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This paper presents measurements of Radar Cross Section (RCS) of five Unmanned Aerial Vehicles (UAVs), comprising both consumer grade and professional small drones, collected in a semi-controlled environment as a function of azimuth aspect angle, polarization and frequency in the range 8.2-18 GHz. The experimental setup and the data pre-processing, which include coherent background subtraction and range gating procedures, are illustrated in detail. Furthermore, a thorough description of the calibration process, which is based on the substitution method, is discussed. Then, a first-order statistical analysis of the measured RCSs is provided by means of the Cramér-von Mises (CVM) distance and the Kolmogorov-Smirnov (KS) test. Finally, radar detection performance is assessed on both measured and bespoke simulated data (leveraging the results of the developed statistical analysis), including, as benchmark terms, the curves for non-fluctuating and Rayleigh fluctuating targets.

This paper deals with Radar Cross Section (RCS) measurements of five small Unmanned Aerial Vehicles (UAVs) in a semi-controlled environment as a function of azimuth aspect angle, polarization, and frequency in the range 8.2-18 GHz. The experimental setup and the data pre-processing, which include coherent background subtraction, range gating procedures, and calibration, are presented. Then, a thorough statistical analysis of the measured RCSs is provided by means of the Cram´er–von Mises (CVM) distance and the Kolmogorov–Smirnov (KS) test.

This paper deals with the effects of Power Amplifiers (PAs) on radar waveforms designed to ensure spectral compatibility with other Radio Frequency (RF) wireless systems cohabitating in the same frequency band. To this end, a specific hardware-in-the-loop test-bed composed of a Personal Computer (PC), an Arbitrary Waveform Generator (AWG), a nonlinear PA, and a Signal Analyser (SA) is designed. It allows to gather measurements of the amplified signal and to grasp unwanted modifications of its spectral features due to the PA nonlinear distortion. Besides, it permits a quantitative assessment of the discrepancies between the nominal and the actual interference power injected by the radar in the shared frequency bandwidths. To alleviate the spectral distortions induced by the PA and improve spectral coexistence, the use of a Digital Pre-Distortion (DPD) stage upstream the PA is investigated. Modeling techniques together with parameters inference are discussed and applied to the measured waveforms. The obtained experimental results highlight that pairing a suitable waveform design strategy with a DPD processing represents a viable means to mitigate the PA distortions and to realize spectral coexistence.

A new algorithm for 3D localization in multiplat-form radar networks, comprising one transmitter and multiple receivers, is proposed. To take advantage of the monostatic sensor radiation pattern features, ad-hoc constraints are imposed in the target localization process. Therefore, the localization problem is formulated as a non-convex constrained Least Square (LS) optimization problem which is globally solved in a quasi-closed-form leveraging Karush-Kuhn-Tucker (KKT) conditions. The results corroborate the effectiveness of the new strategy which is capable of ensuring a lower Root Mean Square Error (RMSE) than counterpart methodologies, especially in the low Signal to Noise Ratio (SNR) regime.

Unmanned Aerial Vehicles (UAVs) have become widely used in civilian and military applications. There is a recognised need for UAVs surveillance against their malicious use. In this context, radar has emerged as a powerful tool in detecting and tracking UAVs. However, the task is quite complicate due to the low-altitude, low velocity and low radar cross section of small UAVs. In this paper, a smart radar signal processing solution in the presence of multiple unresolved UAVs is devised and applied to UAVs surveillance, which fully capitalizes on the coherent integration, constant false alarm rate detection, target multiplicity test and sparse methods to achieve low computational complexity and high angular resolution. At the analysis stage, numerical simulations are carried out to highlight the capability of proposed solution to detect UAVs and resolve them when they are closely spaced.

Considering a SpaceBorne Radar (SBR) for Space Situational Awareness (SSA), the Fading Occurrence Probability (FOP) is computed for fluctuating targets in weak plasma turbulence as a function of the scintillation index $S_{4}$ . While previous works considered a Rayleigh model for both target fluctuation and plasma scintillation, this paper extends the FOP via the Cumulative Distribution Function (CDF) of the echo power modulated by either non-fluctuating, Rayleigh, and Rice target fluctuations along with Rice plasma scintillation. Downstream physical insights on both channel and target phenomenology leading to such statistical models, numerical results corroborated by Monte Carlo (MC) simulations show that a larger value of the scintillation index $S_{4}$ increases the FOP on echoes. In addition, the aforementioned simulation curves provide a conceptual framework to empirically estimate the scintillation index $S_{4}$ in a monostatic radar configuration.

