Guofa Cai

In this paper, an active simultaneous transmitting and reflecting reconfigurable intelligent surface (ASTAR-RIS)-based wireless-powered non-orthogonal multiple access (NOMA) system is proposed. This system is composed of an access point (AP), an ASTAR-RIS, and two non-orthogonal users positioned on both sides of the ASTAR-RIS. Its operation follows a two-phase protocol: firstly, in the downlink phase, the users harvest energy from the radio frequency signals transmitted by the AP; then, in the uplink phase, the users simultaneously utilize the harvested energy to transmit information to the AP, and the energy splitting protocol is utilized throughout both downlink and uplink transmissions. For links from AP to ASTAR-RIS and ASTAR-RIS to users, Nakagami-m distribution is adopted for the fading channels. We derive the probability density functions of the cascaded channel and the channel with the thermal noise of the proposed system. Based on these results, the closed-form outage probability and throughput are derived over Nakagami-m fading channels. All developed analytical results are validated and demonstrated via numerical simulations. Although ASTAR-RIS introduces the thermal noise at the ASTAR-RIS, the proposed system overcomes the inherent multiplicative fading issue of passive STAR-RIS and exhibits superior performances compared with the passive STAR-RIS aided ones.

We propose a multiple chirp rate index modulation (MCR-IM) system based on Zadoff-Chu (ZC) sequences that overcomes the problems of low transmission rate and large-scale access in long-range wide-area network (LoRaWAN) using classical LoRa modulation. We demonstrate the extremely low cross-correlation of MCR-IM signals across different spread factors, showing that the proposed MCR-IM system also inherits the characteristics of ZC sequences modulation. Moreover, we derive an approximate approximate closed-form expression for the bit-error-rate (BER) of the proposed MCR-IM system over Nakagami-m fading channels. Simulation results confirm the accuracy of the derived approximate closed-form expression and demonstrate that the MCR-IM system achieves higher levels of spectral efficiency (SE) compared to existing systems like frequency-shift chirp spread spectrum with index modulation (FSCSS-IM), group-based CSS (GCSS), layered CSS (LCSS), and layered group-based CSS (LGCSS). In this context, assigning multiple chirp rates to each user results in a reduction in the number of parallel channels. To mitigate this issue, we propose a peak detection based successive interference cancellation (PD-SIC) algorithm to accommodate more users. Results further show that the proposed system with the PD-SIC algorithm achieves a lower BER and higher throughput than the orthogonal scatter CSS (OrthoRa) system under collision scenarios, making it a compelling solution for future large-scale, high-throughput Internet of Things network.

Ensuring secure and efficient multi-user (MU) transmission is critical for vehicular communication systems. Chaos-based modulation schemes have garnered considerable interest owing to their inherent advantages in physical layer security. However, most existing MU chaotic communication systems, particularly those based on non-coherent detection, suffer from low spectral efficiency due to reference signal overhead and limited user connectivity under orthogonal multiple access (OMA). Although non-orthogonal schemes such as sparse code multiple access (SCMA)-based differential chaos shift keying (DCSK) have been explored, they incur high computational complexity and exhibit inflexible scalability owing to fixed codebook designs. This paper proposes a deep learning-assisted power-domain non-orthogonal multiple access chaos shift keying (DL-NOMA-CSK) system for vehicular communications. A deep neural network (DNN)-based demodulator is designed to learn the intrinsic characteristics of chaotic signals during offline training, thereby eliminating the need for chaotic synchronization or reference signal transmission. The demodulator employs a dual-domain feature extraction architecture that jointly processes time-domain and frequency-domain information of the received chaotic signals, enhancing feature learning under dynamic channel conditions. The DNN is integrated into a successive interference cancellation (SIC) framework to mitigate error propagation. Theoretical analysis and extensive simulations demonstrate that the proposed system achieves superior performance in terms of spectral efficiency (SE), energy efficiency (EE), bit error rate (BER), security, and robustness compared to conventional MU-DCSK and existing deep learning-aided schemes. These advantages confirm the practical viability of the proposed system for secure vehicular communications.

This letter investigates the performance of a near-field multi-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system with an energy buffer in multipath-dominated environments. The system comprises a base station equipped with a large-scale uniform planar array (UPA) and a single-antenna user equipment operating in the radiative near-field region, where signal propagation needs to be carried out through distributed scatterers due to the direct communication obstruction. By leveraging the Markov chain-based energy buffer model, we derived closed-form outage-probability expression of the proposed system, along with the asymptotic outage-probability expression under high transmit power regime. Theoretical and numerical results demonstrate that the proposed system significantly outperforms buffer-less counterpart. Moreover, increasing the number of scatterers enhances system reliability due to multipath diversity, while square UPA configurations exhibit superior performance over rectangular arrays due to improved projected aperture efficiency. Furthermore, it is revealed that an optimal power-splitting factor can minimize the outage probability. These findings provide foundational insights for designing energy-efficient near-field communications in multipath-dominated environments.

