Orthogonal frequency division multiplexing (OFDM-IM) is a multicarrier transmission technology that modulates information bits not just onto subcarriers by means of M-ary constellation mapping but also onto selected (active) subcarrier indices. Consequently, errors can occur in OFDM-IM systems indices in addition to the errors of M-ary symbols. This paper analyzes the error scenarios and derives mathematical expressions for the error performance based on the maximum likelihood (ML) detection. In evaluating the bit error rate (BER) in the additive white Gaussian noise (AWGN) channel, some assumptions are made and our analytical result show that the BER of OFDM-IM system is a weighted sum of exponential functions and Q-functions. Our general BER expression has been shown to be in excellent agreement with numerical simulation and proven to be accurate and can serve as a reference for the design and evaluation of any arbitrary size and configuration of OFDM-IM systems.
Abstract-Multiple input multiple output-Orthogonal frequency division multiplexing (MIMO-OFDM) is a viable solution for providing high data rate services in harsh channel environments. The optimum receivers for them are those based on the maximum likelihood criterion. However, they have a prohibitive complexity especially when channel dimensions are high and coding is employed. Zero Forcing (ZF) and Linear Minimum Mean Square Error (MMSE) receivers on the other hand provide practicable and low complexity solutions for detection, but require soft demappers to deduce the soft bits information contained in each of the received symbols. In this work, we present the ZF and MMSE receiver analysis of a bit interleaved and coded MIMO-OFDM system and propose a soft output demapper based on MMSE equalizer output to demap the information needed for viterbi decoding. A comparison of the proposed soft demapper with conventional soft demappers in literature show a significant performance improvement. We also noticed that it is more advantageous to apply the proposed demapper on a MIMO-OFDM system employing higher modulation schemes.
TX 75083-3836 U.S.A., fax 1.972.952.9435.
AbstractA large carbonate oil field in Iran is suffering from severe casing collapses. 48 casing collapses have been found to be reservoir compaction and poro-elastic effects and corrosion. The application of neural networks for predicting casing collapses using complex multi-dimensional field data has been undertaken. This paper shows how a neural network (ANN) system can be trained based on the parameters affecting casing collapse to estimate the potential of collapse of wells to be drilled as well as the current wells producing in the field. The potential use of this type of analysis is large in that it can be linked as a critical risking parameter in future field development analysis. Being able to quantify the potential for collapse of a well in the future can give management the foundation for a better financial decision making on what wells and where to drill them with the potential for the larger net return on the investment. The estimated collapse and corresponding depth could also benefit in the type of casing design and completion method to be selected as well as workover designs.Interpretation of the neural network results, together with engineering judgment, allowed us to conclude that using this method is technically feasible for predicting casing collapses in this field.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.