Digital Processing Techniques for WCDMA Cellular Systems » książka
Digital Processing Techniques for WCDMA Cellular Systems
ISBN-13: 9783639173086 / Angielski / Miękka / 2009 / 140 str.
Baseband processing techniques for Code Division Multiple Access (CDMA) systems generally fall into two broad categories: error coding and signal spreading. This thesis addresses these issues for the Wideband CDMA (WCDMA) baseband receiver. The Third Generation Partnership Protocol (3GPP) standards will be used in specifying the system model. 3GPP systems support two forms of error coding, i.e., convolutional coding and Turbo coding. A Viterbi decoder is developed for convolutional codes to optimize the likelihood of the estimated information sequence. For Turbo coding, a Turbo decoder, which attempts to maximize the log- likelihood ratio of each information bit, is introduced. The 3GPP standards also specify long and short codes for signal spreading. For long codes, linear estimation techniques are designed based on specific cost and constraint functions. For short codes, adaptive estimation techniques, which minimize the mean-square error (MSE) cost, are presented.
Baseband processing techniques for Code Division Multiple Access (CDMA) systems generally fall into two broad categories: error coding and signal spreading. This thesis addresses these issues for the Wideband CDMA (WCDMA) baseband receiver. The Third Generation Partnership Protocol (3GPP) standards will be used in specifying the system model. 3GPP systems support two forms of error coding, i.e., convolutional coding and Turbo coding. A Viterbi decoder is developed for convolutional codes to optimize the likelihood of the estimated information sequence. For Turbo coding, a Turbo decoder, which attempts to maximize the log-likelihood ratio of each information bit, is introduced. The 3GPP standards also specify long and short codes for signal spreading. For long codes, linear estimation techniques are designed based on specific cost and constraint functions. For short codes, adaptive estimation techniques, which minimize the mean-square error (MSE) cost, are presented.
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