# Recent Progress on Underwater Wireless Communication Methods and Applications
## 3 Wireless Communication System Applied in Underwater Devices
### Development and Applications of Multimodal Underwater Communication  
To address the inherent limitations of single-mode underwater communication technologies, recent research has shifted toward the development of multimodal communication architectures that integrate acoustic, optical, RF methods. This hybrid strategy aims to exploit the complementary strengths of different communication modes: the long range of acoustic signals, the high data rates of optical links, and the versatility of RF communication in shallow and freshwater environments [82]. Such integration is particularly critical for AUVs, enabling reliable long-range communication, real-time high-speed data transfer, and minimized latency for mission-critical operations. Several practical implementations have demonstrated the benefits of multimodal systems. For instance, hybrid acoustic-optical networks have been proposed for underwater sensor networks, utilizing acoustic links for control signaling and optical links for high-volume data transmission [84]. In shallow water environments, RF communication has been effectively incorporated into Internet of Underwater Things (IoUT) frameworks to establish short-range, low-power communication links between underwater sensors and gateways [85]. Cross-medium communication strategies have also been explored, enabling underwater devices to dynamically switch between acoustic, optical, and RF modes based on environmental conditions and mission requirements [86]. Multimodal systems integrate two or more communication technologies--typically acoustic, optical, and RF--to leverage their complementary strengths. For example, acoustic links offer long-range coverage for basic control messages, while optical or RF links can be activated for high-speed data bursts when conditions allow. One notable implementation is a hybrid acoustic-optical system for AUV clusters, where low-rate acoustic beacons ensure global coordination, and optical modems are selectively engaged for localized high-volume data exchange. Recent studies also propose dynamic switching mechanisms that adjust communication mode based on real-time measurements of turbidity, salinity, or energy availability [85; 86]. For instance, DRL-based frameworks can autonomously select the optimal modality to minimize latency and maximize link quality. Despite their promise, multimodal systems face challenges in hardware complexity, synchronization across channels, and seamless protocol switching. Ongoing efforts aim to address these via unified communication stacks and embedded sensor fusion platforms. And also, hybrid communication systems face significant challenges in actual underwater settings. First, the physical integration of acoustic, optical, and RF transceivers increases payload weight, form factor, and thermal load--especially problematic for micro-AUVs or swarming robots. Second, seamless cross-modality coordination requires reliable environmental sensing and low-latency switching algorithms, which are often hindered by unpredictable underwater dynamics. Third, power consumption across heterogeneous modules is difficult to optimize holistically. Activating multiple high-speed links may rapidly deplete onboard energy reserves. Finally, the lack of standardized multimodal protocol stacks makes it difficult to ensure interoperability between platforms developed by different manufacturers or research groups. Addressing these limitations is an active area of research, with promising solutions emerging in the form of modular AI-assisted control layers, energy-aware scheduling, and open-source hardware frameworks for flexible hybrid integration. In addition to communication integration, ML algorithms are being increasingly adopted to enhance the adaptability and robustness of underwater communication systems [83]. ML techniques enable adaptive signal processing, dynamic resource allocation, and interference mitigation under varying channel conditions.