With the advant of mobile Internet, which provides a convenient means to exchange information ubitiqously, the mobile network is under tremedous pressure due to the proliferation of smartphones, tablets, IPTV, social and media websites. As two of the driving forces behind the information revolution, the computing and the communication industries evolve along separate paths for most of the past decades. Specifically, the purpose of computing is to effective compute a funcation or find a solution based on given information, whereas the traditional wireless communcations try to accomplish data tranfer with miminum amount energy and spectrum resources. In the last 60 years there has been an incredible growth in both the computing and communication disciplines. The modern theory of computing can be traced back to Alan Turing and John von Neumann. Since then, the growth of computing power has been following the uncannily accurate Moore's law. The starting point for the modern theory of digital communication is universally attributed to the 1948 groundbreaking paper of Claude Shannon. The paper reveals that the wireless capacity is fundamentally limited by the spectrum resources and the transmission power, which indicates that any attempt to increase the transmission data rate based solely on the physical layer will.inevitably result a dimisionishing return.
How to break the wireless data delivery bottleneck imposed by the traditional Shannon information theory is at the center stage of wireless research world-wide. As Professor T. Cover, the information theorist of Stanford, once pointed out, “computation is communication limited and communication is computation limited…” Therefore, the key to unleash the ultimate potential of wireless networks may lay in the integration of communication and computing within the mobile networks. Today’s smart mobile devices and networks possess a tremendous amount of computing power. In principle, sophisticated tasks can be accomplished through large-scale collaboration among these mobile stations across the networks. By taking advantage of the abundance of distributed computation power and storage space, significant gains in network capacity can be expected.
纵观无线通信发展历史，网络通信效能的本质提升必须建立在传输理念及网络架构有根本性变革的基础上。“计算通信理论”的核心思想是围绕终端的信息体验（QoE），同时运用计算和通信资源在网络高效地传递函数流（function flow）。与传统的通信系统不同，计算通信系统中的网络效用容量（effective capacity）由三种关键因素决定：①异构网络总体量（mass）、②无线传输能力 （communications）、③网络计算能力 (computing)。虽然无线传输能力的提升已遭遇香农瓶颈效应，网络体量的扩大也附带着巨大的成本，但网络的效用容量仍可通过丰富的计算能力取得跳跃式的增长。
Throughout the history of the wireless development, the breakthrough in network capabilities always came from the fundamental advances on the transmission technologies and network architectures. The core idea behind “computing communication” is the notion of “function flow” (instead of data flow), which allows it to be delivered over the network by leveraging both the computing and the communication resources. In contrast with the classic communication systems where the capacity is measured by the amount of data transfer, we conjecture that the “effective capacity” (which measures the users' QoE) in the computing communication systems is dictated by three factors: ① the mass of wireless heterogeneous network、② the wireless communication capability、and ③ the network computing capability. Due to the Shannon theory, improvement on the wireless transmission capacity is capped. At the same time, increasing the size of the network will also incur huge deployment costs. Nevertheless, it is always possible to achieve significant gains on the effective capacity by exploiting the ever-growing computing power within the network.
(1)超量信息的无线异构传输：异构融合网络，网络资源虚拟化，复杂业务联合适配，协同通信理论，虚拟资源分配，Cloud infrastructures RAN(C-RAN) , 分布式计算、编码、存储共享等；
Motivated by the above promises, the researchers at IWCT of SJTU are developing fundamental theories and enabling techniques at the intersection communications and computing. Our objective is to exploit the powerful computing resources in order to unlock the true potential of a communication network. In particular, we are focusing on the following aspects of the computing communication networks:
(1). Large data over wireless heterogeneous networks: hybrid networks; virtualization of network resources; joint resource allocation for complex sources,;cooperative communication theory; Cloud infrastructures RAN (C-RAN); distributed computing, coding, and storage sharing, etc.
(2). Wireless sensor networks and Ad Hoc networks: compressive sensing-based network computing theory; capacity analysis of the computing wireless sensor networks; information analytics for distributed computing communications.
(3). Fundamental theory for computing communications：The effective capacity of computing communication networks; Network computing-based information theory; effectiveness and applicability of computing communication theory; computing communication in ubiquitous networks.