This paper presents a cost-effective approach to enhancing multi-robot systems through the strategic integration of generative AI and machine learning capabilities. Building upon a traditional master-slave architecture utilizing Raspberry Pi and MSP430 microcontrollers, we introduce an optimized framework that significantly improves swarm robotics performance without requiring extensive hardware modifications. Our enhanced system implements distributed intelligence through federated learning, achieving a 17.9% improvement in object detection accuracy (from 76.4% to 94.3%) and a 65.3% reduction in system response latency (from 245ms to 85ms). The architecture incorporates adaptive model compression techniques and intelligent resource management, reducing communication overhead by 66.7% while maintaining model accuracy. Through comprehensive experimental validation, we demonstrate that the enhanced system achieves a 31.2% reduction in task completion time while operating within existing hardware constraints. The implementation maintains cost-effectiveness with an enhancement cost of $86.37 per unit and achieves return on investment within 1.9 months under standard operational conditions. Our results establish that sophisticated swarm behaviors can be achieved through software optimization rather than hardware upgrades, providing a practical pathway for upgrading existing robotic infrastructure in industrial applications.

The integration of artificial intelligence (AI) within Open Radio Access Network (Open RAN) architectures represents a fundamental transformation in telecommunications infrastructure. This paper presents a comprehensive analysis of AI integration in Open RAN, focusing on its implications for 6G networks and beyond. We examine the effectiveness of various AI models in optimizing network performance, comparing traditional optimization techniques with AI-driven approaches through extensive simulations and real-world deployments. Our results demonstrate significant improvements, including a 23% enhancement in resource utilization efficiency and a 27% reduction in power consumption. Through case studies of commercial deployments, we validate the feasibility and benefits of AI-enhanced Open RAN systems. The research also addresses critical challenges in standardization, security, and multi-vendor integration, proposing practical solutions for implementation. Finally, we provide recommendations for practical deployment and identify promising directions for future research in this rapidly evolving field.

 Reconfigurable Intelligent Surfaces (RIS) represent a transformative technology for achieving the ambitious performance requirements of 6G wireless networks. This comprehensive review examines advanced beamforming techniques incorporating RIS technology, presenting a systematic analysis of theoretical foundations, implementation challenges, and future directions. We develop a rigorous analytical framework that characterizes the fundamental performance limits of RIS-assisted beamforming, demonstrating that system capacity scales as O(log N) with the number of reflecting elements while maintaining energy efficiency that follows O(log N/N). Our analysis reveals that hybrid approaches combining traditional optimization with machine learning techniques can achieve 85-90% of theoretical performance bounds while reducing computational complexity by approximately 80%. We present a detailed examination of practical implementation challenges, including hardware impairments, channel estimation overhead, and real-time optimization requirements. The study establishes that RIS-assisted beamforming can enable a 300% increase in spectral efficiency and 60-70% reduction in power consumption compared to conventional massive MIMO systems. Additionally, we identify critical research directions, including distributed RIS optimization, multi-user scheduling, and integration with artificial intelligence frameworks. This review provides a comprehensive foundation for researchers and practitioners working toward the realization of RIS-enabled 6G networks, offering both theoretical insights and practical implementation guidelines.

This research paper explores the concept of edge computing and the advancements in this technology. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the devices that generate and collect data in order to reduce latency and improve performance. It has been identified as a powerful tool for providing low-latency access to data and applications and is becoming increasingly popular in the IoT and 5G era. This paper focuses on the various advancements in edge computing technology, such as the emergence of edge cloud platforms and the development of edge analytics and machine learning solutions. It also discusses various applications of edge com- puting, such as autonomous driving, augmented/virtual reality, and the Internet of Things, and how they are being used to improve efficiency and reduce latency. Finally, the paper analyses the various challenges and opportunities associated with edge computing, including security, scalability and privacy concerns.