Optimal Beam Pattern Research Articles

A stochastic approach to delays optimization for narrowband transmit beam pattern in medical ultrasound

In ultrasound imaging, state-of-the-art methods for transmit beam pattern (TBP) optimization have well-known drawbacks like non-uniform beam width over depth, significant side lobes, and quick energy drop-out beyond the focal region. To overcome these limitations, we develop a TBP optimization approach by focusing on its narrowband approximation and considering transmit delays as free variables instead of linked to specific focal depths. We formulate a non-linear Least Squares problem to minimize the difference between the TBP corresponding to a set of delays and the desired one, modeled as a 2D rectangle elongated along the beam axis. Three metrics are defined to quantitatively evaluate the results. Results obtained by synthetic simulations show that the main lobe width is considerably more intense (+31.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$+31.5\\%$$\\end{document} on average) and uniform (+28.7%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$+28.7\\%$$\\end{document} on average) over the whole depth range compared to classical focalized Beam Patterns. The application of the method to elastography shows improvements in the ultrasound energy concentration along a desired axis.

Temporal-Assisted Dynamic Beampattern Optimization in Integrated Sensing and Communication Systems

Temporal-Assisted Dynamic Beampattern Optimization in Integrated Sensing and Communication Systems

Optimal audio beam pattern synthesis for an enhanced parametric array loudspeaker

A parametric array loudspeaker (PAL) generates highly directional audible sound in air with a small aperture size compared to a conventional loudspeaker. But in indoor applications, the long propagation distance of a PAL causes reflections, which disturbs the reproduction of narrow audio beams. Moreover, sound distortion appears along the off-axis direction due to the frequency dependence of the beam width. This study proposed an optimal audio beam pattern synthesis for a PAL-based convex optimization, which can design the audio beam of a PAL with an optimal solution. The proposed method overcame the mentioned limitations by applying it to a length-limited PAL for audio spot control and a multichannel PAL array for a constant beam width audio beam. In a length-limited PAL, the proposed method restricts the audio spot to a smaller region and weakens the sound leakage along the off-axis direction. Whereas in a multichannel PAL array, the proposed method also achieves a constant beam width near the radiator axis. Simulations and experiments verify the effectiveness of the proposed method, which will enhance the performance of a PAL in scenarios where control of the audio beam is required.

Robust Transmit Beamforming for Secure Integrated Sensing and Communication

This paper studies a downlink secure integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) transmits confidential messages to a single-antenna communication user (CU) while performing sensing on targets that may act as suspicious eavesdroppers. To ensure the quality of target sensing while preventing their potential eavesdropping, the BS combines the transmit confidential information signals with additional dedicated sensing signals, which play a dual role of artificial noise (AN) for degrading the qualities of eavesdropping channels. Under this setup, we jointly design the transmit information and sensing beamforming, with the objective of minimizing the weighted sum of beampattern matching errors and cross-correlation patterns for sensing subject to secure communication constraints. The robust design takes into account the channel state information (CSI) imperfectness of the eavesdroppers in two practical CSI error scenarios. First, we consider the scenario with bounded CSI errors of eavesdroppers, in which the worst-case secrecy rate constraint is adopted to ensure secure communication performance. In this scenario, we present the optimal solution to the worst-case secrecy rate constrained sensing beampattern optimization problem, by adopting the techniques of S-procedure, semi-definite relaxation (SDR), and a one-dimensional (1D) search, for which the tightness of the SDR is rigorously proved. Next, we consider the scenario with Gaussian CSI errors of eavesdroppers, in which the secrecy outage probability constraint is adopted. In this scenario, we present an efficient algorithm to solve the more challenging secrecy outage-constrained sensing beampattern optimization problem, by exploiting the convex restriction technique based on the Bernstein-type inequality, together with the SDR and 1D search. Finally, numerical results show that the proposed designs can properly adjust the information and sensing beams to balance the tradeoffs among communicating with CU, sensing targets, and confusing eavesdroppers, so as to achieve desirable sensing transmit beampatterns while ensuring the CU’s secrecy requirements for the two scenarios.

