Channel Gain Cartography via Mixture of Experts

Background and Objectives: We propose here joint semi-orthogonal user selection and antenna selection algorithm based on precoding scheme. Methods: The focus of this proposed algorithm is to increase the system sumrate and decrease the complexity. We select and schedule users from a large number of users based on semi-orthogonality condition among them. Here, we select only the maximum channel gain antennas to maximize the system sumrate. Subsequently, the user selection and antenna selection have been scheduled in an adequate manner in order to obtain maximum system sumrate. We calculate the system sumrate for two scenarios: firstly, by considering the interference and secondly without considering the interference. We achieve maximum system sumrate at MMSE and lowest at without precoding while considering the interference. However, when we do not consider the interference we obtain lowest sumrate at MMSE and maximum at without precoding. Results and Conclusion: Here, we apply the precoding scheme to increase the system sumrate and we obtain approximately 20% to 35% higher system sumrate compared to without precoding, when interference is considered. Thus, we achieve higher sumrate in our proposed algorithms compared to other existing work.

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Game-Based Resource Allocation Mechanism in B5G HetNets with Incomplete Information

Applied Sciences ◽

10.3390/app10051557 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1557

Author(s):

Weijia Feng ◽

Xiaohui Li

Keyword(s):

Resource Allocation ◽

Incomplete Information ◽

Stackelberg Game ◽

Superior Performance ◽

Channel Gain ◽

Mobile Users ◽

Pricing Strategies ◽

Game Equilibrium ◽

Allocation Process ◽

Realistic Situation

Ultra-dense and highly heterogeneous network (HetNet) deployments make the allocation of limited wireless resources among ubiquitous Internet of Things (IoT) devices an unprecedented challenge in 5G and beyond (B5G) networks. The interactions among mobile users and HetNets remain to be analyzed, where mobile users choose optimal networks to access and the HetNets adopt proper methods for allocating their own network resource. Existing works always need complete information among mobile users and HetNets. However, it is not practical in a realistic situation where important individual information is protected and will not be public to others. This paper proposes a distributed pricing and resource allocation scheme based on a Stackelberg game with incomplete information. The proposed model proves to be more practical by solving the problem that important information of either mobile users or HetNets is difficult to acquire during the resource allocation process. Considering the unknowability of channel gain information, the follower game among users is modeled as an incomplete information game, and channel gain is regarded as the type of each player. Given the pricing strategies of networks, users will adjust their bandwidth requesting strategies to maximize their expected utility. While based on the sub-equilibrium obtained in the follower game, networks will correspondingly update their pricing strategies to be optimal. The existence and uniqueness of Bayesian Nash equilibrium is proved. A probabilistic prediction method realizes the feasibility of the incomplete information game, and a reverse deduction method is utilized to obtain the game equilibrium. Simulation results show the superior performance of the proposed method.

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Secrecy Performance of AF/DF relaying in NOMA Systems using Average and Instantaneous Channel gain for users’ ranking

2020 International Symposium on Networks, Computers and Communications (ISNCC) ◽

10.1109/isncc49221.2020.9297172 ◽

2020 ◽

Author(s):

Nesrine Zaghdoud ◽

Adel Ben Mnaouer ◽

Wided Hadj Alouane ◽

Hatem Boujemaa ◽

Farid Touat

Keyword(s):

Channel Gain

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Why and how the brain weights contributions from a mixture of experts

Neuroscience & Biobehavioral Reviews ◽

10.1016/j.neubiorev.2020.10.022 ◽

2021 ◽

Vol 123 ◽

pp. 14-23

Author(s):

John P. O’Doherty ◽

Sang Wan Lee ◽

Reza Tadayonnejad ◽

Jeff Cockburn ◽

Kyo Iigaya ◽

...

Keyword(s):

Mixture Of Experts ◽

The Brain

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mvClaim : an R package for multivariate general insurance claims severity modelling

Annals of Actuarial Science ◽

10.1017/s1748499521000099 ◽

2021 ◽

pp. 1-17

Author(s):

Sen Hu ◽

T. Brendan Murphy ◽

Adrian O’Hagan

Keyword(s):

R Package ◽

Finite Mixture ◽

Current Version ◽

Mixture Of Experts ◽

Insurance Claim ◽

General Insurance ◽

Gamma Distributions ◽

Approach Theory ◽

Modelling Approach ◽

Model Family

Abstract The mvClaim package in R provides flexible modelling frameworks for multivariate insurance claim severity modelling. The current version of the package implements a parsimonious mixture of experts (MoE) model family with bivariate gamma distributions, as introduced in Hu et al., and a finite mixture of copula regressions within the MoE framework as in Hu & O’Hagan. This paper presents the modelling approach theory briefly and the usage of the models in the package in detail. This package is hosted on GitHub at https://github.com/senhu/.

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