Chaotic signals are spread-spectrum nonlinear signals with initial value sensitivity that can potentially provide safe and anti-jamming digital communication systems. However, to reduce receiver complexity and achieve smooth demodulation, traditional chaos-based wireless communication systems require the transmission of additional chaotic reference signals, which reduces the spectral efficiency and degrades the security characteristics of the chaotic signals. Recent work has explored deep learning (DL)-aided transceivers to address these issues. However, these techniques have not yet fully exploited the inherent characteristics of chaotic signals, e.g., spectral properties. To maximize the potential of chaotic signals in wireless communication, this paper proposes a power spectral density-based deep learning chaos shift keying (PSD-DLCSK) receiver. The proposed PSD-DLCSK scheme effectively utilizes the spectral properties of chaotic signals, where the PSD of received signals serves as input to a deep neural network (DNN) for symbol detection. The proposed DNN architecture combines long short-term memory networks with self-attention mechanisms to effectively capture the temporal dependencies and spectral properties of chaotic signals, enabling reliable intelligent demodulation without chaotic reference signals. Extensive simulations demonstrate that PSD-DLCSK achieves superior bit error rate (BER) performance compared to traditional receivers as well as other DL-aided schemes over additive white Gaussian noise and multipath Rayleigh fading channels.

We propose a multiple chirp rate index modulation (MCR-IM) system based on Zadoff-Chu (ZC) sequences that overcomes the problems of low transmission rate and large-scale access in classical LoRa networks. We demonstrate the extremely low cross-correlation of MCR-IM signals across different spread factors, showing that the proposed MCR-IM system also inherits the characteristics of ZC sequences modulation. Moreover, we derive an approximate closed-form expression for the bit-error rate (BER) of the proposed MCR-IM system over Nakagami-m fading channels. Simulation results confirm the accuracy of the derived closed-form expression and demonstrate that the MCR-IM system achieves higher levels of spectral efficiency (SE) compared to existing systems. In this context, assigning multiple chirp rates to each user results in a reduction in the number of parallel channels. To mitigate this issue, we propose a peak detection based successive interference cancellation (PD-SIC) algorithm to accommodate more users. Compared to orthogonal scatter chirp spreading spectrum system that names OrthoRa, the MCR-IM system with PD-SIC algorithm achieves lower BER levels. For a similar number of collision signals, the throughput of the MCR-IM system is enhanced by 16% to 21%. Owing to these advantages, the proposed MCR-IM is well suited for large-scale, high-rate LoRa network applications.

This paper introduces a novel neural network framework called M2BeamLLM for beam prediction in millimeter-wave (mmWave) massive multi-input multi-output (mMIMO) communication systems. M2BeamLLM integrates multi-modal sensor data, including images, radar, LiDAR, and GPS, leveraging the powerful reasoning capabilities of large language models (LLMs) such as GPT-2 for beam prediction. By combining sensing data encoding, multimodal alignment and fusion, and supervised fine-tuning (SFT), M2BeamLLM achieves significantly higher beam prediction accuracy and robustness, demonstrably outperforming traditional deep learning (DL) models in both standard and few-shot scenarios. Furthermore, its prediction performance consistently improves with increased diversity in sensing modalities. Our study provides an efficient and intelligent beam prediction solution for vehicle-to-infrastructure (V2I) mmWave communication systems.

In this paper, we propose a new time index modulation (TIM)-based differential chaos shift keying (TIM-DCSK) wireless-powered system. Specifically, we design a time-slot transmission frame, where one time slot is used to carry a bit for wireless information transmission (WIT), while the remaining time slots are used for wireless-powered transmission (WPT). Furthermore, additional index bits for WIT can be transmitted according to the position of the time slot. The results reveal that the proposed system enhances time-slot use, thereby enabling efficient information transfer and energy harvesting for self-sustainability. In addition, we dervie the bit error rate (BER) expression of the WIT for the proposed system and analyze the closed-form energy-shortage probability of the WPT over multipath Rayleigh fading channels. The results reveal that the proposed system achieves better performance as compared to the power-splitting (PS)-based DCSK (PS-DCSK) and time-switching (TS)-based DCSK (TS-DCSK) systems.

Accurate channel state information (CSI) is critical for reconfigurable intelligent surface (RIS)-assisted LoRa systems. Due to the severe channel fading in long-range transmissions, the received LoRa signals have small strength, thus resulting in unsatisfactory channel estimation (CE) performance during convolutional neural network (CNN) training. To properly optimize the network parameters during training, a data pre-processing (DP) method is proposed to pre-process the datasets consisting of small values. Moreover, considering the energy normalization of LoRa pilots, a lightweight residual CNN (LRCNN) is proposed for CE in RIS-assisted LoRa systems. Simulation results show that DP method can significantly improve the performance of CNN-based estimators. Furthermore, the proposed LRCNN can obtain more accurate CSI than the traditional least squares (LS) and linear minimum mean square error (LMMSE) estimators under different spreading factors (SFs) of LoRa modulation. Compared to the existing CNN-based estimators, the proposed LRCNN performs better with lower network complexity.