A Chip-Level BSOR-Based Linear GSIC Multiuser Detector for Long-Code CDMA Systems

We introduce a chip-level linear group-wise successive interference cancellation (GSIC) multiuser structure that is asymptotically equivalent to block successive over-relaxation (BSOR) iteration, which is known to outperform the conventional block Gauss-Seidel iteration by an order of magnitude in terms of convergence speed. The main advantage of the proposed scheme is that it uses directly the spreading codes instead of the cross-correlation matrix and thus does not require the calculation of the cross-correlation matrix (requires 2NK2 floating point operations (flops), where N is the processing gain and K is the number of users) which reduces significantly the overall computational complexity. Thus it is suitable for long-code CDMA systems such as IS-95 and UMTS where the cross-correlation matrix is changing every symbol. We study the convergence behavior of the proposed scheme using two approaches and prove that it converges to the decorrelator detector if the over-relaxation factor is in the interval ]0, 2[. Simulation results are in excellent agreement with theory

A Chip-Level BSOR-Based Linear GSIC Multiuser Detector for Long-Code CDMA Systems

We introduce a chip-level linear group-wise successive interference cancellation (GSIC) multiuser structure that is asymptotically equivalent to block successive over-relaxation (BSOR) iteration, which is known to outperform the conventional block Gauss-Seidel iteration by an order of magnitude in terms of convergence speed. The main advantage of the proposed scheme is that it uses directly the spreading codes instead of the cross-correlation matrix and thus does not require the calculation of the cross-correlation matrix (requires floating point operations (flops), where is the processing gain and is the number of users) which reduces significantly the overall computational complexity. Thus it is suitable for long-code CDMA systems such as IS-95 and UMTS where the cross-correlation matrix is changing every symbol. We study the convergence behavior of the proposed scheme using two approaches and prove that it converges to the decorrelator detector if the over-relaxation factor is in the interval ]0, 2[. Simulation results are in excellent agreement with theory.</p

Auto-nahl: A neural network approach for condition-based maintenance of complex industrial systems

Nowadays, machine learning has emerged as a promising alternative for condition monitoring of industrial processes, making it indispensable for maintenance planning. Such a learning model is able to assess health states in real time provided that both training and testing samples are complete and have the same probability distribution. However, it is rare and difficult in practical applications to meet these requirements due to the continuous change in working conditions. Besides, conventional hyperparameters tuning via grid search or manual tuning requires a lot of human intervention and becomes inflexible for users. Two objectives are targeted in this work. In an attempt to remedy the data distribution mismatch issue, we firstly introduce a feature extraction and selection approach built upon correlation analysis and dimensionality reduction. Secondly, to diminish human intervention burdens, we propose an Automatic artificial Neural network with an Augmented Hidden Layer (Auto-NAHL) for the classification of health states. Within the designed network, it is worthy to mention that the novelty of the implemented neural architecture is attributed to the new multiple feature mappings of the inputs, where such configuration allows the hidden layer to learn multiple representations from several random linear mappings and produce a single final efficient representation. Hyperparameters tuning including the network architecture, is fully automated by incorporating Particle Swarm Optimization (PSO) technique. The designed learning process is evaluated on a complex industrial plant as well as various classification problems. Based on the obtained results, it can be claimed that our proposal yields better response to new hidden representations by obtaining a higher approximation compared to some previous works.Scopu