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Optimization and Heuristics for Cognitive Radio Design
thesisposted on 12.10.2021, 15:05 by Bharath KeshavamurthyBharath Keshavamurthy
Cognitive Radio technologies have been touted to be instrumental in solving resource-allocation problems in resource-constrained radio environments. The adaptive computational intelligence of these radios facilitates the dynamic allocation of network resources--particularly, the spectrum, a scarce physical asset. In addition to consumer-driven innovation that is governing the wireless communication ecosystem, its associated infrastructure is being increasingly viewed by governments around the world as critical national security interests--the US Military instituted the DARPA Spectrum Collaboration Challenge which requires competitors to design intelligent radios that leverage optimization, A.I., and game-theoretic strategies in order to efficiently access the RF spectrum in an environment wherein every other competitor is vying for the same limited resources. In this work, we detail the design of our radio, i.e., the design choices made in each layer of the network protocol stack, strategies rigorously derived from convex optimization, the collaboration API, and heuristics tailor-made to tackle the unique scenarios emulated in this DARPA Grand Challenge. We present performance evaluations of key components of our radio in a variety of military and disaster-relief deployment scenarios that mimic similar real-world situations. Furthermore, specifically focusing on channel access in the MAC, we formulate the spectrum sensing and access problem as a POMDP; derive an optimal policy using approximate value iteration methods; prove that our strategy outperforms the state-of-the-art, and facilitates means to control the trade-off between secondary network throughput and incumbent interference; and evaluate this policy on an ad-hoc distributed wireless platform constituting ESP32 radios, in order to study its implementation feasibility.