Pipelined functional link artificial recurrent neural network with the decision feedback structure for nonlinear channel equalization

Application of Bat Algorithm and Its Modified Form Trained with ANN in Channel Equalization

The transmission of high-speed data over communication channels is the function of digital communication systems. Due to linear and nonlinear distortions, data transmitted through this process is distorted. In a communication system, the channel is the medium through which signals are transmitted. The useful signal received at the receiver becomes corrupted because it is associated with noise, ISI, CCI, etc. The equalizers function at the front end of the receiver to eliminate these factors, and they are designed to make them work efficiently with proper network topology and parameters. In the case of highly dispersive and nonlinear channels, it is well known that neural network-based equalizers are more effective than linear equalizers, which use finite impulse response filters. An alternative approach to training neural network-based equalizers is to use metaheuristic algorithms. Here, in this work, to develop the symmetry-based efficient channel equalization in wireless communication, this paper proposes a modified form of bat algorithm trained with ANN for channel equalization. It adopts a population-based and local search algorithm to exploit the advantages of bats’ echolocation. The foremost initiative is to boost the flexibility of both the variants of the proposed algorithm and the utilization of proper weight, topology, and the transfer function of ANN in channel equalization. To evaluate the equalizer’s performance, MSE and BER can be calculated by considering popular nonlinear channels and adding nonlinearities. Experimental and statistical analyses show that, in comparison with the bat as well as variants of the bat and state-of-the-art algorithms, the proposed algorithm substantially outperforms them significantly, based on MSE and BER.

An Optimal Three-Dimensional Drone Layout Method for Maximum Signal Coverage and Minimum Interference in Complex Pipeline Networks

In daily pipeline inspection, it is significant to ensure good network communication and security. With the development of drone technology, it is possible to apply drones as air routers to collect information from pipeline networks and transmit it to pipeline inspectors. It is also crucial to achieve optimal drone deployment in pipeline networks. This article proposes a two-phase evolution optimal 3-D drone layout algorithm to deploy drones in pipeline networks. First, a 3-D pipeline graph model is designed to represent the possible projection position of drones, and the objective function is proposed for optimal drone deployment. Then, in the first phase, based on the features of the 3-D pipeline graph, the drone flight rules and constraint conditions are presented to calculate the number of drones and the initial layout sequence. In the second phase, according to the objective function and the above results, every drone is continuously moved in a small area to achieve a tradeoff between signal coverage and interference. Moreover, the key parameters of the objective function can be discussed to further optimize drone deployment. Simulation results are presented to illustrate the effectiveness and advantages of the proposed algorithm.