Output Signal-to-interference-plus-noise Ratio Research Articles

Joint Design of Horizontal and Vertical Polarization Waveforms for Polarimetric Radar via SINR Maximization

For an extended target with different polarimetric response, one way of improving the detection performance is to exploit waveform diversity on the dimension of polarization. In this paper, we focus on joint design of transmit signal and receive filter for polarimetric radars with local waveform constraints. Considering the signal-to-interference-plus-noise ratio (SINR) as the figure of merit to optimize, where the average Target-Impulse-Response Matrix (TIRM) within a certain Target-Aspect-Angle (TAA) interval is employed as the target response, the waveform is decomposed and then designed for both horizontal and vertical polarization segments, subject to energy and similarity constraints. An iterative algorithm is proposed based on the majorization-minimization (MM) method to solve the formulated problem. The developed algorithm guarantees the convergence to a B-stationary point, where in each iteration, optimal horizontal and vertical transmit waveforms are respectively solved by using the feasible point pursuit and successive convex approximation (FPP-SCA) technique. Experiment results show the effectiveness of the proposed algorithm, the robustness of the output SINR against the TAA change, and the advantages of polarization diversity and local design.

Robust joint transmit and receive beamforming by sequential optimization for bistatic radar system

AbstractThe conventional joint transmit and receive beamforming algorithms maximise the output signal‐to‐interference‐plus‐noise ratio (SINR) by designing the weight coefficients simultaneously at the transmitter and receiver. However, their performance degrades significantly when the model mismatch exists. In this paper, the robust version of the joint transmit and receive beamforming method is considered in the context of the bistatic radar system. In particular, both the transmit array and the receive array suffer model mismatch. The designed beamformer aims to maximise its output SINR subjected to a robustness constraint by simultaneously designing the transmit beamforming vector and the receive beamforming vector. Two types of robustness constraints, namely the worst‐case constraint and the probability constraint, are considered in this paper. The proposed formulations are non‐convex, and the sub‐optimal solutions are obtained by a sequential optimization approach. In addition, a novel method to estimate the interference‐plus‐noise covariance matrices at the transmitter and receiver is proposed, which makes the proposed robust joint beamforming algorithms adapt to the variation of the surrounding environment. Numerical simulations verify the effectiveness of the proposed algorithms.

A novel beamforming technique using mmWave antenna arrays for 5G wireless communication networks

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) and beamforming technologies are most likely the integral parts and the key enablers of 5G-and-Beyond (5G-B) wireless communication systems. From that perspective, this manuscript proposes an efficient digital beamforming technique for 5G applications. The suggested approach is formulated based on a reliable sampling technique that effectively samples the interference-plus-noise covariance matrix (IPNCM) to construct an efficient projection matrix (PM). The proposed technique is therefore named interference-plus-noise PM (IPNPM) beamformer. Next, a compact-size rectangular microstrip antenna operating at 49.3 GHz having 1.7 GHz potential bandwidth is designed using CST microwave studio. The designed antenna is subsequently used to model a 32-element uniform linear array (ULA) representing a typical 5G base station. The elements of the modeled ULA are then manually fed with the complex-weights generated from the proposed beamformer to produce high-power beams and deep nulls toward the desired and undesired locations, respectively. To further improve the beamformer performance in terms of minimizing radiating power within undesired regions, an iterative sidelobe level (SLL) suppression technique is developed and integrated with the proposed beamformer. The conducted theoretical analysis justifies that the IPNPM beamformer requires less arithmetic operations than the well-known beamforming approaches. To reveal the benefits of the proposed technique, the array radiation pattern based on the proposed beamformer is plotted and compared with those for the three widely used approaches. An intensive Monte Carlo simulation is also carried out in which the IPNPM beamformer performance is systemically compared with the popular approaches based on the output signal to interference-plus-noise ratio (SINR) criterion. The achieved results demonstrate that the proposed approach is superior and surpasses the rival algorithms in terms of generating deeper nulls, enhanced output SINR, and lower execution time. It is also demonstrated that, unlike the existing SLL reduction techniques, the newly developed SLL suppressor reduces the SLLs of the proposed beamformer to a predefined threshold value (i.e., −24 dB) after few iterations.

