No abstract

In the 1940s, Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel. Guided by this fundamental work, the main theme of wireless system design up until the fifth generation (5G) was the data rate maximization. In Shannon's theory, the semantic aspect and meaning of messages were treated as largely irrelevant to communication. The classic theory started to reveal its limitations in the modern era of machine intelligence, consisting of the synergy between Internet-of-things (IoT) and artificial intelligence (AI). By broadening the scope of the classic communication-theoretic framework, in this article, we present a view of semantic communication (SemCom) and conveying meaning through the communication systems. We address three communication modalities: human-to-human (H2H), human-to-machine (H2M), and machine-to-machine (M2M) communications. The latter two represent the paradigm shift in communication and computing, and define the main theme of this article. H2M SemCom refers to semantic techniques for convey-

No abstract

Edge machine learning involves the deployment of learning algorithms at the network edge to leverage massive distributed data and computation resources to train artificial intelligence (AI) models.Among others, the framework of federated edge learning (FEEL) is popular for its data-privacy preservation. FEEL coordinates global model training at an edge server and local model training at edge devices that are connected by wireless links. This work contributes to the energy-efficient implementation of FEEL in wireless networks by designing joint computation-and-communication resource management (C 2 RM). The design targets the state-of-the-art heterogeneous mobile architecture where parallel computing using both a CPU and a GPU, called heterogeneous computing, can significantly improve both the performance and energy efficiency. To minimize the sum energy consumption of devices, we propose a novel C 2 RM framework featuring multi-dimensional control including bandwidth allocation, CPU-GPU workload partitioning and speed scaling at each device, and C 2 time division for each link. The key component of the framework is a set of equilibriums in energy rates with respect to different control variables that are proved to exist among devices or between processing units at each device. The results are applied to designing efficient algorithms for computing the optimal C 2 RM policies faster than the standard optimization tools. Based on the equilibriums, we further design energy-efficient schemes for device scheduling and greedy spectrum sharing that scavenges "spectrum holes" resulting from heterogeneous C 2 time divisions among devices. Using a real dataset, experiments are conducted to demonstrate the effectiveness of C 2 RM on improving the energy efficiency of a FEEL system.

High pressure in situ Raman scattering and electrical resistivity measurements were performed to investigate the phase transitions in a semimetal 1T-TiTe2 single crystal up to 17 GPa. Combining anomalous experimental results with the electronic band structures and Z2 topological invariants in calculations, two topological phase transitions and one structural phase transition were confirmed at 1.7 GPa, 3 GPa, and 8 GPa, respectively. These two topological transformations are due to the enhanced orbital hybridization followed by a few of band inversions near the Fermi level, and the further parity analysis manifested that the phases II and III correspond to a strong topological state and a weak topological state, respectively. The rich topology variation of 1T-TiTe2 under high pressure provides a potential candidate for understanding the relevant topology physics and probable applications. The current results also demonstrate that Raman spectroscopy and electrical transport measurements are efficient tools to detect the topological phase transition under high pressure.

Low-rate noiseless label channel High-rate noisy data channel TDMA Communication System Model Edge Devices Edge Server Channel State Information

No abstract

In 1940s, Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel. Guided by this fundamental work, the main theme of wireless system design up until the fifth generation (5G) was the data rate maximization. In Shannon's theory, the semantic aspect and meaning of messages were treated as largely irrelevant to communication. The classic theory started to reveal its limitations in the modern era of machine intelligence, consisting of the synergy between Internet-of-Things (IoT) and artificial intelligence (AI).By broadening the scope of the classic communication-theoretic framework, in this article we present a view of semantic communication (SemCom) and conveying meaning through the communication systems. We address three communication modalities: human-to-human (H2H), human-to-machine (H2M), and machine-to-machine (M2M) communications. The latter two represent the paradigm shift in communication and computing, and define the main theme of this article. H2M SemCom refers to semantic techniques for conveying meanings understandable not only by humans but also by machines so that they can have interaction and "dialogue". On the other hand, M2M SemCom refers to effectiveness techniques for efficiently connecting multiple machines such that they can effectively execute a specific computation task in a wireless network. The first part of this article focuses on introducing the SemCom principles including encoding, layered system architecture, and two design approaches: (1) layer-coupling design; and (2) end-to-end design using a neural network. The second part focuses on discussion of specific techniques for different application areas of H2M SemCom (including human and AI symbiosis, recommendation, human sensing and care, and virtual reality (VR)/augmented reality (AR)) and M2M SemCom (including distributed learning, split inference, distributed consensus, and machine-vision cameras). Finally, we discuss the approach for designing SemCom systems based on knowledge graphs. We believe that this comprehensive introduction will provide a useful guide into the emerging area of SemCom that is expected to play an important role in sixth generation (6G) featuring connected intelligence and integrated sensing, computing, communication, and control.