Waveform Design of a Cognitive MIMO Radar via an Improved Adaptive Gradient Descent Genetic Algorithm

Multiple input multiple output (MIMO) radars have been intensively studied for their superior performance merits compared to conventional radars including improved spatial resolution and parameter estimation. To achieve these merits, the transmitted waveforms from the MIMO transmitting antennas should be mutually orthogonal. Herein, the orthogonality is attained in the Doppler domain, namely Doppler division multiple access (DDMA), as it enjoys the simplest structure among other orthogonality schemes. Heretofore, the research in MIMO radars has focused on optimizing MIMO radar performance from different aspects including efficient waveform design, antenna beam-forming, and adaptive processing techniques. However, MIMO radars have been also a target of deliberate jamming scenarios. In this paper, the focus is turned to the evaluation of the detection performance of DDMA MIMO radars in jamming environments by creating a two-sided engagement scenario. On the jammer side, a jamming technique is proposed, Doppler-range block jamming (DRBJ), to deny the detection of a DDMA MIMO radar equipped with a moving target detector (MTD). On the radar side, a comparative detection performance analysis of two different MIMO radar detectors, normalized adaptive matched filter (NAMF) and fast 3D constant false alarm rate (CFAR), is conducted using Monte Carlo simulations. This analysis unveils divergent design philosophies of the two detectors, with each prioritizing distinct performance objectives.

In this paper, we address the design of optimal waveform for detecting extended target in signal-dependent interference. A two-step waveform design algorithm is proposed to obtain the optimal phase-modulated (PM) waveform with constant modulus. We first obtain the normally called water-filling waveform in frequency domain and subsequently synthesize the corresponding time-domain waveform using the minimum spectral mean-squares error (MSE) as a cost function. The phase gradient algorithm is employed to seek the optimal phase of the PM waveform, which can monotonously decrease the spectral MSE for each iterative step. Simulations demonstrate that the proposed algorithm can obtain asymptotic optimal waveform and its performance outperforms the iterative-estimate algorithm.

In this paper, we propose a waveform design based electronic counter-countermeasures (ECCM) scheme against interrupted sampling repeater jamming (ISRJ) for wideband multiple-input multiple-output (MIMO) radar. The ECCM scheme includes two steps: ISRJ parameters estimation and ISRJ signal suppression. The ISRJ signal interpretations based on time-frequency (TF) domain and Doppler domain are proposed respectively. Based on the Doppler interpretation, a waveform with thumbtack ambiguity function and a corresponding multiple Doppler channels processing structure are proposed to obtain ISRJ parameters. According to the TF interpretation and the known ISRJ parameters, a waveform with desired TF distribution and a corresponding frequency filter processing strategy are proposed to suppress ISRJ signal. The proposed ECCM scheme can be directly used in phased-array mode without waveform optimization. Considering the multiple targets scenario, the simultaneous multiple beams technology in MIMO radar is preferred. Therefore, a constant modulus waveform design method is proposed to match the desired transmit beampattern and TF distribution. Besides, in order to further suppress the sidelobe output of ISRJ signal, an extended version of the proposed ECCM scheme in the slow-time dimension is proposed. Finally, the numerical simulations are provided to demonstrate the effectiveness of the proposed ECCM scheme in multiple targets scenario.

Peak to average power ratio (PAPR) constrained power allocation in single carrier multiuser (MU) single-input multiple-output (SIMO) systems with iterative frequency domain (FD) soft cancelation (SC) minimum mean squared error (MMSE) equalization is considered in this paper. To obtain full benefit of the iterative receiver, its convergence properties need to be taken into account also at the transmitter side. In this paper, we extend the existing results on the area of convergence constrained power allocation (CCPA) to consider the instantaneous PAPR at the transmit antenna of each user. In other words, we will introduce a constraint that PAPR cannot exceed a predetermined threshold. By adding the aforementioned constraint into the CCPA optimization framework, the power efficiency of a power amplifier (PA) can be significantly enhanced by enabling it to operate on its linear operation range. Hence, PAPR constraint is especially beneficial for power limited cell-edge users. In this paper, we will derive the instantaneous PAPR constraint as a function of transmit power allocation. Furthermore, successive convex approximation is derived for the PAPR constrained problem. Numerical results show that the proposed method can achieve the objectives described above.

