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Coherent Sensing Approaches for Dynamic Spectrum Allocation


OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): FutureG; Trusted AI and Autonomy OBJECTIVE: Design a distributed, coherent sensing solution to generate a spectrum map of available channels in sparse or dense spectral environments for channel allocation in a decentralized multi-hop network. Develop a scheme for sharing spectrum sensing results across the network for all channels to reach distributed consensus on the spectrum map between multiple geographically-dispersed nodes. DESCRIPTION: Wireless multi-hop networks, and the understanding thereof, are key components to military and commercial communications. These networks are non-centralized, highly dynamic, and often either sparse or dense. With wireless communications and networking now necessitating dynamic spectrum allocation, such as with fifth generation (5G) and sixth generation (6G) cellular technology, military and commercial users must develop a more coherent approach to understanding spectral environments for maintaining reliable connectivity. There are many signal processing solutions for detection and classification of spectral energy, such as those based on machine learning (ML). Standard ML metrics are often used to evaluate the accuracy of these techniques, such as number of false positives/negatives. System performance may also be quantified with temporal metrics such as latency and speed. While being agnostic to any particular sensing approach is the objective, it may be through the course of this STTR topic that a greater understanding of ML characteristics specific to the radio frequency (RF) modality is a key component to coherently linking multiple, geographically-dispersed sensing systems for the complete spectral mapping that fully supports dynamic allocation in sparse and/or dense environments. Indeed, sharing ML data model updates instead of in-phase and quadrature (I/Q) data is much more efficient. An adequate understanding to baseline spectral activity can be achieved through sensing at a single node, but distributed spectrum sensing would provide much greater fidelity on activity for dynamic spectrum allocation. Metrics similar to those used for quantifying accuracy and performance of a single node may also be used for evaluating these distributed sensing systems with multiple nodes. Overcoming the coherence challenges behind a distributed spectrum sensing approach would enable a network-wide mapping of available channels. This awareness of the spectral environment would then inform proper channel allocation for resilient communications that support both military and commercial users. It would also enable adaptation within the spectrum, as well as identification of primary and secondary users. The solution to these problems must be computationally efficient and require little overhead to share sensing results/updates to nodes across the network. The goal is for coherency to be as agnostic to the sensing technique as possible. This STTR topic will develop the foundational mathematical analysis to address coherence for distributed sensing in dynamic spectral environments. This topic also seeks an initial design of a methodology for disseminating results and awareness across the network to achieve distributed consensus among the sensing nodes for applications such as adapting communications within the spectrum and identifying primary and secondary users. The innovation and focus is on solving coherency issues in order to map out spectral environments for dynamic spectrum allocation, and on a means for efficiently sharing results to multiple nodes across the network to reach a desired end state of distributed consensus on spectral activity for dynamic allocation. PHASE I: Conduct analysis to design an approach for coherent distributed sensing. Use this analysis to inform and demonstrate a proof of concept that provides a network-wide mapping of available channels. Perform a trade study and literature survey on sharing results for distributed consensus of spectral activity. PHASE II: Refine, test, and prototype a scalable, mathematically-founded approach to coherent distributed sensing for network-wide mapping of available channels with means for sharing results/updates across the network to reach consensus amongst the nodes. PHASE III DUAL USE APPLICATIONS: Support knowledge transfer and demonstrations/testing of the capability developed as a result of Phase II. The transition of a spectral mapping capability will help both military and commercial users with spectrum sharing and dynamic spectrum allocation amid the current proliferation of 5G, and future proliferation of 6G, communications and networking technologies. The envisioned coherency techniques for distributed sensing and means for disseminating consensus to enable dynamic spectrum allocation can provide many opportunities with military and commercial transition. REFERENCES: 1. Olivieri, M.P., Barnett, G., Lackpour, A., Davis, A., and Ngo, P. “A Scalable Dynamic Spectrum Allocation System with Interference Mitigation for Teams of Spectrally Agile Software Defined Radios,” First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005., 2005, pp. 170-179, doi: 10.1109/DYSPAN.2005.1542632. 2. Rajesh Babu, C., Ramana, K., Jeya, R., and Srinivasulu, A. “COCO: Coherent Consensus Scheme for Dynamic Spectrum Allocation for 5G.” Complexity and Robustness Trade-Off for Traditional and Deep Models 2022 (Special Issue). 6 Jul 2022. 3. Tran, C. et al., “Dynamic Spectrum Access: Architectures and Implications,” MILCOM 2008-2008 IEEE Military Communications Conference, 2008, pp. 1-7, doi:10.1109/MILCOM.2008.4753454. 4. Zhao, Q. and Sadler, B.M. “A Survey of Dynamic Spectrum Access,” in IEEE Signal Processing Magazine, vol. 24, no. 3, pp. 79-89, May 2007, doi:10.1109/MSP.2007.361604. KEYWORDS: Distributed Sensing; Network Mapping; Dynamic Spectrum Allocation; Spectrum Sharing; 5G; 6G; Wireless Communications; Distributed Consensus; Wireless Multi-hop Networks
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