Bayesian networks are a powerful approach for representation and reasoning under conditions of uncertainty. Of the many good algorithms for learning Bayesian networks from data, the bio-inspired search algorithm is one of the most effective. In this paper, we propose a hybrid mutual information-modified binary particle swarm optimization (MI-MBPSO) algorithm. This technique first constructs a network based on MI to improve the quality of the initial population, and then uses the decomposability of the scoring function to modify the BPSO algorithm. Experimental results show that, the proposed hybrid algorithm outperforms various other state-of-the-art structure learning algorithms.

The traffic load of cellular networks varies in both time and spatial domains, causing many base stations (BS) to be under-utilized. Assisted by cell zooming, dynamic BS sleep control is considered as an effective way to improve energy efficiency during low traffic hours. Therefore, how densely the BSs should be deployed with cell zooming and BS sleeping is an important issue. In this paper, we explore the energy-optimal cellular network planning problem with dynamic BS sleeping and cell zooming for the cases in which traffic is uniformly distributed in space but time-varying. To guarantee the quality of multi-class services, an approximation method based on Erlang formula is proposed. Extensive simulations under our predefined scenarios show that about half of energy consumption can be saved through dynamic BS sleeping and power control. Surprisingly, the energy-optimal BS density we obtained is larger than the one without considering BS sleeping. In other words, deploying more BSs may help to save energy if dynamic BS sleeping is executed.

Two adaptive energy detectors are proposed for cognitive radio systems to detect the primary users. Unlike the conventional energy detector (CED) where a decision is made after receiving all samples, our detectors make a decision with the sequential arrival of samples. Hence, the sample size of the proposed detectors is adaptive. Simulation results show that for a desired performance, the average sample size of the proposed detectors is much less than that of the CED. Therefore, they are more agile than the CED.

This paper presents a non-crossover and multi-mutation based genetic algorithm (NMGA) for the Flexible Job-shop Scheduling problem (FJSP) with the criterion to minimize the maximum completion time (makespan). Aiming at the characteristics of FJSP, three mutation operators based on operation sequence coding and machine assignment coding are proposed: flip, slide, and swap. Meanwhile, the NMGA framework, coding scheme, as well as the decoding algorithm are also specially designed for the FJSP. In the framework, recombination operator crossover is not included and a special selection strategy is employed. Computational results based on a set of representative benchmark problems were provided. The evidence indicates that the proposed algorithm is superior to several recently published genetic algorithms in terms of solution quality and convergence ability.

Channel estimation is one of the key technologies in mobile communications. Channel estimation is critical in providing high data rate services and to overcome fast fading in very high-speed mobile communications. This paper presents a novel channel estimation based on hybrid spreading of I and Q signals (CEHS). Simulation results show that it can effectively mitigate the influence of fast fading and enable to provide high data rates for very high speed mobile systems.

In order to reduce the convergence time in an iterative procedure, some gradient based preliminary processes are employed to eliminate outliers. The adaptive variable block size is also introduced to balance the accuracy and computational complexity. Moreover, the use of Canberra distance instead of Euclidean distance illustrates higher performance in measuring motion similarity.

In order to estimate fast time-varying channels exactly, the Inter-Carrier Interference (ICI) caused by time-varying fading channels in Orthogonal Frequency Division Multiplexing (OFDM) systems is analyzed based on the Basis Expansion Model (BEM). A channel estimation and ICI cancellation algorithm with low complexity is proposed. A special pilot sequence is designed to minimize the cost of computing the channel state information in the proposed algorithm. Based on the property of channel frequency impulse matrix, the ICI can be canceled iteratively in frequency domain. The complexity of the algorithm is analyzed theoretically. Through simulation, the algorithm is shown to be effective in estimating channel state information and in cancelling ICI.

A method is proposed for estimating code and carrier phase parameters of GNSS reflected signals in low SNR (signal-to-noise ratio) environments. Simulation results show that the multipath impact on code and carrier with 0.022 C/A chips delay can be estimated in 0 dB SNR in the condition of 46 MHz sampling rate.