Those host-based intrusion detection models like VPStatic first construct a model of acceptable behaviors for each monitored program via static analysis, and then perform intrusion detection by comparing them with programs' runtime behaviors. These models usually share the highly desirable feature that they do not produce false alarms but face the conflicts between accuracy and efficiency. For instance, the high accuracy of the VPStatic model is at the cost of high space complexity. In this paper, we use a statically-constructed state transition table (STT), which records expected transitions among system calls as well as their stack states (return address lists), as a behavior model to perform context-sensitive intrusion detection. According to our analysis, our STT model improves the space efficiency of the VPStatic model without decreasing its high precision and time efficiency. Experiments show that for three test programs, memory uses of our STT models are all much less than half of the VPStatic models'. Thereby, we alleviate the conflicts between the accuracy and the efficiency.

In [1], an algorithm based on phase variations of received pilot symbols was proposed to estimate one of the most important channel parameters, maximum Doppler shift, fd. However, AWGN (Additive white gauss noise) will cause large estimation error in some cases. In order to analyze the influence of noise, we extended the phase probability density function (pdf) in [1] to the scenario with both fading and AWGN, then the estimation error is characterized in closed-form expression. By this error expression, we found that power control will affect the estimator of [1] and we proposed a modification method based on SNR estimation to obtain accurate Doppler shift estimation in moderate low SNRs (signal-to-noise ratio). Simulation results show high accuracy in wide range of velocities and SNRs.

Since the accuracy of channel prediction influences the performance of adaptive multiple-input-multiple-output (MIMO) systems in Rayleigh fading channels, we analyze the performance of adaptive V-BLAST systems with a minimum-mean-square-error (MMSE) channel predictor in this paper. Considering the channel prediction error, we obtain the closed-form expressions for the bit-error-rate (BER) and throughput of adaptive V-BLAST systems, and maximize system transmission rate by choosing optimum block length under BER constraint. The numerical results reveal the critical value of channel prediction error below which systems can match the transmission rate of the systems with perfect channel prediction.

In this letter, we propose two antenna grouping schemes for uplink Nx SC-FDMA MIMO systems, where the multiple component carriers can be divided into several groups which are handled by different antennas, thus the number of component carriers on each antenna will be reduced by the group method. As a result, the peak-to-average power ratio (PAPR) of each antenna has been reduced. To further enhance the performance, an interleaving method is proposed to achieve better diversity gain due to the channel varying in the spatial domain and the frequency domain during one turbo coded stream. Our simulation figures clearly demonstrate that in all examples, the proposed schemes are shown to be effective in improving the Block Error Rate (BLER) performance while reducing the PAPR.

In this letter, we first investigate the bias of Doppler shift estimator based on autocorrelation function (ACF). Then we derive a signal-to-noise ratio (SNR) independent condition for Doppler shift estimation and achieve this condition by a adaptive process. Moreover, we present theoretical analysis about the convergency of our adaptive Doppler shift estimator, and derive a close-form expression for its mean square error (MSE). We verify the proposed estimator by computer simulation, the results of which are in agreement with the analysis, i.e., the proposed method achieves a good SNR-independent performance in a wide range of velocities and SNRs.