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Feixiang Zhang

Bio: Feixiang Zhang is an academic researcher from Louisiana State University. The author has contributed to research in topics: Vickrey auction & Channel allocation schemes. The author has an hindex of 4, co-authored 10 publications receiving 29 citations. Previous affiliations of Feixiang Zhang include Southern Illinois University Carbondale.

Papers
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Proceedings ArticleDOI
01 Dec 2014
TL;DR: This paper proposes a method for multiple PUs to share with multiple SUs different idle channels in overlapped licensed areas to obtain quotas of evolutionary stable strategy (ESS) and best integer quotas that render payoffs close to each other.
Abstract: Spectrum sharing between primary users (PUs) and secondary users (SUs) can be realized using economic approaches. In this paper, we propose a method for multiple PUs to share with multiple SUs different idle channels in overlapped licensed areas. Due to the fluctuations of supply and demand in different areas, the SUs are grouped according to their suppliers and the PUs set channel transaction quotas for these SU groups. By applying evolutionary games, the PUs can obtain quotas of evolutionary stable strategy (ESS) and achieve their maximum payoffs theoretically. Furthermore, we design a learning process for the PUs to attain best integer quotas that are realizable. In our simulation, we obtain under two pricing schemes both ESS and best integer quotas that render payoffs close to each other.

9 citations

Journal ArticleDOI
TL;DR: A model where geographic information, including licensed areas of primary users (PUs) and locations of secondary users (SUs), plays an important role in the spectrum sharing system and the existence and uniqueness of the evolutionary stable strategy quota vector of each PU is proved.
Abstract: For a spectrum sharing system using economic approaches, conventional models without geographic considerations are oversimplified. In this paper, we develop a model where geographic information, including licensed areas of primary users (PUs) and locations of secondary users (SUs), plays an important role in the spectrum sharing system. We consider a multi-price policy and the pricing power of non-cooperative PUs in multiple geographic areas. Meanwhile, the value assessment of a channel is price-related and the demand from the SUs is price-elastic. To maximize the payoffs of the PUs, we propose a unique quota transaction process. By applying an evolutionary procedure defined as replicator dynamics, we prove the existence and uniqueness of the evolutionary stable strategy quota vector of each PU, which leads to the optimal payoff for each PU selling channels without reserve. In the scenario of selling channels with reserve, we predict the channel prices for the PUs leading to the optimal supplies of the PUs and hence the optimal payoffs. Furthermore, we introduce a grouping mechanism to simplify the process. In our simulation, the effectiveness of the learning processes designed for the two scenarios is verified and our spectrum sharing scheme is shown efficient in utilizing the frequency resources.

8 citations

Journal ArticleDOI
TL;DR: This work proves that truthful bidding is the optimal strategy for the SUs even though they do not participate in the VCG auction for MIGs directly, and approximate and simplify the optimal channel allocation with a greedy algorithm, Dijkstra's algorithm, and batch allocation.
Abstract: Spatial spectrum reuse significantly enhances spectrum utilization but requires delicate design to avoid cochannel interference. Instead of focusing solely on spectrum efficiency, we consider maximizing social welfare via channel allocation. Specifically, we propose an on-demand receiver-centric mechanism with a new factor, the supply and demand relationship. The optimal channel allocation that maximizes social welfare can be achieved by the constrained Vickrey–Clarke–Groves (VCG) auction for secondary users (SUs), but with high complexity. To simplify the constrained VCG auction, we group the SUs into maximal independent groups (MIGs) using a modified Bron–Kerbosch algorithm. We prove that truthful bidding is the optimal strategy for the SUs even though they do not participate in the VCG auction for MIGs directly. Therefore, the MIGs are bidding truthfully and social welfare is maximized. We also prove that the optimal channel allocation is not unique and thus a weakly-dominant strategy for the primary user, and the VCG style pricing based on truthful bidding can be implemented by using a decision tree repeatedly. Furthermore, we approximate and simplify the optimal channel allocation with a greedy algorithm, Dijkstra's algorithm, and batch allocation. In our simulation, we compare the proposed methods and demonstrate that on-demand channel allocation increases social welfare.

7 citations

Proceedings ArticleDOI
01 Dec 2017
TL;DR: A spectrum reuse mechanism for non-symmetric networks, in which the optimal channel allocation that maximizes social welfare is the result of an appropriate bidding method of secondary users in the constrained Vickrey-Clarke-Groves (VCG) auction, is designed.
Abstract: Spatial spectrum reuse significantly enhances spectrum utilization but requires delicate design to avoid co-channel interference. Instead of focusing solely on spectrum efficiency, we consider maximizing social welfare via on-demand channel allocation in this paper. We design a spectrum reuse mechanism for non-symmetric networks, in which the optimal channel allocation that maximizes social welfare is the result of an appropriate bidding method of secondary users (SUs) in the constrained Vickrey-Clarke-Groves (VCG) auction. To simplify the constrained VCG auction, we group the SUs into interference-free maximal independent groups (MIGs) using a modified Bron-Kerbosch algorithm. We introduce the VCG auction for MIGs, in which truthful bidding is the optimal strategy for the MIGs. We build a decision process such that the MIGs as representatives of the SUs can update their channel evaluations in each step and submit truthful bids. Furthermore, we approximate and simplify the optimal channel allocation with a greedy algorithm and Dijkstra's algorithm. In our simulation, we compare the proposed methods and demonstrate that our on- demand channel allocation increases social welfare.

