This paper investigates the non-data-aided (NDA) carrier frequency estimation of amplitude and phase shift keying (APSK) signals. The true Cramer-Rao bound (CRB) for NDA frequency estimation of APSK signals are derived and evaluated numerically. Characteristic and jitter variance of NDA Luise and Reggiannini (L&R) frequency estimator are analyzed. Verified by Monte Carlo simulations, the analytical results are shown to be accurate for medium-to-high signal-to-noise ratio (SNR) values. Using the proposed closed-form expression, parameters of the algorithm are optimized efficiently to minimize the jitter variance.

In cognitive radar systems (CRSs), target scattering coefficients (TSC) can be utilized to improve the performance of target identification and classification. This work considers the problem of TSC estimation for temporally correlated target. Multiple receive antennas are adopted to receive the echo waveforms, which are interfered by the signal-dependent clutter. Unlike existing estimation methods in time domain, a novel estimation method based on Kalman filtering (KF) is proposed in frequency domain to exploit the temporal TSC correlation, and reduce the complexity of subsequent waveform optimization. Additionally, to minimize the mean square error of estimated TSC at each KF iteration, in contrary to existing works, we directly model the design process as an optimization problem, which is non-convex and cannot be solved efficiently. Therefore, we propose a novel method, similar in some way to semi-definite programming (SDP), to convert the non-convex problem into a convex one. Simulation results demonstrate that the estimation performance can be significantly improved by the KF estimation with optimized waveform.

A low complexity log-likelihood ratio (LLR) calculation for high-order amplitude phase shift keying (APSK) signals is proposed. Using proper constellation partitioning together with a look-up table, the number of terms for the comparison of Euclidean distances can be significantly reduced. Compared with the log-sum LLR approximation, the proposed method reduces the computational complexity by more than 65% and 75% for 16-APSK and 32-APSK signals, respectively, with very small bit error rate performance degradation.

In this letter, a micro-expression recognition method is investigated by integrating both spatio-temporal facial features and a regression model. To this end, we first perform a multi-scale facial region division for each facial image and then extract a set of local binary patterns on three orthogonal planes (LBP-TOP) features corresponding to divided facial regions of the micro-expression videos. Furthermore, we use GSLSR model to build the linear regression relationship between the LBP-TOP facial feature vectors and the micro expressions label vectors. Finally, the learned GSLSR model is applied to the prediction of the micro-expression categories for each test micro-expression video. Experiments are conducted on both CASME II and SMIC micro-expression databases to evaluate the performance of the proposed method, and the results demonstrate that the proposed method is better than the baseline micro-expression recognition method.

Aiming to ease the parallel programming for heterogeneous architectures, we propose and implement a high-level OpenCL runtime that conceptually merges multiple heterogeneous hardware devices into one virtual heterogeneous compute device (VHCD). Moreover, automated workload distribution among the devices is based on offline profiling, together with new programming directives that define the device-independent data access range per work-group. Therefore, an OpenCL program originally written for a single compute device can, after inserting a small number of programming directives, run efficiently on a platform consisting of heterogeneous compute devices. Performance is ensured by introducing the technique of virtual cache management, which minimizes the amount of host-device data transfer. Our new OpenCL runtime is evaluated by a diverse set of OpenCL benchmarks, demonstrating good performance on various configurations of a heterogeneous system.

To overcome the shortcomings of conventional cellular positioning, a novel cooperative location algorithm that uses the available peer-to-peer communication between the mobile terminals (MTs) is proposed. The main idea behind the proposed approach is to incorporate the long- and short-range location information to improve the estimation of the MT's coordinates. Since short-range communications among MTs are characterized by high line-of-sight (LOS) probability, an improved spring-model-based cooperative location method can be exploited to provide low-cost improvement for cellular-based location in the non-line-of-sight (NLOS) environments.

In this letter, the sparse recovery algorithm orthogonal matching pursuit (OMP) and subspace pursuit (SP) are applied for MIMO OFDM channel estimation. A new algorithm named SOMP is proposed, which combines the advantage of OMP and SP. Simulation results based on 3GPP spatial channel model (SCM) demonstrate that SOMP performs better than OMP and SP in terms of normalized mean square error (NMSE).

