In this letter, a multiuser cooperative network with multiple relays is introduced, and two decode-and-forward (DF) cooperation schemes are proposed aiming at outage-optimal and fair user scheduling, respectively. The outage probability and asymptotic expressions of symbol error probability (SEP) are derived to evaluate these two schemes. Analysis and simulations show that both schemes can achieve full diversity order, which is the combination of cooperative diversity and multiuser diversity.

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Selected mapping (SLM) is a promising distortionless technique for controlling the high peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) systems. In SLM, phase sequence selection plays an important role for efficient PAPR reduction. Although some phase sequence sets have been proposed in past studies, we show that an optimal selection is still desired for the phase sequences from the same set. Therefore, this letter develops a measureable phase sequence selection criterion to optimally select the phase sequences from both the same and different sets, so as to achieve near optimum PAPR reduction performance in SLM-OFDM systems.

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In recent years, techniques such as multiple input multiple output (MIMO) and orthogonal frequency division multiplexing (OFDM) have been developed and combined in MIMO-OFDM systems to provide higher data rates. In addition, the system can be optimized by setting modulation and coding adaptively according to the channel conditions. The overall system performance depends on how accurately the system obtains the channel state information (CSI) and feeds it back to the transmitter. In this paper, we propose a signal-to-noise-ratio (SNR) estimation algorithm in which the preamble is known by both sides of the transceiver. Through simulations of several channel environments, we prove that our proposed algorithm is more accurate than traditional algorithms.

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We propose optimal power allocation schemes for a secondary cognitive user sharing spectrum with a primary user under different interference power constraints in Rayleigh fading channels. Specifically, we consider a practical scenario in which the secondary user has a fixed transmission rate and the instantaneous channel state of the interference channel is not available to the secondary user. Simulation results verify the feasibility of the proposed schemes and evaluate the effective transmission rate loss due to the incomplete channel state information.

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Vehicle routing is an important combinatorial optimization problem. In real transport networks,the travel speed and travel time of roads have large time-variability and randomness. The study of vehicle routing problem in time-dependent network has even more practical value than static network VRP problem. This paper combines the features of time-dependent networks and gives the mathematical models of the time-dependent vehicle routing problem. On this basis, the traditional ant colony optimization algorithm is improved. A new path transfer strategy of ants and new dynamic pheromone update strategy applicable to time-dependent network are proposed. Based on these strategies, the improved ant colony algorithm is given for solving the vehicle routing problem in time-dependent networks. The simulation results show that the algorithm can effectively solve the vehicle routing problem in time-dependent network and has better computational efficiency and convergence speed.

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The high-speed downlink packet access (HSPDA) system is expected to effectively support voice services when Release 7/8 features are adopted in the system. In this letter, we point out that a voice packet can be transmitted at a lower power level than normal in the HSDPA network. We propose a power allocation scheme that adjusts the transmission power of voice packets optimally and allocates the remaining transmission power to data services in order to improve data throughput. The proposed power allocation is analyzed with a Rayleigh fading channel model and compared to conventional fixed power allocation.

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In this letter, a new terrain type classifier is proposed for polarimetric Synthetic Aperture Radar (Pol-SAR) images. This classifier uses the binary tree structure. The homogenous and inhomogeneous areas are first classified by the support vector machine (SVM) classifier based on the texture features extracted from the span image. Then the homogenous and inhomogeneous areas are, respectively, classified by the traditional Wishart classifier and the SVM classifier based on the texture features. Using a NASA/JPL AIRSAR image, the authors achieve the classification accuracy of up to 98%, demonstrating the effectiveness of the proposed method.

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Based on the reiterative maximum signal minus interference level (MSMIL) criterion and adaptive beamforming, a novel interference suppression algorithm is proposed for shared-spectrum multistatic radar that must contend with clutter. In this algorithm, two-dimensional adaptive beamformers are designed for azimuths and range cells. Numerical results show advantages of the proposed method.

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