A UoI-Optimal Policy for Timely Status Updates with Resource Constraint

Timely status updates are critical in remote control systems such as autonomous driving and the industrial Internet of Things, where timeliness requirements are usually context dependent. Accordingly, the Urgency of Information (UoI) has been proposed beyond the well-known Age of Information (AoI) by further including context-aware weights which indicate whether the monitored process is in an emergency. However, the optimal updating and scheduling strategies in terms of UoI remain open. In this paper, we propose a UoI-optimal updating policy for timely status information with resource constraint. We first formulate the problem in a constrained Markov decision process and prove that the UoI-optimal policy has a threshold structure. When the context-aware weights are known, we propose a numerical method based on linear programming. When the weights are unknown, we further design a reinforcement learning (RL)-based scheduling policy. The simulation reveals that the threshold of the UoI-optimal policy increases as the resource constraint tightens. In addition, the UoI-optimal policy outperforms the AoI-optimal policy in terms of average squared estimation error, and the proposed RL-based updating policy achieves a near-optimal performance without the advanced knowledge of the system model.     

Attention-Shared Multi-Agent Actor–Critic-Based Deep Reinforcement Learning Approach for Mobile Charging Dynamic Scheduling in Wireless Rechargeable Sensor Networks

The breakthrough of wireless energy transmission (WET) technology has greatly promoted the wireless rechargeable sensor networks (WRSNs). A promising method to overcome the energy constraint problem in WRSNs is mobile charging by employing a mobile charger to charge sensors via WET. Recently, more and more studies have been conducted for mobile charging scheduling under dynamic charging environments, ignoring the consideration of the joint charging sequence scheduling and charging ratio control (JSSRC) optimal design. This paper will propose a novel attention-shared multi-agent actor–critic-based deep reinforcement learning approach for JSSRC (AMADRL-JSSRC). In AMADRL-JSSRC, we employ two heterogeneous agents named charging sequence scheduler and charging ratio controller with an independent actor network and critic network. Meanwhile, we design the reward function for them, respectively, by considering the tour length and the number of dead sensors. The AMADRL-JSSRC trains decentralized policies in multi-agent environments, using a centralized computing critic network to share an attention mechanism, and it selects relevant policy information for each agent at every charging decision. Simulation results demonstrate that the proposed AMADRL-JSSRC can efficiently prolong the lifetime of the network and reduce the number of death sensors compared with the baseline algorithms.     

Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning

Multicasting in wireless systems is a natural way to exploit the redundancy in user requests in a content centric network. Power control and optimal scheduling can significantly improve the wireless multicast network’s performance under fading. However, the model-based approaches for power control and scheduling studied earlier are not scalable to large state spaces or changing system dynamics. In this paper, we use deep reinforcement learning, where we use function approximation of the Q-function via a deep neural network to obtain a power control policy that matches the optimal policy for a small network. We show that power control policy can be learned for reasonably large systems via this approach. Further, we use multi-timescale stochastic optimization to maintain the average power constraint. We demonstrate that a slight modification of the learning algorithm allows tracking of time varying system statistics. Finally, we extend the multi-time scale approach to simultaneously learn the optimal queuing strategy along with power control. We demonstrate the scalability, tracking and cross-layer optimization capabilities of our algorithms via simulations. The proposed multi-time scale approach can be used in general large state-space dynamical systems with multiple objectives and constraints, and may be of independent interest.     

A robust super-resolution approach with sparsity constraint in acoustic imaging

Acoustic imaging is a standard technique for mapping acoustic source powers and positions from limited observations on microphone sensors, which often causes an ill-conditioned inverse problem. In this article, we firstly improve the forward model of acoustic power propagation by considering background noises at the sensor array, and the propagation uncertainty caused by wind tunnel effects. We then propose a robust super-resolution approach via sparsity constraint for acoustic imaging in strong background noises. The sparsity parameter is adaptively derived from the sparse distribution of source powers. The proposed approach can jointly reconstruct source powers and positions, as well as the background noise power. Our approach is compared with the conventional beamforming, deconvolution and sparse regularization methods by simulated, wind tunnel data and hybrid data respectively. It is feasible to apply the proposed approach for effectively mapping monopole sources in wind tunnel tests.     

