This paper investigates an explicit polar coding scheme for a two-user discrete memoryless multiple access wiretap channel with partial rate-limited feedback (MAC-WT-PLF). Feedback is exploited to increase channel-input correlation, inject dummy messages, and encrypt messages using a one-time pad. Existing MAC-WT polar coding schemes assume independent channel inputs, which cannot support the correlation requirement. Therefore, we propose an explicit polar mapping that leverages feedback to introduce correlation between the channel inputs. This mapping allows both the transmitter and the receiver to restructure the correlation in opposite decoding directions. The proposed scheme relies on source polarization, block Markov coding, superposition coding, lossy source coding, and ratesplitting, without making symmetry or degradation assumptions on the channel model. Rigorous information-theoretic asymptotic analysis establishes that the proposed scheme ensures both reliability and strong secrecy, and attains the entire achievable secrecy rate region given in prior work.

This paper focuses on the performance analysis of multi-functional RIS (MF-RIS) assisted ambient backscatter communication (AmBC) networks. To be specific, we derive the closed-form and asymptotic expressions of outage probability for both data signal and backscatter signal with the consideration of imperfect and perfect successive interference cancellation (SIC) process. On this basis, the diversity orders, which are related to the number of MF-RIS elements and levels of imperfect SIC, are also derived. Numerical results indicate that the outage performance of MF-RIS-assisted AmBC networks outperforms that of passive and conventional relaying schemes.

Recently proliferated deep learning-based semantic communications (DLSC) focus on how transmitted symbols efficiently convey a desired meaning to the destination. However, the sensitivity of neural models and the openness of wireless channels cause the DLSC system to be extremely fragile to various malicious attacks. This inspires us to ask a question: “Can we further exploit the advantages of transmission efficiency in wireless semantic communications while also alleviating its security disadvantages?”. Keeping this in mind, we propose SemEntropy, a novel method that answers the above question by exploring the semantics of data for both adaptive transmission and physical layer encryption. Specifically, we first introduce semantic entropy, which indicates the expectation of various semantic scores regarding the transmission goal of the DLSC. Equipped with such semantic entropy, we can dynamically assign informative semantics to Orthogonal Frequency Division Multiplexing (OFDM) subcarriers with better channel conditions in a fine-grained manner. We also use the entropy to guide semantic key generation to safeguard communications over open wireless channels. By doing so, both transmission efficiency and channel security can be simultaneously improved. Extensive experiments over various benchmarks show the effectiveness of the proposed SemEntropy. We discuss the reason why our proposed method benefits secure transmission of DLSC, and also give some interesting findings, e.g., SemEntropy can keep the semantic accuracy remain 95% with 60% less transmission.