Communication technology develops greatly, especially in source and channel coding, modulation mechanisms and ultra-wideband communications. From the aspect of signal processing, current technologies have already approached the Shannon capacity. Directions for future researches in traditional data and signal transmission-based communication industry become unclear. According to the recent state-of-the-art artificial intelligence technologies and their influences on the revolution of the communication industry, a new communication mechanism, namely semantic communications, is proposed. Compared with the traditional communication technology based on pattern transmission, the key point of semantic communications is idea-passing communication, and it can also be referred to as content transmission. In such a revolutionary communication mechanism, the transmission is conducted upon ideas instead of data through the construction of a certain knowledge library. The error tolerance of the channel can also be improved via the matching between the idea transmitter and receiver. It could be considered as the artificial intelligence induced communication with its true meaning since it is a brain-resembling communication mechanism. The introduced brain-resembling communication mechanism based on idea-passing significantly reduces the data amount to be transmitted, and it is an efficient way to address the challenge to the communication industry due to the arrival of big data era. In this paper, the tentative idea of semantic communications is presented and the fundamental elements, semantic coding and decoding, future research directions and major challenges in semantic communications are discussed.
[1] Cisco. Visual Networking Index[EB/OL]. [2017-11-20]. www.cisco.com.
[2] LUGER G F. Artificial Intelligence: Structures and Strategies for Complex Problem Solving. 5th Edition. Addison, USA: Wesley, 2004.
[3] MANYIKA J, CHUI M, BROWN B, et al. Big Data: The Next Frontier for Innovation, Competition, and Productivity. New York, USA: McKinsey Global Institute, 2011.
[4] TSE D, VISWANATH P. Fundamentals of Wireless Communication. Cambridge, UK: Cambridge University Press, 2005.
[5] PROAKIS J, SALEHI M. Digital Communications. New York, USA: McGraw-Hill, 1995.
[6] RAPPAPORT T S. Wireless Communications: Principles and Practice. Upper Saddle River, USA: Prentice Hall, 1996.
[7] GESBERT D, SHAFI M, SHIU D, et al. From Theory to Practice: An Overview of MIMO Space-Time Coded Wireless Systems. IEEE Journal on Selected Areas in Communications, 2003, 21(3): 281-302.
[8] VETRIVEL S, SUBA K, ATHISHA G. An Overview of H.26x Series and Its Applications. International Journal of Engineering Science and Technology, 2010, 2(9): 4622-4631.
[9] PENNEBAKER W B. JPEG: Still Image Data Compression Stan-dard. Berlin, Germany: Springer Science & Business Media, 1992.
[10] SHANNON C E. A Mathematical Theory of Communication. The Bell System Technical Journal, 1948, 27(3): 379-423.
[11] COVER T M, THOMAS J A. Elements of Information Theory. New York, USA: John Wiley & Sons, 2012.
[12] SHANNON C E, WEAVER W, BURKS A W. The Mathematical Theory of Communication. Illinois, USA: The University of Illinois Press, 1951.
[13] BRILLOUIN L. Science and Information Theory. New York, USA: Dover Publications, 2013.
[14] SUDAN M. Communication Amid Uncertainty // Proc of the IEEE
Information Theory Workshop. Washington, USA: IEEE, 2012: 158-161.
[15] NICHOLLS J G, MARTIN A R, WALLACE B G, et al. From Neuron to Brain: A Cellular and Molecular Approach to the Function of the Nervous System. 4th Edition. Sunderland, USA: Sinauer Associates, 2001.