Abstract: Disclosed are a long-range radio communication method and device based on a spreading factor index, the method including: within one symbol period, dividing a plurality of information bits into index bits and modulated bits; mapping the index bits into a spreading factor sequence, and generating first chirp signals corresponding to various spreading factors (SFs); and converting the modulated bits to decimal numbers associated with the various SFs, modulating the decimal numbers into carrying signals, multiplying the carrying signals by the first chirp signals associated with the SFs to obtain second chirp signals associated with the various SFs, and superposing the second chirp signals to obtain a transmitted signal. According to the disclosure, by combining different SFs, an additional dimension is created to carry more information bits, so as to achieve the effect of increasing a data rate.

Abstract: A power-imbalanced multi-level decoding method for sparse code multiple access includes: encoded bits of all users are mapped to multi-dimensional sparse codewords through predetermined codebooks; a factor graph matrix is constructed using the predetermined codebooks, all users are classified according to the factor graph matrix under predetermined constraints to determine Z levels; based on a predetermined total transmission power, a progressive multi-level power optimization algorithm is employed to perform power-imbalanced allocation for all users according to the Z levels, thereby determining a locally optimal power vector; transmission signals corresponding to the multi-dimensional sparse codewords are transmitted according to the locally optimal power vector; SCMA detection and power-oriented decoding are sequentially performed for users each level, and outputs decoded bit sequences for the L levels.

Abstract: Disclosed are a long-range radio communication method and device based on a spreading factor index, the method including: within one symbol period, dividing a plurality of information bits into index bits and modulated bits; mapping the index bits into a spreading factor sequence, and generating first chirp signals corresponding to various spreading factors (SFs); and converting the modulated bits to decimal numbers associated with the various SFs, modulating the decimal numbers into carrying signals, multiplying the carrying signals by the first chirp signals associated with the SFs to obtain second chirp signals associated with the various SFs, and superposing the second chirp signals to obtain a transmitted signal. According to the disclosure, by combining different SFs, an additional dimension is created to carry more information bits, so as to achieve the effect of increasing a data rate.

Abstract: A coding method based on multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM) is provided. A coded bit sequence is divided into an index-layer sequence and a symbol-layer sequence. According to the index-layer sequence and the symbol-layer sequence, a plurality of constellation modes respectively corresponding to the plurality of subcarriers and which one of a plurality of constellation symbols respectively corresponding to the plurality of constellation modes is carried by each of the plurality of subcarriers are determined. A constellation mode permutation corresponding to each of the plurality of subcarriers is in a symmetrical distribution. The coded bit sequence is converted into the plurality of constellation symbols. The plurality of constellation symbols are respectively loaded to the plurality of subcarriers. A coding device based on MM-OFDM-IM is also provided.

Abstract: A coding method based on multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM) is provided. A coded bit sequence is divided into an index-layer sequence and a symbol-layer sequence. According to the index-layer sequence and the symbol-layer sequence, a plurality of constellation modes respectively corresponding to the plurality of subcarriers and which one of a plurality of constellation symbols respectively corresponding to the plurality of constellation modes is carried by each of the plurality of subcarriers are determined. A constellation mode permutation corresponding to each of the plurality of subcarriers is in a symmetrical distribution. The coded bit sequence is converted into the plurality of constellation symbols. The plurality of constellation symbols are respectively loaded to the plurality of subcarriers. A coding device based on MM-OFDM-IM is also provided.

Abstract: A training method based on an improved protograph neural decoder includes the following steps: a to-be-trained decoding network is constructed based on an initial variable sub-network layer, an initial check sub-network layer and a preset shuffled belief-propagation (BP) sub-network layer; the initial variable sub-network layer, the initial check sub-network layer and the preset shuffled BP sub-network layer are updated and trained by calculating log-likelihood ratio (LLR) based on a preset mean square error loss function and a preset decoder objective function to obtain a target protograph neural decoder; and the preset mean square error loss function is configured to calculate a loss value between output information of the check sub-network layer and the preset shuffled BP sub-network layer. The target protograph neural decoder includes an optimized variable sub-network layer, an optimized check sub-network layer and an optimized shuffled BP sub-network layer. A training device is also provided.

Abstract: A training method based on an improved protograph neural decoder includes the following steps. A to-be-trained decoding network is constructed based on an initial variable sub-network layer, an initial check sub-network layer and a preset shuffled belief-propagation (BP) sub-network layer. The initial variable sub-network layer, the initial check sub-network layer and the preset shuffled BP sub-network layer are updated and trained by calculating log-likelihood ratio (LLR) based on a preset mean square error loss function and a preset decoder objective function to obtain a target protograph neural decoder. The preset mean square error loss function is configured to calculate a loss value between output information of the check sub-network layer and the preset shuffled BP sub-network layer. The target protograph neural decoder includes an optimized variable sub-network layer, an optimized check sub-network layer and an optimized shuffled BP sub-network layer. A training device is also provided.