This paper deals with the use of Reconfigurable Intelligent Surfaces (RISs) for radar surveillance in Non-Line Of Sight (N-LOS) scenarios. First of all, the geometry of the scene and the new system concept is described with emphasis on the required operative modes and the role played by the RIS. Then, the specific radar equation (including the RIS effect) is developed to manage the coverage requirements in the challenging region where the LOS is not present. Hence, a digression on the use of the radar timeline for the new operative mode is presented together with a discussion on the resolution issues for the range, azimuth, and Doppler domains. Finally, the interplay among the system parameters and, in particular, those involving the RIS is discussed and analyzed via numerical simulations.

The problem of missing sensor measurements can emerge in a variety of radar signal processing applications as for instance beamforming, direction of arrival estimation, interference cancellation, and target detection. The mentioned applications rely on reliable data covariance matrix estimates and suitable procedures, based on the expectation-maximization (EM) algorithm, have been proposed in the open literature to cope with the lack of some entries within specific spatial snapshots. In this paper, the effectiveness of a recent structured covariance matrix estimator [1], accounting for missing data and leveraging possible structural knowledge, is assessed on measured data. Specifically, the estimation procedure is framed in the context of two practically relevant radar applications: beamforming and detection of the number of sources. At the analysis stage, results highlight the effectiveness of the procedure to tackle missing data in the considered radar scenarios.

A new algorithm for Passive Bistatic Radar localization is proposed capitalizing measurements gathered by a colocated active radar and exploiting multiple illuminators of opportunity. Prior information, related to the Passive Bistatic Radar (PBR) receive antenna main-beam size and to the uncertainty characterizing active radar data, is accounted for formalizing ad-hoc constraints for the localization process. Hence, the estimation task is cast as an elliptic positioning problem, according to the constrained Least Squares (LS) framework. The performance of the proposed estimator exhibits sensible improvement with respect to some counterparts.

This paper deals with the joint design of transmit waveform and receive filter improving radar Signal to Interference plus Noise Ratio (SINR) in signal-dependent interference and spectrally contested-congested environments. The injected interference energy on each common band is precisely controlled for spectral compatibility. Besides, the transmit signal complies with constant envelop and similarity constraints to bestow some attractive waveform characteristics. To handle the resulting transceiver design problem, an iterative optimization procedure with a polynomial computational complexity is developed leveraging the Coordinate Descent (CD) framework. Finally, numerical results are provided to highlight the effectiveness of the proposed joint transmitter and receiver design technique.

Adaptive target detection in the presence of Gaussian interference considering a frequency diverse array multipleinput multiple-output (FDA-MIMO) radar is investigated. At the design stage, adaptive detectors are devised according to the generalized likelihood ratio test (GLRT) criterion, where the target range is assumed unknown within the radar cell. Hence, the maximum likelihood (ML) estimate of the target range under the H 1 hypothesis is approximated either assuming a discrete grid or resorting to a Newton-based optimization procedure. Numerical results are provided to illustrate the effectiveness of the devised detectors.

This paper studies adaptive radar detection of pointlike targets in the presence of Gaussian interference and possible diffuse multipath. For this problem, a Tunable Adaptive Matched Filter (T-AMF) detector which contains a tuning parameter ruled by the strength of multipath effects was proposed. This paper develops a two-stage strategy for the selection of the tuning parameter by jointly using a two-step energy detector and the expected likelihood (EL)-based principle. Once plugged into the T-AMF, the selected parameter yields a fully adaptive detector free of any user parameter. Remarkably, the new architecture possesses the desired constant false alarm rate (CFAR) property with respect to the disturbance covariance. Finally, its detection performance is assessed and validated via numerical examples.

