62,583 research outputs found
Capacity estimation of two-dimensional channels using Sequential Monte Carlo
We derive a new Sequential-Monte-Carlo-based algorithm to estimate the
capacity of two-dimensional channel models. The focus is on computing the
noiseless capacity of the 2-D one-infinity run-length limited constrained
channel, but the underlying idea is generally applicable. The proposed
algorithm is profiled against a state-of-the-art method, yielding more than an
order of magnitude improvement in estimation accuracy for a given computation
time
Monte Carlo Algorithms for the Partition Function and Information Rates of Two-Dimensional Channels
The paper proposes Monte Carlo algorithms for the computation of the
information rate of two-dimensional source/channel models. The focus of the
paper is on binary-input channels with constraints on the allowed input
configurations. The problem of numerically computing the information rate, and
even the noiseless capacity, of such channels has so far remained largely
unsolved. Both problems can be reduced to computing a Monte Carlo estimate of a
partition function. The proposed algorithms use tree-based Gibbs sampling and
multilayer (multitemperature) importance sampling. The viability of the
proposed algorithms is demonstrated by simulation results
A Cross-layer Perspective on Energy Harvesting Aided Green Communications over Fading Channels
We consider the power allocation of the physical layer and the buffer delay
of the upper application layer in energy harvesting green networks. The total
power required for reliable transmission includes the transmission power and
the circuit power. The harvested power (which is stored in a battery) and the
grid power constitute the power resource. The uncertainty of data generated
from the upper layer, the intermittence of the harvested energy, and the
variation of the fading channel are taken into account and described as
independent Markov processes. In each transmission, the transmitter decides the
transmission rate as well as the allocated power from the battery, and the rest
of the required power will be supplied by the power grid. The objective is to
find an allocation sequence of transmission rate and battery power to minimize
the long-term average buffer delay under the average grid power constraint. A
stochastic optimization problem is formulated accordingly to find such
transmission rate and battery power sequence. Furthermore, the optimization
problem is reformulated as a constrained MDP problem whose policy is a
two-dimensional vector with the transmission rate and the power allocation of
the battery as its elements. We prove that the optimal policy of the
constrained MDP can be obtained by solving the unconstrained MDP. Then we focus
on the analysis of the unconstrained average-cost MDP. The structural
properties of the average optimal policy are derived. Moreover, we discuss the
relations between elements of the two-dimensional policy. Next, based on the
theoretical analysis, the algorithm to find the constrained optimal policy is
presented for the finite state space scenario. In addition, heuristic policies
with low-complexity are given for the general state space. Finally, simulations
are performed under these policies to demonstrate the effectiveness
Energy Harvesting Wireless Communications: A Review of Recent Advances
This article summarizes recent contributions in the broad area of energy
harvesting wireless communications. In particular, we provide the current state
of the art for wireless networks composed of energy harvesting nodes, starting
from the information-theoretic performance limits to transmission scheduling
policies and resource allocation, medium access and networking issues. The
emerging related area of energy transfer for self-sustaining energy harvesting
wireless networks is considered in detail covering both energy cooperation
aspects and simultaneous energy and information transfer. Various potential
models with energy harvesting nodes at different network scales are reviewed as
well as models for energy consumption at the nodes.Comment: To appear in the IEEE Journal of Selected Areas in Communications
(Special Issue: Wireless Communications Powered by Energy Harvesting and
Wireless Energy Transfer
Time-Space Constrained Codes for Phase-Change Memories
Phase-change memory (PCM) is a promising non-volatile solid-state memory
technology. A PCM cell stores data by using its amorphous and crystalline
states. The cell changes between these two states using high temperature.
However, since the cells are sensitive to high temperature, it is important,
when programming cells, to balance the heat both in time and space.
In this paper, we study the time-space constraint for PCM, which was
originally proposed by Jiang et al. A code is called an
\emph{-constrained code} if for any consecutive
rewrites and for any segment of contiguous cells, the total rewrite
cost of the cells over those rewrites is at most . Here,
the cells are binary and the rewrite cost is defined to be the Hamming distance
between the current and next memory states. First, we show a general upper
bound on the achievable rate of these codes which extends the results of Jiang
et al. Then, we generalize their construction for -constrained codes and show another construction for -constrained codes. Finally, we show that these two
constructions can be used to construct codes for all values of ,
, and
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