Kun Yang

Orthogonal time frequency space (OTFS) modulation has emerged as a promising candidate to overcome the performance degradation of orthogonal frequency division multiplexing (OFDM), which are commonly encountered in high-mobility wireless communication scenarios. However, conventional OTFS transceivers rely on multiple separately designed signal-processing modules, whose isolated optimization often limits global optimal performance. To overcome limitations, this paper proposes a modular deep learning (DL) based end-to-end OTFS transceiver framework that consists of trainable and interchangeable neural network (NN) modules, including constellation mapping/demapping, superimposed pilot placement, inverse Zak (IZak)/Zak transforms, and a U-Net-enhanced NN tailored for joint channel estimation and detection (JCED), while explicitly accounting for the impact of the cyclic prefix. This physics-informed modular architecture provides flexibility for integration with conventional OTFS systems and adaptability to different communication configurations. Simulations demonstrate that the proposed design significantly outperforms baseline methods in terms of both normalized mean squared error (NMSE) and detection reliability, maintaining robustness under integer and fractional Doppler conditions. The results highlight the potential of DL-based end-to-end optimization to enable practical and high-performance OTFS transceivers for next-generation high-mobility networks.

The evolution toward sixth-generation (6G) wireless networks demands high-performance transceiver architectures capable of handling complex and dynamic environments. Conventional orthogonal frequency-division multiplexing (OFDM) receivers rely on cascaded discrete Fourier transform (DFT) and demodulation blocks, which are prone to inter-stage error propagation and suboptimal global performance. In this work, we propose two neural network (NN) models DFT-Net and Demodulation-Net (Demod-Net) to jointly replace the IDFT/DFT and demodulation modules in an OFDM transceiver. The models are trained end-to-end (E2E) to minimize bit error rate (BER) while preserving operator equivalence for hybrid deployment. A customized DFT-Demodulation Net Accelerator (DDNA) is further developed to efficiently map the proposed networks onto field-programmable gate array (FPGA) platforms. Leveraging fine-grained pipelining and block matrix operations, DDNA achieves high throughput and flexibility under stringent latency constraints. Experimental results show that the DL-based transceiver consistently outperforms the conventional OFDM system across multiple modulation schemes. With only a modest increase in hardware resource usage, it achieves approximately 1.5 dB BER gain and up to 66\% lower execution time.

In response to the security blind zone challenges faced by traditional reconfigurable intelligent surface (RIS)-aided covert communication (CC) systems, the joint distance-angle beamforming capability of frequency diverse RIS (FD-RIS) shows significant potential for addressing these limitations. Therefore, this paper initially incorporates the FD-RIS into the CC systems and proposes the corresponding CC transmission scheme. Specifically, we first develop the signal processing model of the FD-RIS, which considers effective control of harmonic signals by leveraging the time-delay techniques. The joint distance-angle beamforming capability is then validated through its normalized beampattern. Based on this model, we then construct an FD-RIS-assisted CC system under a multi-warden scenario and derive an approximate closed-form expression for the covert constraints by considering the worst-case eavesdropping conditions and utilizing the logarithmic moment-generating function. An optimization problem is formulated which aims at maximizing the covert user's achievable rate under covert constrains by jointly designing the time delays and modulation frequencies. To tackle this non-convex problem, an iterative algorithm with assured convergence is proposed to effectively solve the time-delay and modulation frequency variables. To evaluate the performance of the proposed scheme, we consider three communication scenarios with varying spatial correlations between the covert user and wardens. Simulation results demonstrate that FD-RIS can significantly improve covert performance, particularly in angular-overlap scenarios where traditional RIS experiences severe degradation. These findings further highlight the effectiveness of FD-RIS in enhancing CC robustness under challenging spatial environments.

Reconfigurable holographic surface (RHS) enables cost-effective large-scale arrays with high spatial gain. However, its amplitude-controlled holographic beamforming suffers from directional fluctuations, making it difficult to fully exploit the spatial gain of RHS. Fortunately, the promising 6D movable antenna (6DMA) provides a potential solution to this problem. In this paper, we study a 6D movable holographic surface (6DMHS) integrated data and energy transfer (IDET) system, where a three-stage protocol is proposed, consisting of an uplink sensing stage, an orientation adjustment stage and a downlink transmission stage, to coordinate the 6DMHS and effectively serve the IDET receivers. Firstly, the holographic-based sensing technology is proposed and the sensing information of the IDET receivers is exploited. Secondly, by fixing the rotations with the sensing information, the orientation optimization problem is formulated for designing the holographic beamforming of the RHS and adjusting the translations of the 6DMHS. As a result, the directions with maximum beamforming gain are aligned with each IDET receiver. Thirdly, by fixing the orientation of the 6DMHS and the holographic beamforming, the equivalent wireless channel is obtained. The IDET performance optimization problem is formulated for obtaining the optimal digital beamforming, power splitting factor and energy harvesting (EH) power. Simulation results demonstrate that the proposed scheme is capable of improving the IDET performance compared to the benchmarks.