This paper outlines a conceptual and functional digression on a novel Spaceborne Radar (SBR) payload architecture for augmenting space-based measurements and monitoring capabilities for near-Earth Space Situational Awarness (SSA). Taking into account state-of-the art space-qualified technologies in both digital and Radio Frequency (RF) domains, feasible onboard processing subsytems are identified as high level conceptual block schemes allowing for Bayesian inference on small-size debris dynamic states and ancillary scattering signatures. A description of a bespoke Active Electronically Scanned Array (AESA) based Pulse Doppler Radar in the Ka Band is illustrated from a functional perspective built upon a digital modular archetype, along with programmable temporal hierarchies for acquiring a complex data hyper-cube. Operative modes are also addressed including the novel Low Pulse Repetion Frequency (Low-PRF) Range & Range-Rate Search (RRRS) in cascade with a Pause While Scan (PWS) strategy for Bayesian Multiple Target Tracking (MTT).

This paper deals with the problem of adaptive detectors design for a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. At the design stage, adaptive detectors are devised according to the generalized likelihood ratio (GLR) criterion, where the target range is assumed unknown within the radar cell. In this respect, the maximum likelihood (ML) estimate of the target range under the H 1 hypothesis is approximated either assuming a discrete grid of the possible values or resorting to a Newton-based optimization procedure. Numerical results are provided to illustrate the effectiveness of the devised detectors.

This paper deals with constant modulus waveform design in spectrally dense environments assuming a discrete phase code alphabet. The goal is to optimize the radar detection performance while rigorously controlling the injected interference energy within each shared band and enforcing a similarity constraint to manage some relevant signal features. To tackle the resulting NP-hard optimization problem, an iterative procedure characterized by a polynomial computational complexity, is introduced leveraging the coordinate descent method. Numerical results are provided to show the effectiveness of the technique.

This paper deals with the robust joint design of a radar transmit waveform and receive filter bank in a background of range unambiguous signal-dependent clutter. Assuming an unknown Doppler shift for the target, the worst-case signal-to-interference-plus-noise-ratio (SINR) at the output of the receive filter bank is considered as the figure of merit. The transceiver design is pursued considering a max-min optimization problem with some constraints on the transmit energy, similarity, and signal dynamic range. Hidden convexity is shown and a procedure to derive optimal waveform and filters is given. Simulation results highlight the effectiveness of the devised method.

The paper starts with a brief retrace of some seminal publications of Dr. K. R. Gerlach on adaptive antennas. This legacy motivates the core of the present paper which addresses the design of SideLobe Blanking (SLB) architectures capable of providing Electronic Protection (EP) against a coherent repeater interference in the presence of continuous jamming signals. In this respect, a generalized adaptive beamforming technique which synthesizes the SLB auxiliary channel output, is introduced. Then, an optimization algorithm is developed to design the adaptive weights so that the resulting outputs fulfill SLB specific requirements. The effectiveness of the resulting architecture is assessed in terms of sidelobe pattern and continuous jamming rejection.

Simultaneous target detection and angle estimation with a multichannel phased array radar system is addressed in this paper. Starting from a linearized expression for the array steering vector around the beam pointing direction, the problem is cast as a composite binary hypothesis test where the unknowns, under the alternative hypothesis, include the target direction cosines displacements with respect to the array nominal steering. The problem is handled via the Generalized Likelihood Ratio (GLR) criterion where a decision statistic leveraging the Maximum Likelihood Estimates (MLEs) of the parameters is compared to a detection threshold. If crossed, target presence is declared and MLEs of the aforementioned displacements directly provide target angular position with respect to the pointing direction. From the analytic point of view ML estimation requires the solution of a constrained fractional quadratic optimization problem whose optimal solution can be found via Dinkelbach's algorithm. The analysis of the proposed architecture is developed in terms of detection performance and angular estimation accuracy also in comparison with some counterparts available in open literature and benchmark limits.

This paper follows the increasing demands for augmenting space-based monitoring capabilities for near-Earth Space Situational Awarness (SSA). The outline comprises an introduction to current SSA as an operative framework, with a particular focus on the possible tantalized and complementary role of Spaceborne Radars (SBR) in the Ka band.

Purpose of the Tutorial: to give a rigorous and academic point of view on the fundamentals of radar detection, receiver performance analysis, Monte Carlo simulation of radar receivers, and CFAR algorithms. The idea is to provide a formative and methodological tutorial on the above topics which represent the base to address more challenging radar detection problems.

