Source fidelity over fading channels: performance of erasure and scalable codes

We consider the transmission of a Gaussian source through a block fading channel. Assuming each block is decoded independently, the received distortion depends on the tradeoff between quantization accuracy and probability of outage. Namely, higher quantization accuracy requires a higher channel code rate, which increases the probability of outage. We first treat an outage as an erasure, and evaluate the received mean distortion with erasure coding across blocks as a function of the code length. We then evaluate the performance of scalable, or multi-resolution coding in which coded layers are superimposed within a coherence block, and the layers are sequentially decoded. Both the rate and power allocated to each layer are optimized. In addition to analyzing the performance with a finite number of layers, we evaluate the mean distortion at high signal-to-noise ratios as the number of layers becomes infinite. As the block length of the erasure code increases to infinity, the received distortion converges to a deterministic limit, which is less than the mean distortion with an infinite-layer scalable coding scheme. However, for the same standard deviation in received distortion, infinite layer scalable coding performs slightly better than erasure coding, and with much less decoding delay.     

Exploring the relationship between energy use and economic growth with bivariate models: New evidence from G-7 countries

There is a rapidly growing literature on the interaction between energy use and economic development, with many analysts drawing policy conclusions on the basis of Granger causality tests that involve only an energy and an economic variable. This paper attempts to demonstrate empirically that such studies, although useful for certain applications, may be of limited use for policy purposes. After outlining theoretical and methodological issues associated with such approaches, I apply bivariate energy–economy causality tests for Canada, France, Germany, Italy, Japan, the United Kingdom and the United States, using aggregate and sectoral data and three different modern econometric methods. The results, which are often contradictory or economically implausible, illustrate explicitly that one should be cautious when drawing policy implications with the aid of bivariate causality tests on small samples. I therefore underline the importance of utilizing as large sample sizes as possible and using multivariate models, which are closer to economic theory, accommodate several mechanisms and causality channels and provide a better representation of real-world interactions between energy use and economic growth.     

An integrated local search method for inventory and routing decisions

The present article studies an inventory routing model which integrates two important components of the supply chain: transportation logistics and inventory control. The distribution system examined consists of customers that face product demand at a deterministic and constant rate. Customer demand is satisfied by a fixed vehicle fleet located at the central depot. The aim of the problem is to determine the timing and size of the replenishment services together with the vehicle routes, so that the total transportation and inventory holding cost of the system is minimized. In methodological terms, we propose a solution approach applying two innovative local search operators for jointly dealing with the inventory and routing aspects of the examined problem, and Tabu Search for further reducing the transportation costs. The proposed algorithmic framework was tested on a set of new benchmark instances of various scales. It produced satisfactory results both in terms of effectiveness and robustness.