TY - GEN
T1 - Learning-based Cognitive Radio Access via Randomized Point-Based Approximate POMDPs
AU - Keshavamurthy, Bharath
AU - Michelusi, Nicolo
N1 - Funding Information:
Extensions to this work have been submitted to IEEE TCCN [1]. Part of this research has been funded by NSF under grant CNS-1642982. B. Keshavamurthy is with ECE, Purdue University, West Lafayette, IN. N. Michelusi is with the School of ECEE, Arizona State University, AZ. Email: bkeshava@purdue.edu, nicolo.michelusi@asu.edu
Publisher Copyright:
© 2021 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - In this paper, a novel spectrum sensing and access strategy based on approximate Partially Observable Markov Decision Processes (POMDPs) is proposed, wherein a cognitive radio learns the time-frequency correlation model defining the occupancy behavior of incumbents, via the Baum-Welch algorithm, and concurrently devises an optimal strategy to perform spectrum sensing and access that exploits this learned correlation model. To ameliorate the complexity of the POMDP optimization, the PERSEUS algorithm, a randomized point-based value iteration method, is designed, with fragmentation and Hamming distance state filters. Evaluating the cognitive radio throughput against incumbent interference, we demonstrate that, with sensing restrictions, our framework achieves a 6% performance gain over that attained by a maximum a-posteriori (MAP) state estimator with prior model knowledge, and outperforms correlation-coefficient based clustering algorithms by an average of 60%; additionally, it surpasses a Neyman-Pearson Detector that assumes independence among channels with no sensing restrictions, by an average of 25%. Furthermore, unlike state-of-the-art algorithms, the proposed design facilitates the regulation of the trade-off between cognitive radio throughput and incumbent interference via a penalty parameter in the underlying MDP.
AB - In this paper, a novel spectrum sensing and access strategy based on approximate Partially Observable Markov Decision Processes (POMDPs) is proposed, wherein a cognitive radio learns the time-frequency correlation model defining the occupancy behavior of incumbents, via the Baum-Welch algorithm, and concurrently devises an optimal strategy to perform spectrum sensing and access that exploits this learned correlation model. To ameliorate the complexity of the POMDP optimization, the PERSEUS algorithm, a randomized point-based value iteration method, is designed, with fragmentation and Hamming distance state filters. Evaluating the cognitive radio throughput against incumbent interference, we demonstrate that, with sensing restrictions, our framework achieves a 6% performance gain over that attained by a maximum a-posteriori (MAP) state estimator with prior model knowledge, and outperforms correlation-coefficient based clustering algorithms by an average of 60%; additionally, it surpasses a Neyman-Pearson Detector that assumes independence among channels with no sensing restrictions, by an average of 25%. Furthermore, unlike state-of-the-art algorithms, the proposed design facilitates the regulation of the trade-off between cognitive radio throughput and incumbent interference via a penalty parameter in the underlying MDP.
KW - Cognitive Radio
KW - HMM
KW - POMDP
UR - http://www.scopus.com/inward/record.url?scp=85115708888&partnerID=8YFLogxK
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U2 - 10.1109/ICC42927.2021.9500752
DO - 10.1109/ICC42927.2021.9500752
M3 - Conference contribution
AN - SCOPUS:85115708888
T3 - IEEE International Conference on Communications
BT - ICC 2021 - IEEE International Conference on Communications, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Communications, ICC 2021
Y2 - 14 June 2021 through 23 June 2021
ER -