses a parametric sound field model that is formulated in the time-frequency domain. It is assumed that each time-frequency instant of a microphone signal can be decomposed into one direct and one diffuse sound component. It is further assumed that the direct component is the response of a single isotropic point-like source (IPLS) of which the position is estimated for each time-frequency instant using distributed microphone arrays. Given the sound components and the position of the IPLS, it is possible to synthesize a signal that corresponds to a virtual microphone at an arbitrary position and with an arbitrary pick-up pattern.

The signal-to-reverberant ratio (SRR) is an important parameter in several applications such as speech enhancement, dereverberation, and parametric spatial audio coding. In this contribution, an SRR estimator is derived from the direction-of-arrival dependent complex spatial coherence function computed via two omnidirectional microphones. It is shown that by employing a computationally inexpensive DOA estimator, the proposed SRR estimator outperforms existing approaches.

Digital audio watermarking has received increasing interest in the last years due to the wide range of applications it can be used for. This contribution presents a novel blind watermarking scheme designed to work in the very specific scenario in which the watermarked audio is played back by a loudspeaker and picked up by the microphone of a mobile device. The proposed scheme is robust against the impairments characterizing this scenario, namely reverberation, movement, microphone self-noise, and interfering sound sources. The conducted performance investigations, as well as the listening tests evaluating the audio quality demonstrate the applicability and efficiency of the proposed system.

Directional audio coding (DirAC) is an efficient technique to capture and reproduce spatial sound. The analysis step outputs a mono DirAC stream, comprising an omnidirectional microphone pressure signal and side information, i.e., direction of arrival and diffuseness of the sound field expressed in time-frequency domain. This contribution proposes a method to merge two or more mono DirAC streams for a joint playback at the reproduction side. This problem arises in applications such as immersive spatial audio teleconferencing. With respect to a trivial direct merging, the proposed method is more efficient as it does not require the synthesis step. From this follows the benefit that the loudspeaker setup at the reproduction side does not have to be known in advance. Simulations and informal listening tests confirm the absence of any artifacts and that the proposed method is practically indistinguishable from the ideal merging.

Directional audio coding (DirAC) is a recent method for spatial audio processing, based on a perceptually motivated representation of spatial sound. Due to its efficiency, DirAC has already been proposed for spatial audio teleconferencing scenarios. Modern hands-free communication systems usually include beamforming techniques to improve speech intelligibility by suppressing diffuse background noise and interfering sources. In this paper, we propose a novel spatial filtering method which can be integrated into the DirAC spatial codec. It uses a spectral weighting of the recorded audio signal, where the design of the corresponding spatial filter transfer function is based on the DirAC parameters, i. e., direction-of-arrival and diffuseness of the sound field. Simulation results show that compared to a standard beamformer the novel technique offers significantly higher interference attenuation, while introducing similar distortion of the desired signal.

The particular topology found in metropolitan areas can be exploited by employing relay nodes, which turn one-hop non-line-of-sight connections into multi-hop line-of-sight ones, thereby reducing the shadowing. However, this topology should also be considered to avoid multi-user interference and thus exploit the spatial resources optimally. Nevertheless, in most approaches which deal with this scenario, the interference is either completely neglected or estimated with strong simplifications. In this paper, we analyze the interference among users in a 4-way junction on the downlink and show that it should not be ignored. Based on this observation, we propose the channel representative interference cancelation (CRIC) scheme. The interference between users in the same street is suppressed by CDMA, whereas the one between users of different streets by the Successive Minimum Mean Square Error (SMMSE) precoder. Due to the dimensionality restrictions given by SMMSE, we introduce a channel representative, which, serving as a virtual user, reflects the spatial features of all users in one street.

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

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.

In this contribution we propose a subspace-based channel model suitable to represent frequency selective time variant MIMO channels. This approach captures the true nature of the MIMO channel maintaining the spatial correlation present between the antenna arrays. Correlation in time and frequency is conserved as well. The decomposition into eigenmodes, which form the channel subspace, gives an interesting interpretation of the channel's eigenstructure. These investigations lead to a very efficient method to synthesize new channels with the same correlation in time, frequency and space of a reference channel. The model allows the interpolation of a channel in order to retrieve more samples in frequency and time to perform statistical analysis such as bit error rate and capacity curves. In addition the model allows the generation of new random channels with the same spatial, time, and frequency correlations of a reference channel, which, for instance, could be obtained from measurements.