Connection management is an important problem for any wireless network to ensure smooth and well-balanced operation throughout. Traditional methods for connection management (specifically user-cell association) consider sub-optimal and greedy solutions such as connection of each user to a cell with maximum receive power. However, network performance can be improved by leveraging machine learning (ML) and artificial intelligence (AI) based solutions. The next generation software defined 5G networks defined by the Open Radio Access Network (O-RAN) alliance facilitates the inclusion of ML/AI based solutions for various network problems. In this paper, we consider intelligent connection management based on the O-RAN network architecture to optimize user association and load balancing in the network. We formulate connection management as a combinatorial graph optimization problem. We propose a deep reinforcement learning (DRL) solution that uses the underlying graph to learn the weights of the graph neural networks (GNN) for optimal user-cell association. We consider three candidate objective functions: sum user throughput, cell coverage, and load balancing. Our results show up to 10% gain in throughput, 45-140% gain cell coverage, 20-45% gain in load balancing depending on network deployment configurations compared to baseline greedy techniques.

Test and validation of autonomous mechatronic systems is a major challenge. Due to more complex tasks as well as dynamic environments, existing test and validation methods are reaching their limits. The complexity and diversity of their elements and interrelations of these as well as interrelations with environmental elements have to be handled because established methods do not consider the characteristics of autonomous mechatronic systems. Therefore Systems Engineering seems to be a proper solution. Based on system thinking an approach for analyzing autonomous mechatronic systems will be developed. For this purpose a procedure for model development will be aligned with the robot's system model. This system model combines hardware and software elements, analyzes their interrelations and prepares a later test and validation. With the help of this new system model, which decomposes to the hardware and software level, new test and validation methods can be developed.

This paper presents a fault simulation environment which accepts pure switch level or mixed switch/RT level descriptions of the design under test. Switch level fault injection strategies for the stuck-at, transition and logic bridge models are presented. A fault simulation algorithm is presented, along with design issues and optimizations. The fault simulation algorithm places no restrictions on the circuit styles used to implement designs. Mixed level simulation issues are discussed. Fault simulation performance numbers on large industrial benchmarks are reported.