MIMO Beam-Forming with Neural Network Channel Prediction Trained by a Novel PSO-EA-DEPSO Algorithm

A new hybrid algorithm based on particle swarm optimization (PSO), evolutionary algorithm (EA), and differential evolution (DE) is presented for training a recurrent neural network (RNN) for multiple-input multiple-output (MIMO) channel prediction. The hybrid algorithm is shown to be superior in performance to PSO and differential evolution PSO (DEPSO) for different channel environments. The received signal-to-noise ratio is derived for un-coded and beam-forming MIMO systems to see how the RNN error affects the performance. This error is shown to deteriorate the accuracy of the weak singular modes, making beam-forming more desirable. Bit error rate simulations are performed to validate these results

Multiple-input multiple-output wireless communications with imperfect channel knowledge

In the first work a recurrent neural network (RNN) is employed for MIMO channel prediction. A novel PSO-EA-DEPSO off-line training algorithm is presented and is shown to outperform PSO, PSO-EA, and DEPSO. This predictor is shown to be robust to varying channel scenarios. New expressions for the received SNR, array gain, average probability of error, and diversity gain are derived. Next, a new expression for the outage capacity of a MIMO system with no CSI at the transmitter and an estimate at the receiver is presented. Since the outage capacity is a function of the first and second moments of the mutual information, new closed form approximations are derived at low and high effective SNR. Also at low effective SNR a new result for the outage capacity is presented. Finally, the outage capacity for a frequency selective channel is derived. This is followed by a MIMO RNN predictor that operates online. A single RNN is constructed to predict all of the MIMO sub-channels instantaneously. The extended Kalman filter (EKF) and real-time recurrent learning (RTRL) algorithms are applied to compare the MSE of the prediction error. A new expression for the channel estimation error of a continuously varying MIMO channel is derived next. The optimal amount of time to send training pilots is investigated for different channel scenarios. Special cases of the new expression for the channel estimation error lead to previously established results. The last work investigates the performance of a MIMO aeronautical system in a two- ray ground reflection scenario. The ergodic capacity is analyzed when the altitude, horizontal displacement, antenna separation, and aircraft velocity are varied --Abstract, page iv

Learning Nonlinear Functions with MLPs and SRNs

In this paper, nonlinear functions generated by randomly initialized multilayer perceptrons (MLPs) and simultaneous recurrent neural networks (SRNs) are learned by MLPs and SRNs. Training SRNs is a challenging task and a new learning algorithm - DEPSO is introduced. DEPSO is a standard particle swarm optimization (PSO) algorithm with the addition of a differential evolution step to aid in swarm convergence. The results from DEPSO are compared with the standard backpropagation (BP) and PSO algorithms. It is further verified that functions generated by SRNs are harder to learn than those generated by MLPs but DEPSO provides better learning capabilities for the functions generated by MLPs and SRNs as compared to BP and PSO. These three algorithms are also trained on several benchmark functions to confirm results

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

A reduced-uncertainty hybrid evolutionary algorithm for solving dynamic shortest-path routing problem

The need for effective packet transmission to deliver advanced performance in wireless networks creates the need to find shortest network paths efficiently and quickly. This paper addresses a Reduced Uncertainty Based Hybrid Evolutionary Algorithm (RUBHEA) to solve Dynamic Shortest Path Routing Problem (DSPRP) effectively and rapidly. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are integrated as a hybrid algorithm to find the best solution within the search space of dynamically changing networks. Both GA and PSO share context of individuals to reduce uncertainty in RUBHEA. Various regions of search space are explored and learned by RUBHEA. By employing a modified priority encoding method, each individual in both GA and PSO are represented as a potential solution for DSPRP. A Complete statistical analysis has been performed to compare the performance of RUBHEA with various state-of-the-art algorithms. It shows that RUBHEA is considerably superior (reducing the failure rate by up to 50%) to similar approaches with increasing number of nodes encountered in the networks