Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning

Proactive edge association is capable of improving wireless connectivity at the cost of increased handover (HO) frequency and energy consumption, while relying on a large amount of private information sharing required for decision making. In order to improve the connectivity-cost trade-off without privacy leakage, we investigate the privacy-preserving joint edge association and power allocation (JEAPA) problem in the face of the environmental uncertainty and the infeasibility of individual learning. Upon modelling the problem by a decentralized partially observable Markov Decision Process (Dec-POMDP), it is solved by federated multi-agent reinforcement learning (FMARL) through only sharing encrypted training data for federatively learning the policy sought. Our simulation results show that the proposed solution strikes a compelling trade-off, while preserving a higher privacy level than the state-of-the-art solutions.Comment: 6 pages, 4 figures, IEEE Trans. on Veh. Techno

3D Positioning Algorithm Design for RIS-aided mmWave Systems

In this paper, we investigate a three-dimensional (3D) positioning algorithm for a millimeter wave (mmWave) system, where the reconfigurable intelligent surfaces (RIS) are leveraged to enhance the positioning performance of mobile users (MUs). We propose a two-stage weight least square (TSWLS) algorithm to obtain the closed-form solution of the MU's position. In the first stage, we construct the pseudolinear equations based on the angle of arrival (AOA) and the time difference of arrival (TDOA) estimation at the RISs, then we obtain a preliminary estimation by solving these equations using the iterative weight least square (WLS) method. Based on the preliminary estimation in the first stage, a new set of pseudolinear equations are obtained, and a finer estimation is obtained by solving the equations using the WLS method in the second stage. By combining the estimation of both stages, the final estimation of the MU's position is obtained. Further, we study the theoretical bias of the proposed algorithm by considering the estimation error in both stages. Simulation results demonstrate the superiority of the proposed positioning algorithm. Furthermore, it is also shown that the proposed algorithm still have good positioning performance with low SNR.Comment: Keywords: Reconfigurable intelligent surface (RIS), intelligent reflecting surface (IRS

Molybdenum disulfide nanoflowers mediated anti-inflammation macrophage modulation for spinal cord injury treatment

Spinal cord injury (SCI) can cause locomotor dysfunctions and sensory deficits. Evidence shows that functional nanodrugs can regulate macrophage polarization and promote anti-inflammatory cytokine expression, which is feasible in SCI immunotherapeutic treatments. Molybdenum disulfide (MoS2) nanomaterials have garnered great attention as potential carriers for therapeutic payload. Herein, we synthesize MoS2@PEG (MoS2 = molybdenum disulfide, PEG = poly (ethylene glycol)) nanoflowers as an effective carrier for loading etanercept (ET) to treat SCI. We characterize drug loading and release properties of MoS2@PEG in vitro and demonstrate that ET-loading MoS2@PEG obviously inhibits the expression of M1-related pro-inflammatory markers (TNF-α, CD86 and iNOS), while promoting M2-related anti-inflammatory markers (Agr1, CD206 and IL-10) levels. In vivo, the mouse model of SCI shows that long-circulating ET-MoS2@PEG nanodrugs can effectively extravasate into the injured spinal cord up to 96 h after SCI, and promote macrophages towards M2 type polarization. As a result, the ET-loading MoS2@PEG administration in mice can protect survival motor neurons, thus, reducing injured areas at central lesion sites, and significantly improving locomotor recovery. This study demonstrates the anti-inflammatory and neuroprotective activities of ET-MoS2@PEG and promising utility of MoS2 nanomaterial-mediated drug delivery

A Robust Secure Hybrid Analog and Digital Receive Beamforming Scheme for Efficient Interference Reduction

Medium-scale or large-scale receive antenna array with digital beamforming can be employed at receiver to make a significant interference reduction but leads to expensive cost and high complexity of the RF-chain circuit. To deal with this issue, classic analog-and-digital beamforming (ADB) structure was proposed in the literature for greatly reducing the number of RF-chains. Based on the ADB structure, in this paper, we propose a robust hybrid ADB scheme to resist directions of arrival (DOAs) estimation errors. The key idea of our scheme is to employ null space projection (NSP) in the analog beamforming domain and diagonal loading (DL) method in digital beamforming domain. The simulation results show that the proposed scheme performs more robustly, and moreover, it has a significant improvement on the receive signal-to-interference-plus-noise ratio compared to NSP ADB scheme and DL method

Power Allocation Strategy of Maximizing Secrecy Rate for Secure Directional Modulation Networks

In this paper, given the beamforming vector of confidential messages and artificial noise (AN) projection matrix and total power constraint, a power allocation (PA) strategy of maximizing secrecy rate (Max-SR) is proposed for secure directional modulation (DM) networks. By the method of Lagrange multiplier, the analytic expression of the proposed PA strategy is derived. To confirm the benefit from the Max-SRbased PA strategy, we take the null-space projection (NSP) beamforming scheme as an example and derive its closed-form expression of optimal PA strategy. From simulation results, we find the following facts: in the medium and high signal-to-noiseratio (SNR) regions, compared with three typical PA parameters such ? = 0:1, 0:5, and 0:9, the optimal PA shows a substantial SR performance gain with maximum gain percent up to more than 60%. Additionally, as the PA factor increases from 0 to 1, the achievable SR increases accordingly in the low SNR region whereas it first increases and then decreases in the medium and high SNR regions, where the SR can be approximately viewed as a convex function of the PA factor. Finally, as the number of antennas increases, the optimal PA factor becomes large and tends to one in the medium and high SNR region. In other words, the contribution of AN to SR can be trivial in such a situation