Beampattern optimization techniques using metaheuristic algorithm for collaborative beamforming: a review

The fast improvements in wireless technology and embedded systems have sparked a renewed interest in collaborative beamforming (CB) approach used in wireless sensor networks (WSNs). Despite the fact that studies on distributed and CB have been conducted for more than 10 years, CB was previously deemed unworkable because of its extreme complexity and difficult-to-attain criteria. It just got well-known in the last several years when cheaply accessible compact wireless communication electronic sensors with high processing capability emerged. These factors contributed as the motivation for this paper's research overview CB in WSNs. We provide the classifications of the static and mobile WSN which is based on the sensor node optimization technique. This paper reviews the metaheuristic algorithms proposed by previous study for beampattern and energy consumption optimization. Finally, this paper also presents a summary analysis of the previous studies in terms of beampattern characteristic and properties, energy consumption and stability.

Beampattern optimization techniques using metaheuristic algorithm for collaborative beamforming: a review

The fast improvements in wireless technology and embedded systems have sparked a renewed interest in collaborative beamforming (CB) approach used in wireless sensor networks (WSNs). Despite the fact that studies on distributed and CB have been conducted for more than 10 years, CB was previously deemed unworkable because of its extreme complexity and difficult-to-attain criteria. It just got well-known in the last several years when cheaply accessible compact wireless communication electronic sensors with high processing capability emerged. These factors contributed as the motivation for this paper's research overview CB in WSNs. We provide the classifications of the static and mobile WSN which is based on the sensor node optimization technique. This paper reviews the metaheuristic algorithms proposed by previous study for beampattern and energy consumption optimization. Finally, this paper also presents a summary analysis of the previous studies in terms of beampattern characteristic and properties, energy consumption and stability.

An optimization method for frequency-invariant beamforming with arbitrary sensor arrays

This paper proposes a beampattern optimization method for frequency-invariant beamforming with arbitrary sensor arrays, which is an extension of the previously proposed method for circular sensor arrays. Based on the criterion of minimizing the mean square error between the synthesized beampatterns at each frequency and the desired beampattern, the relationship between the weighting vectors and the desired weighting vector can be accurately obtained. Then, the relevant performance parameters, such as the directivity factor, error sensitivity function and minimum mean square error, can all be expressed as the accurate closed-form functions of the desired weighting vector. With the above conclusions, a multi-constraint optimization problem is formulated to calculate the optimal desired weighting vector under different constraints. The optimal frequency-invariant beampatterns and their performance parameters can be readily achieved using the derived functions. Thus, the proposed method can not only obtain the optimal desired weighting vector, but also achieve the broadband frequency-invariant beampatterns with only one optimization calculation of the desired weighting vector. Simulations and experimental results show that the proposed method can provide a good trade-off among the directivity, robustness, and frequency invariance.

LPI-constrained collaborative transmit beampattern optimization and resource allocation for maneuvering targets tracking in colocated MIMO radar network

Considering tracking multiple maneuvering targets with the presence of a hostile interceptor in the modern electronic warfare (EW) environment, how to maximize targets tracking performance while ensuring system’s anti-interception capability is of essential significance for the radar network consisting of multiple colocated MIMO radar (CMR) nodes. In this paper, we propose a novel low probability of intercept (LPI) constrained collaborative beampattern optimization and resource allocation (LPI-CBORA) strategy for multiple maneuvering targets tracking (MMTT) in the CMR network (CMRN). More precisely, the LPI-CBORA strategy is formulated as a nonconvex optimization problem, where the node-target assignment, beampattern design and bandwidth allocation are collaboratively considered for the first time. To tackle the nonconvex problem effectively, an efficient two-stage solution algorithm incorporating with the successive convex approximation (SCA) and the cyclic minimization algorithm (CMA) is developed. Numerical simulations are provided to verify the effectiveness and superiority of the proposed LPI-CBORA strategy.