Robust Adaptive Beamforming via Worst-Case SINR Maximization With Nonconvex Uncertainty Sets

This paper considers a formulation of the robust adaptive beamforming (RAB) problem based on worst-case signal-to-interference-plus-noise ratio (SINR) maximization with a nonconvex uncertainty set for the steering vectors. The uncertainty set consists of a similarity constraint and a (nonconvex) double-sided ball constraint. The worst-case SINR maximization problem is turned into a quadratic matrix inequality (QMI) problem using the strong duality of semidefinite programming. Then a linear matrix inequality (LMI) relaxation for the QMI problem is proposed, with an additional valid linear constraint. Necessary and sufficient conditions for the tightened LMI relaxation problem to have a rank-one solution are established. When the tightened LMI relaxation problem still has a high-rank solution, the LMI relaxation problem is further restricted to become a bilinear matrix inequality (BLMI) problem. We then propose an iterative algorithm to solve the BLMI problem that finds an optimal/suboptimal solution for the original RAB problem by solving the BLMI formulations. To validate our results, simulation examples are presented to demonstrate the improved array output SINR of the proposed robust beamformer.

Max-Min Beamforming for Multipath Exploitation

This letter considers the robust spatial-filter design problem for multipath exploitation. The imprecise multipath return results in the output Signal-to-Interference-plus-Noise-Ratio (SINR) performance deterioration of traditional beamformers. We focus on enhancing the worst-case output SINR over the uncertain Direction-Of-Arrival (DOA) sector. To achieve robustness against covariance matrix errors caused by the desired signals contamination, we reconstruct the Interference-Noise Covariance Matrix (INCM) from Sample Covariance Matrix (SCM) based on the proposed residual noise components removal idea. Then, we cast the robust spatial-filter design problem as a max-min optimization problem, aiming at improving the worst-case output SINR. The filter vector is finally obtained via semi-definite relaxation followed by randomization synthesis. Simulation results demonstrate the effectiveness of the proposed algorithm.

Performance Analysis and Optimal Cooperative Cluster Size for Randomly Distributed Small Cells Under CAN Protocol

The ease of imposing multicell coordination mechanisms to enhancing system spectrum efficiency (SE) and performance estimate is one of the key advantages of cloud/centralized control area networks (CAN). Enormous number of cooperative cells should theoretically result in a higher SE, but they may also cause considerable delays due to extra channel state information (CSI) feedback and joint processing computing needs at the cloud data centre, resulting in performance deficit. I partition the network into numerous clusters of cooperating tiny cells and create a throughput optimization problem to study the impact of delays on throughput gains. As a function of cluster size, I figure various delay factors and the network's sum-rate, treating cluster size as the fundamental optimization variable. For both linear and planar network installations, I treat both base station and user geometric locations as random variables in my study. On the basis of the homogeneous Poisson point processing (PPP)model, the output SINR (signal-to-interference- plus-noise ratio) and ergodic sum-rate are calculated. The sum-rate optimization problem is formulated and solved in terms of cluster size. The suggested analytical framework may be used to precisely evaluate the performance of practical cloud-based small cell networks via clustered cooperation, according to simulated study.

An ℓ0-norm-constrained adaptive algorithm for joint beamforming and antenna selection

This paper presents a new adaptive algorithm for joint beamforming and antenna selection in mobile communication systems. Such an algorithm is of particular interest for massive multiple-input multiple-output (mMIMO) antenna systems along with limited number of available radio-frequency chains. The proposed algorithm is based on introducing an ℓ0-norm constraint to an established adaptive-projection beamforming optimization scheme. In doing so, a real-time beamforming optimization can be carried out, resulting in a beamforming vector that tends to be sparse. The higher-magnitude elements of such a vector point out to the antenna-array elements that have the largest contribution to the output signal-to-interference-plus-noise ratio (SINR). Consequently, an effective antenna selection can be achieved directly by considering the magnitude of the beamforming coefficients. Simulation results confirm the effectiveness of the proposed algorithm in enhancing the output SINR.

