Physical layer security has attracted significant attention in the field of wireless communications. The application of artificial noise can reduce the eavesdropping ability of illegal eavesdroppers without affecting legitimate users. However, most current physical layer security schemes only consider static scenarios and do not account for mobility. Some schemes analyze security performance in mobile scenarios with friendly jammers but do not consider the handoff and cooperation of friendly jammers due to mobility. To address this challenge, we propose a scheme for soft-handoff-based cooperative jamming (CJSH) in mobility scenarios. Initially, we consider a common scenario where a base station communicates with a legitimate mobile user, alongside a mobile passive eavesdropper and multiple friendly jammers emitting artificial noise in the circular area covered by the base station’s signal. Next, We measure the connection outage probability (COP) and secrecy outage probability (SOP) under the influence of multiple friendly jammers in the proposed scheme. We also design two corresponding thresholds for jammers to join and exit. To balance security and energy consumption, we define the Power Average Security Gain (PASG) as a measure of system performance. Finally, we provide numerical simulation results to verify the rationality of the proposed handoff scheme, demonstrating its effective improvement of the system’s security performance and power utilization.

While deep neural network-based video denoising methods have achieved promising results, it is still hard to deploy them on mobile devices due to their high computational cost and memory demands. This paper aims to develop a lightweight deep video denoising method that is friendly to resource-constrained mobile devices. Inspired by the facts that 1) consecutive video frames usually contain redundant temporal coherency, and 2) neural networks are usually over-parameterized, we propose a multi-input multi-output (MIMO) paradigm to process consecutive video frames within one-forward-pass. The basic idea is concretized to a novel architecture termed Recurrent Multi-output Network (ReMoNet), which consists of recurrent temporal fusion and temporal aggregation blocks and is further reinforced by similarity-based mutual distillation. We conduct extensive experiments on NVIDIA GPU and Qualcomm Snapdragon 888 mobile platform with Gaussian noise and simulated Image-Signal-Processor (ISP) noise. The experimental results show that ReMoNet is both effective and efficient on video denoising. Moreover, we show that ReMoNet is more robust under higher noise level scenarios.