Study on multi-satellite cooperative spectrum cognitive method integrating Stackelberg game and federated learning by Xiaojin DING, Yehui XU, Wen BAO, Gengxin ZHANG

“…To solve the problem of the weak spectrum-cognitive ability caused by monitoring angle, direction resolution, limited processing ability and peak power for a low-earth-orbit (LEO) satellite, a multi-satellite cooperative spectrum cognitive method integrating Stackelberg game and federated learning was proposed.Firstly, considering the available computing resource, cognitive performance, processing and transmission delay of each spectrum cognitive satellite, a cooperative-satellite selection and computing-resource allocation algorithm was built for multiple spectrum-cognitive tasks.Secondly, based on the selected satellites and the allocated computing resources, a low-complexity multi-satellite cooperative spectrum cognitive strategy was further designed, which could automatically sense the spectrum holes, and detect interference as well as identify the modulation mode.Simulation results demonstrate that compared to the single-node cognitive method, the designed multi-satellite cooperative spectrum cognitive strategy can obtain a better cognitive performance.Moreover, comparing with the existing model, the model utilized in the designed strategy can effectively achieve 96.69% and 93.32% lower number of parameters and required floating point operations per second, whilst maintaining the performance.…”
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Secure communication approach for Internet of things based on C-RAN fronthaul compression by Yong WANG, Mu ZHOU, Zengshan TIAN, Jinjun WU

“…A resource allocation algorithm was proposed for downlink C-RAN secure communication system.This algorithm jointly optimized the parameters of base station (BS) mode,quantized noise and beamforming under the constraints of SINR requirements of Internet of things information receiver,eavesdropped receiver and fronthaul link capacity.Such a design was a non-convex optimization problem,and a step-by step optimization algorithm was proposed to decompose the original optimization problem efficiently.Semi-definite technique and function smoothing approach were adopted,and the local optimization solution of the original problem was obtained by the iterative different of convex method.Bi-section approach was further proposed to determine BS mode,and the transmitting power of the BSs were finally optimized.Simulation results indicate that the proposed joint optimization algorithm achieves better performance than group sparse optimization algorithm and baseline algorithm,and it has approximate optimal performance as the exhaustive search algorithm.…”
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The moderating role of professional grief in the relationship between job stress dimensions and turnover intention among nurses by Xue Liang, Jue Wang

“…Methods This study utilized linear regression models and the Extreme Gradient Boosting machine (XGBoost) learning algorithm to analyze the impact of the total score of job stress and their dimensions on turnover intention. …”
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Gemini: A Cascaded Dual-Agent DRL Framework for Task Chain Planning in UAV-UGV Collaborative Disaster Rescue by Mengxuan Wen, Yunxiao Guo, Changhao Qiu, Bangbang Ren, Mengmeng Zhang, Xueshan Luo

“…Specifically, this framework comprises a chain selection agent and a resource allocation agent: The chain selection agent plans paths for task chains, and the resource allocation agent distributes platform loads along generated paths. …”
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A multi objective optimization framework for smart parking using digital twin pareto front MDP and PSO for smart cities by Dinesh Sahu, Priyanshu Sinha, Shiv Prakash, Tiansheng Yang, Rajkumar Singh Rathore, Lu Wang

“…Evaluation outcomes also show that the proposed algorithm is better than Round Robin, Random Allocation, and Threshold Based algorithms in terms of 25% improvement in the search time, 18% better energy usage, and 30% less traffic congestion. …”
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Multihop cost awareness task migration with networking load balance technology for vehicular edge computing by Shih-Yang Lin, Jie-Qin Wang, Shi-Ming Peng, Miao-Hui Yang, Shuo Jia

“…This paper investigates dynamic resource allocation and task migration mechanisms for cooperative vehicular edge computing (VEC) servers to expand computing capabilities of local server. …”
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Discrete Phase Shift IRS-Assisted Energy Harvesting in Cognitive Radio Networks With Spectrum Sensing by Lilian Chiru Kawala, Guoquan Li, Mihertie Habtamu Demeke, Junzhou Xiong, Hao Xiong, Hang Hu

“…Simulation results demonstrate the superior performance of the proposed framework and the novel resource allocation algorithm based on alternating optimization. …”
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Dynamic Subchannel Assignment-Based Cross-Layer MAC and Network Protocol for Multihop Ad Hoc Networks by Khanh Nguyen Quang, Van Duc Nguyen, Hyunseung Choo

“…The proposed dynamic sub-channel assignment algorithm provides a new interference avoidance mechanism which solves several drawbacks of existing radio resource allocation techniques in wireless networks using OFDMA/TDD, such as the hidden node and exposed node problems, mobility, and cochannels interference in frequency (CCI). …”
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A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study. by Ilaria Amodeo, Giorgio De Nunzio, Genny Raffaeli, Irene Borzani, Alice Griggio, Luana Conte, Francesco Macchini, Valentina Condò, Nicola Persico, Isabella Fabietti, Stefano Ghirardello, Maria Pierro, Benedetta Tafuri, Giuseppe Como, Donato Cascio, Mariarosa Colnaghi, Fabio Mosca, Giacomo Cavallaro