Transmit Beampattern Design for Distributed Satellite Constellation Based on Space–Time–Frequency DoFs

For distributed satellite constellations, detection performance can be equivalently regarded as a single large satellite by the cooperative operation of multiple small satellites, which is a promising research topic of the Next-Generation Radar (NGR) system. However, dense grating lobes inevitably occur in the synthetic transmit pattern due to its distributed configuration, as a result of which the detection performance of dynamic coherent radar is seriously weakened. In this paper, a novel transmit beampattern optimization method for dynamic coherent radar based on a distributed satellite constellation is presented. Firstly, the effective coherent detection range interval is determined by several influence factors, i.e., coherent detection, far-field, and system link constraints. Then, we discuss the quantitative evaluation method for coherent integration in terms of synchronization error, beam pointing error, and high-speed motion characteristics and we allocate the corresponding terms in a reasonable way from the perspective of engineering. Finally, the space–time–frequency degrees of freedom (DOFs), which can be collected from satellite spacing, carrier frequencies, and platform motion characteristics, are utilized to realize a robust transmit beampattern with low sidelobe by invoking a genetic algorithm (GA). Simulation results validate the effectiveness of our theoretic analysis, and unambiguous coherent transmit beamforming with a satellite constellation of limited scale is accomplished.

Biogeography-based optimization with adaptive migration and adaptive mutation with its application in sidelobe reduction of antenna arrays

Biogeography-based optimization (BBO) is a swarm intelligence optimization algorithm based on migration and mutation operations, which is usually used to solve the complex optimization problems. However, it is also a challenging task for conventional BBO to solve some complex and diversified optimization problems with a perfect balance between the performance of exploration and exploitation. In this paper, we propose a variant of BBO approach called BBO with improved migration and adaptive mutation (BBOIMAM) to improve the performance of conventional BBO for dealing with different optimization problems. First, BBOIMAM introduces an improved migration strategy which includes the generalized sinusoidal migration model and immigration strategy based on elite-learning mechanism for the improvement of the local search ability. Second, we propose an adaptive mutation strategy based on the spring vibration to further enhance the population diversity, so that improving the global search ability of the algorithm. By the combination of the proposed improved migration and adaptive mutation strategies, the exploration and exploitation performance of the algorithm can be balanced. We make a large number of experiments on a set of various kinds of benchmark functions, and the experimental results demonstrate that the proposed BBOIMAM approach achieves better performance than several state-of-the-art peer algorithms on CEC 2017 and CEC 2020 test function sets and three cases of the antenna array beam pattern optimization problems.

Energy Saving Technologies and Best Practices for 5G Radio Access Network

This article identifies energy-saving potential of the fifth generation (5G) Radio Access Network, and describes main energy-saving principles and technologies. It explores how to use network energy saving technologies, such as carrier shutdown, channel shutdown, and symbol shutdown in 5G network, that have been inherited from 4G. Some enhanced technologies for 5G like equipment deep sleep and symbol aggregation have also been introduced in this article. However, it is far from enough and an innovative energy-saving solution should be considered. To meet the requirements and development of intelligent and self-adaptive energy-saving solution, Artificial Intelligence (AI) and big data analysis are introduced to form a more precise energy-saving strategy based on site-specific traffic and site-related conditions, thus improving the efficiency and reducing the manpower. Finally, two commercial application practices of AI-based energy-saving solution are elaborated. One is the practice of AI-based service awareness energy saving for 4G/5G collaborative networks, the energy benefits can be improved up to 20%; The other practice is the adoption of a new architecture Active Antenna Unit (AAU) with beam pattern optimization, its energy benefits can be promoted by 30%. These two practices could help mobile network operators (MNOs) to achieve the most energy-efficient network with good network performance and lower Operating Expense (OPEX).

Fast Beampattern Synthesis Algorithm for Flexible Conformal Array

This letter first outlines a flexible conformal array geometric model on variable airborne wings, which can undergo complete shape change according to different combat situations, whereas the deformation may result in array beampattern distorted. In order to realize fast beampattern synthesis for a flexible conformal array, we propose a real-time beampattern optimization algorithm, where we split the protoproblem into two sub-problems based on the alternating direction method of multipliers (ADMM). In the first sub-problem, we transform the quadratic inequality constrained problem into a linear equality constrained problem, and then get the iterative update expression for the variables. Unlike the traditional interior point method, the simulation results demonstrate that the proposed algorithm can efficiently solve large-scale convex optimization problems and greatly reduce the computational complexity.

