Integrated sleep scheduling for multiple relays and UEs in LTE-TDD

Abstract

Energy-saving for the LTE-A network with relay nodes in TDD mode is addressed in this paper, and integrated sleep scheduling schemes for relay nodes and user devices under a base station are designed. The authors' two previously proposed ideas, namely, Load-Based Power Saving (LBPS) and Virtual Time, are adopted in the design, and two strategies, namely, top-down and bottom-up each with three LBPS schemes are proposed. In the top-down strategy, the load as well as the channel quality on the backhaul link is first considered to determine the sleep pattern for all relay nodes, and then the sleep schedule for UEs under each relay node is determined accordingly. On the contrary, the load and the channel quality on the access links are first considered and then integrated into the sleep schedule on the backhaul link. Two associated mechanisms for the proposed LBPS schemes to operate in the virtual time domain are also proposed in the paper, i.e., calculation of the virtual subframe capacity and the mapping mechanisms from the virtual time to the actual time. The benefit of the proposed schemes in power saving over the standard-based scheme is demonstrated by the simulation study, and the bottom-up scheme of BU-Split outperforms the other schemes under equally distributed input load as well as the hotspot scenario. Discussion on the tradeoff of the processing overhead and the performance for the proposed schemes is presented in the paper.

5G New Radio: Dynamic Time Division Duplex Radio Resource Management Approaches

“…For the LTE-A network with relay nodes in TDD mode, Load-Based Power Saving (LBPS) and Virtual Time can be incorporated in the architecture for energy savings. This project was proposed by Yang et al [66], where it integrates sleep timing systems for relay nodes and consumer devices below the base station, such as in Fig. 6 .…”

“…TD-Merge and BU-Aggr will offer a better balance between the overhead operation and output. [66] There is also an energy efficiency resource management algorithm introduced by Zhang et al [67], which uses the power consumption of the heterogeneous cloud radio access networks model. The average energy efficiency of the entire network is adopted as the target of optimization with multiple constraints of each user's maximum transmit power, average power, and minimum data rate [67].…”