KEYWORDS: Image compression, Data compression, Neural networks, Telecommunications, Education and training, Data transmission, Terbium, Algorithm development, Signal attenuation, Environmental monitoring
In response to the demand for data transmission efficiency and accuracy for the diversity of power line carrier services in China, an adaptive data compression scheme is introduced in the paper, which is trained to compress and decompress the data after dividing it into data blocks, and selects an appropriate compression procedure by predicting the data entropy through deep neural network inference. By dividing the data blocks and compressing them according to the characteristics, this compression scheme avoids the worst compression rate and ensures that the compression rate of various types of business data is close to the optimal value. This scheme has some reference value for power line carrier multi-class service data transmission
With the rapid development of new power systems and the stringent communication requirements of electric hybrid services, the 5G interface and access control technologies are required to improve to meet the differentiated demands of low latency, massive connection, and so on. In this paper, we study the 5G interface and access control technology for massive device connection in electric hybrid service scenario. The key 5G interfaces and their functions are firstly introduced. Then, we consider an average access delay minimization problem under the minimum signal-to-noise ratio (SNR) and the maximum delay requirements. A reinforcement learning-based access control algorithm is proposed to learn and dynamically adjust the optimal base station selection strategy to alleviate access congestion and reduce the access delay. Simulation results show the superior performance of the proposed algorithm in the average access delay and collision probability
The wide variety of 5G multi-scenario hybrid power data leads to the problems of poor encryption security and high time cost in traditional data encryption methods. To ensure the 5G network security of power data, a hybrid 5G classified and hierarchical electric power data encryption method in cloud storage with multiple features is proposed. By collecting multi-source power data information in cloud storage space and pre-processing it, multiple features in the data can be extracted and fused, and different types of data will be encrypted. SM4 is used to encrypt the data transmitted by the acquisition system, and the corresponding ciphertext is generated. Based on the feature fusion results of power data, the classification and grading of power data are realized. Simulation results show that encrypting and protecting power data through the proposed method can effectively reduce memory consumption and has better practicability in the actual situation of resource efficiency.
KEYWORDS: Telecommunications, Data transmission, Data conversion, Power grids, Mathematical optimization, Computer simulations, Systems modeling, Network architectures, Mobile communications
With the rapid development of new power systems and 5G communication technology, the demand for electric hybrid services becomes more complex and diversified. However, the existing 5G interface management and resource allocation methods are difficult to adapt to the application scenario of electric hybrid service. In this paper, the 5G uplink communication system adapted to electric hybrid service scenarios is modeled. 5G interface management and resource allocation algorithm based on reinforcement learning is adopted to maximize the uplink transmission rate of the overall network system. Simulation results show that the proposed algorithm has excellent performance compared to the existing algorithms.
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