SBIR-STTR Award

To address the performance degradation of fixed scheduling policies used by TDMA-based tactical networks in contested environments, Bluecom Systems proposes to develop a cognitive anti-jamming and communications scheduling framework.Proposed cognitive protocols can be implemented in hardware that can be integrated in to TDMA-based tactical software-defined radios (SDRs), converting them to autonomous cognitive radios.Technical approach consists of: Designing a machine-learning based real-time hierarchical signal classification framework to distinguish between jammers and inadvertent interference, designing a reinforcement-learning (RL) aided cognitive anti-jamming and scheduling protocol for TDMA-based radios and the integration of these two modules.The most suitable classification engine and the feature inputs will depend on the type of jammers and interference assumed. During Phase I, artificial neural networks (ANN) and convolutional neural networks (CNN) will be explored as the classification engines. Assuming commonly encountered jammer signal models, various feature vectors will be used as inputs to these to determine the most suitable classifier and the feature sets. For cognitive anti-jamming policy learning, several RL algorithms will also be evaluated against these jammer models. A hardware-in-the-loop demo will be developed, utilizing commercial radios, to evaluate the developed cognitive anti-jamming and scheduling system in the presence of real jammer and interference.

To address the performance degradation of fixed scheduling policies used by TDMA-based tactical networks in contested and congested environments, a distributed cognitive TDMA scheduling and anti-jamming protocol is proposed. Technical approach consists of: 1. Developing a deep learning (DL) classifier for jammer/interference identification and automatic detection of network congestion that exchanges information among distributed nodes in an ad-hoc network. 2. Developing a deep reinforcement policy learning algorithm to achieve cognitive anti-jamming communications across channels and timeslots. 3. Developing a distributed cognitive TDMA scheduling algorithm that takes cues from the deep reinforcement learning anti-jamming policy, the DL signal classifier and the congestion/jamming detector. 4. Developing a machine-learning controlled reconfigurable antenna array beamforming technique for interference avoidance that takes cues from the jammer identification algorithm and the congestion/jamming detector. 5. Developing realistic models of jamming, interference, congestion and networks to evaluate performance of the designed machine-learning and scheduling algorithms under various M&S scenarios. 6. Prototyping as FPGA IPs the components of the designed algorithms and protocols most suitable for hardware implementation. 7. Implementing a prototype of the developed cognitive TDMA scheduling and anti-jamming protocol on a COTS SDR to provide a demonstration of the cognitive communications capability in contested and congested environments.