Covariance Matrix Estimation for FDA-MIMO Adaptive Transmit Power Allocation

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar produces an angle-range-dependent and time-varying transmit beampattern due to the small frequency increment across its array elements, which provides potential applications in new radar techniques. In this paper, we first establish a FDA-MIMO radar receiver model in the presence of spectral interferences, which focuses on the interference covariance matrix structure when the transmitted baseband signals are time- or frequency-domain orthogonal. Then, we show that the FDA-MIMO radar has a capability in suppressing spectral interferences with low computational complexity. Specifically, we propose an adaptive transmit weight vector for element-wise power allocation through maximizing the output signal-to-interference-plus-noise ratio (SINR). Furthermore, we propose a shrinkage-to-tapering algorithm for covariance matrix estimation in a coherent process interval. The interference-plus-noise covariance matrix is reconstructed accordingly with prior knowledge including the target, interference and noise covariance matrix structures. Numerical results demonstrate that the FDA-MIMO radar with adaptive power allocation can suppress spectral interferences by the developed method and high output SINR is achieved.

Random Matrix Theory Predictions of Dominant Mode Rejection Beamformer Performance

Adaptive beamformers use a sensor covariance matrix estimated from data snapshots to mitigate directional interference and attenuate uncorrelated noise. Dominant mode rejection (DMR) is a variant of the classic minimum variance distortionless response (MVDR) algorithm that replaces the smallest eigenvalues in the covariance estimate by their average while leaving the largest ones unmodified. Since DMR does not invert the small eigenvalues of the sample covariance, it achieves a higher white noise gain than MVDR while still suppressing loud interferers, thereby yielding a higher signal-to-interference-plus-noise ratio (SINR). MVDR achieves an average output SINR within 3 dB of the SINR achievable with the true covariance with approximately twice as many snapshots as sensors. Prior empirical analyses showed that achieving the same SINR with DMR only requires approximately twice as many snapshots as interferers. This paper derives analytical models of white noise gain and interference leakage for DMR using random matrix theory (RMT), specifically the eigenvalue and eigenvector limiting results of the spiked covariance model. Applying the new analytical models confirms the empirical DMR snapshot requirement. To handle finite cases with a large number of interferers relative to the array size, this paper derives a modified spiked covariance model that improves the accuracy of RMT results, and hence DMR performance predictions.

Enhanced robust adaptive beamforming designs for general-rank signal model via an induced norm of matrix errors

The robust adaptive beamforming (RAB) problem for general-rank signal model with an uncertainty set defined through a matrix induced norm is considered. The worst-case signal-to-interference-plus-noise ratio (SINR) maximization RAB problem is formulated. First, the closed-form optimal value for a minimization problem of the least-squares residual over the matrix errors with an induced lp,q-norm constraint is derived. Then, the maximization problem is reformulated into the maximization of the difference between an l2-norm function and an lq-norm function, with a convex quadratic constraint. It is shown that for any q≥1 in the set of rational numbers, the maximization problem can be approximated by a sequence of second-order cone programming problems, with the ascent optimal values. The resultant beamvector for some q in the set with the maximal actual array output SINR, is treated as the candidate making the RAB design improved the most. In addition, a generalized RAB problem of maximizing the difference between an lp-norm function and an lq-norm function with the convex quadratic constraint is studied, and the actual array output SINR is further enhanced by properly selecting p and q. Simulation examples are presented to demonstrate the improved performance of the robust beamformers for certain matrix induced lp,q-norms.

Joint Design of Colocated MIMO Radar Constant Envelope Waveform and Receive Filter to Reduce SINR Loss.

In this paper, we aim at the problem that MIMO radar’s target detection performance is greatly reduced in the complex multi-signal-dependent interferences environment. We propose a joint design method based on semidefinite relaxation (SDR), fractional programming and randomization technique (JD-SFR) and a joint design method based on coordinate descent (JD-CD) to solve the actual transmit waveform and receive filter bank directly to reduce the loss of strong interference to the output signal-to-interference-plus-noise ratio (SINR) of the radar system. Therefore, the maximization of output SINR is taken as the criterion of the optimization problem. The designed waveforms take into account the radar transmitter’s hardware requirements for constant envelope waveforms and impose similarity constraints on the waveforms. JD-SFR uses SDR, fractional programming and randomization technique to deal with the non-convex optimization problems encountered in the solution process. JD-CD transforms the optimization problem into a function of the phase of the waveform and then solves the transmit waveform based on CD. Compared with other methods, the proposed method has lower output SINR loss under strong power interference and forms deep nulls on the direction beampattern of multiple interference sources, which indicates that it has better anti-interference performance.