“…The native sequences from fetal magnetic resonance imaging (MRI) will be collected. Data from different sources will be integrated and analyzed using ML and DL, and forecasting algorithms will be developed for each outcome. …”
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Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model. by Britta Trautwein, Meinrad Beer, Manfred Blobner, Bettina Jungwirth, Simone Maria Kagerbauer, Michael Götz

“…This would ensure optimal patient care and resource allocation and help initiate adequate patient treatment after being transferred from the recovery room to the ward. …”
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Application of 5G + edge computing technology in intelligent monitoring construction of electricity business office by Wei Cui, Junwei Li, Wei Ge, Bo Zhang, Tianwei Liu

“…This paper proposes a heterogeneous node deployment algorithm based on regional density, which divides the network regionally by raster and heterogeneous node communication range, and determines the optimal location of heterogeneous nodes by regional density, realizing the reasonable allocation and utilization of network resources. …”
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Path Algebra-Driven Classification Solution to Realize User-Centric Performance-Oriented Virtual Network Embeddings by Stelios Prekas, Panagiotis A. Karkazis, Panagiotis Trakadas

“…The intense diversity of the Next-Generation Networking environments like 6G and the forthcoming deployment of immersive applications with varied user-specific requirements transform the efficient allocation of resources into a real challenge. Traditional solutions like the shortest path algorithm and mono-constraint methodologies are inadequate to handle customized user-defined performance parameters and effectively classify physical resources according to these intricate demands. …”
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LEO computing satellite constellation design for heterogeneous QoS requirements by PENG Yuming, SUN Yijing, DI Boya

“…A two-stage constellation design algorithm was proposed, which firstly optimized the resource allocation strategy of computational satellites and then determined the constellation parameters under optimal QoS conditions. …”
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Genomic selection optimization in blueberry: Data‐driven methods for marker and training population design by Paul Adunola, Luis Felipe V. Ferrão, Juliana Benevenuto, Camila F. Azevedo, Patricio R. Munoz

“…Our contribution in this study is threefold: (i) for the genotyping resource allocation, the use of genetic data‐driven methods to select an optimal set of markers slightly improved prediction results for all the traits; (ii) for the long‐term implication, we carried out a simulation study and emphasized that data‐driven method results in a slight improvement in genetic gain over 30 cycles than random marker sampling; and (iii) for the phenotyping resource allocation, we compared different optimization algorithms to select training population, showing that it can be leveraged to increase predictive performances. …”
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Interference Mitigation in mmWave Heterogeneous Cloud-Radio Access Network: For Better Performance and User Connectivity by Najwan M. Swadi, Firas A. Sabir, Hamed S. Al-Raweshidy

“…It integrates User-RRH associations to mitigate interference, enhance network throughput (via Heuristic Algorithm) and RRH-BBU clustering (via k-means) to manage resources in the network. …”
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Improving energy efficiency for intelligent reflecting surface assisted PD-NOMA in EH relaying network by Hong Nguyen-Thi, Thuc Kieu-Xuan, Thang Le-Nhat, Anh Le-Thi

“…In particular, our proposed system consists of a source communicating with some destinations via the help of an energy-harvesting (EH) relay network that employs an amplify–and–forward protocol and a power–splitting (PS) scheme for EH. …”
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Research on Machine Learning-Based Extraction and Classification of Crop Planting Information in Arid Irrigated Areas Using Sentinel-1 and Sentinel-2 Time-Series Data by Lixiran Yu, Hongfei Tao, Qiao Li, Hong Xie, Yan Xu, Aihemaiti Mahemujiang, Youwei Jiang

“…The newly developed framework exhibits exceptional precision in categorization while maintaining impressive adaptability, offering crucial insights for optimizing agricultural operations and sustainable resource allocation in irrigation-dependent arid zones.…”
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Buffer-aided cooperative NOMA with power transfer by Long YANG, Li ZHAO, Yuchen ZHOU, Bingtao HE, Jian CHEN

“…To improve the throughput of multiuser simultaneous wireless information and power transfer systems, a buffer-aided cooperative non-orthogonal multiple access scheme with power transfer was designed to maximize the average throughput under the constraints of average/peak power, user’s rate, and the buffer stability.To reduce the optimization complexity, the Lyapunov’s method was introduced to convert the long-term average optimization problem into a series of time-discrete subproblems, and an adaptive transmission and resource allocation optimization algorithm was proposed, where the working mode, the user scheduling and the power allocation were dynamically optimized according to the time-varying channel/buffer state.Simulation results demonstrate that compared with the existing schemes, the proposed scheme can significantly enhance the average throughput whilst achieving the tradeoff between the delay and throughput.…”
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Content-based dynamic superframe adaptation for Internet of Medical Things by Yousaf Zia, Arshad Farhad, Faisal Bashir, Kashif Naseer Qureshi, Ghufran Ahmed

“…This article presents a content-based dynamic superframe adaptation algorithm for the low-powered Internet of Medical Things devices to address the resource utilization challenges. …”
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Research on deep reinforcement learning in Internet of vehicles edge computing based on Quasi-Newton method by ZHANG Jianwu, LU Zetao, ZHANG Qianhua, ZHAN Ming

“…To address the issues of ineffective task offloading decisions caused by multitasking and resource constraints in vehicular networks, the Quasi-Newton method deep reinforcement learning dual-phase online offloading (QNRLO) algorithm was proposed. …”
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