In a wireless communication system, the nonlinearity of the power amplifier (PA) in the transmitter is a limiting factor of the system performance. To achieve high efficiency, the PA input signal is driven into the nonlinear region. Signals with large peak-to-power ratio (PAPR) suffer uneven distortion where large signals receive additional distortion. Orthogonal linear transformations, such as orthogonal frequency division multiplexing (OFDM) modulation, spread the nonlinear distortion evenly to each data symbol, thus improving the system performance. In this paper, we provide theoretical analysis on the benefit of orthogonal linear transform for a memoryless nonlinear communication system. We show that the multicarrier system based on orthogonal linear transform performs better than the single carrier system in the presence of nonlinearity. Simulation results validate the theoretical analysis.

This paper introduces an unsupervised method for motion pattern learning and abnormality detection from video surveillance. In the preprocessing steps, trajectories are segmented based on their locations, and the sub-trajectories are represented as codebooks. Under our framework, Hidden Markov Models (HMMs) are used to characterize the motion pattern feature of the trajectory groups. The state of trajectory is represented by a HMM and has a probability distribution over the possible output sub-trajectories. Bayesian Information Criterion (BIC) is introduced to measure the similarity between groups. Based on the pairwise similarity scores, an affinity matrix is constructed which indicates the distance between different trajectory groups. An Adaptable Dynamic Hierarchical Clustering (ADHC) tree is proposed to gradually merge the most similar groups and form the trajectory motion patterns, which implements a simpler and more tractable dynamical clustering procedure in updating the clustering results with lower time complexity and avoids the traditional overfitting problem. By using the HMM models generated for the obtained trajectory motion patterns, we may recognize motion patterns and detect anomalies by computing the likelihood of the given trajectory, where a maximum likelihood for HMM indicates a pattern, and a small one below a threshold suggests an anomaly. Experiments are performed on EIFPD trajectory datasets from a structureless scene, where pedestrians choose their walking paths randomly. The experimental results show that our method can accurately learn motion patterns and detect anomalies with better performance.