This paper investigates the performance of an integrated voice and data transmission protocol that can be used in mobile communication networks, e.g. mobile cellular and LEOGEO satellite networks. Voice and data are concentrated at place such as base station in a cellular network. Time sensitive voices are supported by the concentrator in the manner of TDMA. Loss sensitive data are collected, stored, and transmitted using idle TDMA slots. Data users transmit data to the concentration point using ALOHA protocol. Characterization of data arrivals to the concentrator is done by the method of moment matching. The emphasis of this study is on the data performance in terms of packet loss rate, average buffer occupancy, and mean packet waiting time. It is demonstrated through numerical examples that a buffer of reasonable size is good enough to offer satisfactory performance. The analysis is also validated by computer simulations.

This paper presents a study of the applicability of clusters of GPUs to high-resolution 3D simulations of cardiac electrophysiology. By experimenting with representative cardiac cell models and ODE solvers, in association with solving the monodomain equation, we quantitatively analyze the obtainable computational capacity of GPU clusters. It is found that for a 501×501×101 3D mesh, which entails a 0.1mm spatial resolution, a 128-GPU cluster only needs a few minutes to carry out a 100,000-time-step cardiac excitation simulation that involves a four-variable cell model. Even higher spatial and temporal resolutions are achievable for such simplified mathematical models. On the other hand, our experiments also show that a dramatically larger cluster of GPUs is needed to handle a very detailed cardiac cell model.

In next generation mobile multimedia communications, different wireless access networks are expected to cooperate. However, it is a challenging task to choose an optimal transmission path in this scenario. This paper focuses on the problem of selecting the optimal access network for multicast services in the cooperative mobile and broadcasting networks. An algorithm is proposed, which considers multiple decision factors and multiple optimization objectives. An analytic hierarchy process (AHP) method is applied to schedule the service queue and an artificial neural network (ANN) is used to improve the flexibility of the algorithm. Simulation results show that by applying the AHP method, a group of weight ratios can be obtained to improve the performance of multiple objectives. And ANN method is effective to adaptively adjust weight ratios when users' new waiting threshold is generated.

The extended binary phase shift keying (EBPSK) transmission system with ultra narrow bandwidth has excellent BER performance, which raises many doubts with the researchers. Therefore, on the premise of the existence of a special filter that can transform the modulated phase information into amplitude information, the theoretical BER formula of EBPSK system in Additive White Gaussian Noise (AWGN) channel has been deduced. This paper gives the theoretical values of the parameters in the above BER formula and discusses the effects of parameters on BER firstly. Then the paper shows that the special impacting filter satisfies the above assumption, therefore, in the frame of binary detection theory, the excellent performance of high-efficiency EBPSK system can be explained and the correction of the theoretical BER formula can be validated.

This paper studies the performance of code-aided (CA) soft-information based carrier phase recovery, which iteratively exploits the extrinsic information from channel decoder to improve the accuracy of phase synchronization. To tackle the problem of strong coupling between phase recovery and decoding, a semi-analytical model is proposed to express the distribution of extrinsic information as a function of phase offset. Piecewise approximation of the hyperbolic tangent function is employed to linearize the expression of soft symbol decision. Building on this model, open-loop characteristic and closed-loop performance of CA iterative soft decision-directed (ISDD) carrier phase synchronizer are derived in closed-form. Monte Carlo simulation results corroborate that the proposed expressions are able to characterize the performance of CA ISDD carrier phase recovery for systems with different channel codes.

A novel method is proposed to track the position of MS in the mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions in cellular network. A first-order markov model is employed to describe the dynamic transition of LOS/NLOS conditions, which is hidden in the measurement data. This method firstly uses modified EKF banks to jointly estimate both mobile state (position and velocity) and the hidden sight state based on the the data collected by a single BS. A Bayesian data fusion algorithm is then applied to achieve a high estimation accuracy. Simulation results show that the location errors of the proposed method are all significantly smaller than that of the FCC requirement in different LOS/NLOS conditions. In addition, the method is robust in the parameter mismodeling test. Complexity experiments suggest that it supports real-time application. Moreover, this algorithm is flexible enough to support different types of measurement methods and asynchronous or synchronous observations data, which is especially suitable for the future cooperative location systems.

In 1983, Chaum first introduced the concept of blind signature. In 2003, Hwang, Lee and Lai pointed out that the Chaum scheme cannot meet the untraceability property of the blind signature scheme. This letter will demonstrate that Hwang et al.'s claim is incorrect and the Chaum blind signature scheme still keeps the untraceability property.

In this letter, we apply recently proposed compressive projection principal component analysis (CPPCA) for MIMO channel feedback. A novel scheme with compressed feedback and efficient reconstruction is presented. Simulation results based on 3GPP spatial channel model (SCM) demonstrate the scheme is beneficial for large-scale MIMO systems.