results: simulation结果表明，提案的设计可以实现更高的宽带幅增强，而且二层和三层设计的宽带幅增强都高于常用的孤波变换器。一个使用38个单元细节的二层匹配镜被制造并通过打开端面导向管来验证实验结果。Abstract
This paper presents a non-zoned discrete dielectric lens comprising two or three matching layers to reduce the 50-110 GHz frequency range reflections. Based on Chebyshev and binomial multi-section transformers, the designed models use matching layers at the top and bottom. In addition, the presented designs use pins instead of the conventional slots for the matching layers, thus easing the manufacturing process. The results show that the broadband realized gain obtained using the proposed design is higher for both the two- and three-layer design than the commonly used quarter-wave transformer. A Binomial lens with two matchings layers using 38 unit cells is fabricated and illuminated by an open-ended waveguide to validate the simulation results obtained using CST Microwave Studio. The fabrication process uses stereolithography additive manufacturing.
摘要
Translation notes:* "non-zoned" is translated as "无区域" (wú zhōu yì)* "discrete" is translated as "分立" (fēn lí)* "dielectric lens" is translated as "电性透镜" (diàn xìng tōu jìng)* "Chebyshev and binomial multi-section transformers" are translated as "Chebychev和二次多段变换器" (Chébyshev hé èr chóng duō jiān biàn huà)* "matching layers" are translated as "匹配层" (pǐ bèi cè)* "pins" are translated as "钉子" (dī zǐ)* "stereolithography additive manufacturing" is translated as "材料层加工" (cái wù jiā gōng)
Threat-Based Resource Allocation Strategy for Target Tracking in a Cognitive Radar Network
methods: 本研究使用了威胁在干扰 radar资源分配中的使用，并解决了干扰 dwell time 分配问题使用第二个卷积 Program (SOCP)。
results: 数值仿真结果表明，提出的方案可以增强干扰 radar的作战评价和目标跟踪性能。Abstract
Cognitive radar is developed to utilize the feedback of its operating environment obtained from a beam to make resource allocation decisions by solving optimization problems. Previous works focused on target tracking accuracy by designing an evaluation metric for an optimization problem. However, in a real combat situation, not only the tracking performance of the target but also its operational perspective should be considered. In this study, the usage of threats in the allocation of radar resource is proposed for a cognitive radar framework. Resource allocation regarding radar dwell time is considered to reflect the operational importance of target effects. The dwell time allocation problem is solved using a Second-Order Cone Program (SOCP). Numerical simulations are performed to verify the effectiveness of the proposed framework.
摘要
cognitive radar 是为了利用它的运作环境回授，通过解决优化问题进行资源配置决策。 previous works 专注于目标追踪精度，通过设计评估度量来解决优化问题。但在实战情况下，不仅需要追踪目标的追踪性能，还需要考虑目标的运作价值。本研究提出了在 cognitive radar 框架中使用威胁来配置激光资源。这个问题的解决方案是使用第二类拓扑程式 (SOCP)。 numerical simulations 是进行验证的。
Beamforming Design for Hybrid IRS-aided AF Relay Wireless Networks
results: 对比基准方案（通过passive IRS-aided AF relay和只有AF relay网络），提档LC-SCA-FP方法实现的速率均高于基准方案Abstract
In this paper, a hybrid IRS-aided amplify-and-forward (AF) relay wireless network is put forward, where the hybrid IRS is made up of passive and active elements. For maximum signal-to-noise ratio (SNR), a low-complexity method based on successive convex approximation and fractional programming (LC-SCA-FP) is proposed to jointly optimize the beamforming matrix at AF relay and the reflecting coefficient matrices at IRS. Simulation results verify that the rate achieved by the proposed LC-SCA-FP method surpass those of the benchmark schemes, namely the passive IRS-aided AF relay and only AF relay network.
