Optimal linear precoding for opportunistic spectrum sharing under arbitrary input distributions assumption

Abstract

Cognitive radio network with multiple-input multiple-output is an effective method to improve not only spectrum efficiency, but also energy efficiency. In this article, a linear precoding matrix optimization algorithm, named gradient-aided mutual information optimization (GAMIO), is designed to maximize the secondary users' spectrum efficiency. Unlike the previous algorithms which were developed under a specific input assumption, the GAMIO algorithm can work without imposing any input assumption. Furthermore, a framework is also proposed to develop the energy-efficient algorithm which can work with arbitrary spectrum-efficient algorithm. In this way, an energy-efficient algorithm, which can work under arbitrary input assumption, be developed based on the GAMIO algorithm (EEGAMIO). Numerical results indicate that either the GAMIO algorithm or the EEGAMIO algorithm shows the best performance at the present time.

Linear Precoding Design for Mutual Information Maximization in Generalized Spatial Modulation With Finite Alphabet Inputs

“…Thus, we propose an extended ellipsoid algorithm that solves a complex-valued optimization problem while the original ellipsoid algorithm [14] only considers the real-valued optimization problem. The iteration of the proposed algorithm consists of two parts: the constraint iteration and the objective iteration.…”

“…In order to solve this problem, we transform the GSM system into a virtual MIMO system and use the gradient of the mutual information [12], [13]. Since the gradient method considering power constraints of all subchannels is needed in order to find the precoder for the GSM with finite alphabet inputs, an extended ellipsoid algorithm that is a gradient-aided mutual-information optimization (GAMIO)-based method [14] is developed as will be shown later.…”

The Nuclear Techniques and the Selection of Model Parameters in Big Data

“…(1) Construct a universal method, improved the original linear algorithm [7] to join the inner product, using the kernel function for nonlinear algorithm, increased the application areas of kernel function, and the kernel function in the field of multiple classification, also can explore the fast of the wholealgorithm.…”

An Unreliable Opportunistic Spectrum Sharing System with Sensitive Secondary Users

“…Much research about opportunistic spectrum sharing or dynamic spectrum access has been developed in the past a few years [3][4][5][6][7][8][9][10][11][12]. In [3], a multi-channel medium access control (MAC) protocol was developed to enable the interoperation of the primary system and the secondary system.…”

“…The optimal time allocation was derived to maximize the achievable capacity of the secondary system. In [10], a linear precoding matrix optimization algorithm, named gradient-aided mutual information optimization (GAMIO), was designed to maximize the secondary users' spectrum efficiency. A framework was also proposed to develop the energy-efficient algorithm which can work with the GAMIO algorithm.…”