This paper proposes an end-to-end direction-of-arrival (DOA) estimator framework for distributed uniform linear arrays (ULAs) with random massive missing observations. Traditional methods, such as Structured Nyquist Correlation Reconstruction (SNCR) [13] and Vandermonde Decomposition Reconstruction (VDR) [16], are designed based on specific structural constraints (whole-row/column loss). They are becoming invalid when the miss rate exceeds 80% in a random manner, where the unstructured data loss prevents the utilization of prior correlation information. To address this tough challenge, we propose a multi-subarray fusion strategy that utilizes position diversity and angle diversity prior information to correct and integrate the severely missing covariance matrices. This transformation enables them into globally reconstructable matrices, thereby significantly mitigating the impact of missing observations on DOA estimation. Following this, we present a conditional diffusion-based neural network to collaboratively perform matrix completion (MC) and DOA estimation in an end-to-end manner, avoiding the pseudo-peak issues common in traditional algorithms. Simulation results demonstrate that the proposed framework and method maintains robust estimation accuracy even with up to 90% loss rate of covariance elements, significantly outperforming the state-of-the-art recovery algorithms.

Reconfigurable intelligent surfaces (RIS) have emerged as a crucial technology for making sustainable and green communication in future sixth-generation (6G) networks. By adaptively adjusting the wireless environment, RIS facilitates flexible control over signal propagation. When RIS is integrated into a simultaneous wireless information and power transfer (SWIPT) system, it can improve energy efficiency (EE) and link reliability. This enables low-power internet-of-things (IoT) devices to achieve continuous energy acquisition while maintaining reliable data access. This capability aligns well with the increasing demands of future 6G networks for enhancing EE and achieving ubiquitous connectivity. Motivated by this, we investigate a RIS-assisted multi-user SWIPT system that accounts for the nonlinear characteristics of the high power amplifier (HPA) at the transmitting end and the nonlinearity of the energy harvesting (EH) circuits at the receivers. These hardware imperfections pose major challenges for system modeling and optimization, particularly under stringent power limitations and quality of service (QoS) requirements, where improving overall EE is a key objective. Therefore, we proposed a joint optimization framework that simultaneously designs the access point (AP) beamforming, the RIS reflection coefficients, and the power splitting (PS) ratios at the receivers. The resulting problem is non-convex, with strong coupling among these variables. To tackle this issue, we propose an efficient alternating optimization (AO) framework. The fractional EE objective is handled using the Dinkelbach method, while each subproblem is iteratively convexified via successive convex approximation (SCA) and semi-definite relaxation (SDR) algorithms. Simulation results reveal that the proposed AO approach achieves substantial EE gains and confirm that integrating RIS conspicuous boosts the overall system EE performance.

The upcoming sixth generation (6G) driven Internetof- Things (IoT) will face the great challenges of extremely low power demand, high transmission reliability and massive connectivities. To meet these requirements, we propose a novel hybrid ambient backscatter communication (ABCom) or relay communication (RCom) assisted non-orthogonal multiple access (NOMA) network, which simultaneously enables traditional relay networks and ABCom-assisted IoT networks. Specifically, we investigate the reliability and the finite signal-to-noise ratio (SNR) diversity-multiplexing trade-off (f-DMT) of the proposed system to characterize the outage performance of the proposed system in the non-asymptotic SNR region. We derive the outage probability (OP) and the finite SNR diversity gain when two sources aim to communicate through either ABCom or RCom. On the basis that the results of Monte Carlo simulation and analysis are in perfect agreement, we discover that in the high SNR regime, the OP for ABCom tends to be a constant, leading to a zero diversity gain and an error floor, while the OP for RCom is monotone decreasing with respect to the SNR. Also, compared with the imperfect successive interference cancellation (ipSIC) mode, the reliability of the system under the ideal condition is significantly improved; Moreover, in the lower multiplexing gain regime, for both ABCom and RCom, the higher finite SNR diversity gain results in better system reliability, which provides good opportunities for ABCom to adapt f-DMT and improve relevant performance metrics by adapting the reflection parameter.

Internet-of-Vehicle (IoV) enabled Maritime Transportation Systems (MTS) communication is anticipated to support ultra-reliable and low latency, diverse quality-of-service (QoS) and large-scale connectivities. To meet such stringent demands, a cognitive ambient backscatter non-orthogonal multiple access (C-AmBC-NOMA) IoV-MTS network is proposed. We explore the reliable and secure performance of the proposed C-AmBC-NOMA IoV-MTS network with in-phase and quadrature phase imbalance (IQI) at radio-frequency (RF) front-ends and the existence of an eavesdropper. In particular, the analytical expressions on the outage probability (OP) and intercept probability (IP) are obtained after a series of calculations. For a deeper understanding, we discuss the asymptotic behavior of OPs in the high signal-to-noise ratio (SNR) region, the diversity orders of OPs, and IPs in the high main-to-eavesdropper ratio (MER) regime. The results of Monte-Carlo simulation and a series of corresponding theoretical analysis show that: i) As the SNR approaches infinity, the OPs tend to be fixed non-negative values, indicating that the diversity orders of the OPs have error floors; ii) When the MER approaches infinity, the IPs of legitimate users decrease continuously, while the IP of backscatter device (BD) increases; iii) Compared with the system performance under ideal condition, the system performance is less reliable under IQI condition, but the security performance is enhanced; iv) By carefully selecting the system parameters, a trade-off can be achieved between reliability and security.

The upcoming Intelligent Transportation System (ITS) supported by sixth generation (6G) communication technologies is expected to face the great challenges of spectrum scarcity, large-scale connectivity, ultra-low latency, and various security threats. To mitigate these challenges and implement the ITS in practice, we propose an overlay cognitive ambient backscatter communication non-orthogonal multiple access (ABCom-NOMA) network for the ITS. Specifically, we elaborate on the secrecy performance the overlay cognitive ABCom-NOMA based on ITS in the presence of an eavesdropping vehicle by deriving the secrecy outage probability (SOP) between the primary network, overlay secondary network, and the eavesdropping vehicle of the considered networks, respectively. For comparison, the secrecy performance of secondary receiving vehicles is taken into account, and a series of numerical simulations by Monte-Carlo methods are carried out to investigate the secrecy performance. From the numerical results yielded by the simulations, we can conclude: 1) The secrecy performance of the proposed the overlay secondary network is superior to the one of the primary network; 2) The increasing of the power allocation factor yields a positive effect on the secrecy performance of the primary receiving vehicles but a negative effect on that of the secondary receiving vehicles.