Optimal Power Allocation over Multiple Identical Gilbert-Elliott Channels

We study the fundamental problem of power allocation over multiple Gilbert-Elliott communication channels. In a communication system with time varying channel qualities, it is important to allocate the limited transmission power to channels that will be in good state. However, it is very challenging to do so because channel states are usually unknown when the power allocation decision is made. In this paper, we derive an optimal power allocation policy that can maximize the expected discounted number of bits transmitted over an infinite time span by allocating the transmission power only to those channels that are believed to be good in the coming time slot. We use the concept belief to represent the probability that a channel will be good and derive an optimal power allocation policy that establishes a mapping from the channel belief to an allocation decision. Specifically, we first model this problem as a partially observable Markov decision processes (POMDP), and analytically investigate the structure of the optimal policy. Then a simple threshold-based policy is derived for a three-channel communication system. By formulating and solving a linear programming formulation of this power allocation problem, we further verified the derived structure of the optimal policy.Comment: 10 pages, 7 figure

Monotone properties of random geometric graphs have sharp thresholds

Random geometric graphs result from taking $n$ uniformly distributed points in the unit cube, $[0,1]^d$, and connecting two points if their Euclidean distance is at most $r$, for some prescribed $r$. We show that monotone properties for this class of graphs have sharp thresholds by reducing the problem to bounding the bottleneck matching on two sets of $n$ points distributed uniformly in $[0,1]^d$. We present upper bounds on the threshold width, and show that our bound is sharp for $d=1$ and at most a sublogarithmic factor away for $d\ge2$. Interestingly, the threshold width is much sharper for random geometric graphs than for Bernoulli random graphs. Further, a random geometric graph is shown to be a subgraph, with high probability, of another independently drawn random geometric graph with a slightly larger radius; this property is shown to have no analogue for Bernoulli random graphs.Comment: Published at http://dx.doi.org/10.1214/105051605000000575 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org