6 citations

Journal ArticleDOI
TL;DR: This paper introduces a Vickrey–Clarke–Groves (VCG) auction, in which the participants are limited to the allowable user crowds, and designs a channel transaction mechanism for non-symmetric networks and maximize user satisfaction in consideration of multi-level flexible channel valuations of the SUs.
Abstract: Spatial spectrum reuse enables better utilization of limited spectral resources to achieve higher system throughput. However, improving the system throughput or spectrum efficiency does not necessarily translate to the satisfaction of more secondary users (SUs) according to their demands. To improve user satisfaction, user characteristics involving the supply and demand relationship need to be considered and thus enable heterogeneous channel valuations in spatial spectrum reuse. In this paper, we design a channel transaction mechanism for non-symmetric networks and maximize user satisfaction in consideration of multi-level flexible channel valuations of the SUs. Specifically, we introduce a Vickrey–Clarke–Groves (VCG) auction, in which the participants are limited to the allowable user crowds. To facilitate the bid formation, we transform the constrained VCG auction to a step-by-step decision process. Meanwhile, the SUs in a coalition play a coalitional game with transferable utilities. We use the Shapley value to realize fair payoff distribution among the SUs in a coalition. Furthermore, we approach the optimal channel allocation via finding the longest path in a directed acyclic graph, a greedy algorithm, and batch allocation. In our simulation, we compare the low-complexity algorithms and demonstrate the efficiency of the channel transaction mechanism.

6 citations


Cited by
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Journal ArticleDOI
TL;DR: The SpecPSO is proposed for optimizing handovers using supervised machine learning technique for performing dynamic handover by adapting to the environment and make smart decisions compared to the traditional cooperative spectrum sensing (CSS) techniques.
Abstract: Cognitive communication model perform the investigation and surveillance of spectrum in cognitive radio networks abetment in advertent primary users (PUs) and in turn help in allocation of transmission space for secondary users (SUs). In effective performance of regulation of wireless channel handover strategy in cognitive computing systems, new computing models are desired in operating set of tasks to process business model, and interact naturally with humans or machine rather being programmed. Cognitive wireless network are trained via artificial intelligence (AI) and machine learning (ML) algorithms for dynamic processing of spectrum handovers. They assist human experts in making enhanced decisions by penetrating into the complexity of the handovers. This paper focuses on learning and reasoning features of cognitive radio (CR) by analyzing primary user (PU) and secondary user (SU) data communication using home location register (HLR) and visitor location register (VLR) database respectively. The SpecPSO is proposed for optimizing handovers using supervised machine learning technique for performing dynamic handover by adapting to the environment and make smart decisions compared to the traditional cooperative spectrum sensing (CSS) techniques.

287 citations

01 Jan 2009

157 citations

Posted Content
TL;DR: The state-of-the-art of relevant techniques are described, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum and energy-efficient communications in dynamic wireless environments.
Abstract: The ability to intelligently utilize resources to meet the need of growing diversity in services and user behavior marks the future of wireless communication systems. Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. The perception capability and reconfigurability are the essential features of cognitive radio while modern machine learning techniques project great potential in system adaptation. In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication systems. We describe the state-of-the-art of relevant techniques, covering spectrum sensing and access approaches and powerful machine learning algorithms that enable spectrum- and energy-efficient communications in dynamic wireless environments. We also present practical applications of these techniques and identify further research challenges in cognitive radio and machine learning as applied to the existing and future wireless communication systems.

56 citations

Book ChapterDOI
01 Jan 2019
TL;DR: It is found that cooperative spectrum sensing is not only advantageous but is also essential to avoid interference with any primary network users, and a dynamic technique called CUSUM algorithm is devised.
Abstract: Cognitive radio systems require the absorption of cooperative spectrum sensing among cognitive network users to increase the reliability of detection. We have found that cooperative spectrum sensing is not only advantageous but is also essential to avoid interference with any primary network users. Interference by licensed users becomes a chief concern and issue, which affects primary as well as secondary users leading to restrictions in spectrum sensing in cognitive radios. Cognitive radio spectrum sensing ability to identify and make use of vacant spaces in the spectrum without causing any interference to the primary user is elaborately studied. An overview about spectrum sensing is given, and an effective system model based on conventional and cooperative sensing model is proposed. It is reviewed along with various cooperative handover factors, and a dynamic technique called CUSUM algorithm is devised. The efficiency of the proposed cooperative CUSUM spectrum sensing algorithm performs better than existing optimal rules based on a single observation spectrum sensing techniques under cooperative networks.

46 citations

Journal ArticleDOI
TL;DR: A global crowdfunding platform called BitFund is proposed based on the need to an effective crowdfunding platform for developing smart nation and the inherent features of blockchain technology, which yields better results as compared to other generic algorithms for crowdfunding.

38 citations