Sidelobe level minimization for uniform circular smart antenna array using cultural algorithm

Cultural algorithm (CA) is a new evolutionary program inspired by sociology and archaeology theories that assisting formulating cultural evaluation. Its use to solve optimization problems. This paper analyzed the beamforming of a uniform circular antenna array (UCAA) via using the CA algorithm. The sidelobe level (SLL) is minimized by adjusting the appropriate weight for each element. In addition, the optimal beam pattern is achieved by using CA for UCAA, which means that the main beam is steering to the desired user, while the nulls represent the interference signals. The excitation amplitude is supposed to be constant while the elements are assumed isotropic. The circular array number elements and the interspacing distance between them are setting as optimization parameters. The simulation results show that the CA rationally reacts to the changing environments, and it is valuable for SLL reduction. A −25 dB of relative SLL is achieved under beam scanning (0º) and (15º), respectively.

Beamforming against main lobe interference based on radial basis function neural network

Aiming at the problem that the performance of traditional beamforming algorithm deteriorates sharply in the presence of main lobe interference, a beamforming algorithm based on radial basis function (RBF) neural network is proposed. Firstly, the minimum variance distortionless response (MVDR) is used to solve the optimal beam pattern in the presence of side lobe interference. Then, the training set of RBF neural network is constructed according to the optimal beam pattern and the direction information of main lobe interference to train the network, so that the trained RBF neural network can suppress the main lobe interference while maintaining the ability of optimal beamforming. The simulation results show that the method can overcome the limitations of traditional beamforming algorithm, suppress the main lobe interference and side lobe interference, and form the correct beam direction. At the same time, the algorithm also has good real-time performance.

An improved multiobjective evolutionary algorithm based on decomposition approach and its application in antenna array beam pattern synthesis

AbstractAntenna arrays can enhance the performance and reduce the overhead of the wireless communication systems. However, the beam pattern synthesis of antenna arrays are difficult problems since the optimization properties are usually trade‐offs that affect each other. In this paper, we formulate a multiobjective beam pattern optimization problem (MBPOP) to simultaneously reduce the maximum sidelobe level (SLL) and achieve the nulls of the antenna array beam pattern. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a general and effective algorithm to solve the MOPs. However, it may be easy to lose population diversity and converge to local optimum. To overcome the issues above, we propose an improved MOEA/D (IMOEA/D) to deal with the formulated MBPOP. IMOEA/D introduces the normal distribution crossover operator (NDX), Lévy flight strategy and Euclidean distance‐based solution selection mechanism to enhance the performance of conventional MOEA/D to make it more suitable to solve the formulated MBPOP. Experiments are conducted and the results indicate that the proposed IMOEA/D has a better performance in terms of the convergence rate and population diversity compared to other algorithms for solving the formulated MBPOP.

An improved biogeography‐based optimization approach for beam pattern optimizations of linear and circular antenna arrays

AbstractAntenna array makes an increasingly valuable contribution to various fields of communications. The beam pattern synthesis is one of the significant properties of antenna array. In this article, suppressing the maximum sidelobe levels (SLLs) of different antenna arrays is our main objective. Specifically, we first formulate the beam pattern optimization problems of the linear antenna array (LAA) and circular antenna array (CAA), respectively, and propose an improved biogeography‐based optimization (IBBO) approach to tackle the issue. IBBO introduces the hybrid migration and hybrid mutation operators by adopting several improved factors to strengthen the effect of conventional biogeography‐based optimization (BBO) algorithm. Emulate experiments are built to evaluate the performance of the IBBO for dealing with the formulated antenna array beam pattern optimization problems comprehensively.

