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Improved Communications Scheduling in Contested Environments


TECHNOLOGY AREA(S): Electronics 

OBJECTIVE: Develop a set of modular machine learning algorithms, possibly based on deep learning, which effectively avoid or mitigate interference (Red/Blue/Self) and congestion, in order to schedule reliable communications for Army tactical networks 

DESCRIPTION: Military communications waveforms of today are typically Time Division Multiple Access (TDMA)-based. TDMA is a well-understood network access method that enables a group of tactical nodes to communicate amongst each other. Current TDMA scheduling algorithms quickly become ineffective when the communications spectrum is congested and contested. This is because these algorithms are policy driven and have no ability to learn about potential impediments to reliable communications within the operational spectrum. Cognitive techniques are required to reason about the communications spectrum in order to determine when interference and congestion is occurring. These techniques are also required to classify that interference and/or congestion in near real-time. The classification results are fed into the scheduling algorithm so that, as needed, either communications reschedule reliably in a timely fashion to avoid interference, or address the interference/congestion with a robust mitigation technique. 

PHASE I: Develop the feasibility and basic requirements of machine learning techniques that can sweep a segment of communications spectrum while learning and recognizing interference and congestion. Develop the initial training set that minimizes signal feature extraction errors, while enhancing communications by recognizing interference, all classes of jammers, and congestion impairments. 

PHASE II: Design a TDMA scheduling algorithm that takes cues/inputs from a machine-learning algorithm. The machine-learning algorithm exchanges information in a distributed fashion, learning about interference, congestion, and jamming across the nodes of a tactical network. Develop seamless integration of the machine-learning algorithm and the scheduling algorithm in various M&S scenarios and demonstrate the capability. 

PHASE III: Develop and prototype the capability of an integrated scheduling algorithm and machine-learning algorithm on a commercial-of-the-shelf (COTS) Software Defined Radio (SDR) and provide a realistic demonstration of the capability. The demonstration shows near real-time avoidance of interference (EW, self-Interference, co-site, and others) and/or congestion, while reliably scheduling communications amongst networked nodes. This capability can be used in emerging on-the-move tactical (OTM) networks with manned-unmanned Teaming (MUM-T) capabilities for the military, and can be used in emerging commercial self driving vehicle networks. 


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KEYWORDS: Deep Learning, Multi-Layer Neural Network, Time Division Multiple Access, Jamming, Interference, Congestion, Scheduling Algorithms, Communications Spectrum, Tactical Networks, Software Defined Radios 


Gilbert Green 

(443) 395-7629 

Mitesh Patel 

(443) 395-7630 

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