Xiaoshuai Li

Specific emitter identification (SEI) is essential for enhancing the security of Internet of Things (IoT) devices and wireless communication networks. Incremental learning, capable of adapting to real-time data streams with new categories, performs effectively when abundant labeled samples are available. However, the identification accuracy of current incremental learning-based SEI methods decreases in the presence of limited labeled samples. To address this issue, we integrate self-supervised learning (SSL) into incremental learning, structured into three stages: pre-training task, pretext task, and downstream task. The proposed method utilizes contrastive learning in the pretext task to effectively utilize unlabeled samples, thus reducing the necessary number of labeled samples and improving recognition accuracy even with small samples. Experimental results on real radio stations and ADS-B datasets demonstrate that the proposed method achieves an average accuracy of 80.50% on the ADS-B dataset and 92.97% on the radio stations dataset with 100 labeled new category training samples. The code can be downloaded from https://github.com/Simple-up/ISSL.

Elliptic positioning system offers a precise alternative to global navigation satellite system (GNSS). However, ranging measurements upon a single UAV only delineate the location estimates to a spherical region. Data infusion from inertial measurement units (IMUs) may refine these estimates, while its fidelity is undermined by IMUs’ lack of self alignment to a specified reference frame. In this paper, we explore the minimal number of assisted UAVs or anchors for absolute positioning, i.e., location and alignment in a fixed frame, during complete GNSS outages. We first prove that the observability establishes under a UAV in a 3-D trajectory or a pair of static anchors. The two numbers are new theoretical limit, significantly lower than the three anchors in 2-D or four in 3-D scenarios required by traditional theorems. We propose a sequential scheme for the multi-parameter estimation problem ensuring rapid convergence. An iterative solution is derived, flexible to UAV and anchor-based configurations, that provides instant location updates free of computational overhead. Thereafter, we also circumvent NLOS effects by employing inverse estimation of range. Accordingly, we devise a tiered positioning framework that commences with a location-unknown UAV to first cooperate with LOS anchors, and then extend the service to UE via a single NLOS link outside anchors’ coverage. In the experiments, the proposed scheme reaches (10 ⁻² )° orientation alignment and centimeter-level accuracy in NLOS scenarios, which attains the Cramer-Rao lower bound (CRLB) accuracy. Moreover, the accuracy notably exceeds the noise level of ranging measurements at high sampling rate, and also shows robustness against local clock drifting.

The integrated sensing and communication (ISAC) endows the next generation of wireless networks with the new paradigm to the security communication. In this paper, the dual-functional radar-communication (DFRC) with the property of spectral and energy efficiency is considered to strengthen the physical layer security (PLS) under the channel state information (CSI) uncertainty of the eavesdropper, where satisfies the tolerate sensing requirement and the minimum secrecy rate for each legitimate user. Specifically, we propose the PLS optimization model which employs the Cramér-Rao bound (CRB) as the sensing metric, and characterize the sensing-communication trade-off under the PLS enhancement. The efficiency closed-form solution composed of the generalized eigenvector and the power budge is derived for the case of single legitimate user with the perfect CSI of the eavesdropper. Considering the general case of multiple users, we first propose the novel sensing-specific imperfect CSI model to construct the inherent integration between the sensing capacity and the norm bound of the imperfect CSI. Further, the infinite uncertainty is handled by the S-procedure, and we propose the block coordinate descent (BCD) iterative algorithm to solve the PLS optimization problem comprising with the semidefinite relaxation (SDR) and the successive convex approximation (SCA) to address the non-convexity. Simulation results demonstrate the performance gain and the effectiveness of the proposed algorithm in the average secrecy rate over other baseline schemes.

Safety-related V2X communication plays an essential role in road safety and requires stringent high reliability and low latency. In this paper, we propose a novel approach of joint power control and resource allocation mode selection to solve the resource allocation problem of the mixed centralized/distributed V2X communication system under different network load conditions. Two major resource allocation modes are considered for V2X communication: scheduled resource allocation (centralized system) and UE autonomous resource selection mode (distributed system). In order to meet the specific Quality of Service (QoS) related demands of safety-related V2X communication, we aim to maximize overall information value of V2X communication by considering both ProSe Per-Packet Priority (PPPP) and communication link quality of V2X messages while meeting the requirements of minimum SINR and maximum transmit power limitation for both pedestrian user equipments (PUEs) and vehicular user equipments (VUEs). In particular, in scenarios where the network is lightly loaded we propose Vacant Resource Blocks and Power Allocation (VRBPA) algorithm while for scenarios wherein the network is heavily loaded we propose Occupied Resource Blocks and Power Allocation (ORBPA) algorithm. Furthermore, the proposed algorithm can achieve a stable matching between VUEs and their reuse PUEs partners. Extensive simulation results show that both the information value of VUEs as well as the spectral efficiency of PUEs can be improved efficiently by the proposed approach.

As an emerging paradigm, Device-to-Device (D2D) communication has the capability to complement and enhance the conventional cellular network by offering high spectral and energy efficiency. However, the problem of co-channel interference makes the resource allocation very complex and challenging in underlay D2D communication networks. This paper proposes a novel joint power control and resource scheduling scheme to enhance both the network throughput and the users' fairness of the underlay D2D communication networks. Unlike other previous work in this area, our scheme aims at maximizing the sum of all users' proportional fairness functions while simultaneously taking into account factors such as fairness, signal-to-interference-plus-noise ratio (SINR) requirements and severe interference. The proposed scheme offers a practical solution because it works for lengthy time slots, a realistic scenario for underlay D2D communication system. We also take into consideration the time-varying feature of user's channel condition in our proposed solution. Numerical results confirm that our proposed scheme not only dramatically improves the system throughput but also boosts the system fairness while guaranteeing the Quality of Service (QoS) levels of all D2D users (DUs) and cellular users (CUs).