The selection cooperation is a basic and attractive scheme of cooperative diversity in the multiple relays scenario. Most previous schemes of selection cooperation consist only one relay-stage in which one relay is selected to retransmit, and the signal from the selected relay is not utilized by other relays. In this paper, we introduce a two relay-stage selection cooperation scheme. The performance can be improved by letting all other relays to utilize the signal from the first selected relay to make another selection and retransmission in the second relay-stage. We derive the closed-form expression of the outage probability of the proposed scheme in the high SNR regime. Both theoretical and numerical results suggest that the proposed scheme can reduce the outage probability compared with the traditional scheme with only one relay-stage. Furthermore, we demonstrate that more than two relay-stage can not further reduce the outage probability. We also study the dependence of the proposed scheme on stage lengths and topology, and analyze the increased overhead.

In this letter, we analyze the outage performance of decode-and-forward relay systems with imperfect MRC receiver at the destination. Unlike the conventional perfect MRC, the weight of each branch of the imperfect MRC receiver is only the conjugate of the channel impulse response, not being normalized by the noise variance. We derive an exact closed-form expression for the outage probability over dissimilar Nakagami-m fading channels. Various numerical examples confirm the proposed analysis.

In a periodic spectrum sensing framework where each frame consists of a sensing block and a data transmitting block, increasing sensing duration decreases the probabilities of both missed opportunity and interference with primary users, but increasing sensing duration also decreases the energy efficiency and the transmitting efficiency of the cognitive network. Therefore, the sensing duration to use is a trade-off between sensing performance and system efficiencies. The relationships between sensing duration and state transition probability are analyzed firstly, when the licensed channel stays in the idle and busy states respectively. Then a state transition probability based sensing duration optimization algorithm is proposed, which can dynamically optimize the sensing duration of each frame in the current idle/busy state by predicting each frame's state transition probability at the beginning of the current state. Analysis and simulation results reveal that the time-varying optimal sensing duration increases as the state transition probability increases and compared to the existing method, the proposed algorithm can use as little sensing duration in each frame as possible to satisfy the sensing performance constraints so as to maximize the energy and transmitting efficiencies of the cognitive networks.

We propose a simple but effective image segmentation method not based on thresholding but on a merging strategy by evaluating joint probability of gray levels on co-occurrence matrix. The effectiveness of the proposed method is shown through a segmentation experiment.

A novel approach for fast traffic classification for the high speed networks is proposed, which bases on the protocol behavior statistical features. The packet size and a new parameter named "Estimated Protocol Processing Time" are collected from the real data flows. Then a set of joint probability distributions is obtained to describe the protocol behaviors and classify the traffic. Comparing the parameters of an unknown flow with the pre-obtained joint distributions, we can judge which application protocol the unknown flow belongs to. Distinct from other methods based on traditional inter-arrival time, we use the "Estimated Protocol Processing Time" to reduce the location dependence and time dependence and obtain better results than traditional traffic classification method. Since there is no need for character string searching and parallel feature for hardware implementation with pipeline-mode data processing, the proposed approach can be easily deployed in the hardware for real-time classification in the high speed networks.

Based on the characteristics of the thresholds of two detection schemes employing locally optimum test statistics, a sequential detection design procedure is proposed and analyzed. The proposed sequential test, called the sequential locally optimum test (SLOT), inherently provides finite stopping time (terminates with probability one within the finite horizon), and thereby avoids undesirable forced termination. The performance of the SLOT is compared with that of the fixed sample-size test, sequential probability ratio test (SPRT), truncated SPRT, and 2-SPRT. It is observed that the SLOT requires smaller average sample numbers than other schemes at most values of the normalized signal amplitude while maintaining the error performance close to the SPRT.

In this letter, a dual-hop wireless communication network with opportunistic amplify and forward (O-AF) relay is investigated over independent and non-identically distributed Nakagami-m fading channels. Employing Maclaurin series expansion around zero to derive the approximate probability density function of the normalized instantaneous signal-to-noise ratio (SNR), the asymptotic symbol error rate (SER) and outage probability expressions are presented. Simulation results indicate that the derived expressions well match the results of Monte-Carlo simulations at medium and high SNR regions. By comparing the O-AF with all AF relaying analyzed previously, it can be concluded that the former has significantly better performance than the latter in many cases.

