Biblio

Blind identification of channel codes is crucial in intelligent communication and non-cooperative signal processing, and it plays a significant role in wireless physical layer security, information interception, and information confrontation. Previous researches show a high computation complexity by manual feature extractions, in addition, problems of indisposed accuracy and poor robustness are to be resolved in a low signal-to-noise ratio (SNR). For solving these difficulties, based on deep residual shrinkage network (DRSN), this paper proposes a novel recognizer by deep learning technologies to blindly distinguish the type and the parameter of channel codes without any prior knowledge or channel state, furthermore, feature extractions by the neural network from codewords can avoid intricate calculations. We evaluated the performance of this recognizer in AWGN, single-path fading, and multi-path fading channels, the results of the experiments showed that the method we proposed worked well. It could achieve over 85 % of recognition accuracy for channel codes in AWGN channels when SNR is not lower than 4dB, and provide an improvement of more than 5% over the previous research in recognition accuracy, which proves the validation of the proposed method.

Transmission techniques based on channel coding with feedback are proposed in this paper to enhance the security of wireless communications systems at the physical layer. Reliable and secure transmission over an additive noise Gaussian wiretap channel is investigated using Bose-Chaudhuri-Hocquenghem (BCH) and Low-Density Parity-Check (LDPC) channel codes. A hybrid automatic repeat-request (HARQ) protocol is used to allow for the retransmission of coded packets requested by the intended receiver (Bob). It is assumed that an eavesdropper (Eve) has access to all forward and feedback transmitted packets. To limit the information leakage to Eve, retransmitted packets are subdivided into smaller granular subpackets. Retransmissions are stopped as soon as the decoding process at the legitimate (Bob) receiver converges. For the hard decision decoded BCH codes, a framework to compute the frame error probability with granular HARQ is proposed. For LDPC codes, the HARQ retransmission requests are based on received symbols likelihood computations: the legitimate recipient request for the retransmission of the set of bits that are more likely to help for successful LDPC decoding. The performances of the proposed techniques are assessed for nul and negative security gap (SG) values, that is when the eavesdropper's channel benefits from equal or better channel conditions than the legitimate channel.