MALRIS: Malicious Hardware in RIS-Assisted Wireless Communications

Reconfigurable intelligent surfaces (RIS) enhance wireless communication by dynamically shaping the propagation environment, but their integration introduces hardware-level security risks. This paper presents the concept of Malicious RIS (MALRIS), where compromised components behave adversarially, even under passive operation. The focus of this work is on practical threats such as manufacturing time tampering, malicious firmware, and partial element control. Two representative attacks, power-splitting and element-splitting, are modeled to assess their impact. Simulations in a RIS-assisted system reveal that even a limited hardware compromise can significantly degrade performance metrics such as bit error rate, throughput, and secrecy metrics. By exposing this overlooked threat surface, this work aims to promote awareness and support secure, trustworthy RIS deployment in future wireless networks.

Dynamic IRS Allocation for Spectrum-Sharing MIMO Communication and Radar Systems

This paper investigates the use of intelligent reflecting surfaces (IRS) to assist cellular communications and radar sensing operations in a communications and sensing setup. The IRS dynamically allocates reflecting elements to simultaneously localize a target and assist a user's communication. To achieve this, we propose a novel optimization framework that jointly addresses beamforming design and IRS element allocation. Specifically, we formulate a Weighted Minimum Mean Square Error (WMMSE)-based approach that iteratively optimizes the transmit and receive beamforming vectors, IRS phase shifts, and element allocation. The allocation mechanism adaptively balances the number of IRS elements dedicated to communication and sensing subsystems by leveraging the signal-to-noise-plus-interference-ratio (SINR) between the two. The proposed solution ensures efficient resource utilization while maintaining performance trade-offs. Numerical results demonstrate significant improvements in both communication and sensing SINRs under varying system parameters.

Regional Correlation Aided Mobile Traffic Prediction with Spatiotemporal Deep Learning

Mobile traffic data in urban regions shows differentiated patterns during different hours of the day. The exploitation of these patterns enables highly accurate mobile traffic prediction for proactive network management. However, recent Deep Learning (DL) driven studies have only exploited spatiotemporal features and have ignored the geographical correlations, causing high complexity and erroneous mobile traffic predictions. This paper addresses these limitations by proposing an enhanced mobile traffic prediction scheme that combines the clustering strategy of daily mobile traffic peak time and novel multi Temporal Convolutional Network with a Long Short Term Memory (multi TCN-LSTM) model. The mobile network cells that exhibit peak traffic during the same hour of the day are clustered together. Our experiments on large-scale real-world mobile traffic data show up to 28% performance improvement compared to state-of-the-art studies, which confirms the efficacy and viability of the proposed approach.