2 research outputs found
Energy Efficient Scheduling for Loss Tolerant IoT Applications with Uninformed Transmitter
In this work we investigate energy efficient packet scheduling problem for
the loss tolerant applications. We consider slow fading channel for a point to
point connection with no channel state information at the transmitter side
(CSIT). In the absence of CSIT, the slow fading channel has an outage
probability associated with every transmit power. As a function of data loss
tolerance parameters and peak power constraints, we formulate an optimization
problem to minimize the average transmit energy for the user equipment (UE).
The optimization problem is not convex and we use stochastic optimization
technique to solve the problem. The numerical results quantify the effect of
different system parameters on average transmit power and show significant
power savings for the loss tolerant applications.Comment: Published in ICC 201
On optimizing power allocation for reliable communication over fading channels with uninformed transmitter
We investigate energy efficient packet scheduling
and power allocation problem for the services which require
reliable communication to guarantee a certain quality of experience
(QoE). We establish links between average transmit power
and reliability of data transfer, which depends on both average
amount of data transfer and short term rate guarantees. We
consider a slow-fading point-to-point channel without channel
state information at the transmitter side (CSIT). In the absence
of CSIT, the slow fading channel has an outage probability
associated with every transmit power. As a function of data
loss tolerance parameters, and minimum rate and peak power
constraints, we formulate an optimization problem that adapts
rate and power to minimize the average transmit power for
the user equipment (UE). Then, a relaxed optimization problem
is formulated where transmission rate is assumed to be fixed
for each packet transmission. We use Markov chain to model
constraints of the optimization problem. The corresponding
problem is not convex for both of the formulated problems, therefore
a stochastic optimization technique, namely the simulated
annealing algorithm, is used to solve them. The numerical results
quantify the effect of various system parameters on average
transmit power and show significant energy savings when the
service has less stringent requirements on timely and reliable
communication