As the society increasingly emphasizes the need of clean and renewable energy systems,the electric power industry is undergoing profound changes to transform a passive,hierarchical grid into an active and open-access ...As the society increasingly emphasizes the need of clean and renewable energy systems,the electric power industry is undergoing profound changes to transform a passive,hierarchical grid into an active and open-access smart grid.Enabled by advances in sensing,communication,and actuation,future smart grids offer much broader opportunities for cross-fertilization between the traditional power engineering community and the communication community.This special issue pres-展开更多
while cellular networks have continuously evolved in recent years, the industry has clearly seen unprecedented challenges to meet the exponentially growing expectations in the near future. The 5G system is facing gran...while cellular networks have continuously evolved in recent years, the industry has clearly seen unprecedented challenges to meet the exponentially growing expectations in the near future. The 5G system is facing grand challenges such as the everincreasing traffic volumes and remarkably diversified services connecting humans and machines alike.As a result, the future network has to deliver massively increased capacity, greater flexibility, incorporated computing capability,support of significantly extended battery lifetime, and accommodation of vary?ing payloads with fast setup and low latency, etc. In particular, as 5G requires more spectrum resource, higher frequency bands are desirable. Nowadays, millimeter wave has been widely accepted as one of the main communication bands for 5G.展开更多
Federated learning has revolutionized the way we approach machine learning by enabling multiple edge devices to collaboratively learn a shared machine learning model without the need for centralized data collection.Su...Federated learning has revolutionized the way we approach machine learning by enabling multiple edge devices to collaboratively learn a shared machine learning model without the need for centralized data collection.Such a new machine learning paradigm has gained significant attention in recent years due to its ability to address privacy and security concerns associated with centralized learning,as well as its potential to reduce communication overhead and improve scalability.展开更多
Recent breakthroughs in artificial intelligence(AI) give rise to a plethora of intelligent applications and services based on machine learning algorithms such as deep neural networks(DNNs). With the proliferation of I...Recent breakthroughs in artificial intelligence(AI) give rise to a plethora of intelligent applications and services based on machine learning algorithms such as deep neural networks(DNNs). With the proliferation of Internet of things(IoT) and mobile edge computing, these applications are being pushed to the network edge, thus enabling a new paradigm termed as edge intelligence. This provokes the demand for decentralized implementation of learning algorithms over edge networks to distill the intelligence from distributed data, and also calls for new communication-efficient designs in air interfaces to improve the privacy by avoiding raw data exchange. This paper provides a comprehensive overview on edge intelligence, by particularly focusing on two paradigms named edge learning and edge inference, as well as the corresponding communication-efficient solutions for their implementations in wireless systems. Several insightful theoretical results and design guidelines are also provided.展开更多
文摘As the society increasingly emphasizes the need of clean and renewable energy systems,the electric power industry is undergoing profound changes to transform a passive,hierarchical grid into an active and open-access smart grid.Enabled by advances in sensing,communication,and actuation,future smart grids offer much broader opportunities for cross-fertilization between the traditional power engineering community and the communication community.This special issue pres-
文摘while cellular networks have continuously evolved in recent years, the industry has clearly seen unprecedented challenges to meet the exponentially growing expectations in the near future. The 5G system is facing grand challenges such as the everincreasing traffic volumes and remarkably diversified services connecting humans and machines alike.As a result, the future network has to deliver massively increased capacity, greater flexibility, incorporated computing capability,support of significantly extended battery lifetime, and accommodation of vary?ing payloads with fast setup and low latency, etc. In particular, as 5G requires more spectrum resource, higher frequency bands are desirable. Nowadays, millimeter wave has been widely accepted as one of the main communication bands for 5G.
文摘Federated learning has revolutionized the way we approach machine learning by enabling multiple edge devices to collaboratively learn a shared machine learning model without the need for centralized data collection.Such a new machine learning paradigm has gained significant attention in recent years due to its ability to address privacy and security concerns associated with centralized learning,as well as its potential to reduce communication overhead and improve scalability.
文摘Recent breakthroughs in artificial intelligence(AI) give rise to a plethora of intelligent applications and services based on machine learning algorithms such as deep neural networks(DNNs). With the proliferation of Internet of things(IoT) and mobile edge computing, these applications are being pushed to the network edge, thus enabling a new paradigm termed as edge intelligence. This provokes the demand for decentralized implementation of learning algorithms over edge networks to distill the intelligence from distributed data, and also calls for new communication-efficient designs in air interfaces to improve the privacy by avoiding raw data exchange. This paper provides a comprehensive overview on edge intelligence, by particularly focusing on two paradigms named edge learning and edge inference, as well as the corresponding communication-efficient solutions for their implementations in wireless systems. Several insightful theoretical results and design guidelines are also provided.
