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As 4G wireless networks are vastly and rapidly deployed worldwide, 5G with its advanced vision of all connected world and zero distance communications is already at the corner. Along with the super quality of user experience brought by these new networks, the shockingly increasing energy consumption of wireless networks has become a worrying economic issue for operators and a big challenge for sustainable development. Green Transmission Technologies (GTT) is a project focusing on the energy-efficient design of physical-layer transmission technologies and MAC-layer radio resource management in wireless networks. In particular, fundamental trade-offs between spectrum efficiency and energy efficiency have been identified and explored for energy-efficiency-oriented design and optimization. In this article, four selected GTT solutions are introduced, focusing on how they utilize the degrees of freedom in different resource domains, as well as how they balance the trade-off between energy and spectrum efficiency. On top of the elaboration of separated solutions, the GTT toolbox is introduced as a systematic tool and unified simulation platform to integrate the proposed GTT solutions together

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The power-saving class of type I (PSC I), which is one of the sleep-mode operations specified in the IEEE 802.16e standard, is designed to reduce power consumption for nonrealtime traffic. However, the inefficiency of the PSC I comes from the configuration of its operation and the utilized mechanism of binary-exponential traffic detection. Based on the concepts of IEEE 802.16m sleep-mode operation, a statistical sleep window control (SSWC) approach is proposed to improve the energy efficiency of a mobile station (MS) with nonreal-time downlink traffic in this paper. The SSWC approach constructs a discrete-time Markov-modulated Poisson process (dMMPP) for representing the states of nonreal-time traffic. Furthermore, a partially observable Markov decision process (POMDP) is exploited within the SSWC approach to conjecture the present traffic state. Based on the estimated traffic state and the considerations of tolerable delay and/or queue size, two suboptimal policies, including the sleep ratio-based (SR) and energy cost-based (EC) policies, are proposed within the SSWC approach. The efficiency of the proposed SSWC approach is evaluated and compared via simulations. Simulation results show that the proposed SSWC approach outperforms the conventional IEEE 802.16e PSC I and the evolutional PSC I of the IEEE 802.16m system in terms of both energy conservation and packet delay.

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