提高空间目标温度测量精度的波段优选方法

为提高空间目标温度的测量精度而进行波段优选方法的探索。建立空间目标温度测量数学模型和波段优选评价函数数学模型,仿真分析探测波段对红外系统的测温灵敏度、温度分辨力和信噪比的影响,实现系统探测波段的优选。仿真结果表明,优选的探测波段为中心波长范围8.0 m～9.6 m,波段宽度60 nm,温度分辨力最高可达到0.06 K,同时可获得大于5的信噪比。结论:提出的方法可以使测温灵敏度和温度分辨力分别平均有8.7%和11.3%的提高。     

Research on Joint Resource Allocation for Multibeam Satellite Based on Metaheuristic Algorithms

With the rapid growth of satellite communication demand and the continuous development of high-throughput satellite systems, the satellite resource allocation problem—also called the dynamic resources management (DRM) problem—has become increasingly complex in recent years. The use of metaheuristic algorithms to obtain acceptable optimal solutions has become a hot topic in research and has the potential to be explored further. In particular, the treatment of invalid solutions is the key to algorithm performance. At present, the unused bandwidth allocation (UBA) method is commonly used to address the bandwidth constraint in the DRM problem. However, this method reduces the algorithm’s flexibility in the solution space, diminishes the quality of the optimized solution, and increases the computational complexity. In this paper, we propose a bandwidth constraint handling approach based on the non-dominated beam coding (NDBC) method, which can eliminate the bandwidth overlap constraint in the algorithm’s population evolution and achieve complete bandwidth flexibility in order to increase the quality of the optimal solution while decreasing the computational complexity. We develop a generic application architecture for metaheuristic algorithms using the NDBC method and successfully apply it to four typical algorithms. The results indicate that NDBC can enhance the quality of the optimized solution by 9–33% while simultaneously reducing computational complexity by 9–21%.     

Data-Based Optimal Bandwidth for Kernel Density Estimation of Statistical Samples

It is a common practice to evaluate probability density function or matter spatial density function from statistical samples. Kernel density estimation is a frequently used method, but to select an optimal bandwidth of kernel estimation, which is completely based on data samples, is a long-term issue that has not been well settled so far. There exist analytic formulae of optimal kernel bandwidth, but they cannot be applied directly to data samples, since they depend on the unknown underlying density functions from which the samples are drawn. In this work, we devise an approach to pick out the totally data-based optimal bandwidth. First, we derive correction formulae for the analytic formulae of optimal bandwidth to compute the roughness of the sample's density function. Then substitute the correction formulae into the analytic formulae for optimal bandwidth, and through iteration we obtain the sample's optimal bandwidth. Compared with analytic formulae, our approach gives very good results, with relative differences from the analytic formulae being only 2%~3% for sample size larger than 10⁴. This approach can also be generalized easily to cases of variable kernel estimations.     

Optimal Information Update for Energy Harvesting Sensor with Reliable Backup Energy

The timely delivery of status information collected from sensors is critical in many real-time applications, e.g., monitoring and control. In this paper, we consider a scenario where a wireless sensor sends updates to the destination over an erasure channel with the supply of harvested energy and reliable backup energy. We adopt the metric age of information (AoI) to measure the timeliness of the received updates at the destination. We aim to find the optimal information updating policy that minimizes the time-average weighted sum of the AoI and the reliable backup energy cost. First, when all the environmental statistics are assumed to be known, the optimal information updating policy exists and is proved to have a threshold structure. Based on this special structure, an algorithm for efficiently computing the optimal policy is proposed. Then, for the unknown environment, a learning-based algorithm is employed to find a near-optimal policy. The simulation results verify the correctness of the theoretical derivation and the effectiveness of the proposed method.     

Joint offloading design and bandwidth allocation for RIS-aided multiuser MEC networks

This article examines a multiuser intelligent reflecting surface (RIS) aided mobile edge computing (MEC) system, where multiple edge nodes (ENs) with powerful calculating resources at the network can help compute the calculating tasks from the users through wireless channels. We evaluate the system performance by using the performance metric of communication and computing delay. To enhance the system performance by reducing the network delay, we jointly optimize the unpacking design and wireless bandwidth allocation, whereas the task unpacking optimization is solved by using the deep deterministic policy gradient (DDPG) algorithm. As to the bandwidth allocation, we propose three analytical solutions, where criterion I performs an equal bandwidth allocation, criterion II performs the allocation based on the transmission data rate, while criterion III performs the allocation based on the transmission delay. We finally provide simulation results to show that the proposed optimization on the task unpacking and bandwidth allocation is effective in decreasing the network delay.     

Dual adaption based evolutionary algorithm for optimized the smart healthcare communication service of the Internet of Things in smart city

The Internet of Things (IoT) is a revolutionary technique of sharing data for smart devices that generates huge amounts of data from smart healthcare systems. Therefore, healthcare systems utilize the convergence power and traffic analysis of sensors that cannot be satisfactorily handled by the IoT. In this article, a novel mutation operator is devised and incorporated with the differential evolution (DE) algorithm. Two tests have been conducted in the validation process. Firstly, the newly dual adaption-based operators incorporated with the differential evolution algorithm are being proposed. The proposed approach provides sufficient diversity and enhances the search speed of nature’s local and global search environments in the problem. The proposed method incorporates the application of IoT-based smart healthcare. Second, an application-based test has been conducted, in which the proposed approach is applied to the application in the smart healthcare system. Therefore, IoT sensor deployment is an optimization problem to minimize service time, delay, and energy loss by considering the communication constraint between sensors(objects). The proposed algorithm is applied in this article to solve this optimization problem. Further, in the experimentation and comparative study, the proposed method is superior to the standard evolutionary algorithms in IoT applications concerning the minimum number of function evaluations and minimization of traffic services. The proposed approach also achieves efficiency in the minimum loss of energy in each service and reduces load and delay.     

