Learning methods for temporal-spatial opportunistic spectrum access in cognitive radio networks

As a natural resource, the radio spectrum is usually regulated by government agencies and static spectrum allocation policies are widely adopted by most countries. With the increasing popularity of mobile devices and need for high speed data transmission, static spectrum allocation policies can no l...

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Main Author: Zhang, Yi
Other Authors: Tay Wee Peng
Format: Theses and Dissertations
Language:English
Published: 2017
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Online Access:http://hdl.handle.net/10356/69589
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-695892023-07-04T17:12:43Z Learning methods for temporal-spatial opportunistic spectrum access in cognitive radio networks Zhang, Yi Tay Wee Peng School of Electrical and Electronic Engineering University of Technology of Troyes DRNTU::Engineering::Electrical and electronic engineering As a natural resource, the radio spectrum is usually regulated by government agencies and static spectrum allocation policies are widely adopted by most countries. With the increasing popularity of mobile devices and need for high speed data transmission, static spectrum allocation policies can no longer satisfy all demands for spectrum. In cognitive radio networks (CRNs), opportunistic spectrum access (OSA) alleviates the spectrum under-utilization problem by allowing unlicensed secondary users (SUs) to identify and exploit the unused spectrum owned by primary users (PUs) temporally and spatially while limiting the interference to PUs below a predefined threshold. Designing effective methods for temporal-spatial OSA is thus crucial for improving spectrum utilization nowadays. We first consider the problem of estimating the no-talk region of the PU for temporal-spatial OSA, i.e., the region outside which SUs may utilize the PU's spectrum opportunistically regardless of whether the PU is transmitting or not. Based on a distributed learning framework, we propose a distributed boundary estimation algorithm that allows SUs to determine the boundary of the no-talk region collaboratively through message passing between SUs. We analyze the trade-offs between estimation error, communication cost, setup complexity, throughput and robustness. Simulation results suggest that our proposed algorithm have lower estimation errors and better robustness compared to various other methods. Within the no-talk region of the PU, SUs who do not interfere with each other can make use of the same PU channel. We then formulate and study a multi-user multi-armed bandit (MAB) problem that exploits the temporal-spatial OSA of PU channels for these SUs located inside the region. We first propose a centralized channel allocation policy for finding an optimal channel allocation and learning the statistics of the channels. We show that this policy is order-optimal with logarithmic regret, but requires solving a NP-complete optimization problem at exponentially increasing time intervals. To overcome the high computation complexity at the central processor, we also propose heuristic distributed learning policies that however have linear regrets. We compare the performance of our proposed policies with other distributed policies recently proposed for temporal (but not spatial) OSA. Simulation results suggest that our policies perform significantly better in terms of average regret than the benchmark policies. Finally, we also propose three collaborative channel learning policies for temporal-spatial OSA, which embed collaboration in the channel statistics learning process. We identify spectrum access opportunities via information exchange among neighboring SUs by applying consensus algorithms on their channel sensing observations, empirical estimates of channel idle probabilities and estimated channel ranks. We compare the performance of these policies with a distributed channel allocation policy. Simulation results suggest that our proposed collaborative policies outperform the distributed access rank learning policy which does not consider collaborations in the learning process. Doctor of Philosophy (EEE) 2017-02-27T04:42:35Z 2017-02-27T04:42:35Z 2017 Thesis Zhang, Y. (2017). Learning methods for temporal-spatial opportunistic spectrum access in cognitive radio networks. Doctoral thesis, Nanyang Technological University, Singapore. http://hdl.handle.net/10356/69589 10.32657/10356/69589 en 154 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Zhang, Yi
Learning methods for temporal-spatial opportunistic spectrum access in cognitive radio networks
description As a natural resource, the radio spectrum is usually regulated by government agencies and static spectrum allocation policies are widely adopted by most countries. With the increasing popularity of mobile devices and need for high speed data transmission, static spectrum allocation policies can no longer satisfy all demands for spectrum. In cognitive radio networks (CRNs), opportunistic spectrum access (OSA) alleviates the spectrum under-utilization problem by allowing unlicensed secondary users (SUs) to identify and exploit the unused spectrum owned by primary users (PUs) temporally and spatially while limiting the interference to PUs below a predefined threshold. Designing effective methods for temporal-spatial OSA is thus crucial for improving spectrum utilization nowadays. We first consider the problem of estimating the no-talk region of the PU for temporal-spatial OSA, i.e., the region outside which SUs may utilize the PU's spectrum opportunistically regardless of whether the PU is transmitting or not. Based on a distributed learning framework, we propose a distributed boundary estimation algorithm that allows SUs to determine the boundary of the no-talk region collaboratively through message passing between SUs. We analyze the trade-offs between estimation error, communication cost, setup complexity, throughput and robustness. Simulation results suggest that our proposed algorithm have lower estimation errors and better robustness compared to various other methods. Within the no-talk region of the PU, SUs who do not interfere with each other can make use of the same PU channel. We then formulate and study a multi-user multi-armed bandit (MAB) problem that exploits the temporal-spatial OSA of PU channels for these SUs located inside the region. We first propose a centralized channel allocation policy for finding an optimal channel allocation and learning the statistics of the channels. We show that this policy is order-optimal with logarithmic regret, but requires solving a NP-complete optimization problem at exponentially increasing time intervals. To overcome the high computation complexity at the central processor, we also propose heuristic distributed learning policies that however have linear regrets. We compare the performance of our proposed policies with other distributed policies recently proposed for temporal (but not spatial) OSA. Simulation results suggest that our policies perform significantly better in terms of average regret than the benchmark policies. Finally, we also propose three collaborative channel learning policies for temporal-spatial OSA, which embed collaboration in the channel statistics learning process. We identify spectrum access opportunities via information exchange among neighboring SUs by applying consensus algorithms on their channel sensing observations, empirical estimates of channel idle probabilities and estimated channel ranks. We compare the performance of these policies with a distributed channel allocation policy. Simulation results suggest that our proposed collaborative policies outperform the distributed access rank learning policy which does not consider collaborations in the learning process.
author2 Tay Wee Peng
author_facet Tay Wee Peng
Zhang, Yi
format Theses and Dissertations
author Zhang, Yi
author_sort Zhang, Yi
title Learning methods for temporal-spatial opportunistic spectrum access in cognitive radio networks
title_short Learning methods for temporal-spatial opportunistic spectrum access in cognitive radio networks
title_full Learning methods for temporal-spatial opportunistic spectrum access in cognitive radio networks
title_fullStr Learning methods for temporal-spatial opportunistic spectrum access in cognitive radio networks
title_full_unstemmed Learning methods for temporal-spatial opportunistic spectrum access in cognitive radio networks
title_sort learning methods for temporal-spatial opportunistic spectrum access in cognitive radio networks
publishDate 2017
url http://hdl.handle.net/10356/69589
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