Jiajie Xu

Maritime communication demands have surged due to diverse applications such as transoceanic shipping, offshore resource exploration, and emergency rescue. However, current communication systems are unable to meet the diverse data rate demands of maritime applications. In this paper, we propose a space-air-ground-sea integrated network (SAGSIN) that combines onshore base stations, uncrewed aerial vehicles, high-altitude platform stations, and low Earth orbit satellites to provide seamless and reliable communication across vast oceanic regions. Based on stochastic geometry, we model the spatial distribution of network devices on spherical surfaces. The Matérn hard-core point process (MHCPP) is used to model the deployment of both ground and aerial devices, considering the minimum spacing to avoid overdense and collisions. Aerial relays are used to extend the coverage of coastal base stations, with relay selection aimed at maximizing the end-to-end channel capacity. Analytical expressions for the uplink coverage probability are derived by considering the receiver’s decoding threshold. Furthermore, the analysis is extended to the communication probability by incorporating quality of service (QoS) with data rate as the metric. Numerical results validate the accuracy of the proposed model and demonstrate that SAGSIN can effectively extend reliable communication from coastal regions to the deep ocean. Furthermore, dynamic QoS scheduling across heterogeneous links, each with different transmission capabilities, can enhance overall system efficiency to meet diverse QoS demands.

The intelligent omni-surface (IOS), evolved from the reconfigurable intelligent surface (RIS), is viewed as a promising technology for next-generation wireless communications thanks to its more flexible deployment. Unlike conventional RISs that can only operate on one side to receive and reflect signals, an IOS can simultaneously reflect and refract incident waves, thereby achieving full-space coverage. In this paper, tools from stochastic geometry are applied to model and analyze the performance of IOS-assisted millimeter-wave (mmWave) communications, with a particular focus on indoor scenarios. The dimensions of the indoor environment and the IOS aperture are explicitly taken into account to evaluate how many end users an IOS can serve, i.e., its user capacity. To this end, we characterize the users' coverage performance under practical propagation and service constraints, where near-field effects and heterogeneous user-type signal-to-interference-plus-noise-ratio (SINR) thresholds are incorporated to enable accurate capacity evaluation. Both near-field and far-field regions are considered to capture realistic indoor operation. Furthermore, a comprehensive set of system parameters, including node heights and the depth-of-focus (DF) effect, is incorporated into the mathematical analysis. We also compare the user capacity of IOS-assisted and RIS-assisted indoor mmWave systems under hybrid near-/far-field and pure far-field conditions. The analytical results are validated through Monte Carlo simulations, confirming the accuracy of the proposed framework. The obtained insights show that near-field effects can substantially affect user capacity at certain IOS/RIS aperture sizes, highlighting the importance of accounting for such effects for accurate modeling and design of IOS-assisted indoor networks.

Underwater optical wireless communication (UOWC) is a key enabling technology for future space–air–ground–sea integrated networks. However, UOWC faces critical hurdles from spatial randomness and stringent energy constraints. These challenges fundamentally limit network lifetime and sustainability. This article develops a comprehensive stochastic geometry (SG) framework to perform a differential energy analysis of UOWC links. Instead of relying on simplified models, we employ a 3-D truncated Poisson point process (TPPP) to accurately capture the anisotropic nature of the underwater environment, specifically the disparity between horizontal spread and vertical depth. It incorporates a Lambertian emission pattern, random receiver (Rx) positions and orientations, and a realistic channel model with extinction effects. Under this model, we derive a full suite of closed-form expressions for key performance indicators (KPIs). These include the nearest-neighbor distance distribution, expected received power, signal-to-noise ratio (SNR), and bit error rate (BER). A principal and counter-intuitive finding of our analysis is an “offset-pointing” strategy. This strategy involves intentionally misaligning the Rx by a deterministically optimal angle. This approach maximizes the integrated received power across the aperture, contrary to the conventional pursuit of perfect alignment. We formulate and solve an energy-efficiency optimization problem. Our results demonstrate that this strategy enhances system robustness and yields substantial performance gains. Simulation results validate our analytical models. They show that the optimal offset strategy can reduce the required transmit power by nearly 20% to achieve a target BER. This reduction directly translates into extended network lifetime and higher total data throughput. These findings offer a new design paradigm for deploying robust, cost-effective, and sustainable UOWC networks.

Jointly optimizing the base station (BS) precoding matrix and reconfigurable intelligent surface (RIS) phases is challenging in RIS-assisted multi-user multiple-input single-output (MU-MISO) systems, particularly with extremely large RISs. This paper proposes a deep reinforcement learning (DRL) approach based on a sequential multi-agent advantage actor-critic (A2C) framework that accounts for discrete RIS phases, imperfect channel state information (CSI), and inter-user channel correlation. The computational complexity is analyzed, and the proposed method is benchmarked against the zero-forcing (ZF), minimum mean square error (MMSE), and single-agent A2C beamformers in terms of sum spectral efficiency (SE). Simulation results show that the proposed algorithm achieves higher SE and a certain degree of robustness to moderate channel estimation errors.

The wealth of spectral resources in millimeter-wave (mmWave) and terahertz (THz) bands has the potential to support data transmission with Gbps, yet the narrow beams required at these frequencies introduce critical challenges in beam management. This paper investigates the application of integrated sensing and communications (ISAC) in the fifth-generation (5G) and beyond networks. By configuring synchronization signal blocks (SSB) and positioning reference signals (PRS), the proposed ISAC scheme enables intelligent inter-cell handovers and adaptive beam alignment for mobile terminals (MTs). With the tools from stochastic geometry, we first develop a unified analytical framework that yields tractable expressions for sensing and communication coverage probability and beam misalignment probability, explicitly capturing blockage process, beam dynamics, and multi-cell interference. Subsequently, we derive the optimal pilot design pattern that minimizes the beam misalignment probability. Taking into account both coverage performance and beam management overhead, the effective area spectral efficiency (ASE) metric is proposed to quantify the network-level benefits of ISAC. In addition, Monte Carlo simulation results are introduced to validate the theoretical analysis. Our study demonstrates that the proposed ISAC scheme can effectively enhance the ASE of the network and reduce the beam management overhead, especially in scenarios of dense base station (BS) deployments and large beam numbers. Furthermore, a fundamental beamwidth tradeoff is revealed: Although narrower beams improve the coverage for sensing, they can increase mobility-induced misalignment probabilities for communications. These insights provide concrete guidelines for practical ISAC-assisted beam management in future networks.

Underwater wireless sensor networks (UWSNs) have received significant attention due to their various applications, with underwater target localization playing a vital role in enhancing network performance. Given the challenges and high costs associated with UWSN deployments, Received Signal Strength (RSS)-based localization offers a viable solution due to its minimal hardware requirements and cost-effectiveness. In this paper, we assign distance-based weights to RSS measurements, providing higher reliability to closer anchor nodes. Using the weighted RSS measurements and generalized trust region subproblem (GTRS), we propose the GTRS-based localization technique with Unknown Transmit Power (GUTP), which can be solved by a simple bisection method. Unlike conventional localization methods that require prior knowledge of the target node's transmit power, GUTP jointly estimates both the location and transmit power of the target node, broadening its practical use. Additionally, we derive the Cramer-Rao lower bounds (CRLBs) for RSS-based underwater localization with known and unknown transmit power, respectively. Extensive simulations demonstrate that GUTP achieves enhanced accuracy and significantly lower computational complexity in estimating the target node's location and transmit power compared to existing semidefinite programming (SDP)-based techniques.