Abstract-To keep pace with the rapid growth of mobile traffic demands, dense deployment of small cells in millimeter wave (mmWave) bands has become a promising candidate for next generation wireless communication systems. With a greatly increased data rate from huge bandwidth of mmWave communications, energy consumption should be mitigated for higher energy efficiency. Due to content popularity, many contentbased mobile applications can be supported by the multicast service. mmWave communications exploit directional antennas to overcome high path loss, and concurrent transmissions can be enabled for better multicast service. On the other hand, deviceto-device (D2D) communications in physical proximity should be exploited to improve multicast performance. In this paper, we propose an energy efficient multicast scheduling scheme, referred to as EMS, which utilizes both D2D communications and concurrent transmissions to achieve high energy efficiency. In EMS, a D2D path planning algorithm establishes multi-hop D2D transmission paths, and a concurrent scheduling algorithm allocates the links on the D2D paths into different pairings. Then the transmission power of links is adjusted by the power control algorithm. Furthermore, we theoretically analyze the roles of D2D communications and concurrent transmissions in reducing energy consumption. Extensive simulations under various system parameters demonstrate the superior performance of EMS in terms of energy consumption compared with the state-of-theart schemes. Furthermore, we also investigate the choice of the interference threshold to optimize network performance.
In REF , both D2D communications and concurrent transmissions are exploited to improve the energy efficiency of multicast transmission in mmWave small cells, where power control is performed after concurrent transmission and D2D transmission scheduling to reduce energy consumption with the achieved throughput ensured.
Device-to-Device Communications Enabled Energy Efficient Multicast Scheduling in mmWave Small Cells
IEEE Transactions on Communications, 2017
Computer Science