A new methodology is proposed to deceive an anomalous trajectory detector by designing ship paths that deviate from the nominal traffic routes in an optimized way. The route planning is formalized as a min-max problem (with respect to surveillance system acquisition instants) focusing on the Kullback-Leibler (KL) divergence between the statistical hypotheses of the nominal and the anomalous trajectories as key performance measure. Modeling the vessel’s dynamic according to the Ornstein-Uhlenbeck (OU) mean-reverting stochastic process, physical, practical, and kinematic requirements are also accounted for forcing several constraints at the design stage. A computationally efficient technique is proposed to handle the resulting non-convex optimization problem, and some case studies are reported to assess its effectiveness.

Covariance matrix estimation is a crucial task in adaptive signal processing applied to several surveillance systems, including radar and sonar. In this paper we propose a dynamic environment learning strategy to track both the covariance matrix and its class; the class represents a set of structured covariance matrices. We assume that the posterior distribution of the covariance given the class, is basically a mixture of inverse Wishart, while the class posterior distribution evolves according to a Markov chain. The proposed multi-class inverse Wishart mixture filter is shown to outperform the class-clairvoyant maximum likelihood estimator in terms of covariance estimate accuracy, as well as the Bayesian information criterion rule in terms of classification performance.

The two-dimensional (2-D) spectrum sensing problem is addressed in this paper. Specifically, assuming a sensor equipped with multiple receive antennas, three signal processing techniques able to recover the space-frequency occupancy map are proposed. At the analysis stage, an interesting case study is reported to compare the different strategies and highlight the effectiveness of the developed procedures.

The problem of High Resolution Range Profile (HRRP) estimation is considered in this paper. In particular, stepped frequency waveforms are devised to enhance target Range Profile (RP) estimation accuracy. The basic idea relies on the dynamic optimization of the probing waveform accounting for some feedback information to minimize the profile estimation error. The results highlight the capabilities of the cognitive approach to provide significant benefits with respect to the classic linear stepped frequency strategy. © 2017 IEEE.

In this paper the long standing major challenge of designing binary sequences with good (aperiodic) autocorrelation properties in terms of Peak Sidelobe Level (PSL) and Integrated Sidelobe Level (ISL) is considered. The problem is formulated as a bi-objective Pareto optimization forcing the binary constraint at the design stage. An iterative novel FFT-based approach exploiting the coordinate descent method is devised to deal with the resulting optimization problem which is non-convex and NP-hard in general. Simulation results illustrate that the proposed algorithm can outperform some counterparts providing sequences with desirable PSL as well as ISL.

Cognitive radar is a new paradigm to conceive the next radar generation characterized by unique and amazing features inspired to mental abilities and processes related to knowledge. It goes beyond the usual extraction of information from the measurements and requires the radar to get intelligence. Introduced by Haykin [1] and Guerci [2], it is attracting huge attention within the radar community during the last few years. The key concept is that radar system performance can be enhanced through a continuous and coordinated feedback between the transmitter and receiver which implies a dynamic adaptation of the sensor algorithms to the operational context and environmental replies. This paper discusses the biological inspiring principles of cognitive radar and describes the resulting conceptual architecture. Then, a radar signal processing application, which can significantly benefit from cognition, is illustrated highlighting the potential performance improvements achievable with the awesome pro-active paradigm. © 2016 IEEE.

We focus on the robust joint design of the transmit waveform and filtering structure for Polarimetrie radar. Assuming signal-dependent interference and Target Aspect Angle (TAA), we consider the worst-case Signal to Interference plus Noise Ratio (SINR) at the output of the filter bank as the figure of merit to optimize under both a similarity and an energy constraint on the transmit signal. We develop an iterative optimization procedure that monotonically improves the worst-case SINR and converges to a stationary point. The proposed method is validated through experimental results and compared with existing “TAA-aware” alternatives, thus showing its attractiveness.

We deal with the robust design of the Multiple-Input Multiple-Output (MIMO) waveform covariance matrix optimizing the worst case (over steering mismatches) transmit beampattern Peak Sidelobe Level (PSL). To this end, we model the uncertainty set associated with each steering vector through two double-sided, potentially non-convex, quadratic constraints. Additionally, we force two suitable constraints on the optimization variable. The former accounts for the width of the mainbeam, whereas the latter is either a uniform or a relaxed elemental power requirement allowing to control the amount of transmitted power. We prove that the mentioned design leads to a non-convex optimization problem which remarkably shares some hidden convexity. Hence, we devise a polynomial time procedure aimed at synthesizing an optimal MIMO waveform covariance. Finally, at the analysis stage, we assess the performance of the proposed design technique showing that it ensures an improved worst case performance than some counterparts available in open literature.