In this paper, we propose a general target model for multiple-input multiple-output (MIMO) radar. Two types of targets are presented. For each type of target, we develop a corresponding optimum waveform designing method to optimize detection performance. Based on Chernoff-Stein lemma, we use relative entropy as the performance measure. We show that the optimum waveform designing method can obtain better detection performance than traditional orthogonal waveform designing method.

Controlling peak side-lobe level (PSL) is of great importance in\nhigh-resolution applications of multiple-input multiple-output (MIMO) radars.\nIn this paper, designing sequences with good autocorrelation properties are\nstudied. The PSL of the autocorrelation is regarded as the main merit and is\noptimized through newly introduced cyclic algorithms, namely; PSL Minimization\nQuadratic Approach (PMQA), PSL Minimization Algorithm, the smallest Rectangular\n(PMAR), and PSL Optimization Cyclic Algorithm (POCA). It is revealed that\nminimizing PSL results in better sequences in terms of autocorrelation\nside-lobes when compared with traditional integrated side-lobe level (ISL)\nminimization. In order to improve the performance of these algorithms,\nfast-randomized Singular Value Decomposition (SVD) is utilized. To achieve\nwaveform design for MIMO radars, this algorithm is applied to the waveform\ngenerated from a modified Bernoulli chaotic system. The numerical experiments\nconfirm the superiority of the newly developed algorithms compared to\nhigh-performance algorithms in mono-static and MIMO radars.\n

In this study, two iterative design algorithms for multiple-input multiple-output (MIMO) radars with co-located antennas with point targets are proposed. In these algorithms, by joint design of the receiver filter and transmit waveform using linear combination of orthogonal waveforms, the authors aim to maximise the signal-to-interference plus-noise ratio (SINR) in the presence of signal dependent interference. In the first proposed algorithm, in each iteration, transmit waveforms and receive filter are designed in closed form to decrease the computational complexity. In the second method, by adding constant envelope criteria, the final waveform would be a linear combination of orthogonal waveforms and because of using a constant envelope, combining coefficients have equal magnitudes and different phases. Therefore, it is more practical for hardware implementation in comparison to the first proposed method. Simulation results show that both proposed methods have better SINR performances compared with other methods proposed in MIMO radar literature. The outperformance of the second proposed method with respect to phased arrays shows that by only using phase shifted combination of orthogonal waveforms, better performance in comparison to phased array radars can be achieved.

In this article, we explore the use of neural networks (NNs) to enhance the performance evaluation of Universal Filtered Multi-Carrier (UFMC) systems, a key technology for modern communication networks such as 5G and beyond. Traditional methods of evaluating the Bit Error Rate (BER) and system efficiency in UFMC system can be computationally intensive and less accurate under dynamic conditions. To address these challenges, we propose a NN based approach that not only improves the accuracy of BER prediction but also significantly optimizes the system's overall efficiency. Channel state estimation (CSE) plays a major role for UFMC system to address the phenomenon of multipath channel fading. In order to achieve a high data rate using UFMC technology, it is necessary to have an effective CSE and very accurate signal detection. Recently, there has been significant interest in utilizing deep learning (DL) to enhance channel estimations. This article introduces a new method for channel estimation (CE) in UFMC system. The suggested approach utilizes DL models to improve the CE. For the UFMC system, we propose a detector based on bidirectional long short-term memory (Bi-LSTM). To identify the transmitted symbols, the suggested detector uses DL training data directly. Currently, a significant drawback of UFMC systems is the presence of a high peak-to-average power ratio (PAPR). The approach aims to reduce the BER and enhances the efficiency of the UFMC system. This is achieved by dynamically setting the constellation mapping and symbol damping on each subcarrier and sub-symbol. The results illustrate that the proposed model can accurately and efficiently recognize UFMC signals. The suggested model is being compared to Least Square (LS), LSTM, and Minimum Mean Square Error (MMSE) channel estimators. Through extensive simulations, our results demonstrate that the NN model reduces BER and enhances efficiency. The proposed model gives more effective performance in terms of enhanced efficiency and reduction of BER. The findings offer valuable insights for the design and optimization of next-generation communication systems, where accurate and efficient performance evaluation is critical.

