Controlling the peak-to-mean envelope power ratio (PMEPR) of orthogonal frequency-division multiplexed (OFDM) transmissions is a significant obstacle in many low-cost applications of OFDM. An coding approach proposed by H.R. Sadjadpour presents non-square M-QAM symbols as a combination of QPSK and BPSK signals when M=22n+1, and then uses QPSK and BPSK Golay (or Golay-like) sequences with a constant PMEPR to generate M-QAM sequences. This paper proposes a new scheme in which M-QAM sequences are generated by QPSK and BPSK sequences with variable PMEPRs. In other words, this new scheme is a general case of the existing approach. As a result, the code rate of the new sequence is significantly improved, while the upper bound of its PMEPR remains at a comparative level.

In CRYPTO 2019, Gohr first introduced the deep learning method to cryptanalysis for SPECK32/64. A differential-neural distinguisher was obtained using ResNet neural network. Zhang et al. used multiple parallel convolutional layers with different kernel sizes to capture information from multiple dimensions, thus improving the accuracy or obtaining a more round of distinguisher for SPECK32/64 and SIMON32/64. Inspired by Zhang's work, we apply the network structure to other ciphers. We not only improve the accuracy of the distinguisher, but also increase the number of rounds of the distinguisher, that is, distinguish more rounds of ciphertext and random number for DES, Chaskey and PRESENT.

Multicarrier communications including orthogonal frequency-division multiplexing (OFDM) is a technique which has been adopted for various wireless applications. However, a major drawback to the widespread acceptance of OFDM is the high peak-to-mean envelope power ratio (PMEPR) of uncoded OFDM signals. Finding methods for construction of sequences with low PMEPR is an active research area. In this paper, by employing some new shortened and extended Golay complementary pairs as the seeds, we enlarge the family size of near-complementary sequences given by Yu and Gong. We also show that the new set of sequences we obtained is just a reversal of the original set. Numerical results show that the enlarged family size is almost twice of the original one. Besides, the Hamming distances of the binary near-complementary sequences are also analyzed.

Based on the framework of a multi-stage key recovery attack for a large block cipher, 2 and 3-round differential-neural distinguishers were trained for AES using partial ciphertext bits. The study introduces the differential characteristics employed for the 2-round ciphertext pairs and explores the reasons behind the near 100% accuracy of the 2-round differential neural distinguisher. Utilizing the trained 2-round distinguisher, the 3-round subkey of AES is successfully recovered through a multi-stage key guessing. Additionally, a complexity analysis of the attack is provided, validating the effectiveness of the proposed method.