Practical Non-Linear Responsivity Model and Outage Analysis for SLIPT/RF Networks

Simultaneous lightwave information and power transfer (SLIPT) is considered as an effective method of transferring information and energy via illuminated lightwaves. In this paper, we investigate the effect of non-linear responsivity on the SLIPT/radio frequency (RF) performance. We develop a tractable mathematical model for the non-linearity of responsivity and use it to study the end-to-end signal-to-interference-plus-noise ratio (SINR). Based on that, we derive the closed-form expressions for the probability density function and cumulative distribution function of the output SINR at the receiver. These statistical distributions are then used to derive the closed-form expressions of the outage probability of SLIPT/RF system over Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> fading channels. Numerical results indicate that the simplified model used in previous studies leads to inaccurate performance evaluation results, and that our model provides more accurate evaluation. We also verify the analytical findings by simulation.

Cognitive FDA radar transmit power allocation for target tracking in spectrally dense scenario

In this paper, we propose a cognitive radar paradigm based on frequency diverse array (FDA), which allows flexible spectrum control via element-wise transmit power allocation. As an emerging array technique, FDA differs from conventional phased array (PA) in that it imposes an additional frequency increment across the array elements. The use of frequency increment provides the FDA radar with the ability of flexible spectrum adjustment. We propose the cognitive FDA radar for target tracking in spectrally dense scenarios. Two optimization criteria, i.e., signal-to-interference-plus-noise ratio (SINR) maximization and Cramér-Rao bound (CRB) minimization, are employed to adaptively update the array weight vector for power allocation at each transmission. Numerical results show that the proposed cognitive FDA radar can adjust the signal spectrum to avoid the interfered frequencies for better output SINR. The resulting tracking errors of FDA radar with adaptive power allocation are lower than that for fixed power allocation. Moreover, the CRB criterion further improves the tracking performance.

Sparse Array Beamforming Design for Coherently Distributed Sources

Recently, nonstructured sparse array designs have attracted wide interest due to their capability of providing optimum performance for environment-dependent adaptive beamforming. In this article, we develop a sparse array design approach for adaptive beamforming in the presence of spatially coherently distributed (CD) sources. The proposed approach formulates the design problem via maximizing the output signal-to-interference-plus-noise ratio (SINR), but it exploits the generalized array manifold of CD sources. Moreover, sequential convex programming is utilized to convert the nonconvex optimization problem into a series of convex subproblems. We also analyze the impact of source distributed shape on optimum array configuration and theoretically prove that when the CD sources are Gaussian-shaped, the uniform linear array (ULA) is exactly the optimum array configuration. The resulting optimum sparse arrays yield higher output SINR and better-shaped beampattern than the structured sparse arrays. Numerical results verify the superiority of the optimum sparse arrays obtained by the proposed approach over the structured sparse arrays.

Joint Design of Transmit and Receive Beamforming for Transmit Subaperturing MIMO Radar

We consider the joint design of the transmit and receive beamforming in transmit subaperturing multiple-input-multiple-output (TS-MIMO) systems by utilizing the prior knowledge about the locations and power of the target and inherent interferences. The joint design is solved by an iterative algorithm to maximize the output signal-to-interference-plus-noise-ratio (SINR). The proposed approach can benefit from the sum co-array and joint transmit and receive optimization. Simulation results show that the approach provides more directions of freedom (DOFs) and/or directional gain compared to the original TS-MIMO and omni-MIMO, which leads to better output SINR results and interference rejection.

Waveform Optimization of Compressed Sensing Radar without Signal Recovery

Radar signal processing mainly focuses on target detection, classification, estimation, filtering, and so on. Compressed sensing radar (CSR) technology can potentially provide additional tools to simultaneously reduce computational complexity and effectively solve inference problems. CSR allows direct compressive signal processing without the need to reconstruct the signal. This study aimed to solve the problem of CSR detection without signal recovery by optimizing the transmit waveform. Therefore, a waveform optimization method was introduced to improve the output signal-to-interference-plus-noise ratio (SINR) in the case where the target signal is corrupted by colored interference and noise having known statistical characteristics. Two different target models are discussed: deterministic and random. In the case of a deterministic target, the optimum transmit waveform is derived by maximizing the SINR and a suboptimum solution is also presented. In the case of random target, an iterative waveform optimization method is proposed to maximize the output SINR. This approach ensures that SINR performance is improved in each iteration step. The performance of these methods is illustrated by computer simulation.

