958 research outputs found
An Optimal Transmission Strategy for Kalman Filtering over Packet Dropping Links with Imperfect Acknowledgements
This paper presents a novel design methodology for optimal transmission
policies at a smart sensor to remotely estimate the state of a stable linear
stochastic dynamical system. The sensor makes measurements of the process and
forms estimates of the state using a local Kalman filter. The sensor transmits
quantized information over a packet dropping link to the remote receiver. The
receiver sends packet receipt acknowledgments back to the sensor via an
erroneous feedback communication channel which is itself packet dropping. The
key novelty of this formulation is that the smart sensor decides, at each
discrete time instant, whether to transmit a quantized version of either its
local state estimate or its local innovation. The objective is to design
optimal transmission policies in order to minimize a long term average cost
function as a convex combination of the receiver's expected estimation error
covariance and the energy needed to transmit the packets. The optimal
transmission policy is obtained by the use of dynamic programming techniques.
Using the concept of submodularity, the optimality of a threshold policy in the
case of scalar systems with perfect packet receipt acknowledgments is proved.
Suboptimal solutions and their structural results are also discussed. Numerical
results are presented illustrating the performance of the optimal and
suboptimal transmission policies.Comment: Conditionally accepted in IEEE Transactions on Control of Network
System
Event-Driven Optimal Feedback Control for Multi-Antenna Beamforming
Transmit beamforming is a simple multi-antenna technique for increasing
throughput and the transmission range of a wireless communication system. The
required feedback of channel state information (CSI) can potentially result in
excessive overhead especially for high mobility or many antennas. This work
concerns efficient feedback for transmit beamforming and establishes a new
approach of controlling feedback for maximizing net throughput, defined as
throughput minus average feedback cost. The feedback controller using a
stationary policy turns CSI feedback on/off according to the system state that
comprises the channel state and transmit beamformer. Assuming channel isotropy
and Markovity, the controller's state reduces to two scalars. This allows the
optimal control policy to be efficiently computed using dynamic programming.
Consider the perfect feedback channel free of error, where each feedback
instant pays a fixed price. The corresponding optimal feedback control policy
is proved to be of the threshold type. This result holds regardless of whether
the controller's state space is discretized or continuous. Under the
threshold-type policy, feedback is performed whenever a state variable
indicating the accuracy of transmit CSI is below a threshold, which varies with
channel power. The practical finite-rate feedback channel is also considered.
The optimal policy for quantized feedback is proved to be also of the threshold
type. The effect of CSI quantization is shown to be equivalent to an increment
on the feedback price. Moreover, the increment is upper bounded by the expected
logarithm of one minus the quantization error. Finally, simulation shows that
feedback control increases net throughput of the conventional periodic feedback
by up to 0.5 bit/s/Hz without requiring additional bandwidth or antennas.Comment: 29 pages; submitted for publicatio
Upper Bounds on the Capacities of Noncontrollable Finite-State Channels with/without Feedback
Noncontrollable finite-state channels (FSCs) are FSCs in which the channel
inputs have no influence on the channel states, i.e., the channel states evolve
freely. Since single-letter formulae for the channel capacities are rarely
available for general noncontrollable FSCs, computable bounds are usually
utilized to numerically bound the capacities. In this paper, we take the
delayed channel state as part of the channel input and then define the {\em
directed information rate} from the new channel input (including the source and
the delayed channel state) sequence to the channel output sequence. With this
technique, we derive a series of upper bounds on the capacities of
noncontrollable FSCs with/without feedback. These upper bounds can be achieved
by conditional Markov sources and computed by solving an average reward per
stage stochastic control problem (ARSCP) with a compact state space and a
compact action space. By showing that the ARSCP has a uniformly continuous
reward function, we transform the original ARSCP into a finite-state and
finite-action ARSCP that can be solved by a value iteration method. Under a
mild assumption, the value iteration algorithm is convergent and delivers a
near-optimal stationary policy and a numerical upper bound.Comment: 15 pages, Two columns, 6 figures; appears in IEEE Transaction on
Information Theor
Optimal Energy Management Policies for Energy Harvesting Sensor Nodes
We study a sensor node with an energy harvesting source. The generated energy
can be stored in a buffer. The sensor node periodically senses a random field
and generates a packet. These packets are stored in a queue and transmitted
using the energy available at that time. We obtain energy management policies
that are throughput optimal, i.e., the data queue stays stable for the largest
possible data rate. Next we obtain energy management policies which minimize
the mean delay in the queue.We also compare performance of several easily
implementable sub-optimal energy management policies. A greedy policy is
identified which, in low SNR regime, is throughput optimal and also minimizes
mean delay.Comment: Submitted to the IEEE Transactions on Wireless Communications; 22
pages with 10 figure
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