It is important to design a spectrum-efficient and power-efficient wireless communication system, which is the main design target in cellular and wireless communication engineering. Basically, to make it, the desired system must have a low peak-to-average power ratio (PAPR), low-level of out-of-band (OOB) power emission and can provide a high throughput. In this paper, we propose a high-throughput cellular communication system, namely Discrete Fourier Transform-Spread Windowing and Restructuring Orthogonal Frequency Division Multiplexing (DFT-Spread WR-OFDM), which has considerably low PAPR and low OOB power emission for upcoming next-generation beyond 5G (B5G) and 6G cellular communication systems. High PAPR is still one of the challenging issues for the MIMO-OFDM system. The proposed system is an effective arrangement of DFT-Spreading and windowing techniques to reduce PAPR and OOB power emission, respectively. Multiuser diversity is obtained using localized subcarrier mapping. We take advantages of spatial multiplexing to enhance the data throughput significantly. Additionally, frequency-domain equalization (FDE)-minimum mean squared error (MMSE) is used to combat the inter-symbol interference (ISI) effect. Simulation results verify that the suggested system considerably lowers PAPR and OOB power emission compared to the conventional systems. Furthermore, the proposed scheme shows better bit error rate (BER) performance over the MIMO Rayleigh fading channel environment. Simulation results also confirmed that the channel capacity of our suggested system is better than the conventional multi-carrier systems.

This study investigates the problem of low probability of intercept (LPI)-based distributed multiple-input multiple-output (MIMO) radar waveform design against barrage jamming in signal-dependent clutter and coloured noise. Given the priori knowledge of the extended target impulse response, signal-dependent clutter, barrage jamming signals and coloured noise, the LPI-based scheme for optimal radar waveform design is proposed to minimise the total power consumption of the MIMO radar system by optimising the transmitted waveforms of different transmitters with a predetermined mutual information (MI) constraint for target characterisation performance. Firstly, the MI between the received echoes from the target at each receiver and the target impulse response is derived as a practical metric to characterise the parameter estimation performance of a target. Then, the LPI-based distributed MIMO radar waveform design strategy is developed. The resulting radar waveform optimisation problem is convex and solved analytically, whose solutions represent the optimum power allocation for each transmitter in the MIMO radar system. With the aid of numerical simulations, it is illustrated that to minimise the total transmission power, the optimal waveform should match with the target, clutter, jamming and coloured noise. In addition, it is also demonstrated that the LPI performance of the MIMO radar system can be significantly improved by employing the proposed radar waveform design scheme.

In the radar target tracking stage, the repeater deception jamming can produce multiple false targets around the real target after matched filtering, which makes radar lose tracking of the real target. In this paper, a frequency-phase joint agility anti-repeater deception jamming waveform for Multiple Input Multiple Output (MIMO) radar is designed by the memristive radial basis function neural network (memristive-RBFNN), and the optimization of the waveform modulation coding is transformed into the optimization of the basis function parameters of the memristive-RBFNN. The memristive-RBFNN generates the frequency-phase joint agile waveform modulation sequence directly, thus greatly reduced the computational complexity of the waveform optimization algorithm. The designed frequency-phase joint agile waveform reduced the correlation between real target echo signal and repeater deception jamming to realize the suppression of repeater deception jamming. The simulation analyzes the computation complexity of proposed algorithm, and the jamming suppression performance is also given to verify the feasibility of this method proposed in this paper.

Information theory, and particularly the mutual information (MI), has provided fundamental guidance for communications research. In Bell's 1993 paper, the MI was first applied to radar waveform design. Similar to its communications counterpart, the solution comes in a water-filling form. However, the practical meaning of MI in the sensing context remains unclear to date. Recently, Yang and Blum's 2007 paper shows that under the white noise assumption, the optimum water-filling scheme simultaneously maximizes the MI and minimizes the estimation minimum mean square error (MMSE). Such an equivalence, however, does not hold when the target parameter statistics are not perfectly known as shown in Yang and Blum's subsequent work. To further the understanding of the practical meaning of MI and to establish a connection between the MI and commonly adopted MSE measures for sensing, this paper takes a fresh look at the target estimation problem. We consider the general colored noise, incorporate the normalized MSE (NMSE), and develop joint robust designs for both the transmitter (waveforms) and the receiver (estimator) under various target and noise uncertainty models. Our results show that i) the optimum waveform designs resulted from the MI, MMSE and NMSE criteria are all different and ii) compared to MMSE, the NMSE-based designs share more similarities with the MI-based ones, especially when the target and noise statistics are not perfectly known.