Collaborative Beamforming in Wireless Sensor Networks Using a Novel Particle Swarm Optimization Algorithm Variant

Wireless Sensor Network (WSN) nodes inherently feature limited transmission range. A recent solution to the long range transmission problem is Collaborative Beamforming (CBF). The outcome of an ideal CBF process ought to be a radiation pattern whose main beam is oriented towards the intended transmission destination (WSN sink), inherently increasing the transmission range. Owing to the random and complex nature of WSNs, development and use of improved metaheuristic algorithms in CBF is of essence. This paper delves into the development and application of an improved Particle Swarm Optimization (PSO) algorithm in CBF from the perspective of a 3 dimension WSN configuration (wherein the sink is elevated from the nodes’ plane). The modifications done on the PSO algorithm entail use of fuzzy-adaptive confidence and inertia weight parameters alongside a particle culling procedure. The modified PSO algorithm has been christened Culled Fuzzy Adaptive Particle Swarm Optimization (CFAPSO) algorithm. Comparisons against use of a linearly-adaptive PSO algorithm variant, basic PSO algorithm and the Genetic Algorithm (GA) in CBF have established the superiority of the proposed CFAPSO algorithm. The CFAPSO algorithm is found to generate a beamsteering outcome statistically identical to that of conventional beamsteering. It is noteworthy that conventional beamsteering is inapplicable to beam-pattern optimization with respect to processes such as sidelobe minimization, nulling among others. The developed CFAPSO algorithm befits utilization in a plethora of CBF schemes.

A beamforming study of the linear antenna array using grey wolf optimization algorithm

<p><br />The grey wolf optimization (GWO) algorithm is considered an inspired meta-heuristic algorithm, which inspired by the social hierarchy and hunting behavior of the grey wolves. GWO has a high-performance capability of solving constrained, as well as unconstrained optimization problems. In this paper, the beamforming of smart antennas in a code division multiple access system based on the GWO algorithm is investigated. The sidelobe level (SLL) is minimized along with peak sidelobe level reduction, as well as an optimal beam pattern has been accomplished by using GWO to uniform linear antenna arrays. In this work, an amplitude is introduced as constant, while the interspacing distance between antenna array elements and the number of elements in a linear array are variables. The simulation results show that a faster convergence and likely high accurate beamforming are gained using GWO based method. Finally, it is shown that the GWO outperforms the genetic algorithm (GA) based method.</p>

An improved cuckoo search with reverse learning and invasive weed operators for suppressing sidelobe level of antenna arrays

AbstractIn order to overcome some shortcomings including the premature convergence and slow convergence speed at later evolution stage of conventional cuckoo search (CS) algorithm for the sidelobe suppressions of antenna arrays, an improved CS with reverse learning and invasive weed operators (ICSRLIWO) is proposed. First, ICSRLIWO algorithm generates reverse populations through opposition‐based learning method, then selects better individuals in the mixed population consists of the original and reversed population to form a high‐quality initial population. Second, ICSRLIWO introduces the reproducing, space diffusion and survival competition operators of invasive weed optimization to improve the population diversity and global search ability of conventional CS algorithm. Simulations are conducted based on 30 test functions in the CEC 2014 test suit to verify the performance of the proposed approach, and the results show that ICSRLIWO has higher solution accuracy and faster convergence speed than other comparison algorithms. In addition, ICSRLIWO also has advantages in solving the array antenna beam pattern optimization problems.

Transmit beamspace design for FDA–MIMO radar with alternating direction method of multipliers

Hybridization of a frequency diverse array (FDA) generated range-angle-time dependent beampattern with multiple input multiple output (MIMO) radar waveform diversity, namely, FDA-MIMO radar, provides more degrees-of-freedom to improve overall system performance. As the beampattern optimization needs an efficient method to approximate the desired beampattern and simultaneously minimize the cross-correlation sidelobes, we optimize the transmit beampattern over the beamspace matrix in this paper. That is, the transmit FDA antennas are divided into multiple overlapping subarrays and each subarray transmits an orthogonal waveform towards the target, whereas the weight vectors of all subarrays jointly form a beamspace matrix. Since the optimization problem turns to be a non-convex problem with a constant energy constraint, we propose an efficient algorithm based on alternating direction method of multipliers (ADMM) to solve it, which has a very fast convergence speed. The effectiveness and superiorities of the proposed method over existing methods are verified by extensive simulation results.