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Network slicing has become a primary function of 5G/6G network resource management. However, the existing slicing schemes have not sufficiently discussed the reconfiguration optimization schemes brought by user behavior changes and mobile network environment fluctuations, leading to excessive service interruption rates and slice reconfiguration costs in dynamic environments. To address this problem, this paper proposes an End-to-end Steady-state Adaptive slicing method for Dynamic network state and load (ESAD). To realize the steady-state slicing decisions, ESAD takes the steady-state degree of network slicing and reconfiguration cost as the objective and constructs the slicing reconfiguration probability evaluation function based on the service load dynamics function and the time-varying function of the network channel conditions. To improve the predictability and steady-state degree of the slicing decision, ESAD introduces an ensemble deep learning method to predict the load service fluctuation based on the user behavior model and employs reinforcement learning to compute the channel dynamics boundary, which guides the slicing decision to balance the network dynamics factors. Experiments on quality of service assurance for 5G cloud game rendering class prove that ESAD can reduce reconfiguration probability and long-term reconfiguration cost by 49.45%–58.50% while improving system QoS assurance and capacity.

Reconfigurable intelligent surfaces (RISs) are deemed as a potential technique for the future of the Internet of Things (IoT) due to their capability of smartly reconfiguring the wireless propagation environment using a large number of low-cost passive elements. To benefit from RIS technology, the problem of RIS-assisted channel state information (CSI) acquisition needs to be carefully considered. Existing channel estimation methods usually ignored the different channel characteristics of direct channel and reflected channels. In fact, the reflected channel can be smartly configured by adjusting the phase shifts of the RIS, which is different from the direct channel due to the different path loss exponents between the transmitter and receiver. Therefore, it is necessary to further develop a RIS-assisted channel estimation to determine the direct and reflected channels, respectively. In this paper, we study a RIS-assisted channel estimation that jointly exploits the properties of the direct and the reflected channel to provide more accurate CSI. The direct channel is estimated using weighted $\ell _{1}$ norm minimization, while the reflected channel is modeled based upon the robust $\ell _{1,\tau }$ norm minimization to sequentially estimate the channel parameters. Moreover, by combining the gradient descent and the alternating minimization method, a flexible and fast algorithm is developed to provide a feasible solution. Simulation results demonstrate that an RIS-aided MIMO system significantly reduces the active antennas/RF chains compared to other benchmark schemes.

With the proliferation and rapid evolution of new psychoactive substances (NPSs), traditional database-based search methods face increasing challenges in identifying NPS seizures with complex compositions, thereby complicating their regulation and early warning. To address this issue, CBMAFF-Net (CNN BiLSTM Multistep Attentional Feature Fusion Network) is proposed as an intelligent screening method to rapidly classify unknown confiscated substances using 13C nuclear magnetic resonance (NMR) and 1H NMR data. Initially, we utilize the synergy of a convolutional neural network (CNN) and bidirectional long short-term memory network (BiLSTM) to extract the global and local features of the NMR data. These features are sequentially fused through a weighted approach guided by an attention mechanism, thoroughly capturing the essential NPS information. We evaluated the model on a generated simulated data set, where it performed with 99.8% accuracy and a 99.8% F1 score. Additionally, testing on 42 actual seizure cases yielded a recognition accuracy of 97.6%, significantly surpassing the performance of conventional database-based similarity search algorithms. These findings suggest that the proposed method holds substantial promise for the rapid screening and classification of NPSs.