In this paper, an effective approach is proposed to recognize the order among codewords from the randomly binary and non-binary interleaved linear block coded streams if the code minimum distance is known, where the interleaver lengthNn+dis not the multiple of the linear block code lengthn. Then, an efficient approach is developed without knowing linear block code parameters such as code rate and the code generator matrix (except the code length) to recognize the whole random interleaving pattern if the greatest common divisor (GCD) ofnanddis GCD(n, d) = 1 for both binary and non-binary interleaved linear block coded symbols. Further, for the case GCD(n, d) =q̸= 1, the whole random interleaving pattern can be recognized with the probability of no more than (q!)−(Nn+d)/qfor the following cases: (1) the non-binary interleaved coded symbols without knowing the non-binary code generator matrix; (2) the binary interleaved coded symbols.

In this paper, we construct non-binary codes of lengthNwhich correct errors under a non-binary segment-Nmaxburst-Ddeletion and maxburst-Sinsertion (NB-SBDI(N, D, S)) channel without knowing the codeword boundaries. In this NB-SBDI(N, D, S) channel, at most a single non-binary burst (a block of consecutive bits/symbols) of deletions or insertions of length up toDorS, respectively, exists in a block ofNconsecutive non-binary symbols. One code named as BM-DB-MDS consists of a maximum distance separable (MDS) code, a block of periodic de Bruijn (DB) symbols, and a block of proposed periodic binary marker (BM) patterns with a period ofS+D+ 2. The other code called BM-MDS code consists of a BM code and an MDS code. We show that the rates of BM-DB-MDS and BM-MDS codes achieve λ/λ+1 (1 - 1/2t), λ ∈ N+and 1 - 1/t, respectively, whenN→ +∞, where λ represent the MDS code shortening factor, and 1/tis the rough proportion of the maximum length of burst deletions or insertions allowed in a code.

Radio frequency fingerprint (RFF) identification is emerging as an attractive paradigm for physical layer security. Despite the exceptional accuracy achieved by deep learning (DL) based schemes, few works consider the cross-receiver scenario. The performance deteriorates significantly when the model is deployed on new receivers directly. To this end, a cross-receiver RFF learning scheme is proposed. First, an unsupervised pre-training method based on contrastive learning is utilized to extract receiver-agnostic features. Then, the model is optimized by subdomain adaptation to further improve identification performance. The proposed scheme does not require multiple labeled datasets from different receivers. And experimental results indicate that the proposed scheme effectively alleviates performance degradation in the cross-receiver scenario.

Direct sequence spread spectrum (DSSS) signals are widely used in various military and civilian communication systems owing to its useful properties, such as lower working signal‐to‐noise ratio, strong anti‐jamming capability, and robustness to multi‐path fading. In past few years, the estimation for spreading sequence in non‐cooperative context has attracted lots of attention. Several methods are developed to realise a recovery of the sequence. Eigenvalue decomposition (EVD) is a classical method to obtain an effective estimation by extracting the main eigenvectors. The only obstacle to apply EVD method into practical systems is high complexity of computation when spreading sequence is long. Alternative algorithms such as neural network and clustering method have lower complexity, but an evident loss in performance. In this work, the authors propose to generalise the EVD algorithm to intermediate frequency (IF) short‐code DSSS with an unknown carrier offset. The blind despreading process is designed to complete correlation demodulation. As a result, the computational afford is largely decreased due to the operation on IF real signal. To evaluate the performance, they derive the Crammer‐Rao bound for spreading code estimation and compare the performance of several methods. Simulation demonstrates the superiority of the proposed method in estimation accuracy and runtime.