The increasing complexity and scale of modern network environments, particularly with the advent of the Internet of Things (IoT), demand flexible and adaptable routing protocols. Our study delves into the first known ns-3 implementation of Ad Hoc On-Demand Distance Vector version 2 (AODVv2), offering researchers a valuable platform to explore its capabilities. This implementation facilitates rigorous testing and comparative analysis under diverse network conditions, leading to new insights on the protocol’s performance and reliability. Key findings from our preliminary evaluations indicate that AODVv2’s overhead is higher than that of legacy AODV, but its route maintenance is more robust in dynamic topologies. These results accelerate innovation in mobile ad hoc networking and IoT domains, encouraging the development of more adaptive and resilient networks. Furthermore, our work offers empirical results to inform future standardization efforts for AODVv2.
The IEEE 802.15.6 standard for Wireless Body Area Networks (WBANs) enables wireless communication inside the human body. In a network scenario where a signal travels through various body parts, it is important to consider the propagation loss caused by the body. This paper presents a body propagation loss model for WBAN implemented in the network simulator ns-3. Our proposed propagation loss model calculates path-loss and interference caused by the presence of a human body in the network. The experiments in this study are close to real-life scenarios in which the transmitters are placed in or on the human body, and the receivers are placed in the vicinity of the body. We evaluated the effectiveness of our body propagation loss model signals using the physical layers of different standards. Our results show that for WBAN, the packet success rate is better than that of the Low-rate Wireless Personal Area Network (Lr-WPAN) standard. Our proposed propagation loss model is supported by all WBAN narrowband frequencies using DBPSK modulation. We analyzed different frequency bands within the WBAN standard for in-body and on-body communications and concluded that the 402 MHz band achieved the best performance among the WBAN narrowband frequencies. The code for our simulation model is publicly available.
Flying Ad-hoc Networks (FANETs) are considered to be ad-hoc network formed by Unmanned Arial Vehicles (UAVs). FANETs are widely used in both military and civilian domains, like surveillance or emergency response. Due to the highly dynamic movement of UAVs in FANETs, communication protocols must be thoroughly tested before deployment. To ensure consistent performance, thorough testing must be performed in both simulated and real-world contexts. In this study, the FANS (Flying Ad-hoc Network Simulator) co-simulation platform is expanded. This platform combines ns-3 network simulation, ROS robotics software development, and Gazebo drone simulation. It is further connected to hardware emulation using an embedded system platform, which includes the NVIDIA Jetson Nano and Pixhawk-4 flight controller. The simulation framework is designed to measure important network parameters such as end-to-end delay and Packet Delivery Ratio (PDR) in a decentralized Wi-Fi ad-hoc network setup. In addition to the simulation, a hardware testbed consisting of three drones is created. In this configuration, UAVs communicate while flying in a straight line formation. The findings of this real-world implementation are contrasted with those acquired through simulation. The simulation and hardware test results are presented to show that the hardware integration was successful. This work lays the groundwork for future research and testing of various networking protocols in FANETs.
3rd Generation Partnership Project (3GPP) 5G New Radio (NR) requires users to provide feedback for the base stations, named Next Generation Node Bs (gNBs), to select the number of spatial streams and the precoding matrix, optimizing metrics like channel capacity. 5G-LENA 3.1 offers an exhaustive search for the optimal Rank Indicator (RI) and Precoding Matrix Indicator (PMI) pair, constrained by the codebook. We also implement the unconstrained ideal precoding matrix and two codebook-based PMI/RI selection techniques from the literature. We propose a new heuristic method that reduces the search space with minimal system performance loss. Simulations show that it achieves 3.7x speedup with only a 3.3% throughput reduction compared to the exhaustive search.
Artificial Intelligence (AI) will play a crucial role in next generation networks, where adaptability and self-optimization are essential for achieving zero-touch, autonomous network operation. As AI techniques are integrated into the architecture of next generation networks, such as 6G, having tools that will allow the application of AI for testing and experimentation is of high importance. Moreover, AI techniques are increasingly being employed to enhance the performance of applications with challenging Quality of Service (QoS) and latency requirements. In this work we present an enhanced version of the ns-3 5G-LENA simulator, extended with AI capabilities through integration with the ns3-gym module. In addition, we introduce a novel Reinforcement Learning (RL)-based resource scheduler, namely Delay-Responsive Intelligent Learning for Latency-sensitive QoS (DRILL-Q), designed to optimize resource allocation with a special focus on latency-sensitive, multi-flow traffic. Through extensive simulations we demonstrate that the proposed scheduler significantly reduces in QoS violations, while improves fairness across flows, compared to the traditional schedulers provided in 5G-LENA, the 5G NR module for the ns-3 simulator.
