Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/168432
Title: Reputation-based power allocation for NOMA cognitive radio networks
Authors: Li, Feng
Sun, Zhongming
Lam, Kwok-Yan
Zhang, Songbo
Sun, Lianzhong
Wang, Li
Keywords: Engineering::Computer science and engineering
Issue Date: 2023
Source: Li, F., Sun, Z., Lam, K., Zhang, S., Sun, L. & Wang, L. (2023). Reputation-based power allocation for NOMA cognitive radio networks. Wireless Networks, 29(1), 449-457. https://dx.doi.org/10.1007/s11276-022-03139-x
Journal: Wireless Networks 
Abstract: In this paper, a power optimization scheme based on user’s reputation in non-orthogonal multiple access (NOMA) Cognitive Radio Networks (CRN) is proposed. By combining NOMA and CRN, the spectrum utilization and network throughput can be further improved, in which secondary users can access the authorized spectrum without worrying about the co-channel interference. In NOMA systems, how to optimize the user power so as to realize the effective decoding in receivers and enhance the system capacity is a key issue. In this work, the concept of user reputation is introduced which denotes the spectrum sensing capability of a secondary user, depending on the ratio of the channel number sensed by the secondary user and the actual number of available channels provided by the primary systems. High user reputation means a precise spectrum sensing capability which leads to less channel collision and better network capacity. When the secondary users with qualified reputation level aim to access the idle channels, an optimal power allocation strategy is required to facilitate the decoding for the receivers in NOMA systems and maximize the overall system throughput. Due to the complexity of the objective functions achieved, the genetic algorithm, which has good performances in global searching is applied for ascertaining the final power solutions. Furthermore, numerical results are provided to evaluate the proposed method on system throughput, power level and access probability.
URI: https://hdl.handle.net/10356/168432
ISSN: 1022-0038
DOI: 10.1007/s11276-022-03139-x
Schools: School of Computer Science and Engineering 
Rights: © 2022 The Author(s), under exclusive licence to Springer Science Business Media, LLC, part of Springer Nature. All rights reserved. This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11276-022-03139-x.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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