Random and unwanted fluctuations, that perturb the phase of an ideal reference sinusoidal signal, may cause significant performance degradation in radar systems exploiting coherent integration techniques. To quantify the resulting performance loss, we develop a fast-time/slow-time data matrix radar signal representation, modeling the undesired phase fluctuations via multivariate circular distributions and describing the phase noise Power Spectral Density (PSD) through a composite power-law model. Hence, we accurately predict the performance degradation experienced by Moving Target Indication (MTI) algorithms for clutter cancellation, providing a closed form expression for the improvement factor I. The subsequent analysis shows that phase noise affects I directly through its Characteristic Function (CF). Additionally, I shares a robust behavior with respect to the actual phase noise multivariate circular distribution, as long as the phase noise PSD correctly represents the available measurements.

We propose a novel robust design method to jointly optimize the radar transmit code and receive filter, exploiting the signal-to-interference-plus-noise ratio (SINR) at the receiver end as design figure of merit. We confer robustness to our method against uncertainties on the target impulse response (TIR) using a worst-case optimization approach. Precisely, we model the uncertainty set as a finite collection of TIRs, obtained sampling the actual TIR at some aspect angles. We further enforce a peak-to-average power ratio (PAR) constraint to the transmit code, which is very important for radar applications, where the transmitter operates close to saturation. The design problem is tackled using a sequential optimization procedure alternating between two semi-definite programming (SDP) problems, followed by randomization steps. Our numerical results highlight the robustness and applicability of the proposed method.

We deal with the problem of detecting point-like targets in diffuse multipath environments. We model the target echo as the superposition of a deterministic signal with an unknown scaling factor (due to the direct path) plus a zero-mean Gaussian random vector with an unknown covariance matrix (accounting for echoes from the glistening surface). Hence, we devise a constrained Generalized Likelihood Ratio Test (GLRT) for the resulting hypothesis testing problem, enforcing the primary data covariance matrix (due to both interference and multipath echoes) to belong to a neighborhood of the secondary data sample covariance matrix. Remarkably, the proposed decision scheme ensures the desirable Constant False Alarm Rate (CFAR) property with respect to the unknown parameters of the interference. The performance assessment is conducted on simulated data in terms of detection probability (P d ) also in comparison with existing solutions in open literature. The results highlight the effectiveness of the new approach to cope with diffuse multipath environments.

In this work we propose an adaptive receiver with enhanced range estimation capabilities, which jointly exploits the over-sampling of the noisy returns and the spillover of target energy to adjacent range samples. To this end, a proper discrete-time model for the received signal is introduced. Then, the Generalized Likelihood Ratio Test (GLRT) is derived and assessed. The performance analysis highlights that better detection performances and increased range estimation accuracies can be achieved exploiting the oversampling at the price of an additional processing cost.

In this paper, we study the joint design of Doppler robust transmit sequence and receive filter to improve the performance of an active sensing system dealing with signal-dependent interference. The signal-to-interference-plus-noise ratio (SINR) of the filter output is considered as the performance measure of the system. The design problem is cast as a max-min optimization problem to robustify the system SINR with respect to the unknown Doppler shifts of the targets. To tackle the design problem, we devise a novel method to obtain optimized pairs of transmit sequence and receive filter sharing the desired robustness property.

In the present paper, the synthesis of optimized radar waveforms ensuring spectral compatibility with the overlayed electromagnetic radiators is addressed. Cognition provided by a Radio Environmental Map (REM) is exploited so as to induces dynamic spectral constraints on the radar waveform. A waveform optimization aiming at improving some radar performances is proposed, whose feasibility is extensively studied. The procedure requires the relaxation of the original problem into a convex optimization problem and involves a polynomial computational complexity. At the analysis stage, the waveform performance is studied in terms of trade-off among the achievable Signal to Interference plus Noise Ratio (SINR), spectral shape, and the resulting AutoCorrelation Function (ACF).