摘要
在本文中，一种混合式IRS受助的增强前向（AF）无线网络被提出，其中IRS由pasive和活动元素组成。为了最大化信号噪听比（SNR），一种低复杂度的LC-SCA-FP方法被提议来同时优化AF关键矩阵和IRS反射矩阵。实验结果表明，提议的LC-SCA-FP方法可以超越参考方案，即pasive IRS受助的AF关键矩阵和只有AF关键矩阵网络。Here's the breakdown of the translation:* "In this paper" is translated as "在本文中" (在本文中).* "a hybrid IRS-aided amplify-and-forward (AF) relay wireless network" is translated as "一种混合式IRS受助的增强前向（AF）无线网络" (一种混合式IRS受助的增强前向（AF）无线网络).* "where the hybrid IRS is made up of passive and active elements" is translated as "其中IRS由pasive和活动元素组成" (其中IRS由pasive和活动元素组成).* "For maximum signal-to-noise ratio (SNR)" is translated as "为了最大化信号噪听比（SNR）" (为了最大化信号噪听比（SNR）).* "a low-complexity method based on successive convex approximation and fractional programming (LC-SCA-FP)" is translated as "一种低复杂度的LC-SCA-FP方法" (一种低复杂度的LC-SCA-FP方法).* "is proposed to jointly optimize the beamforming matrix at AF relay and the reflecting coefficient matrices at IRS" is translated as "提议同时优化AF关键矩阵和IRS反射矩阵" (提议同时优化AF关键矩阵和IRS反射矩阵).* "Simulation results verify that the rate achieved by the proposed LC-SCA-FP method surpass those of the benchmark schemes" is translated as "实验结果表明，提议的LC-SCA-FP方法可以超越参考方案" (实验结果表明，提议的LC-SCA-FP方法可以超越参考方案).* "namely the passive IRS-aided AF relay and only AF relay network" is translated as "即pasive IRS受助的AF关键矩阵和只有AF关键矩阵网络" (即pasive IRS受助的AF关键矩阵和只有AF关键矩阵网络).
A Deep Reinforcement Learning Approach for Improving Age of Information in Mission-Critical IoT
results: 比较related-work的结果，本研究的方法可以实现信息新鲜度的优化，并且实现了AoI过衡概率的下降。Abstract
The emerging mission-critical Internet of Things (IoT) play a vital role in remote healthcare, haptic interaction, and industrial automation, where timely delivery of status updates is crucial. The Age of Information (AoI) is an effective metric to capture and evaluate information freshness at the destination. A system design based solely on the optimization of the average AoI might not be adequate to capture the requirements of mission-critical applications, since averaging eliminates the effects of extreme events. In this paper, we introduce a Deep Reinforcement Learning (DRL)-based algorithm to improve AoI in mission-critical IoT applications. The objective is to minimize an AoI-based metric consisting of the weighted sum of the average AoI and the probability of exceeding an AoI threshold. We utilize the actor-critic method to train the algorithm to achieve optimized scheduling policy to solve the formulated problem. The performance of our proposed method is evaluated in a simulated setup and the results show a significant improvement in terms of the average AoI and the AoI violation probability compared to the related-work.
摘要
“现代互联网Of Things（IoT）在远程医疗、感觉交互和工业自动化等mission-critical应用中扮演着重要的角色，其中快速传递状态更新是关键。信息年龄（AoI）是一个有效的度量器，用于捕捉和评估目标端信息的新鲜度。然而，基于AoI的平均值的系统设计可能不够 capture mission-critical应用的需求，因为平均值会消除EXTREME事件的影响。在本文中，我们提出了基于深度强化学习（DRL）的算法，用于提高mission-critical IoT应用中的AoI。我们的目标是将AoI-based度量器中的平均AoI和超过AoI阈值的概率加权和。我们使用actor-critic方法来训练算法，以实现优化的排程策略，解决表述的问题。我们的提出的方法在模拟setup中进行了评估，结果显示与相关工作相比，有显著的提高 average AoI和AoI违反概率。”Note: The translation is in Simplified Chinese, which is the standard writing system used in mainland China. If you need the translation in Traditional Chinese, please let me know.
for: This paper proposes new bounds for parameter estimation in statistical signal processing, which can be used to evaluate the performance of estimation methods.
methods: The proposed bounds are based on the Bobrovsky–Mayer-Wolf–Zakai class of bounds and are optimized using a weighting function.
results: The proposed bounds are asymptotically attainable and coincide with the expected Cramér-Rao bound (ECRB) in several fundamental signal processing examples. Unlike the Bayesian Cramér-Rao bound (BCRB), the proposed bounds are valid for any estimator and do not require uniformly unbiasedness.Abstract
Performance bounds for parameter estimation play a crucial role in statistical signal processing theory and applications. Two widely recognized bounds are the Cram\'{e}r-Rao bound (CRB) in the non-Bayesian framework, and the Bayesian CRB (BCRB) in the Bayesian framework. However, unlike the CRB, the BCRB is asymptotically unattainable in general, and its equality condition is restrictive. This paper introduces an extension of the Bobrovsky--Mayer-Wolf--Zakai class of bounds, also known as the weighted BCRB (WBCRB). The WBCRB is optimized by tuning the weighting function in the scalar case. Based on this result, we propose an asymptotically tight version of the bound called AT-BCRB. We prove that the AT-BCRB is asymptotically attained by the maximum {\it a-posteriori} probability (MAP) estimator. Furthermore, we extend the WBCRB and the AT-BCRB to the case of vector parameters. The proposed bounds are evaluated in several fundamental signal processing examples, such as variance estimation of white Gaussian process, direction-of-arrival estimation, and mean estimation of Gaussian process with unknown variance and prior statistical information. It is shown that unlike the BCRB, the proposed bounds are asymptotically attainable and coincide with the expected CRB (ECRB). The ECRB, which imposes uniformly unbiasedness, cannot serve as a valid lower bound in the Bayesian framework, while the proposed bounds are valid for any estimator.
