Partial transmit sequence (PTS) is an effective method to reduce the peak‐to‐average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) system, but it is not suitable for the filter bank multi‐carrier with offset quadrature amplitude modulation (FBMC/OQAM) system because of the overlapping structure of FBMC/OQAM signals. And PTS needs the traversal method to search for the optimal solution, which will increase the complexity of the system. To overcome this shortcoming, a PTS based on discrete particle swarm optimization with threshold (PTS‐DPSO‐TH) is proposed and applied to FBMC/OQAM system in this paper. PTS‐DPSO‐TH uses discrete particle swarm optimization (DPSO) to search for the optimal solution, effectively reducing the excessively high PAPR of the FBMC/OQAM system and avoiding the increase of system complexity. Secondly, the threshold is introduced to decrease the number of iterations and further reduce the system's complexity on the premise of guaranteeing the performance of PAPR reduction. Furthermore, through simulation, the effects of multiple parameters on PAPR reduction performance of PTS‐DPSO‐TH are compared and analysed.

Tropospheric scatter is a promising method for over‐the‐horizon propagation. Transmission loss caused by the three mainstream troposcatter mechanisms is analysed, namely turbulent incoherent scattering theory, coherent reflection by stable layers theory, and incoherent reflection by irregular layers theory. Then an experiment is conducted to explore the relationships among the three mechanisms. Based on this experiment, the troposcatter transmission loss prediction model is established in different climate zones by a particle swarm optimisation algorithm and experimental data from the global troposcatter databank. The simulation shows that this model is more effective than the existing International Telecommunication Union‐Radiocommunication Sector (ITU‐R) P.617, P.452, and P.2001. Furthermore, by analysing the training parameters' proportion of the new model in different climate zones, the specific composition of three troposcatter mechanisms can be obtained.

In this paper, we investigate the resource block (RB) allocation problem in cellular vehicle-to-everything (C-V2X) networks mode 3, where the cellular networks schedule the RBs for direct vehicular communications. First, we establish the communication model and introduce the effective capacity and queuing theory to describe the reliability of vehicle-to-vehicle (V2V) links. Then, we introduce the α-fair function and formulate the joint power control and RB allocation problem considering the allocation fairness and the different quality-of-service (QoS) requirements for vehicle-to-infrastructure (V2I) and V2V links. Our objective is to maximize the sum capacity of all V2I links with the α-fair function while guaranteeing the allocation fairness among V2I links and the transmission reliability for each V2V pair. To achieve this objective, we propose a novel matching game theory algorithm based on the exchanged preference profiles between the two participant sets, i.e., V2V and V2I links. Simulation results show that our proposed algorithm is adaptive to the dynamic vehicular network and achieves better efficiency and fairness trade-offs, outperforming the classic allocation method.

We provide a novel nonlinear frequency domain equalization algorithm for the frequency domain equalization of an SC-FDE system by improving the classical iterative block decision feedback equalization (IBDFE) algorithm and applying δ estimation to the improved algorithm. The improvement of the IBDFE algorithm is carried out by replacing the ZF equalization in the feedback branch with the MMSE equalization and eliminating the iteration of the correlation factor, thus reducing the noise error and the computational complexity of the original algorithm. δ estimation can estimate residual inter-symbol interference in the signal after MMSE equalization and reject it, thus further improving the equalization accuracy. The simulation results show that the performance of the novel algorithm is better than that of the IBDFE algorithm with similar complexity, or the complexity of the novel algorithm is lower than that of the IBDFE algorithm with similar performance.

No abstract