Browsing by Subject "Bayesian estimate"

Now showing 1 - 3 of 3

Open Access
Blind phase noise estimation in OFDM systems by sequential Monte Carlo method
(Springer, 2006) Panayırcı, Erdal; Çırpan, H. A.; Moeneclaey, M.; Noels, N.
In this paper, based on a sequential Monte Carlo method, a computationally efficient algorithm is presented for estimating the residual phase noise, blindly, generated at the output the phase tracking loop employed in OFDM systems. The basic idea is to treat the transmitted symbols as "missing data" and draw samples sequentially of them based on the observed signal samples up to time t. This way, the Bayesian estimates of the phase noise is obtained through these samples, sequentially drawn, together with their importance weights. The proposed receiver structure is seen to be ideally suited for high-speed parallel implementation using VLSI technology.
Open Access
Multi-item quick response system with budget constraint
(2012) Serel, D. A.
Quick response mechanisms based on effective use of up-to-date demand information help retailers to reduce their inventory management costs. We formulate a single-period inventory model for multiple products with dependent (multivariate normal) demand distributions and a given overall procurement budget. After placing orders based on an initial demand forecast, new market information is gathered and demand forecast is updated. Using this more accurate second forecast, the retailer decides the total stocking level for the selling season. The second order is based on an improved demand forecast, but it also involves a higher unit supply cost. To determine the optimal ordering policy, we use a computational procedure that entails solving capacitated multi-item newsboy problems embedded within a dynamic programming model. Various numerical examples illustrate the effects of demand variability and financial constraint on the optimal policy. It is found that existence of a budget constraint may lead to an increase in the initial order size. It is also observed that as the budget available decreases, the products with more predictable demand make up a larger share of the procurement expenditure.
Open Access
Optimal ordering and pricing in a quick response system
(Elsevier, 2009-10) Serel, D. A.
Quick response systems enable retailers to estimate customer demand more accurately, and improve stocking decisions for perishable products with uncertain demand. Retailers place separate orders for a product at two different times before the selling season. Following the initial order, additional market information is obtained, and the second-order amount is decided based on an improved demand forecast. In some cases, purchase cost associated with the second order is uncertain, and demand for the product during the season depends on the selling price. We present a solution procedure for finding the optimal order quantity and selling price in this setting. We also study the case where any desired portion of the initial order can be cancelled after updating the demand forecast. In the numerical study. the optimal price is observed to be relatively insensitive to changes in demand variability.