Multiple-input multiple-output (MIMO) systems can achieve high data-rate and high capacity transmission. In MIMO systems, the scheme that weight substreams based on minimum mean square error (MMSE) criterion at the transmitter using feedback channel state information (CSI) achieves good performance (denoted hereafter as MMSE precoder). However, if the optimum number of substreams is not selected based on CSI and methods for reducing the performance degradation due to feedback delay is not used, MMSE precoder cannot achieve the full performance possible. In this paper, we propose an MMSE precoder with mode selection for MIMO systems, which selects both the number of substreams and the modulation scheme for each substream to minimize the average BER at a fixed rate. We evaluate the BER of the proposed MMSE precoder with two methods for reducing performance degradation due to feedback delay, channel prediction and receive weight robust to feedback delay. We also evaluate the BER of the proposed MMSE precoder with the method that combines channel prediction with receive weight robust to feedback delay. Simulation results show that the BER of the proposed MMSE precoder is improved compared to that of the conventional MMSE precoder using the fixed number of substreams. We also show that the method that combines channel prediction with receive weight robust to feedback delay can achieve good BER even when the large feedback delay exists.

In orthogonal frequency-division multiplexing (OFDM) systems, it is generally assumed that the channel response is static in an OFDM symbol period. However, the assumption does not hold in high-mobility environments. As a result, intercarrier interference (ICI) is induced and the system performance is degraded. A simple remedy for this problem is the application of the zero-forcing (ZF) and minimum mean square error (MMSE) equalizers. Unfortunately, the direct ZF method requires the inversion of an NxN ICI matrix, where N is the number of subcarriers. When N is large, the computational complexity can become prohibitively high. As for the direct MMSE method, in addition to an NxN matrix inverse, it requires an extra NxN matrix multiplication, making the required computational complexity higher compared to the direct ZF method. In this dissertation, we first propose a low-complexity ZF method to solve the problem in single-input-single-output (SISO) OFDM systems. The main idea is to explore the special structure inherent in the ICI matrix and to apply Newton's iteration for matrix inversion. With our formulation, fast Fourier transforms (FFTs) can be used in the iterative process, reducing the complexity from O(N^3) to O(Nlog_2 N) . Also, the required number of the iteration is typically one or two. We also analyze the convergence behavior of the proposed method and derive the theoretical output signal-to-interference-noise-ratio (SINR). For the MMSE method, we first reformulate the MMSE solution in a way that the extra matrix multiplication can be avoided. Similar to the ZF method, we then exploit the structure of the ICI matrix and apply Newton's iteration to reduce the complexity of the matrix inversion. For a multiple-input-multiple-output (MIMO) OFDM system, the required complexity of the ZF and MMSE methods becomes more intractable. We then manage to extend the proposed ZF and MMSE methods for SISO-OFDM systems to MIMO-OFDM systems. It turns out that the computational complexity can be reduced even more significantly. Simulation results show that the performance of the proposed methods is almost as good as that of the direct ZF and MMSE methods, while the required computational complexity is reduced dramatically. Finally, we explore the application of the proposed methods in mobility-induced ICI mitigation for OFDM multiple access (OFDMA) systems, and in carrier frequency offset (CFO) induced ICI mitigation for OFDMA uplink systems. As that in OFDM systems, the proposed methods can reduce the required computational complexity, effectively.

We generalize the multiuser (CDMA spread spectrum) communication systems to the fading environments. We first extend Verdu's (1986) conventional optimum receiver to Rayleigh-fading environments and then evaluate its performance. Having no knowledge of the received power at the receiving end, we therefore need an estimator to efficiently estimate the received signal strength of each user in fading environments. A linear minimum mean square error (LMMSE) unbiased estimator is proposed to attain this goal. By using the minimum mean square error (MMSE) Bayesian estimation, we further propose the LMMSE bit estimator for efficient demodulation. Its performance is close to the optimum multiuser receiver but with a much simpler polynomial complexity. To further reduce the complexity, we extend the LMMSE estimator to the sequential LMMSE estimator. In sequential estimation, we do not need to implement the matched filter banks and to perform the matrix inversion when estimating. In addition, it converges after approximately 2k iterations, where k is the number of users. With this fast convergence property and the simple structure, the sequential LMMSE estimator provides an attractive alternative to the implementation of a multiuser system.