Name
Title | ||
|---|---|---|
Upper Bounds on the Capacities of Noncontrollable Finite-State Channels With/Without Feedback |
Abstract | ||
|---|---|---|
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 formulas 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 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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TIT.2012.2201341 | IEEE Transactions on Information Theory |
Keywords | DocType | Volume |
average reward per stage stochastic control problem (arscp),noncontrollable fsc,near-optimal stationary policy,noncontrollable finite-state channel,stochastic systems,compact action space,uniformly continuous reward function,channel input,value iteration method,numerical upper bound,finite-state arscp,information rate,delayed channel state sequence,conditional markov sources,markov processes,feedback,channel output sequence,computable bounds,channel capacity,finite-action arscp,delayed feedback,upper bound,directed information,dynamic programming,feedback capacity,iterative methods,noncontrollable finite-state channel (fsc),average reward per stage stochastic control problem,compact state space,feedforward neural networks,value iteration,markov process,state space,stochastic control,feedforward neural network,vectors | Journal | 58 |
Issue | ISSN | Citations |
8 | 0018-9448 | 1 |
PageRank | References | Authors |
0.36 | 25 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiujie Huang | 1 | 28 | 5.53 |
| Aleksandar Kavcic | 2 | 191 | 20.83 |
| Xiao Ma | 3 | 487 | 64.77 |