Wireless Channel-based Secret Key Generation (WC-SKG) offers a promising alternative for wireless communication security, yet suffers from an extremely low key generation rate (KGR) in quasi-static environments (e.g., indoor Internet of Things), where key refresh cycles can extend for hours, creating a security vulnerability. Reconfigurable Intelligent Surfaces (RIS) can boost KGR by introducing artificial randomness, yet existing schemes only randomize the RIS phase shifts without using environmental priors, failing to maximize the KGR and meet the communication signal-to-noise ratio (SNR) requirement, causing service outages. To overcome these limitations, we propose the RIS-assisted Integrated Communication and WC-SKG (RICK) scheme. First, we treat the RIS-adjusted channel as a designable random variable and derive its optimal probability density function (PDF) using a novel geometric-algebraic framework to maximize the KGR under the communication SNR constraint. Second, we design a constrained-clustering-based quantization region division scheme tailored to this optimal non-uniform PDF, guaranteeing uniformly distributed secret keys. Simulation results show RICK achieves a KGR approximately 3.5-5 times higher than the state-of-the-art scheme while saving at least 20 dB of transmit power for the same KGR, confirming its effectiveness in quasi-static scenarios.

ABSTRACT The real‐time reconfigurable characteristics of meta‐surface antennas can be used to separate multi‐stream signals under the condition of single radio frequency (RF). However, with the increase of the symbol rate and the number of antenna arrays in the future, it will face the problem that the state switch rate of the electromagnetic unit is not enough to reach the upper limit of array effective degrees of freedom (DOF) of the meta‐surface antenna. To solve this problem, a theory of asynchronous control meta‐surface antenna is proposed in this paper. By designing the starting time of different element state switching, different electromagnetic element states are staggered to improve the array effective DOF of the meta‐surface antenna. Then, an electromagnetic unit state design algorithm of asynchronous control meta‐surface antenna based on the minimum condition number of equivalent channel matrix is proposed. We improve the sparrow search algorithm to solve the condition number minimization problem in order to obtain the better multi‐stream signal separation performance. The simulation results show that compared with the synchronous control meta‐surface antenna, theory proposed in this paper can improve the effective DOF of array under the condition of limited switch rate, and can effectively reduce the receiving bit error rate and improve spectral efficiency when separating multi‐stream signals.

Predicting user's next location is of great importance for a wide spectrum of location-based applications. However, most prediction methods do not take advantage of the rich semantic information contained in trajectory data. Meanwhile, the traditional LSTM-based model can not capture the spatio-temporal dependencies well. In this paper, we propose a Semantic and Attention Spatio-temporal Recurrent Model (SASRM) for next location prediction. Firstly, the SASRM put forward a method for encoding semantic vectors and concatenating vectors (location, time and semantic vectors) as input to the model. To capture the spatio-temporal dependencies, we design a variant recurrent unit based on LSTM. Further, an attention layer is used to weight hidden state to capture the influence of the historical locations on the next location prediction. We perform experiments on two real-life semantic trajectory datasets, and evaluation results demonstrate that our model outperforms several state-of-the-art models in accuracy.