摘要
perf bounds for parameter estimation play a crucial role in statistical signal processing theory and applications. Two widely recognized bounds are the Cramér-Rao bound (CRB) in the non-Bayesian framework, and the Bayesian CRB (BCRB) in the Bayesian framework. However, unlike the CRB, the BCRB is asymptotically unattainable in general, and its equality condition is restrictive. This paper introduces an extension of the Bobrovsky--Mayer-Wolf--Zakai class of bounds, also known as the weighted BCRB (WBCRB). The WBCRB is optimized by tuning the weighting function in the scalar case. Based on this result, we propose an asymptotically tight version of the bound called AT-BCRB. We prove that the AT-BCRB is asymptotically attained by the maximum \emph{a-posteriori} probability (MAP) estimator. Furthermore, we extend the WBCRB and the AT-BCRB to the case of vector parameters. The proposed bounds are evaluated in several fundamental signal processing examples, such as variance estimation of white Gaussian process, direction-of-arrival estimation, and mean estimation of Gaussian process with unknown variance and prior statistical information. It is shown that unlike the BCRB, the proposed bounds are asymptotically attainable and coincide with the expected CRB (ECRB). The ECRB, which imposes uniformly unbiasedness, cannot serve as a valid lower bound in the Bayesian framework, while the proposed bounds are valid for any estimator.Here's the translation in Traditional Chinese: perf bounds for parameter estimation play a crucial role in statistical signal processing theory and applications. Two widely recognized bounds are the Cramér-Rao bound (CRB) in the non-Bayesian framework, and the Bayesian CRB (BCRB) in the Bayesian framework. However, unlike the CRB, the BCRB is asymptotically unattainable in general, and its equality condition is restrictive. This paper introduces an extension of the Bobrovsky--Mayer-Wolf--Zakai class of bounds, also known as the weighted BCRB (WBCRB). The WBCRB is optimized by tuning the weighting function in the scalar case. Based on this result, we propose an asymptotically tight version of the bound called AT-BCRB. We prove that the AT-BCRB is asymptotically attained by the maximum \emph{a-posteriori} probability (MAP) estimator. Furthermore, we extend the WBCRB and the AT-BCRB to the case of vector parameters. The proposed bounds are evaluated in several fundamental signal processing examples, such as variance estimation of white Gaussian process, direction-of-arrival estimation, and mean estimation of Gaussian process with unknown variance and prior statistical information. It is shown that unlike the BCRB, the proposed bounds are asymptotically attainable and coincide with the expected CRB (ECRB). The ECRB, which imposes uniformly unbiasedness, cannot serve as a valid lower bound in the Bayesian framework, while the proposed bounds are valid for any estimator.
A Fast Power Spectrum Sensing Solution for Generalized Coprime Sampling
results: simulations 结果显示，这个方案比现有的方法更具有低复杂度和快速执行速度，同时保持了相同的性能水准，而且在分布式群体enario中，模型误差的影响也仅对测定性能有轻微的影响。Abstract
The growing scarcity of spectrum resources, wideband spectrum sensing is required to process a prohibitive volume of data at a high sampling rate. For some applications, spectrum estimation only requires second-order statistics. In this case, a fast power spectrum sensing solution is proposed based on the generalized coprime sampling. By exploring the sensing vector inherent structure, the autocorrelation sequence of inputs can be reconstructed from sub-Nyquist samples by only utilizing the parallel Fourier transform and simple multiplication operations. Thus, it takes less time than the state-of-the-art methods while maintaining the same performance, and it achieves higher performance than the existing methods within the same execution time, without the need for pre-estimating the number of inputs. Furthermore, the influence of the model mismatch has only a minor impact on the estimation performance, which allows for more efficient use of the spectrum resource in a distributed swarm scenario. Simulation results demonstrate the low complexity in sampling and computation, making it a more practical solution for real-time and distributed wideband spectrum sensing applications.
摘要
随着频率资源的增加稀缺，宽带频率感知成为处理极高采样率的离散数据的必要手段。有些应用程序只需第二阶统计来进行频率估计。在这种情况下，一种快速的电力 спектrum感知解决方案基于泛函 coprime 采样。通过探索感知向量的内在结构，输入序列的自相关可以从低于 Nyquist 采样点重建，只需要平行傅里叶变换和简单的乘法操作。因此，它比现有方法更快速，同时保持同样的性能，而不需要预先估算输入的数量。此外，模型匹配的影响也很小，使得频率资源的使用更加高效，在分布式群组enario下。 simulation 结果表明，采样和计算的低复杂性使得这种解决方案在实时和分布式宽带频率感知应用中更加实用。