Social media platforms connect users with users around the world and allow everyone to post their opinions freely. This facilitates the spread of rumors. Identifying rumors on social media becomes a thorny challenge. Existing research has achieved good results by using the propagation characteristics of rumors. However, these researches only consider the impact of rumor propagation and ignore users' feedback on rumors. In this paper, we propose a bottom-up node aggregation method based on user feedback trees and look at how to use pre-training to model the structure of feed back trees.

Recently, integrated sensing and communications (ISAC) design has attracted great attention for B5G/6G networks. Existing ISAC studies are mainly focused on monostatic sensing with a full-duplex radio. However, full-duplex radio requires complicated self-interference cancellations and many current devices are half-duplex radios. Therefore, it is still interesting to investigate passive bistatic sensing with half-duplex radios. In this paper, integrating passive bistatic sensing into mmWave B5G/6G networks is investigated. The public reference signals are leveraged to extract the frequency-domain channel state information (CSI) for passive sensing. To address the problems of sampling-timing-offset and random phase error, a line-of-sight (LoS) path aided calibration mechanism is first developed. To achieve accurate localization for multiple targets, a novel super-resolution channel impulse response (CIR) based mechanism is then developed. The super-resolution CIR is achieved from CSI through a joint design of spatial-smoothing multiple signal classification (MUSIC) algorithm and template-based tap estimation. With the super-resolution CIR, multiple targets can be first distinguished in CIR taps and then localized by further estimating their angle of arrivals (AoAs). The proposed design is implemented and validated on a prototype system at 28 GHz with 500 MHz bandwidth in indoor environments. Experimental results show that our design can achieve accurate single-target and multi-target localization and tracking. The localization error is 26 cm at 80thpercentile for single-person and 29 cm on average for multi-person setups. The average error to the planned path after the moving-averaged filter is 11 cm and 19 cm for single-person and two-person tracking, respectively.

Backscatter communication is visioned as one of the promising technologies for future ultra-low power Internet of Things (IoT). The orthogonal-frequency-division-multiplexing physical-layer (OFDM-PHY) WiFi backscatter communications attract great attention in recent years. However, the severe tag transmission collisions highly degrade the system performance in a backscatter network, since complicated multiple access mechanisms, e.g., carrier sense multiple access, cannot be applied on backscatter tags. To address this problem, a novel parallel decoding design called FreeCollision is developed to enable concurrent OFDM-PHY WiFi backscatter communications. Without the prior knowledge of the number of collided tags, their channel state information, and modulation types, FreeCollsion can just use the I-Q symbols to resolve the collision by the design of a series of mechanisms: collided constellation recovery, concurrent virtual channel estimation, QPSK tag detection, and parallel demodulation. Simulation results verify the effectiveness of our proposed scheme. The successful decoding rate is more than 95% for 4 collided tags with BPSK modulation. The maximum successful transmission probability can be improved from 36.7% to 88% for slotted Aloha.

Intelligent vehicles require both communications and radar sensing. Recently, the development of joint communication and automotive radar sensing has attracted great attention. In this paper, the reference signal (RS) in the orthogonal frequency division multiplexing (OFDM) waveform is leveraged for radar sensing in 5G vehicle-to-everything (V2X) communications. Unlike the existing studies that use the whole subcarriers as pilot subcarriers for radar sensing, the scattered RS pattern adopted in the 5G system is considered in our design. More specifically, the scattered RS placement and power allocation between RS and data are optimized to minimize the total transmission power for energy-efficient joint signal transmission, while satisfying both the communication and radar sensing requirements. The optimization problem is a mixed-integer non-linear programming (MINLP) problem. The optimal solution is solved by enumerating all possible RS placements with the optimal allocated power by solving a transformed convex optimization problem. Such an approach suffers a high computing complexity when there are a large number of possible RS placements. Therefore, an alternative heuristic approach is further developed to resolve the problem via successive convex approximation (SCA) method. Simulation results show that the heuristic method can well approach the optimal solution. Compared with existing studies, our proposed scheme can effectively improve energy efficiency.