Dynamic Contract Incentives Mechanism for Traffic Offloading in Multi-UAV Networks

Abstract

Traffic offloading is considered to be a promising technology in the Unmanned Aerial Vehicles- (UAVs-) assisted cellular networks. Due to their selfishness property, UAVs may be reluctant to take part in traffic offloading without any incentive. Moreover, considering the dynamic position of UAVs and the dynamic condition of the transmission channel, it is challenging to design a long-term effective incentive mechanism for multi-UAV networks. In this work, the dynamic contract incentive approach is studied to attract UAVs to participate in traffic offloading effectively. The two-stage contract incentive method is introduced under the information symmetric scenario and the information asymmetric scenario. Considering the sufficient conditions and necessary conditions in the contract design, a sequence optimization algorithm is investigated to acquire the maximum expected utility of the base station. The simulation experiment shows that the designed two-stage dynamic contract improves the performance of traffic offloading effectively.

Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems

“…The presented AM-MISG algorithm is also numerically effective in the sense of different innovation length and noise variance. The proposed approaches in the article can combine other mathematical tools and statistical strategies 61,62 to study the parameter estimation algorithms of other linear stochastic systems and nonlinear stochastic systems with different structures and disturbance noises 63,64,65,66 and can be applied to literatures 67,68,69,70,71 such as article-making systems and chemical engineering systems.…”

Three‐stage least squares‐based iterative estimation algorithms for bilinear state‐space systems based on the bilinear state estimator

“…In the future work, we will further investigate whether these algorithms can be applied to systems with missing data. The iterative algorithm in this paper is proposed for bilinear stochastic systems but the idea can be extended to other linear and nonlinear stochastic systems with colored noises [66][67][68][69][70][71][72][73][74][75] and can be applied to other literatures [76][77][78][79][80][81][82][83] such as signal modeling, pattern cognition, information processing, and engineering application systems.…”

Recursive coupled projection algorithms for multivariable output‐error‐like systems with coloured noises

“…(2) Collect the input-output data u t = [u 1 t , u 2 t , …, u r t ] T and y t = [y 1 t , y 2 t , …, y m t ] T and construct φ t using (81) (3) Form ψ s t and ψ n t using (84) and (85) construct ψ t using (87), then read ψ i t from ψ t in (83), i = 1, 2, 3, …, m (4) Compute the step-size μ 1 t using (77), and update the estimates β 1 t and θ^1 t using (75) and (76). (5) For i = 2, 3, …, m, compute the step-size μ i t using (80) and update the estimates β i t and θ^i t using (78) and (79).…”