10 research outputs found
Broadcasting with an Energy Harvesting Rechargeable Transmitter
In this paper, we investigate the transmission completion time minimization
problem in a two-user additive white Gaussian noise (AWGN) broadcast channel,
where the transmitter is able to harvest energy from the nature, using a
rechargeable battery. The harvested energy is modeled to arrive at the
transmitter randomly during the course of transmissions. The transmitter has a
fixed number of packets to be delivered to each receiver. Our goal is to
minimize the time by which all of the packets for both users are delivered to
their respective destinations. To this end, we optimize the transmit powers and
transmission rates intended for both users. We first analyze the structural
properties of the optimal transmission policy. We prove that the optimal total
transmit power has the same structure as the optimal single-user transmit
power. We also prove that there exists a cut-off power level for the stronger
user. If the optimal total transmit power is lower than this cut-off level, all
transmit power is allocated to the stronger user, and when the optimal total
transmit power is larger than this cut-off level, all transmit power above this
level is allocated to the weaker user. Based on these structural properties of
the optimal policy, we propose an algorithm that yields the globally optimal
off-line scheduling policy. Our algorithm is based on the idea of reducing the
two-user broadcast channel problem into a single-user problem as much as
possible.Comment: Submitted to IEEE Transactions on Wireless Communications, October
201
Energy-Efficient Transmission Scheduling with Strict Underflow Constraints
We consider a single source transmitting data to one or more receivers/users
over a shared wireless channel. Due to random fading, the wireless channel
conditions vary with time and from user to user. Each user has a buffer to
store received packets before they are drained. At each time step, the source
determines how much power to use for transmission to each user. The source's
objective is to allocate power in a manner that minimizes an expected cost
measure, while satisfying strict buffer underflow constraints and a total power
constraint in each slot. The expected cost measure is composed of costs
associated with power consumption from transmission and packet holding costs.
The primary application motivating this problem is wireless media streaming.
For this application, the buffer underflow constraints prevent the user buffers
from emptying, so as to maintain playout quality. In the case of a single user
with linear power-rate curves, we show that a modified base-stock policy is
optimal under the finite horizon, infinite horizon discounted, and infinite
horizon average expected cost criteria. For a single user with piecewise-linear
convex power-rate curves, we show that a finite generalized base-stock policy
is optimal under all three expected cost criteria. We also present the
sequences of critical numbers that complete the characterization of the optimal
control laws in each of these cases when some additional technical conditions
are satisfied. We then analyze the structure of the optimal policy for the case
of two users. We conclude with a discussion of methods to identify
implementable near-optimal policies for the most general case of M users.Comment: 109 pages, 11 pdf figures, template.tex is main file. We have
significantly revised the paper from version 1. Additions include the case of
a single receiver with piecewise-linear convex power-rate curves, the case of
two receivers, and the infinite horizon average expected cost proble
From Sleeping to Stockpiling: Energy Conservation via Stochastic Scheduling in Wireless Networks.
Motivated by the need to conserve energy in wireless networks, we study three stochastic dynamic scheduling problems.
In the first problem, we consider a wireless sensor node that can turn its radio off for fixed durations of time in order to conserve energy. We formulate finite horizon expected cost and infinite horizon average expected cost problems to model the fundamental tradeoff between packet delay and energy consumption. Through analysis of the dynamic programming equations, we derive structural results on the optimal policies for both formulations. For the infinite horizon problem, we identify a threshold decision rule to determine the optimal control action when the queue is empty.
In the second problem, we consider a sensor node with an inaccurate timer in the ultra-low power sleep mode. The loss in timing accuracy in the sleep mode can result in unnecessary energy consumption from two unsynchronized devices trying to communicate. We develop a novel method for the node to calibrate its timer: occasionally waking up to measure the ambient temperature, upon which the timer speed depends. The objective is to dynamically schedule a limited number of temperature measurements in a manner most useful to improving the accuracy of the timer. We formulate optimization problems with both continuous and discrete underlying time scales, and implement a numerical solution to an equivalent reduction of the second formulation.
In the third problem, we consider a single source transmitting data to one or more receivers over a shared wireless channel. Each receiver has a buffer to store received packets before they are drained. The transmitter's goal is to minimize total power consumption by exploiting the temporal and spatial variation of the channel, while preventing the receivers' buffers from emptying. In the case of a single receiver, we show that modified base-stock and finite generalized base-stock policies are optimal when the power-rate curves are linear and piecewise-linear convex, respectively. We also present the sequences of critical numbers that complete the characterizations of the optimal policies when additional technical conditions are satisfied. We then analyze the structure of the optimal policy for the case of two receivers.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/77839/1/dishuman_1.pd