In this paper we deal with the problem of estimating the disturbance covariance matrix for radar signal processing applications, when a limited number of training data is present. We determine the Maximum Likelihood (ML) estimator of the covariance matrix starting from a set of secondary data, assuming a special covariance structure (i.e. the sum of a positive semidefinite matrix plus a term proportional to the identity), and a condition number upper-bound constraint. We show that the formulated constrained optimization problem falls within the class of MAXDET problems and develop an efficient procedure for its solution in closed form. Remarkably, the computational complexity of the algorithm is of the same order as the eigenvalue decomposition of the sample covariance matrix. At the analysis stage, we assess the performance of the proposed algorithm in terms of detection capability of an Adaptive Matched Filter (AMF) receiver with the proposed estimator in place of the sample covariance matrix, for a spatial processing. The results show that the AMF with the structured constrained covariance matrix estimator can achieve higher Detection Probabilities (PD), than some counterparts available in open literature. © 2012 IEEE.

In this paper, we consider the problem of knowledge-aided transmit signal and receive filter design for a point-like target embedded in a high reverberating signal dependent environment. We focus on phase-only waveforms and devise a constrained optimization procedure which sequentially improves the Signal to Interference plus Noise Ratio (SINR), forcing a similarity constraint between the transmitted signal and a known radar waveform sharing a good ambiguity function. The computational complexity of the proposed algorithm is linear with the number of iterations and polynomial with the receive filter length. At the analysis stage, the performances of the technique are assessed in the presence of a homogeneous clutter scenario.

This paper considers sidelobe blanking (SLB) system design in the presence of both homogeneous Gaussian clutter plus noise and jammer entering through the sidelobes of the antenna. A coherent SLB structure using Kelly's receiver [6] on the two channels is introduced and analyzed. Closed-form performance probabilities in the blanking and detection regions are derived and the Constant False Alarm Rate (CFAR) behavior of the architecture is remarked. Finally, several numerical simulations of the proposed SLB system with typical parameters are obtained and discussed.

In this paper, the problem of cognitively designing the transmit signal and the receive filter for a point-like target embedded in a high reverberating environment is addressed. Precisely, a dynamic environmental database on the actual scattering environment is assumed available. Then, an optimization procedure which sequentially improves the Signal to Interference plus Noise Ratio (SINR), exploiting the aforementioned information, is presented. A quantized phase-only condition, and a similarity constraint between the transmitted signal and a known radar waveform sharing a good ambiguity function, are enforced. The computational complexity of the proposed algorithm is linear with the number of iterations and polynomial with the receive filter length. At the analysis stage, the performances of the technique in the presence of a homogeneous clutter scenario are assessed.

We consider a multiple-input multiple-output (MIMO) detection problem with widely-spaced antennas at both the transmitter and the receiver, and we assume that target scattering is modeled as an exchangeable and unitarily-invariant process. We illustrate optimal signal design (i.e., space-time coding) at the transmitter for two criteria, i.e. the lower Chernoff bound (LCB) to the detection probability for fixed probability of false alarm and the mutual information (MI) between the observations and the target scattering matrix, under a semi-definite rank constraint and a transmit power constraint, showing that the Gaussian scattering assumption is robust. A by-product, of not secondary importance, of our derivation is the proof of a number of new properties concerning concavity and Schur-concavity of MI and LCB.

We consider a multiple-input multiple-output (MIMO) detection problem with M widely-spaced transmit antennas and L widely-spaced receive antennas, and we optimize the signal waveforms transmitted by each source node. Two figures of merit are investigated for space-time code optimization under a semi-definite rank constraint and a received signal-to-clutter ratio constraint: 1) the lower Chernoff bound (LCB) to the detection probability for fixed probability of false alarm, and 2) the mutual information (MI) between the observations and the target scattering matrix. If the scattering distribution possesses some properties of exchangeability and unitary invariance, we show that MI-optimal and LCB-optimal space-time coding admit a simple closed-form solution. As an application, the detection of a compound-Gaussian target is examined. The robustness of code design under Gaussian scattering is also investigated.

Coordinating the reception and transmission of signals across spatially distributed base stations has been shown to improve sum-rate performance by mitigating the effects of intercell interference in Multiple-Input-Multiple-Output (MIMO) cellular networks. Relying on recent results on the freeness of certain non-Gaussian random matrices, we provide an information theoretic analysis of cooperative MIMO networks. This analysis applies to the case where full channel state information is known at a subset of the bases and where statistical information is known at all others. Tools for evaluating random matrix transforms traditionally exploited in Mean Square Error (MSE) and mutual information analysis are provided, and the general model formulation paves the way for future work, where specific scheduling and/or power assignment schemes could be embodied in the newly presented framework.