一种基于延时和带宽约束的纳米级互连线优化模型

基于RLC互连线延时模型,通过缓冲器插入和改变互连线宽及线间距,提出了一种基于延时和带宽约束的互连功耗-缓冲器面积的乘积优化模型.基于90 nm,65 nm和45 nm CMOS工艺验证了互连线优化模型,在牺牲1/3和1/2的带宽的前提下,平均能够节省46%和61%的互连功耗,以及65%和83%的缓冲器面积,能应用于纳米级SOC的计算机辅助设计. 关键词： 纳米互连功耗 缓冲器面积 延时 带宽     

Optimally balancing data and pilot power for adaptive MIMO-OFDM systems

While pilot symbols facilitate channel estimation, they reduce the transmit energy for data symbols per OFDM symbol under a fixed total transmit power constraint. In this paper, we investigate the effect of the pilot-to-data power ratio (PDPR) on multilevel quadrature amplitude modulation (M-QAM) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with adaptive modulation in order to provide a basic framework for finding the optimal PDPR in current and emerging standards using MIMO-OFDM. In particular, we derive the optimal PDPR in terms of average symbol error rate (SER) and spectral efficiency according to different receiver types such as zero-forcing (ZF) and minimum mean square error (MMSE). Employing the optimal PDPR results in higher spectral efficiency and lower SER without using any additional resource.     

基于物联网的小区智能照明管理系统设计与实现

针对国内中小型LED照明企业的艰难处境和政府倡导低碳,绿色环保照明政策现状,提出了一种基于电力载波通信技术的小区智能照明管理系统的设计方案。采用微控制器、PLC芯片和传感器构建智能控制终端,实现对单灯电能、光强度等数据采集。集中器集成GPRS通信模块负责与远程控制中心建立连接,控制终端与集中器通过电力线相连,实现PC端和移动端对LED照明设备的多平台控制。重点阐述了智能控制终端的软硬件设计、系统的自定义通信协议和远程控制方法。系统实测结果表明,该系统数据传输可靠、响应时间短、成本低,易于进行远程管理和控制。     

Joint optimal fair cooperative spectrum sensing and transmission in cognitive radio

Since the sensing power consumption of cooperative spectrum sensing (CSS) will decrease the throughput of secondary users (SU) in cognitive radio (CR), a joint optimal model of fair CSS and transmission is proposed in this paper, which can compensate the sensing overhead of cooperative SUs. The model uses the periodic listen-before-transmission method, where each SU is assigned a portion of channel bandwidth, when the primary user (PU) is estimated to be free by the coordinator. Then, a joint optimization problem of local sensing time, number of cooperative SUs, transmission bandwidth and power is formulated, which can compensate the sensing overhead of cooperative SUs appropriately through choosing suitable compensating parameter. The proposed optimization problem can be solved by the Polyblock algorithm. Simulation results show that compared with the traditional model, the total system throughput of the fairness cooperation model decreases slightly, but the total throughput of the cooperative SUs improves obviously.     

Channel Quality-Based Optimal Status Update for Information Freshness in Internet of Things

This paper investigates the status updating policy for information freshness in Internet of things (IoT) systems, where the channel quality is fed back to the sensor at the beginning of each time slot. Based on the channel quality, we aim to strike a balance between the information freshness and the update cost by minimizing the weighted sum of the age of information (AoI) and the energy consumption. The optimal status updating problem is formulated as a Markov decision process (MDP), and the structure of the optimal updating policy is investigated. We prove that, given the channel quality, the optimal policy is of a threshold type with respect to the AoI. In particular, the sensor remains idle when the AoI is smaller than the threshold, while the sensor transmits the update packet when the AoI is greater than the threshold. Moreover, the threshold is proven to be a non-increasing function of channel state. A numerical-based algorithm for efficiently computing the optimal thresholds is proposed for a special case where the channel is quantized into two states. Simulation results show that our proposed policy performs better than two baseline policies.     

Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment

An Unmanned Aerial Vehicle (UAV) can greatly reduce manpower in the agricultural plant protection such as watering, sowing, and pesticide spraying. It is essential to develop a Decision-making Support System (DSS) for UAVs to help them choose the correct action in states according to the policy. In an unknown environment, the method of formulating rules for UAVs to help them choose actions is not applicable, and it is a feasible solution to obtain the optimal policy through reinforcement learning. However, experiments show that the existing reinforcement learning algorithms cannot get the optimal policy for a UAV in the agricultural plant protection environment. In this work we propose an improved Q-learning algorithm based on similar state matching, and we prove theoretically that there has a greater probability for UAV choosing the optimal action according to the policy learned by the algorithm we proposed than the classic Q-learning algorithm in the agricultural plant protection environment. This proposed algorithm is implemented and tested on datasets that are evenly distributed based on real UAV parameters and real farm information. The performance evaluation of the algorithm is discussed in detail. Experimental results show that the algorithm we proposed can efficiently learn the optimal policy for UAVs in the agricultural plant protection environment.     

Wideband optical absorber based on plasmonic metamaterial cross structure

The optical absorber with Fano response is valuable for various applications such as solar cells or optical sensors. In this paper, we have modeled an optical plasmonic metamaterial absorber which contains a broken cross as an elementary cell along with four rectangular loads to improve the absorbance and achieve a Fano response within a wide bandwidth at 190–245 THz (25%). The bandwidth of the proposed structure is more than conventional metamaterial absorbers. The prototype absorber has a remarkable enhancement in the electric field in comparison with the simple cross model and the reflection value has reduced to ??47 dB. The parametric studies show how the gap capacitance controls the bandwidth, resonance frequency and the reflection value of the absorber, therefore we can consider this technique as a way to enhance the metamaterial absorber’s bandwidth. The proposed structure can be used as an optical refractive index sensor while the Fano line-shape provides a higher figure of merit (FOM) compared with many others. For this structure, the FOM has obtained as 10,660. The Finite Integration Technique with Perfect Boundary Approximation used for the simulation.     

An energy efficient scheme by exploiting multi-hop D2D communications for 5G networks

The multi-hop Device-to-Device (M-D2D) communication has a potential to serve as a promising technology for upcoming 5G networks. The prominent reason is that the M-D2D communication has the potential to improve coverage, enhanced spectrum efficiency, better link quality, and energy-efficient communication. One of the major challenges for M-D2D communication is the mitigation of interference between the cellular user (CUs) and M-D2D users. Considering this mutual interference constraint, this work investigates the problem of optimal matching of M-D2D links and CUs to form spectrum-sharing partners to maximize overall sum rates of the cell under QoS and energy efficiency (EE) constraints. In this paper, we investigate the interference management for multi-hop (more than one-hop) D2D communication scenarios where we propose a channel assignment scheme along with a power allocation scheme. The proposed channel assignment scheme is based on the Hungarian method in which the channel assignment for M-D2D pairs is done by minimum interference value. The power allocation scheme is based on Binary Particle swarm optimization (BPSO). This scheme calculates the specific power values for all the individual M-D2D links. We have done a comprehensive simulation and the result portrays that our proposed scheme performs better compared to the previous work mentioned in the literature. The results clearly indicate that the proposed scheme enhances the EE of up to 13% by producing the optimal assignment of channels and power for the CUs and M-D2D users.     

Adaptive Diagnosis for Fault Tolerant Data Fusion Based on α-Rényi Divergence Strategy for Vehicle Localization

When applying a diagnostic technique to complex systems, whose dynamics, constraints, and environment evolve over time, being able to re-evaluate the residuals that are capable of detecting defaults and proposing the most appropriate ones can quickly prove to make sense. For this purpose, the concept of adaptive diagnosis is introduced. In this work, the contributions of information theory are investigated in order to propose a Fault-Tolerant multi-sensor data fusion framework. This work is part of studies proposing an architecture combining a stochastic filter for state estimation with a diagnostic layer with the aim of proposing a safe and accurate state estimation from potentially inconsistent or erroneous sensors measurements. From the design of the residuals, using α-Rényi Divergence (α-RD), to the optimization of the decision threshold, through the establishment of a function that is dedicated to the choice of α at each moment, we detail each step of the proposed automated decision-support framework. We also dwell on: (1) the consequences of the degree of freedom provided by this α parameter and on (2) the application-dictated policy to design the α tuning function playing on the overall performance of the system (detection rate, false alarms, and missed detection rates). Finally, we present a real application case on which this framework has been tested. The problem of multi-sensor localization, integrating sensors whose operating range is variable according to the environment crossed, is a case study to illustrate the contributions of such an approach and show the performance.     

考虑自热效应的互连线功耗优化模型

基于互连线的分布式功耗模型,考虑自热效应的同时采用非均匀互连线结构,提出了一种基于延时、带宽、面积、最小线宽和最小线间距约束的互连动态功耗优化模型.分别在90和65 nm互补金属氧化物半导体工艺节点下验证了功耗优化模型的有效性,在工艺约束下同时不牺牲延时、带宽和面积所提模型能够降低高达35％互连线功耗.该模型适用于片上网络构架中大型互连路由结构和时钟网络优化设计.