Waveform Design for MIMO Radar with One-Bit DACs via Block-Sparse Semidefinite Relaxation

Multiple-input multiple-output(MIMO)systems which deploy one-bit DACs are at-tractive in many fields,such as wireless communications and radar.In this paper,the problem of transmit waveform design in MIMO radar system with one-bit DACs is investigated.By appropri-ately designing the transmitted QPSK signal waveforms,the majority of radiated energy can be fo-cused into the mainlobe region(s)by minimizing the integrated sidelobe to mainlobe ratio(ISMR)of beampattern,such that the intensity of backscattered signals from targets can be enhanced.However,the resulting optimization problem which consists of constrained fractional quadratic problem(CFQP)is noconvex.To tackle this problem,a block-sparse semidefinite relaxation meth-od is first utilized to reformulate the CFQP into a reduced convex semidefinite programming(SDP).Further,a customized interior point algorithm(IPA)is developed to solve the small-scale SDP.Finally,the desirable one-bit transmit waveform sequence can be properly synthesized by us-ing Gaussian randomization method.Numerical simulation results demonstrate that the proposed method offer better performance than the state-of-the-art algorithms.

A Reliable Iteration Algorithm for One-Bit Compressive Sensing on the Unit Sphere

The one-bit compressed sensing problem is of fundamental importance in many areas,such as wireless communication,statistics,and so on.However,the optimization of one-bit problem coustrained on the unit sphere lacks an algorithm with rigorous mathematical proof of convergence and validity.In this paper,an iteration algorithm is established based on difference-of-convex algorithm for the one-bit compressed sensing problem constrained on the unit sphere,with iterating formula■,where C is the convex cone generated by the one-bit measurements andη_(1)>η_(2)>1/2.The new algorithm is proved to converge as long as the initial point is on the unit sphere and accords with the measurements,and the convergence to the global minimum point of the l_(1)norm is discussed.

A survey on one-bit compressed sensing： theory and applications

In the past few decades, with the growing popularity of compressed sensing （CS） in the signal processing field, the quantization step in CS has received significant attention. Current research generally considers multi-bit quantization. For systems employing quantization with a sufficient number of bits, a sparse signal can be reliably recovered using various CS reconstruction algorithms. Recently, many researchers have begun studying the one- bit quantization case for CS. As an extreme case of CS, one- bit CS preserves only the sign information of measurements, which reduces storage costs and hardware complexity. By treating one-bit measurements as sign constraints, it has been shown that sparse signals can be recovered using certain reconstruction algorithms with a high probability. Based on the merits of one-bit CS, it has been widely applied to many fields, such as radar, source location, spectrum sensing, and wireless sensing network. In this paper, the characteristics of one-bit CS and related works are reviewed. First, the framework of one-bit CS is introduced. Next, we summarize existing reconstruction algorithms. Additionally, some extensions and practical applications of one-bit CS are categorized and discussed. Finally, our conclusions and the further research topics are summarized.

Stochastic Approximation for Expensive One-Bit Feedback Systems

One-bit feedback systems generate binary data as their output and the system performance is usually measured by the success rate with a fixed parameter combination. Traditional methods need many executions for parameter optimization. Hence, it is impractical to utilize these methods in Expensive One-Bit Feedback Systems （EOBFSs）, where a single system execution is costly in terms of time or money. In this paper, we propose a novel algorithm, named Iterative Regression and Optimization （IRO）, for parameter optimization and its corresponding scheme based on the Maximum Likelihood Estimation （MLE） method and Particle Swarm Optimization （PSO） method, named MLEPSO-IRO, for parameter optimization in EOBFSs. The IRO algorithm is an iterative algorithm, with each iteration comprising two parts： regression and optimization. Considering the structure of IRO and the Bernoulli distribution property of the output of EOBFSs, MLE and a modified PSO are selected to implement the regression and optimization sections, respectively, in MLEPSO-IRO. We also provide a theoretical analysis for the convergence of MLEPSO-IRO and provide numerical experiments on hypothesized EOBFSs and one real EOBFS in comparison to traditional methods. The results indicate that MLEPSO-IRO can provide a much better result with only a small amount of system executions.

Millimeter wave receive beamforming with one-bit quantization

Nowadays,the increase amounts of mobile data has resulted in great demand on existing communication networks.The millimeter-wave band offers large bandwidths to be exploited,which eases the spectrum crunch for bands below 3 GHz.In order to mitigate the path loss from high frequencies,a large antenna array and beamforming technology are adopted to increase the link gain.However,a challenge arises from the new band.Given the order of gigahertz bandwidth and the high sampling rate,the high-resolution ADC(Analog-to-Digital Converter)used in the large array creates a power consumption bottleneck.One solution is to use a low-resolution ADC to replace the full-precision ADC.In this paper,we propose a mixed LMS(Least Mean Square)receiver beamforming method for a millimeter-wave one-bit antenna array.We first use a grid-based approach to roughly estimate the DOA(Direction of Arrival)of the incoming signal.Then,we use the steering vector for the DOA as an initial value for the LMS method.A simulation shows that our proposed mixed LMS receiver beamforming method for the one-bit antenna array attains a performance level near the optimal one that obtained with an accurate DOA.The radiation pattern of the mixed LMS method almost overlaps the pattern for the accurate DOA,and the spectral efficiency of the mixed LMS method reach up with that obtained from accurate DOA.Furthermore,owing to the use of initial values from rough DOA estimation,the mixed LMS method has a fast convergence.

Optimal one-bit perturbation in Boolean networks based on cascading aggregation

We investigate the problem of finding optimal one-bit perturbation that maximizes the size of the basin of attractions(BOAs)of desired attractors and minimizes the size of the BOAs of undesired attractors for large-scale Boolean networks by cascading aggregation.First,via the aggregation,a necessary and sufficient condition is given to ensure the invariance of desired attractors after one-bit perturbation.Second,an algorithm is proposed to identify whether the one-bit perturbation will cause the emergence of new attractors or not.Next,the change of the size of BOAs after one-bit perturbation is provided in an algorithm.Finally,the efficiency of the proposed method is verified by a T-cell receptor network.

One-bit compressed sensing recovery algorithm robust to perturbation

One-bit compressed sensing （CS） technology reconstructs the sparse signal when the available measurements are reduced to only their sign-bit. It is well known that CS reconstruction should know the measurement matrix exactly to obtain a correct result. However, the measurement matrix is probably perturbed in many practical scenarios. An iterative algorithm called perturbed binary iterative hard thresholding （PBIHT） is proposed to reconstruct the sparse signal from the binary measurements （sign measurements） where the measurement matrix experiences a general perturbation. The proposed algorithm can reconstruct the original data without any prior knowledge about the perturbation. Specifically, using the ideas of the gradient descent, PBIHT iteratively estimates signal and perturbation until the estimation converges. Simulation results demonstrate that, under certain conditions, PBIHT improves the performance of signal reconstruction in the perturbation scenario.

Collaborative beamforming based on directional perturbation using one bit feedback

A novel collaborative beamforming algorithm is proposed in a wireless communication system with multiple transmitters and one receiver. All transmitters take part in the collaboration and the weighted message is transmitted simultaneously. In order to maximize the beamforming gain, the transmitters use one bit feedback information to adjust the phase offset. It tracks the direction in which the signal strength at the receiver can increase. The directional search and perturbation theory is used to achieve the phase alignment. The feasibility of the proposed algorithm is proved both experimentally and theoretically. Simulation results show that the proposed algorithm can improve the convergent speed of the phase alignment.