Map-based Channel Modeling and Generation for U2V mmWave Communication

Unmanned aerial vehicle (UAV) aided millimeter wave (mmWave) technologies have a promising prospect in the future communication networks. By considering the factors of three-dimensional (3D) scattering space, 3D trajectory, and 3D antenna array, a non-stationary channel model for UAV-to-vehicle (U2V) mmWave communications is proposed. The computation and generation methods of channel parameters including interpath and intra-path are analyzed in detail. The inter-path parameters are calculated in a deterministic way, while the parameters of intra-path rays are generated in a stochastic way. The statistical properties are obtained by using a Gaussian mixture model (GMM) on the massive ray tracing (RT) data. Then, a modified method of equal areas (MMEA) is developed to generate the random intra-path variables. Meanwhile, to reduce the complexity of RT method, the 3D propagation space is reconstructed based on the user-defined digital map. The simulated and analyzed results show that the proposed model and generation method can reproduce non-stationary U2V channels in accord with U2V scenarios. The generated statistical properties are consistent with the theoretical and measured ones as well

A Realistic 3D Non-Stationary Channel Model for UAV-to-Vehicle Communications Incorporating Fuselage Posture

Considering the unmanned aerial vehicle (UAV) three-dimensional (3D) posture, a novel 3D non-stationary geometry-based stochastic model (GBSM) is proposed for multiple-input multiple-output (MIMO) UAV-to-vehicle (U2V) channels. It consists of a line-of-sight (LoS) and non-line-of-sight (NLoS) components. The factor of fuselage posture is considered by introducing a time-variant 3D posture matrix. Some important statistical properties, i.e. the temporal autocorrelation function (ACF) and spatial cross correlation function (CCF), are derived and investigated. Simulation results show that the fuselage posture has significant impact on the U2V channel characteristic and aggravate the non-stationarity. The agreements between analytical, simulated, and measured results verify the correctness of proposed model and derivations. Moreover, it is demonstrated that the proposed model is also compatible to the existing GBSM without considering fuselage posture.Comment: 12 pages, 8 figures, CNCO

Research on carrier frequency offset estimation algorithm based on PN sequence preamble in OFDM system

Carrier frequency offset (CFO) due to Doppler frequency shift or frequency mismatch between the transmitter’s and receiver’s oscillators can introduce severe inter-symbol and inter-carrier interference into OFDM systems. A simplified OFDM system model is considered to analyze effects of CFO in theory and simulation. The article briefly reviews some traditional CFO estimation algorithms. Relying on relatively good correlation characteristic of pseudo-noise (PN) sequence, the PN preamble based algorithm of CFO estimation is developed. Performance characteristics of traditional and the new improved algorithms are simulated under different conditions. Results indicate that the PN preamble based algorithm of CFO estimation is more accurate, resource-saving and robust even under poor communications channel condition, such as low SNR and big normalized CFO

Joint optimization of sensing threshold and transmission power in wideband cognitive radio with energy detection

[1] In this paper, we consider a wideband cognitive radio system that operates over multiple idle subchannels. A joint optimization of sensing threshold and transmission power is proposed, which maximizes the total throughput subject to the constraints on the total interference, the total power, and the probabilities of false alarm and detection of each subchannel. An alternative joint optimization is proposed, which minimizes the total interference under the constraint of the total throughput. The bilevel optimization method is used to solve the proposed optimization problems with a minimized iteration complexity. The mixed-variable optimization problem is divided into two single-variable convex optimization subproblems: the upper level for threshold optimization and the lower level for power optimization. Weighed cooperative sensing is proposed to maximize the detection probability by choosing the optimal weighed factors. The simulations show that the proposed joint optimization algorithm can achieve desirable improvement on the throughput of cognitive radio at the same interference level to primary user, or vice versa within the limits on the probabilities of false alarm and miss detection, and the weighed cooperative sensing can considerably improve sensing performance compared with the unweighed cooperative sensing and single-user sensing.Published versio

Identity-Preserving Talking Face Generation with Landmark and Appearance Priors

Generating talking face videos from audio attracts lots of research interest. A few person-specific methods can generate vivid videos but require the target speaker's videos for training or fine-tuning. Existing person-generic methods have difficulty in generating realistic and lip-synced videos while preserving identity information. To tackle this problem, we propose a two-stage framework consisting of audio-to-landmark generation and landmark-to-video rendering procedures. First, we devise a novel Transformer-based landmark generator to infer lip and jaw landmarks from the audio. Prior landmark characteristics of the speaker's face are employed to make the generated landmarks coincide with the facial outline of the speaker. Then, a video rendering model is built to translate the generated landmarks into face images. During this stage, prior appearance information is extracted from the lower-half occluded target face and static reference images, which helps generate realistic and identity-preserving visual content. For effectively exploring the prior information of static reference images, we align static reference images with the target face's pose and expression based on motion fields. Moreover, auditory features are reused to guarantee that the generated face images are well synchronized with the audio. Extensive experiments demonstrate that our method can produce more realistic, lip-synced, and identity-preserving videos than existing person-generic talking face generation methods.Comment: CVPR2023, Code: https://github.com/Weizhi-Zhong/IP_LA