In this paper, we analyze the performance of the 21 Alamouti scheme suggested by Alamouti, composed of the transmit space-time code and the simple linear decoding processing, in perfectly time-varying and spatially correlated channels. We derive the closed-form probability density function (PDF) of output signal-to-noise ratio (SNR) and outage probability of the Alamouti scheme as a function of spatial correlation coefficient in the consideration of no correlation in time. We observe that the performance of the Alamouti scheme is severely degraded when the channels are time-varying and spatially correlated.

This article presents a unified analytical framework to evaluate the bit error probability (BEP) of M-QAM, R-QAM and M-PAM modulation schemes for different types of fading channels, modeled with Hoyt, Rice, Rayleigh, Nakagami and Log-normal distributions. The mathematical development is obtained for maximal-ratio combining multichannel reception and assumes independent fading paths. The new BEP expressions are written in terms of the integral of the moment generating funcion of the instantaneos signal-to-noise ratio. The advantage of this approach is that it can be applied to any type of fading, and the integrals, even though they do not provide exact expressions, can be numerically evaluated.

Kernel logistic regression (KLR) is a powerful and flexible classification algorithm, which possesses an ability to provide the confidence of class prediction. However, its training--typically carried out by (quasi-)Newton methods--is rather time-consuming. In this paper, we propose an alternative probabilistic classification algorithm called Least-Squares Probabilistic Classifier (LSPC). KLR models the class-posterior probability by the log-linear combination of kernel functions and its parameters are learned by (regularized) maximum likelihood. In contrast, LSPC employs the linear combination of kernel functions and its parameters are learned by regularized least-squares fitting of the true class-posterior probability. Thanks to this linear regularized least-squares formulation, the solution of LSPC can be computed analytically just by solving a regularized system of linear equations in a class-wise manner. Thus LSPC is computationally very efficient and numerically stable. Through experiments, we show that the computation time of LSPC is faster than that of KLR by two orders of magnitude, with comparable classification accuracy.

We propose a scheme of MultiCast Routing and Wavelength Assignment (MC-RWA) to establish light-tree for dynamic multicast session for the Wavelength Division Multiplex (WDM) network by choosing the wavelength that leads to a reduction in blocking probabilities by using a parameter Δ. Δ is defined as the overall reduction of connectivity of the nodes in the network caused by a wavelength assignment process when using a particular wavelength, and we assign wavelength resources to the multicast session by choosing the Δ which leads to smallest reduction in connectivity. Through computer simulation, we show that the proposed scheme has lower blocking probabilities when compared with minimum cost scheme under the condition that wavelength conversion is not allowed.

The asymptotic properties of node distribution and speed distribution in random direction model were analyzed, respectively, by the tools of geometric probability and palm calculus. The probability density function for node distribution in circular regions was obtained which indicated that mobile nodes tended to disperse as simulation advancing. The speed decay phenomenon was confirmed in this model. Moreover, the hypostasis of speed decay was proved to be the correlation between speed and duration within any movement period.

In this paper, we propose an exact analytical technique to evaluate the average capacity of a dual-hop OFDM relay system with decode-and-forward protocol in an independent and identical distribution (i.i.d.) Rayleigh fading channel. Four schemes, (no) matching "and" or "or" (no) power allocation, will be considered. First, the probability density function (pdf) for the end-to-end power channel gain for each scheme is described. Then, based on these pdf functions, we will give the expressions of the average capacity. Monte Carlo simulation results will be shown to confirm the analytical results for both the pdf functions and average capacities.

This paper presents a method to monitor information of a large-sized multicast group that can follow the group's dynamics in real-time while avoiding feedback implosion by using probabilistic polling. In particular, this paper improves the probabilistic-polling-based approach by deriving a reference mean value as the reference control value for the number of expected feedback from the properties of a binomial estimation model. As a result, our method adaptively changes its estimation parameters depending on the feedback from receivers in order to achieve a fast estimate time with high accuracy, while preventing the possible occurrence of feedback implosion. Our experimental implementation and evaluation on PlanetLab showed that the proposed method effectively controls the number of feedback and accurately estimates the size of a dynamic multicast group.

