This paper is concerned with an attractive multiantenna multi-carrier modulation scheme, termed as generalized space-frequency index modulation (GSFIM), which is a promising modulation scheme for next generation wirel...
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ISBN:
(纸本)9781467395267
This paper is concerned with an attractive multiantenna multi-carrier modulation scheme, termed as generalized space-frequency index modulation (GSFIM), which is a promising modulation scheme for next generation wireless systems. GSFIM uses both spatial domain and frequency domain to encode bits through indexing. In GSFIM, information bits are mapped through antenna indexing in the spatial domain, subcarrier indexing in the frequency domain, and M-ary modulation. GSFIM can offer higher rates using fewer transmit radio frequency (RF) chains and better performance compared to conventional MIMO-OFDM. In this paper, we address the problem of low-complexity encoding and detection of large-dimensional GSFIM signals. The proposed encoding procedure exploits the lowcomplexity computation of combinadics in combinatorial number system. This allows 'on-the-fly' computation of GSFIM encoding maps. For detecting GSFIM signals, we propose a lowcomplexity detection algorithm based on a multi-stage message passing approach. The proposed lowcomplexityencoding/detection algorithms allow practical implementation of large-dimension GSFIM systems.
This paper is concerned with an attractive multi-antenna multi-carrier modulation scheme, termed as generalized space-frequency index modulation (GSFIM), which is a promising modulation scheme for next generation wire...
详细信息
ISBN:
(纸本)9781467395274
This paper is concerned with an attractive multi-antenna multi-carrier modulation scheme, termed as generalized space-frequency index modulation (GSFIM), which is a promising modulation scheme for next generation wireless systems. GSFIM uses both spatial domain and frequency domain to encode bits through indexing. In GSFIM, information bits are mapped through antenna indexing in the spatial domain, subcarrier indexing in the frequency domain, and M-ary modulation. GSFIM can offer higher rates using fewer transmit radio frequency (RF) chains and better performance compared to conventional MIMO-OFDM. In this paper, we address the problem of low-complexity encoding and detection of large-dimensional GSFIM signals. The proposed encoding procedure exploits the lowcomplexity computation of combinadics in combinatorial number system. This allows 'on-the-fly' computation of GSFIM encoding maps. For detecting GSFIM signals, we propose a lowcomplexity detection algorithm based on a multi-stage message passing approach. The proposed lowcomplexityencoding/detection algorithms allow practical implementation of large-dimension GSFIM systems.
Multilevel coding (MLC) provides a low-complexity encoding scheme for lattices obtained via construction D. On the other hand, multistage decoding is also a practical decoding scheme for MLC provided that the underlyi...
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Multilevel coding (MLC) provides a low-complexity encoding scheme for lattices obtained via construction D. On the other hand, multistage decoding is also a practical decoding scheme for MLC provided that the underlying error correcting codes are capacity achieving, which requires using a powerful soft decoder for each code. Proposed is an approximation of the log-likelihood ratio (LLR) which used for the soft decoding of the different error correcting codes employed in the lattice construction. This approximation is based on the von Mises distribution and achieves, with lower complexity, the same error rate performance obtained with the exact LLR calculation.
In this paper we propose a novel distributed lossless compression scheme for hyperspectral images. All the images/bands are encoded independently, and the spectral correlation is exploited using distributed coding tec...
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ISBN:
(纸本)9781424479948
In this paper we propose a novel distributed lossless compression scheme for hyperspectral images. All the images/bands are encoded independently, and the spectral correlation is exploited using distributed coding technologies in order to achieve lowencodingcomplexity. At the encoder, sub-sampled images are successively encoded and transmitted. At the decoder, side information is generated with the knowledge of decoded sub-sampled images and other previously decoded bands. Reference bands are adaptively selected, and sliding window prediction or k nearest neighbor prediction is performed to capture the spatially varying spectral characteristics. Experimental results on AVRIS data demonstrate that the proposed scheme achieves competitive compression performance with respect to other state-of-the-art 3D codecs and with even lower encodingcomplexity than 2D codecs.
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