Optimal Transmission Scheduling for Energy-Efficient Wireless Networks

Abstract

It has been shown that using appropriate channel coding schemes in wireless environments, transmission energy can be significantly reduced by controlling the packet transmission time. This paper seeks optimal solutions for transmission control problems, motivated by this observation and by the need to minimize energy consumption in wireless networks. A downlink scheduling problem is formulated, and it is shown that its solution can be efficiently obtained through an approach decomposing an optimal sample path through certain "critical tasks" which in turn can be efficiently identified. Our approach deals with a more general setting than the one in [1] where the "MoveRight" algorithm is proposed. We include simulation results showing that our algorithm is significantly faster than the MoveRight algorithm. Finally, we analyze the transmission scheduling problem in a multiple node path and show that this problem can be transformed to a much simpler downlink transmission control problem.

Online Energy Efficient Packet Scheduling with Delay Constraints in Wireless Networks

“…Example service disciplines include earliest deadline first [23], first-come-first-served [2], [3], [15], and weighted fair queuing [13]. By separating rate assignment and packet scheduling, we make algorithm design simple and enable the use of independent policies at either level.…”

“…Researchers have studied the energy-delay trade-off from different prospectives. A recent focus is to use explicit deadline for each packet [2], [3], [13], [15], [18], [19], [23], [26]. For example, Schurgers et al analyzed energy-efficient packet scheduling in a time-invariant channel [19].…”

“…Extensive studies were conducted along the line for offline packet scheduling, in which all packet timing information is available offline in order to determine packet transmission rates, or service times similarly. Researchers have studied the problem with different focuses, such as on a special application model (periodic streams) [19], offline generalization [23], distinguished characteristics of different packets [15], properties of offline schedulers [2] [3].…”

“…A poisson input with known packet statistics is studied in [23]. Knowledge about future packets releases in a small timing window is assumed in [15]. The knowledge about input correlation and/or distribution is not always available; accurate prediction of future packet arrivals even in a small window is hard, if possible.…”

“…We assume the channel between the transmitter and the receivers is an Additive White Gaussian Noise (AWGN) channel and the interference to the receiver is negligible, as in [2], [3], [13], [15], [23]. Using adaptive modulation scaling or channel coding can reduce the energy consumption in wireless communication.…”