This paper shows experimental results of packet error rates (PERs) in wireless-LAN mounted printed circuit boards and gives a discussion on a mechanism of electromagnetic noise coupling that affects the PER. We utilized the amplitude probability distribution to investigate the noise coupling channel. We measured the magnetic near-field distribution to obtain information about noise sources. Based on measurement results, we also performed parallel plate resonance analysis to find out electromagnetic interference antennas. We confirmed that noise radiates from a power supply system of a digital circuit and its coupling to a receiving antenna causes an increase of the PER.

ICI (Inter-Cell Interference) mitigation schemes at the cell border are frequently dealt with as a special issue in 3GPP LTE (Long Term Evolution). However, few papers have analyzed the outage performance for the ICI mitigation schemes. In this paper, we propose a generalized cell planning scheme termed QBPA (Quality of Service based Band Power Allocation). Utilizing the QBPA scheme, we measure how much increase in channel capacity can be obtained through the flexible control of bandwidth and power in multi-cell forward-link environments. In addition, the feasible performance of the conventional schemes can be evaluated as long as those schemes are specific forms of the QBPA.

Free probability theory, which has become a main branch of random matrix theory, is a valuable tool for describing the asymptotic behavior of multiple systems, especially for large matrices. In this paper, using asymptotic free probability theory, a new cooperative scheme for spectrum sensing is proposed, which shows how the asymptotic free behavior of random matrices and the property of Wishart distribution can be used to assist spectrum sensing for cognitive radio. Simulations over Rayleigh fading and AWGN channels demonstrate the proposed scheme has better detection performance than the energy detection techniques and the Maximum-minimum eigenvalue (MME) scheme even for the case of a small sample of observations.

In this letter, the outage performance of multi-hop multiple-input multiple-output (MIMO) relaying systems is analyzed for spatially correlated Rayleigh fading channels. We focus on nonregenerative MIMO decouple-and-forward (DCF) relaying in orthogonal space-time block code (OSTBC) transmission and provide its outage probability given the assumption of ideal relay gain. The outage obtained here is shown a lower bound for using practical gains, which gets tight at high SNR. We conduct numerical studies to assess the impact of the spatial correlation between antennas on the outage probability.

In this paper, an efficient radio resource allocation scheme for OFDMA systems is proposed, which follows two steps to take care of real-time traffic characterized with multi-level delay constraints. Urgent packets, those with imminent deadlines, are released first in step 1. After that the remaining channel resources are managed in such a way that overall throughput is maximized at Step 2. In this work, 2-dimensional diversity over multiple sub-bands and multiple users are jointly considered. The proposed scheme is compared with existing schemes designed for real-time traffic such as Exponential Scheduling (EXP) scheme, Modified Largest Weighted Delay First (M-LWDF) scheme, and Round robin scheme in terms of the packet discard probability and throughput. Numerical results show that the proposed scheme performs much better than the aforementioned ones in terms of the packet discard probability, while slightly better in terms of throughput.

Dynamic Voltage Scaling (DVS) is a well-known low-power design technique, which adjusts the clock speed and supply voltage dynamically to reduce the energy consumption of real-time systems. Previous studies considered the probabilistic distribution of tasks' workloads to assist DVS in task scheduling. These studies use probability information for intra-task frequency scheduling but do not sufficiently explore the opportunities for the system workload to save more energy. This paper presents a novel DVS algorithm for periodic real-time tasks based on the analysis of the system workload to reduce its power consumption. This algorithm takes full advantage of the probabilistic distribution characteristics of the system workload under priority-driven scheduling such as Earliest-Deadline-First (EDF). Experimental results show that the proposed algorithm reduces processor idle time and spends more busy time in lower-power speeds. The measurement indicates that compared to the relative DVS algorithms, this algorithm saves energy by at least 30% while delivering statistical performance guarantees.