7 research outputs found
On the Capacity of Multilevel NAND Flash Memory Channels
In this paper, we initiate a first information-theoretic study on multilevel
NAND flash memory channels with intercell interference. More specifically, for
a multilevel NAND flash memory channel under mild assumptions, we first prove
that such a channel is indecomposable and it features asymptotic equipartition
property; we then further prove that stationary processes achieve its
information capacity, and consequently, as its order tends to infinity, its
Markov capacity converges to its information capacity; eventually, we establish
that its operational capacity is equal to its information capacity. Our results
suggest that it is highly plausible to apply the ideas and techniques in the
computation of the capacity of finite-state channels, which are relatively
better explored, to that of the capacity of multilevel NAND flash memory
channels.Comment: Submitted to IEEE Transactions on Information Theor
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