Channel capacity and state estimation for state-dependent Gaussian channels

We formulate a problem of state information transmission over a state-dependent channel with states known at the transmitter. In particular, we solve a problem of minimizing the mean-squared channel state estimation error E/spl par/S/sup n/ - S/spl circ//sup n//spl par/ for a state-dependent additive Gaussian channel Y/sup n/ = X/sup n/ + S/sup n/ + Z/sup n/ with an independent and identically distributed (i.i.d.) Gaussian state sequence S/sup n/ = (S/sub 1/, ..., S/sub n/) known at the transmitter and an unknown i.i.d. additive Gaussian noise Z/sup n/. We show that a simple technique of direct state amplification (i.e., X/sup n/ = /spl alpha/S/sup n/), where the transmitter uses its entire power budget to amplify the channel state, yields the minimum mean-squared state estimation error. This same channel can also be used to send additional independent information at the expense of a higher channel state estimation error. We characterize the optimal tradeoff between the rate R of the independent information that can be reliably transmitted and the mean-squared state estimation error D. We show that any optimal (R, D) tradeoff pair can be achieved via a simple power-sharing technique, whereby the transmitter power is appropriately allocated between pure information transmission and state amplification.     

Admission Control Algorithms for Cellular Systems

This paper evaluates call admission control algorithms for a cellular or microcellular system. Algorithms are evaluated based on two Quality of Service (QoS) metrics: the new call blocking probability, which is the probability that a new call is denied access to the system, and the forced-termination probability, which is the probability that a call that has been admitted will be terminated prior to the call's completion. Three novel algorithms are presented: the Weighted Sum Scheme, the Probability Index Scheme, and the Hybrid Control Scheme. The weighted sum scheme uses the weighted sum of the number of calls underway in various cells when making the admission decision. The probability index scheme computes a probability index, which reflects the forced-termination probability of a new call arrival, and admits those calls with low probability indexes. The hybrid control scheme combines these two approaches. These novel algorithms are compared with three known algorithms: the Reservation Scheme in which a specific number of channels are reserved in each cell for handoffs, the Linear Weighting Scheme in which the admission decision depends on the total number of calls underway in a group of cells, and the Distributed Admission Control Scheme in which the admission decision depends on the projected overload probabilities in the cell at which the new call arrives and adjacent cells. We show that the Hybrid Control Scheme yields the best performance, particularly during periods when load differs from the expected level. We also show that the simple Reservation Scheme performs remarkably well, often superior to more complex schemes that have been proposed.     

State Amplification

We consider the problem of transmitting data at rate over a state-dependent channel with state information available at the sender and at the same time conveying the information about the channel state itself to the receiver. The amount of state information that can be learned at the receiver is captured by the mutual information between the state sequence and the channel output . The optimal tradeoff is characterized between the information transmission rate and the state uncertainty reduction rate , when the state information is either causally or noncausally available at the sender. In particular, when state transmission is the only goal, the maximum uncertainty reduction rate is given by . This result is closely related and in a sense dual to a recent study by Merhav and Shamai, which solves the problem of masking the state information from the receiver rather than conveying it.     

Real options approach to evaluating genetic algorithms

The real options technique has emerged as an evaluation tool for investment under uncertainty. It explicitly recognizes future decisions, and the exercise strategy is based on the optimal decisions in future periods. This paper employs the optimal stopping policy derived from real options approach to analyze and evaluate genetic algorithms, specifically for the new branches namely Estimation of Distribution Algorithms (EDAs). As an example, we focus on their simple class called univariate EDAs, which include the population-based incremental learning (PBIL), the univariate marginal distribution algorithm (UMDA), and the compact genetic algorithm (cGA). Although these algorithms are classified in the same class, the characteristics of their optimal stopping policy are different. These observations are useful in answering the question “which algorithm is suitable for a particular problem”. The results from the simulations indicate that the option values can be used as a quantitative measurement for comparing algorithms.