人工智能驱动的国防战术通信与网络：提升现代战争中的态势感知、安全性与自主决策

Dr.Mohammad Atif KhanAssistant Professor, Military Studies (Defence And Strategic Studies)Rajendra Prasad Degree College, Meergunj, Bareilly, U.P. (India)(Affiliated to M.J.P Rohilkhand University Bareilly UP. INDIA) Abstract:The integration of Artificial Intelligence (AI) into tactical communications and networking has redefined military operations, offering advanced capabilities for real-time decision-making, enhanced situational awareness, andsecure data transmission. This research explores the potential and impact of AI technologies on defensecommunication infrastructures, focusing on key applications such as radar-based data transmission, UAV-assisted relay systems, and the fusion of AI with emerging technologies like the Internet of Things (IoT),blockchain, and augmented reality (AR). AI-enhanced systems facilitate the automation of threat detection,dynamic encryption, and autonomous decision-making, significantly improving operational efficiency andsecurity in contested environments. However, the deployment of AI in military contexts also raises criticalchallenges related to adversarial threats, ethical concerns, and interoperability across multinational forces. Thisstudy provides an in-depth analysis of these advancements and challenges, offering insights into the future of AIin defense communications and its potential to reshape the battlefield. The paper concludes with a discussion ofthe technical, ethical, and strategic considerations necessary for the responsible and effective implementation ofAI in military operations.Keywords:AI-driven communication networks, Situational awareness, Autonomous decision-making, Radar systems in defense, UAV-assisted relay systems, Military cybersecurity I.Introduction 1.1 BackgroundThe integration of Artificial Intelligence (AI) into tactical communications and defense networking represents one of the most significant technological shifts in modern military doctrine. With the emergence ofincreasingly complex and asymmetric warfare environments, traditional communication paradigms have proveninsufficient in ensuring rapid, secure, and context-aware responses. Consequently, AI has emerged as a strategicenabler, allowing for real-time data processing, autonomous decision-making, and enhanced situational awarenessacross operational theaters (Monzon Baeza et al., 2025). In contemporary defense scenarios, military units areoften required to operate in dynamic, high-threat environments where communication systems must be agile,resilient, and capable of adapting to evolving threats. AI addresses this imperative by automating the collection,analysis, and dissemination of large volumes of heterogeneous data from diverse sensors and platforms (MilitaryKnowledge Base, n.d.-a). This automation significantly reduces the cognitive burden on military personnel,enabling faster and more informed decisions. Machine learning (ML) algorithms, particularly deep learning andreinforcement learning, empower these systems to detect anomalies, optimize bandwidth allocation, and predictnetwork congestion—functions that are vital for mission success.One of the critical contributions of AI in defense communications is its role in enhancing battlefield situational awareness. AI-driven platforms synthesize information from multiple domains—land, sea, air, space,and cyberspace—into cohesive operational pictures, allowing commanders to understand threats, opportunities,and unit positions with unprecedented clarity (Financial Times, 2025). The Joint All-Domain Command andControl (JADC2) initiative, for instance, underscores the significance of AI in integrating sensors, shooters, anddecision-makers across the military spectrum. AI serves as the connective tissue in this system, facilitatingseamless information flow and multi-layered threat assessment (Wikipedia, n.d.-a). Radar-based communicationsystems, long a mainstay of military surveillance and targeting, have also benefited immensely from AIintegration. Traditionally reliant on manual signal interpretation, these systems now utilize AI to process radarreturns in real-time, filtering noise and extracting actionable insights. Monzon Baeza et al. (2025) detail how AIalgorithms can classify targets, track their movements, and even predict their trajectories, thereby improvingoperational precision. This capability is especially vital in contested or electronically degraded environments,where signal jamming and deception are commonly employed by adversaries. Moreover, the increasing use of Unmanned Aerial Vehicles (UAVs) in military operations has expandedthe scope of AI-driven networking. UAVs function as mobile relay nodes in tactical mesh networks, extendingcommunication ranges and ensuring line-of-sight transmission in obstructed terrains. AI optimizes their flightpaths and relaying behavior based on environmental data and mission objectives. Abohashish et al. (2023)demonstrated that reinforcement learning models significantly improve