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Two gains play key roles in recently developed‎ MIMO ‎wireless communication systems‎: ‎"spatial diversity‎" ‎gain and "spatial multiplexing‎" ‎gain‎. ‎The diversity gain refers to the capability to decrease the error rate of the MIMO channel‎, ‎while the multiplexing gain implicitly refers to the amount of increase in the capacity of the MIMO channel‎. ‎It has been shown that there is a fundamental tradeoff between these two types of gains‎, ‎meaning interplay between increasing reliability (via an increase in the diversity gain) and increasing data rate (via an increase in the multiplexing gain)‎. ‎On the other hand‎, ‎recently‎, ‎MIMO radars have attracted much attention for their superior ability to enhance the system's performance‎. ‎As a MIMO system‎, ‎it is expected that the mentioned diversity-multiplexing tradeoff exists in a MIMO radar‎, ‎too‎. ‎In this paper‎, ‎this tradeoff is studied and verified in MIMO radars with widely separated antennas‎. ‎In more details‎, ‎it will be shown that increasing dependency between transmit-receive links results in higher diversity gain and lower multiplexing gain‎, ‎and vice versa‎. ‎Then‎, ‎the optimal tradeoff is introduced‎, ‎i.e.‎, ‎the conditions that the MIMO radar system should have‎, ‎so that the diversity-multiplexing tradeoff is at its optimum point‎, ‎are driven‎.

Two gains play key roles in recently developed multiple-input–multiple-output (MIMO) wireless communication systems: ‘spatial diversity’ gain and ‘spatial multiplexing’ gain. The diversity gain (DG) refers to the capability to decrease the error rate of the MIMO channel, whereas the multiplexing gain (MG) implicitly refers to the amount of increase in the capacity of the MIMO channel. It has been shown that there is a fundamental tradeoff between these two types of gains, meaning interplay between increasing reliability (via an increase in the DG) and increasing data rate (via an increase in the MG). On the other hand, recently, MIMO radars have attracted much attention for their superior ability to enhance the system's performance. As an MIMO system, it is expected that the mentioned diversity-multiplexing tradeoff exists in an MIMO radar, too. In this study, first, these two gains are obtained in an MIMO radar system in a closed-form solution. The authors have shown that the DG of an MIMO radar is directly related to its detection performance and the MG represents its capability in estimating the target's parameters. Finally, the tradeoff between these two gains in an MIMO radar is studied and verified theoretically and through simulations.

In this paper, we consider an exponentially windowed sample covariance matrix (EWSCM) and propose an improved estimator for its eigenvalues. We use new advances in random matrix theory, which describe the limiting spectral distribution of the large dimensional doubly correlated Wishart matrices to find the support and distribution of the eigenvalues of the EWSCM. We then employ the complex integration and residue theorem to design an estimator for the eigenvalues, which satisfies the cluster separability condition, assuming that the eigenvalue multiplicities are known. We show that the proposed estimator is consistent in the asymptotic regime and has good performance in finite sample size situations. Simulation results show that the proposed estimator outperforms the traditional estimator, significantly.

The presence of the desired signal (DS) in the training snapshots makes the adaptive beamformer sensitive to any steering vector mismatch and dramatically reduces the convergence rate. Even the performance of the most of the existing robust adaptive beamformers is degraded when the signal-to-noise ratio (SNR) is increased. In this study, a high converging rate robust adaptive beamformer is proposed. This method is a promoted eigenspace-based beamformer. In this paper, a new signal-plus-interferences (SPI) covariance matrix estimator is proposed. The subspace of the ideal SPI covariance matrices is exploited and the estimated covariance matrix is projected into this subspace. This projection effectively reduces the covariance matrix estimation error and the proposed estimator yields a more accurate estimation of the SPI covariance matrix. In addition, a computationally efficient steering vector estimator has been proposed. To prevent the absence of the DS steering vector in the estimated SPI subspace, the estimated SPI covariance matrix is compensated. Hence, the proposed method can attain the optimal beamformer in the both high and low SNR cases. The numerical examples indicate that this method has excellent signal-to-interference plus noise ratio performance and offers a higher converging rate compared with the existing robust adaptive beamforming algorithms.

The presence of the desired signal (DS) in the training snapshots makes the adaptive beamformer sensitive to any steering vector mismatch and dramatically reduces the convergence rate. The objective of the present study is to propose a new adaptive beamformer which is robust against direction-of-arrival (DOA) mismatch and its convergence rate is not sensitive to the presence of the DS. This method is applicable to the arrays with specific structure such as the linear array. Our approach is based on the DS elimination from the training snapshots and the sub-array beamforming technique. To accomplish this goal, a blocking matrix which converts the primary data to the DS-free data is synthesized. The Synthesis process is based on the desired degree of freedom and the uncertainty of DOA of the DS. Using the signal-free data, the beamforming vector is calculated through the presented algorithm. Owing to elimination of the DS from the training snapshots and performing the adaptive operations in the sub-array level, our algorithm has high convergence rate and excellent performance even in cases with the small sample size. Simulations show that the proposed beamformer can achieve a much better performance in terms of output SINR compared to the existing ones.

Many papers have investigated phased array radars beam-forming so far. In these papers the power at a specific point of interest is written by a combination of transmitted power by means of each antenna. In this paper we will introduce a new approach which will use of cross correlation matric like MIMO radars to define beam-pattern of array and then by introducing an optimization problem to steering the available power at desire directions, it is shown that with an approach to optimum choosing of antenna placement, it is possible to concentrate the transmitter power on desire direction better than conventional phased array antennas with half wave length spacing. It should be say that some papers have investigated non uniform arrays but those papers usually use of multiple of half wave length in their elements spacing and in addition their approach to choosing these spaces is different and pretty more complicated from presented approach. Simulation is performed to analyze the presented algorithm and compare it's performance with conventional phased arrays with half wave length spacing for linear and planar array. MATLAB software is being employed for simulations.

In this article, we investigate the limiting spectral distribution of the sample covariance matrix (SCM) of weighted/windowed complex data. We use recent advances in random matrix theory and describe the distribution of eigenvalues of the doubly correlated Wishart matrices. We obtain an approximation for the spectral distribution of the SCM obtained from windowed data. We also determine a condition on the coefficients of the window, under which the fragmentation of the support of noise eigenvalues can be avoided, in the noise-only data case. For the commonly used exponential window, we derive an explicit expression for the l.s.d of the noise-only data. In addition, we present a method to identify the support of eigenvalues in the general case of signal-plus-noise. Simulations are performed to support our theoretical claims. The results of this article can be directly employed in many applications working with windowed array data such as source enumeration and subspace tracking algorithms.