To improve the radio spectral efficiency for 5G and beyond, novel radio access techniques need to be designed to accommodate unprecedented number of connected devices, and non-orthogonal multiple access (NOMA) has become a promising candidate. Additionally, power allocation and NOMA-secondary user(SU) assignment technology is an efficient way to enhance the resource utilization efficiency at the power domain and the spectral domain for underlaying cognitive NOMA networks. In this paper, firstly, a joint power allocation and SU assignment problem is formulated for NOMA downlink transmission in an underlaying cognitive radio network. The worst-case achievable rate for the NOMA-SU is maximized. To solve this mixedinteger non-linear programming (MINLP) problem, we divide the original optimization problem into two sub-problems: NOMA-SU assignment and power allocation. Next, a heuristic algorithm is adopted to solve the NOMA-SU assignment subproblem, and successive convex approximation (SCA) based method is utilized to design a suboptimal power allocation algorithm. Furthermore, an alternative joint NOMA-SU assignment and power allocation scheme is proposed with its average computational complexity analysis given. Finally, numerical results show that the total throughput for the proposed algorithm outperforms more than 30 percent compared with an existing benchmark scheme at least.

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In this paper, we will study the recursive density estimators of the probability density function for widely orthant dependent WOD random variables. The complete consistency and complete convergence rate are established under some general conditions.

Consider the following nonparametric model: Y ni =g(x ni )+ε ni , 1 ⩽ i ⩽ n, where x n i ∈ A $ x_{ni}\in \mathbb{A} $ are the nonrandom design points and A $ \mathbb{A} $ is a compact set of ℝ m for some m=1, g (·) is a real valued function defined on A $ \mathbb{A} $ , and ε n1, ⋅, ε nn are zero mean φ-mixing random errors with finite moment of 2+δ order for some δ>0. Under some general conditions, we obtain the uniformly asymptotic normality of the weighted estimator of g (·). The rate of Berry-Esseen bound can approximate O(n −1/4) under some appropriate conditions. The validity of the main results is partially illustrated via a numerical simulation.

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