Abstract: System for adaptive routing in time division multiple access-based wideband mobile adhoc network employs an enhanced OLSR-based routing protocol designed for TDMA-based mobile adhoc network to spread the link quality information and calculates adaptive routing paths based on the link quality. The system employs a signal-to-noise ratio calculation, demodulation and decoding block, link quality estimator, FIFO, link quality broadcast, network layer link state broadcast, link state table, adaptive routing, path calculation block and routing table blocks to achieve the desired results.

Abstract: In order to avoid various jamming attacks from intelligent jammers in modern complex wireless environments, a system and method is presented for a user radio to generate and implement an adaptive anti-jamming communication strategy. The said adaptive anti-jamming communication strategy is obtained via the training process for a specific neural network using Deep Double-Q Reinforcement learning algorithm in the strategy generation phase. The objective of this process is to discover a strategy to select the optimal radio action including transmission channel and transmission power for the user radio, which is changed adaptively to different jamming patterns to maximize the successful transmission rate (“jamming-free”) while retaining the power consumption of user radio as low as possible.

Abstract: A system and method for live video streaming over tactical wireless networks based on distortion estimation using multilayer perceptron neural network (MLP). The system and method can adaptively adjust the parameters of transport and application layers according to wireless network conditions, based on video distortion estimation using MLP neural network. The system includes a video motion and detail estimation block, a redundancy and video bitrate optimizer, a MLP neural network-based video distortion estimator, and video transport blocks, a packet error rate (PER) and bandwidth measurement block.

Abstract: A system and method for live video streaming over tactical wireless networks based on distortion estimation using multilayer perceptron neural network (MLP). The system and method can adaptively adjust the parameters of transport and application layers according to wireless network conditions, based on video distortion estimation using MLP neural network. The system includes a video motion and detail estimation block, a redundancy and video bitrate optimizer, a MLP neural network-based video distortion estimator, and video transport blocks, a packet error rate (PER) and bandwidth measurement block.

Abstract: In order to avoid various jamming attacks from intelligent jammers in modern complex wireless environments, a system and method is presented for a user radio to generate and implement an adaptive anti-jamming communication strategy. The said adaptive anti-jamming communication strategy is obtained via the training process for a specific neural network using Deep Double-Q Reinforcement learning algorithm in the strategy generation phase. The objective of this process is to discover a strategy to select the optimal radio action including transmission channel and transmission power for the user radio, which is changed adaptively to different jamming patterns to maximize the successful transmission rate (“jamming-free”) while retaining the power consumption of user radio as low as possible.