Search

Conventional non-orthogonal multiple access (NOMA)-based spatial modulation (SM) systems utilize only the real part of amplitude-phase modulation (APM) symbols for transmission, but quadrature spatial modulation (QSM) can extend this to two orthogonal components to improve the spectral efficiency. In this way, we propose a NOMA-based QSM system in multiple-input multiple-output scenarios, where APM symbols are divided into real and imaginary components for transmission and then these two components are sent by real and imaginary transmit antennas, respectively. To further improve the performance of our proposed system, we also propose a user grouping and a power allocation with relatively low complexity. Different from previous schemes, the user grouping scheme is designed for considering channel conditions, user locations, and channel correlations jointly. Moreover, the power allocation scheme is performed in each group to meet users' quality-of-service requirements, i.e., target-rate requirements. Simulation results demonstrate the effectiveness of proposed schemes by comparing with existing ones.

To improve energy efficiency and reduce the interference to macro users (MUs), this paper investigates the robust resource allocation (RA) problem for maximizing the interference efficiency of users (i.e., total rate/sum interference) in heterogeneous networks. Under perfect channel state information (CSI), the considered RA problem is formulated as a multivariate nonlinear programming problem with constraints on the maximum interference power of MUs, the minimum rate requirement of each femto user, and the maximum transmit power of the femto base station. The original fractional programming problem is converted into a convex optimization problem by using Dinkelbach's method, the logarithmic transformation method, and the successive convex approximation method, where the closed-form solution is obtained with the Lagrangian dual approach. Moreover, with imperfect CSI consideration, the original problem is reformulated as a robust RA problem that is transformed into a deterministic and convex optimization problem by using an inequality transformation approach. In addition, the computational complexity and the cost of robustness are also analyzed. The simulation results verify that the proposed algorithm has better interference efficiency and robustness by comparing with the existing algorithms.

This paper proposes a method for gas leakage early warning system based on Kalman filter and back-propagation (BP) neural network to address the issue of inaccurate gas leakage detection and incapability of predicting concentration change of gas. First, Kalman filter is adopted to filter the noise from the gas concentration that is measured by a sensor. Then, predictions about the change of gas concentration are made using the BP neural network that is optimized by genetic algorithm. Next, the gas leakage early warning system, based on the proposed method, is designed. Last, to verify the effectiveness of the method proposed by simulation, methane, the main component of gas is chosen as an example. Also introduced in this paper are the determinant coefficient, mean absolute error, correlation coefficient and root-mean-square error-the four evaluation indicators methods to demonstrate the effectiveness and feasibility of the algorithm this paper proposed by comparing with Support Vector Machine (SVM), Long Short-term Memory (LSTM) and general Back Propagation Neural Network (BPNN). The best validation performance of BP neural network through simulation experiments and is 0.013518, and the probability of the relative error between the predicted value and the actual value within 10% is 0.7692. The proposed method can effectively improve the accuracy of gas concentration prediction as comparison results show, and it has advantage in fitting degree and error fluctuations.