Non-orthogonal multiple access (NOMA) has been widely investigated as a promising technique for supporting massive connectivity and efficient spectrum utilization in next generation wireless networks. Among different NOMA schemes, power-domain NOMA (PD-NOMA) has attracted considerable attention as it separates users in the power domain, but its reliance on successive interference cancellation (SIC) increases receiver complexity and error propagation. Recently, quadrature NOMA (Q-NOMA) has emerged as a low-complexity alternative by employing coordinate interleaving (CI) to separate users across the in-phase and quadrature components, thereby reducing the number of SIC operations. While existing Q-NOMA studies have mainly focused on theoretical and simulation based analyses, its practical feasibility remains largely unexplored. This paper presents a hardware implementation and experimental validation of a downlink Q-NOMA system using a modular Simulink/ Software-defined radio (SDR) platform. The proposed architecture is evaluated through over-the-air experiments in laboratory, corridor, and outdoor environments, and its performance is benchmarked against conventional PD NOMA and orthogonal multiple access (OMA). The results show that Q-NOMA achieves higher throughput than both PD NOMA and OMA, while also providing improved bit error rate (BER) performance compared with PD-NOMA. The obtained experimental results exhibit trends similar to those observed in the simulation outcomes, thereby validating the proposed SDR based implementation. These findings demonstrate the practical feasibility of Q-NOMA as a low-complexity and scalable multiple access technique for next-generation wireless systems.
To address the multi-objective real-time control requirements of the QRA-type pure electric bus dual-motor system and the deficiencies in stability and torque smoothness of current real-time torque allocation algorithms, this paper proposes an improved QPSO-based dual-motor multi-objective real-time torque optimization strategy for 1-millisecond-level high-precision control. A multi-objective model is constructed in Matlab/Simulink with drive/brake torque allocation, torque compensation, and mechanical brake compensation as decision variables, achieving dynamic co-optimization of economy, torque allocation smoothness, and vehicle speed tracking accuracy. Simulation results demonstrate that under NEDC, CHTC-B, and CHTC-C driving cycles, the proposed strategy successfully achieves high vehicle speed tracking accuracy and high overall dual-motor efficiency, while maintaining highly smooth dynamic torque allocation within a 1-ms control period. Hardware-in-the loop (HIL) tests further validate the engineering feasibility of the strategy in terms of real-time performance, stability, and safety. Real-vehicle road tests show that, compared with conventional schemes, the improved QPSO-based 1-ms strategy achieves energy savings of 15.9%, 6.9%, and 18.3% under the three aforementioned driving cycles, respectively. In summary, the proposed strategy realizes high-precision and stable torque control within a millisecond-level period, significantly improves overall vehicle energy efficiency, and provides a viable technical pathway for green energy-saving control of pure electric bus powertrains.
In urban areas, GNSS/INS integrated navigation systems suffer from degraded observations due to non-line-of-sight (NLOS) reception and multipath interference (MI), and even satellite signal blockages caused by surrounding buildings. In a loosely coupled architecture, these effects appear at the integration interface as abrupt or persistent, nonstationary GNSS position errors rather than as simple zero-mean measurement noise. Furthermore, during periods of GNSS unavailability or interference, the INS accumulates propagation errors. Once GNSS measurements become available again, these errors may manifest as anomalies in the integrated system. Consequently, it becomes difficult to distinguish whether the inconsistencies originate from GNSS measurement faults or INS model drifts. Improper use of robust or adaptive filtering can lead to filter divergence and performance degradation. To address this challenge, this paper proposes a resilient loosely coupled GNSS/INS integration method with a fuzzy-based quality control algorithm that combines GNSS observation quality assessment with an online covariance-inflation proxy. Fuzzy rules constructed from the variance of time-differenced pseudorange innovation (TDPR $S^{2}$) and the mean of standardized pseudorange residuals (resiAvg) are designed to evaluate GNSS observation quality and estimate adaptive factors. When quality is high, the adaptive factor adjusts the effective measurement covariance; when quality is poor, the short-term accuracy of INS is exploited to form a direction-dependent covariance proxy from GNSS/INS position-increment disagreement. Rather than classifying or correcting individual NLOS/MI signals, the method targets their position-domain consequences at the loosely coupled GNSS/INS interface. Field experiments demonstrate that the proposed method achieves a 65.2% improvement in 3D positioning accuracy and a 91.0% reduction in maximum 3D error compared with the EKF baseline.
Ultra-wideband (UWB) communication provides a robust defense against signal-relaying attacks in proximity verification systems, such as secure access control and user authentication. However, despite the adoption of the IEEE 802.15.4z standard, high-rate pulse repetition frequency (HRP) UWB ranging remains vulnerable to distance reduction attacks, undermining its reliability in mission-critical applications. To address this, we propose a time-reversal-based dynamic response (TR-DR) scheme. By exploiting channel reciprocity, time reversal (TR) produces a spatial self-filtering effect that couples the two propagation phases of two-way ranging into a single coherent process. This concentrates multipath energy into a sharp temporal peak, substantially suppressing multipath-induced delay spread and improving time-of-arrival (ToA) estimation accuracy. The proposed scheme also employs a dynamic scrambled timestamp sequence (STS) architecture with an adjustable inter-segment gap and amplitude weighting. This provides a verifiable physical-layer signature for authenticating the legitimate responder and detecting and rejecting forged ranging responses. Furthermore, the complete TR-DR ranging process is formulated, a least-squares dual-peak detection algorithm is developed, and closed-form expressions are derived for the legitimate-signal detection performance and the attack success probabilities of two representative distance reduction attacks. Simulation results under various channel conditions demonstrate that TR-DR offers ToA accuracy comparable to that of the baseline scheme while significantly enhancing resilience to distance reduction attacks. Specifically, even with an attack-to-legitimate-signal power ratio of 30 dB, the attack success rate remains below the incorrect rate of TR-DR, whereas the attack success rate of the baseline scheme approaches one. These results demonstrate the potential of TR-DR to enhance ranging security and provide technical insights for future UWB ranging standards.
Full-duplex reconfigurable intelligent surface (RIS) assisted integrated sensing and communication cell-free massive multiple-input multiple-output systems suffer from strong cross functional interference between communication and sensing, which is further aggravated by RIS-induced electromagnetic interference (EMI) and environmental clutter. To cope with this highly coupled interference environment, we propose a deep rein forcement learning (DRL)-enabled joint optimization framework that maximizes the uplink sum rate while satisfying sensing performance requirements. Specifically, the proposed framework jointly optimizes uplink user power allocation, precoding at access points, and discrete RIS phase shifts by integrating a Gumbel-based lightweight convolutional neural network for discrete phase learning with a twin-delayed deep deterministic policy gradient-based DRL module for continuous resource optimization. Simulation results show that the proposed scheme effectively mitigates RIS-induced EMI, achieves a favorable communication-sensing tradeoff, and provides more pronounced performance gains in large-scale RIS deployments.
