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The multi‐mode heterogeneous network combines the advantages of high‐speed power line communication (HPLC) and high radio frequency (HRF), ensuring service quality and meeting the requirements for data transmission delay and reliability even when devices are flexibly deployed. Queuing delay and privacy entropy are important metrics for managing multi‐mode heterogeneous internet of things (IoT) networks, which require collaborative optimization of the transmission phase (server selection and sub‐channel allocation) and the computing phase (computing resource allocation) to ensure low latency and high privacy entropy. However, existing communication‐computing collaborative optimization methods face issues such as low privacy security of electricity‐carbon service data, high difficulty in solving the joint optimization problem, and resource competition. Therefore, this paper proposes a privacy preservation‐driven communication‐computing collaboration method for the management of multi‐mode heterogeneous IoT networks. Firstly, the architecture for the management of multi‐mode heterogeneous IoT networks is constructed and a privacy entropy model for electricity‐carbon computing service data is established to measure the privacy security performance of the network management. Secondly, a joint optimization problem of queuing delay and privacy entropy under long‐term privacy entropy constraints are constructed and the long‐term privacy entropy constraints from short‐term decisions is decoupled based on Lyapunov optimization. Finally, a joint optimization algorithm for server selection and multi‐mode sub‐channel allocation driven by privacy protection is proposed. This algorithm reduces the three‐dimensional matching optimization problem among different devices, servers, and channels, and uses auction matching to solve the conflict of resource block selection, further optimizing the computing resource allocation of edge servers based on the Karush–Kuhn–Tucker (KKT) conditions. Simulation results show that the proposed algorithm effectively reduces queuing delay and improves privacy security of data transmission. This paper proposes a privacy protection‐driven communication‐computation collaboration method for managing multi‐mode heterogeneous Internet of Things networks, combining high power line communication and high radio frequency to ensure low latency and high privacy entropy. The method includes a joint optimization algorithm for server selection, sub‐channel allocation, and computational resource allocation, using auction matching and Lyapunov optimization to address queuing delay, resource competition, and privacy protection in internet of things network management. Simulation results show significant improvements in queuing delay and privacy security performance compared to baseline methods.

\"While some related books cover separate aspects of digital media and cloud computing, none integrate both of these areas together. Bridging the gap between digital media and cloud computing, this book brings together technologies for media/data communication, elastic media/data storage, security, authentication, cross-network media/data fusion, inter-device media interaction/reaction, data centers, platform as a service (PaaS), and software as a service (SaaS). The book also covers interesting applications involving digital media in the cloud. \"-- Provided by publisher.

With the rapid increase in traffic in the Internet era, new services on 5G mobile communication networks are gradually facing many problems such as high return bandwidth and low latency. The use of computers can quickly solve the above problems. This article has sent a computer-based 5G mobile communication network architecture. According to the construction of the network architecture, it can be concluded that the 5G mobile communication network is a network of communication and multi-level computing. Virtualization technology can be used to achieve communication, computing, and storage. Efficient sharing of resources. Facing the collaboration of communication and computing, the main challenge of the 5G network that integrates computers in terms of basic theory is network capacity analysis.

\"This handbook offers an unparalleled view of wireless personal area networking technologies and their associated protocols. It lifts the lid on their growing adoption within the consumer electronics, home automation, sports, and health and well-being markets. Bluetooth low energy, ZigBee, EnOcean, and ANT+ are comprehensively covered, along with other WPAN technologies including NFC, Wi-Fi, Bluetooth classic and high speed, and WHDI. It also features 802.11ac, the Internet of Things, Wireless USB, WiGig, and WirelessHD. The handbook shows how white space radio, cellular, and femtocells have inadvertently blurred the boundaries between personal and wide area communications, creating disruptive topologies through technology convergence. It explores how pervasive WAN technologies have spawned a new generation of consumers through the Lawnmower Man Effect and explains how our personal space has become integral to social media streams, including Twitter, Facebook, and Pinterest. An essential read for students, software engineers and developers, product planners, technical marketers, and analysts\"-- Provided by publisher.

We discuss principles of measuring a topological charge or representation that travels in a set of anyons. We describe the procedure and analyze how it works for different values of theory parameters. We also show how it can be modified to be more efficient.

One of the main applications of quantum communication is quantum key distribution, which solves the problem of secure distribution of cryptographic keys between remote users. This paper investigates the performance of a phase-time coding quantum key distribution protocol belonging to the BB84 protocol family. We construct a simulation model that takes into account the peculiarities of hardware operation and physical properties of the transmission medium. Computational experiments with this model have shown that stable operation of the considered protocol is possible over communication lines up to 210 km long, but this parameter can be improved by constructing a more efficient error-correcting code.

The continuous traffic increase of mobile communication systems has the collateral effect of higher energy consumption, affecting battery lifetime in the user equipment (UE). An effective solution for energy saving is to implement a discontinuous reception (DRX) mode. However, guaranteeing a desired quality of experience (QoE) while simultaneously saving energy is a challenge; but undoubtedly both energy efficiency and the QoE have been essential aspects for the provision of real‐time services, such as voice over Internet protocol (VoIP), voice over LTE, and mobile broadband in 4G networks and beyond. This paper focuses on human voice communications and proposes a Gaussian process regression algorithm that is capable of recognizing patterns of silence and predicts its duration in human conversations, with a prediction error as low as 1.87%. The proposed machine learning mechanism saves energy by switching OFF/ON the radio frequency interface, in order to extend the UE autonomy without harming QoE. Simulation results validate the effectiveness of the proposed mechanism compared with the related literature, showing improvements in energy savings of more than 30% while ensuring a desired QoE level with low computational cost.

NOMA and MIMO are considered to be the promising technologies to meet huge access demands and high data rate requirements of 5G wireless networks. In this paper, the power allocation problem in a downlink MIMO‐NOMA system to maximize the energy efficiency while ensuring the quality‐of‐service of all users is investigated. Two deep reinforcement learning‐based frameworks are proposed to solve this non‐convex and dynamic optimization problem, referred to as the multi‐agent DDPG/TD3‐based power allocation framework. In particular, with current channel conditions as input, every single agent of two multi‐agent frameworks dynamically outputs the optimum power allocation policy for all users in every cluster by DDPG/TD3 algorithm, and the additional actor network is also added to the conventional multi‐agent model in order to adjust power volumes allocated to clusters to improve overall performance of the system. Finally, both frameworks adjust the entire power allocation policy by updating the weights of neural networks according to the feedback of the system. Simulation results show that the proposed multi‐agent deep reinforcement learning based power allocation frameworks can significantly improve the energy efficiency of the MIMO‐NOMA system under various transmit power limitations and minimum data rates compared with other approaches, including the performance comparison over MIMO‐OMA.

In this letter, the problem of jamming data prediction in wireless communications is investigated. Both time and frequency domains are considered to construct the multi‐domain historical jamming data tensor. Besides, due to the perceiver's limited ability, false alarm data and missing detection data are considered. Two neural network prediction models are proposed to predict the jammers' future actions based on deep learning techniques. One is the multi‐variate long‐short‐term‐memory (multi‐variate LSTM) model, and the other is the 2‐D convolutional long‐short‐term‐memory model. Simulation results show that the proposed models have better prediction accuracy and robustness than the benchmark method.