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To address the explosive traffic demands, the capacity of the fading channel is increasingly becoming a prime concern in the designing of the wireless communication system. The channel capacity is an extremely important quantity, since it allows the transmission of the data through the channel with an arbitrarily small probability of error. In other words, capacity dictates the maximum rate of information transmission, called as ‘capacity’ of channel, determined by the intrinsic properties of the channel and is independent of the content of the transmitted information. In this paper, we present a comprehensive survey of the existing work related to the channel capacity model over various fading channels. With an elaborated explanation of the theory of channel capacity, definitions of channel capacity based on the channel state information are reviewed. To compliment this, review of the technique to enhance the channel capacity is discussed and reviewed. An effective capacity model to overcome the channel capacity limitation is also explained. Furthermore, as the secure transmission of data is of utmost importance, to address this physical layer security model is also reviewed. We also summarize the work related to channel capacity in various types of wireless networks. We finally cover the future research directions, including less explored aspects of the channel capacity that can be studied to design efficient communication systems.
相似文献Content based image retrieval (CBIR) is an extrusive technique of retrieving the relevant images from vast image archives by extracting their low level features. In this research paper, the pursuance of five most prominent texture feature extraction techniques used in CBIR systems are experimentally compared in detail. The main issue with the CBIR systems is the proper selection of techniques for the extraction of low level features which comprises of color, texture and shape. Among these features, texture is one of the most decisive and dominant features. This selection of features completely depends upon the type of images to be retrieved from the database. The texture techniques explored here are Grey level co-occurrence matrix (GLCM), Discrete wavelet transform (DWT), Gabor transform, Curvelet and Local binary pattern (LBP). These are experimented on three touchstone databases which are Wang, Corel-5 K and Corel-10 K. The chief parameters of CBIR systems are evaluated here such as precision, recall and F-measure on all these databases using all the techniques. After detailed investigation it is figured out that LBP, GLCM and DWT provide highlighted and comparable results in all these datasets in terms of average precision. Besides practical implementation, the précised conceptual examination of these three texture techniques is also proposed in this article. So, this analysis is extremely beneficial for selecting the appropriate feature extraction technique by taking into consideration the experimental results along with image conditions such as noise, rotation etc.
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