Wireless Communications and Mobile Computing

Global routing is a crucial step in the design of Very Large-Scale Integration (VLSI) circuits. However, most of the existing methods are heuristic algorithms, which cannot conjointly optimize the subproblems of global routing, resulting in congestion and overflow. In response to this challenge, an enhanced Deep Reinforcement Learning- (DRL-) based global router has been proposed, which comprises the following effective strategies. First, to avoid the overestimation problem generated by -learning, the proposed global router adopts the Double Deep -Network (DDQN) model. The DDQN-based global router has better performance in wire length optimization and convergence. Second, to avoid the agent from learning redundant information, an action elimination method is added to the action selection part, which significantly enhances the convergence performance of the training process. Third, to avoid the unfair allocation problem of routing resources in serial training, concurrent training is proposed to enhance the routability. Fourth, to reduce wire length and disperse routing resources, a new reward function is proposed to guide the agent to learn better routing solutions regarding wire length and congestion standard deviation. Experimental results demonstrate that the proposed algorithm outperforms others in several important performance metrics, including wire length, convergence performance, routability, and congestion standard deviation. In conclusion, the proposed enhanced DRL-based global router is a promising approach for solving the global routing problem in VLSI design, which can achieve superior performance compared to the heuristic method and DRL-based global router.

The integration of the industrial internet of things (IIoT) and blockchain has become a popular concept that provides IIoT with a trustworthy computing environment. Numerous IIoT nodes together form a decentralized network with rich location-aware computation resources, which can offer great data processing capabilities and low-latency services. However, we still face the challenges of how to efficiently process the massive IIoT data on resource-constrained IIoT nodes by blockchain smart contracts, as their storage capacity only allows them to store limited blockchain data. This work is aimed at improving the smart contract execution efficiency on these IIoT nodes by caching based on deep reinforcement learning. On the one hand, focusing on the characteristics of IIoT, the ledger structure, network architecture, and transaction flow are optimized. IIoT nodes are enabled to store and cache part of block data without affecting global data consistency. On the other hand, we formulated the blockchain caching problem as a Markov decision process and implemented a lightweight caching agent based on deep Q-learning. Proper features and a reward function are defined to minimize the execution delay of smart contracts. The extensive experimental results show that our proposed scheme can effectively reduce the data dissemination costs and smart contract execution delays of IIoT nodes that hold limited blockchain data.

Mobile edge computing (MEC) has produced incredible outcomes in the context of computationally intensive mobile applications by offloading computation to a neighboring server to limit the energy usage of user equipment (UE). However, choosing a pool of application components to offload in addition to the volume of data transfer along with the latency in communication is an intricate issue. In this article, we introduce a novel energy-efficient offloading scheme based on deep neural networks. The proposed scheme trains an intelligent decision-making model that picks a robust pool of application components. The selection is based on factors such as the remaining UE battery power, network conditions, the volume of data transfer, required energy by the application components, postponements in communication, and computational load. We have designed the cost function taking all the mentioned factors, get the cost for all conceivable combinations of component offloading decisions, pick the robust decisions over an extensive dataset, and train a deep neural network as a substitute for the exhaustive computations associated. Model outcomes illustrate that our proposed scheme is proficient in the context of accuracy, root mean square error (RMSE), mean absolute error (MAE), and energy usage of UE.

In this paper, we present an extremely compact ultrawideband (UWB) monopole microstrip patch antenna for a wireless body area network (WBAN). The proposed antenna is fabricated on a flexible Rogers RT-5880 dielectric substrate of thickness 0.5 mm and has an overall size of . The proposed antenna achieves a wideband characteristic with the help of a modified ground plane with a monopole pair. The monopole antenna is fed through a microstrip line and has a good impedance matching over a frequency band of 3.2 to 15 GHz (and beyond), with an axial ratio below 3 dB and a high efficiency of 77–95%. This antenna is designed to cover almost the complete UWB range; bandwidth for antenna is 11.52 GHz (3.48-15 GHz). The antenna has a realized gain of 2.3–7.2 dBi throughout the frequency band and has been tested for conformality. Measured results are found to be in good correlation with the simulated results. The antenna has also been tested for specific absorption rate (SAR) values within the simulation to compare with Federal Communications Commission (FCC) limits and verify their suitability for the Internet of Things- (IoT-) based wearable body area network.

With the commercialization of the fifth-generation mobile communication network (5G), the scale of the unmanned aerial vehicle (UAV) industry has continued to expand. However, the unregistered UAV has caused frequent harassment incidents at international airports, and the problem of UAV crimes is increasing. Radio technology supports long-distance detection of unregistered UAV and can be used as an efficient early warning method for unregistered UAV, which has attracted extensive attention from academia and industry. The classic UAV detection based on remote control signal method faces technical bottlenecks such as being easily affected by environmental noise, high complexity, and low detection accuracy. In the paper, an UAV remote control signal detection method is proposed based on cyclic spectrum features. More specifically, a dataset of UAV remote control signal UAV-CYCset is firstly constructed in the frequency domain. Based on UAV-CYCset dataset, a network architecture is proposed based on improved AlexNet, and the average detection accuracy of the improved model reaches 85% (from -10 dB to 10 dB) according to the simulation experiments.

This paper presents a novel approach, based on the whale optimization algorithm (WOA), for channel estimation in wireless communication systems. The proposed method provides a means to accurately estimate the wireless channel, while not requiring the statistical characteristics of the channel. This method uses the WOA to search for the best channel statistical characteristics toward the ultimate goal of having the smallest bit error rate (BER). The proposed approach is aimed at enhancing the efficiency of pilot-based OFDM systems under frequency-selective fading channels used in the performance testing of 5G New Radio gNodeB. In terms of BER and mean square error (MSE), the performance of the proposed WOA-based channel estimation algorithm is evaluated and compared with the conventional least square (LS) and minimum mean square error (MMSE) algorithms. The simulation results demonstrate that the proposed algorithm provides highly competitive performance over the MMSE algorithm and significantly outperforms the LS algorithm in a variety of system configurations. Since the requirement on prior channel statistics information makes the MMSE algorithm impractical or extremely complex, the proposed WOA-based channel estimation algorithm should be a suitable and promising candidate for dealing with channel estimation problems. The simulation framework is implemented in MATLAB and available upon request.