SINR Distribution for the Persymmetric SMI Beamformer With Steering Vector Mismatches

In this paper, we examine the normalized output signal-to-interference-plus-noise ratio (SINR) of a sample matrix inversion beamformer exploiting the persymmetry of the received data. The persymmetry commonly exists in the received data, when a symmetrically spaced linear array and/or symmetrically spaced pulse trains are utilized. An exact expression for the probability density function (PDF) of the normalized output SINR is derived in the mismatched case. This PDF is verified using Monte Carlo simulations. Numerical examples indicate that the exploitation of persymmetry is equivalent to doubling the training data size, and hence can greatly alleviate the requirement of training data in adaptive processing.

Toeplitz covariance matrix of colocated MIMO radar waveforms for SINR maximization

Abstract Focusing on the signal to interference-plus-noise ratio (SINR) maximization in colocated multiple-input multiple-output (MIMO) radars, using the transmit covariance matrix (TCM) design of transmit waveforms, we have proposed a TCM Rpm with the form of symmetrical Toeplitz matrix, where m is the control parameter to generate different TCM. The main features include (a) full rank, to exploit the waveform diversity advantage of MIMO radar and to further suppress the maximum number of interfering sources, (b) positive semi-definition of sin ((π/2)Rpm) to guarantee that Rpm can be synthesized with binary phase shift keying (BPSK) waveforms in closed form, (c) higher output SINR level using the Rpm with smaller m in the receiver, with the prior knowledge of target and interference locations, (d) lower receive sidelobe levels (SLLs) using the Rpm with certain larger m (e.g. m = 1.5 or 2) for the unwanted and unknown sidelobe interference suppression. Simulation results validate the better performance of our proposed TCM compared to the phased-array, omnidirectional MIMO radar, phased-MIMO radar and the recently proposed TCMs.

Joint Optimization of Transmit Waveform and Receive Filter for Target Detection in MIMO Radar

In this paper, we consider the joint optimization of transmit waveform and receive filter in colocated multiple-input multiple-output (MIMO) radar to enhance target detection performance in the presence of signal-dependent interference. It is noticed that in the detection stage, the output energy is mainly concentrated in the mainlobe region. Therefore, we decompose the receive filter as the cascade of a receive beamformer and a temporal filter. Then, under the peak-to-average ratio (PAR) constraint, a problem is formulated to realize a trade-off between the signal-to-interference-plus-noise ratio (SINR) and the integrated sidelobe level (ISL) at the pulse compression output of mainlobe synthesized signal. A non-decreasing algorithm, which is the combination of sequential optimization algorithm and minorization-maximization (MM) method, is developed to solve this problem. Besides, in order to reduce computation burden, a special case is proposed, where we fix the temporal filter as the mainlobe synthesized signal. Then, another non-decreasing algorithm based on the MM method is proposed to solve the special case. Numerical experiments show that the proposed algorithms can obtain high output SINR and low output ISL efficiently.

Time-Invariant Joint Transmit and Receive Beampattern Optimization for Polarization-Subarray Based Frequency Diverse Array Radar

We propose a polarization-subarray based frequency diverse array (FDA) radar with the subarray-based FDA as the transmit (Tx) array and the polarization-sensitive subarray-based FDA (PSFDA) as the receive (Rx) array. The subarray-based FDA has the capability to achieve a single maximum beampattern at the target location, while the PSFDA can provide an extra degree of freedom to further suppress the interference and, thus, to improve the radar's signal-to-interference-plus-noise ratio (SINR). The time-dependent frequency offsets are designed for the proposed radar to realize the time-invariant beampattern at the desired target location over the whole pulse duration. To further improve the target detection performance, the time-invariant joint Tx–Rx beampattern design is considered based on the output SINR maximization. To effectively solve the nonconvex output SINR maximization problem, a suboptimal alternating optimization algorithm is proposed to iteratively optimize the FDA Tx beamforming, the PSFDA spatial pointings, and the PSFDA Rx beamforming. Numerical experiments illustrate that the time-invariant and single-maximum joint Tx–Rx beampattern at the target location is achieved. Moreover, compared to the basic FDA and logarithmic frequency offset FDA as well as conventional phased array radars, the proposed polarization-subarray based FDA radar achieves a significant SINR improvement, particularly when the desired target and the interferences are spatially indistinguishable.