The pinching-antenna systems (PASS), which activate small dielectric particles along a dielectric waveguide, has recently emerged as a promising paradigm for flexible antenna deployment in next-generation wireless communication networks. While most existing studies assume rectangular indoor layouts with full coverage waveguide, practical deployments may involve geometric constraints, partial coverage, and non-negligible waveguide attenuation. This paper presents the first analytical investigation of PASS in a circular indoor environment, encompassing both full coverage and partial coverage waveguide configurations with/without propagation loss. A unified geometric-propagation framework is developed that jointly captures pinching-antenna placement, Internet of Things (IoT) device location distribution, and waveguide attenuation. Closed-form expressions for the outage probability and average achievable rate are derived for four scenarios, with accuracy validated via extensive Monte-Carlo simulations. The analysis reveals that, under the partial coverage waveguide scenario with propagation loss, the system performance demonstrates a non-monotonic trend with respect to the waveguide length, and the optimal length decreases as the attenuation coefficient increases. Numerical results further quantify the interplay between deployment strategy, waveguide propagation loss, and coverage geometry, offering practical guidelines for performance-oriented PASS design.

In this paper, we studies the performance of a novel simultaneous wireless information and power transfer (SWIPT) system enabled by a flexible pinching-antenna. To support flexible deployment and optimize energy-rate performance, we propose three practical pinching antenna placement-schemes: the edge deployment scheme (EDS), the center deployment scheme (CDS), and the diagonal deployment scheme (DDS). Moreover, a hybrid time-switching (TS) and power-splitting (PS) protocol is introduced, allowing dynamic adjustment between energy harvesting and information decoding. Under each deployment strategy and the transmission protocol, closed-form expressions for the average harvested energy and average achievable rate of a randomly located user equipment (UE) are derived based on the optimal positioning of the pinching-antenna. Numerical simulations confirm the accuracy of the theoretical analysis and illustrate the trade-off between rate and energy harvesting under different schemes.

In the evolving landscape of high-speed communication, the shift from traditional pilot-based methods to a Sensing-Oriented Approach (SOA) is anticipated to gain momentum. This paper delves into the development of an innovative Integrated Sensing and Communication (ISAC) framework, specifically tailored for beamforming and trajectory prediction processes. Central to this research is the exploration of an Unmanned Aerial Vehicle (UAV)-enabled communication system, which seamlessly integrates ISAC technology. This integration underscores the synergistic interplay between sensing and communication capabilities. The proposed system initially deploys omnidirectional beams for the sensing-focused phase, subsequently transitioning to directional beams for precise object tracking. This process incorporates an Extended Kalman Filtering (EKF) methodology for the accurate estimation and prediction of object states. A novel frame structure is introduced, employing historical sensing data to optimize beamforming in real-time for subsequent time slots, a strategy we refer to as 'temporal-assisted' beamforming. To refine the temporal-assisted beamforming technique, we employ Successive Convex Approximation (SCA) in tandem with Iterative Rank Minimization (IRM), yielding high-quality suboptimal solutions. Comparative analysis with conventional pilot-based systems reveals that our approach yields a substantial improvement of 156\% in multi-object scenarios and 136\% in single-object scenarios.

In this paper, an integrated sensing and communication (ISAC) system is investigated. Initially, we introduce a design criterion wherein sensing data acquired from the preceding time slot is employed for instantaneous optimal beamforming in the succeeding time slot, aiming to enhance the communication rate. Subsequently, the development of optimal beamforming is addressed, and a high-caliber suboptimal resolution is derived utilizing successive convex approximation (SCA) techniques combined with the iterative rank minimization (IRM) methodology. Our evaluations, grounded on numerical analyses, reveal that the communication rate of the introduced beamforming strategy surpasses that of conventional omnidirectional sensing and pilot based approaches.