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Articles by Yuan Wu
Total Records ( 5 ) for Yuan Wu
  Kang Wang , Zhi-Jiang Xu , Yuan Wu and Jing-Yu Hua
  As a non-Gaussian model, the alpha stable distribution has gained much attention because of its generality to model the heavy-tail and impulsive noise which is widely observed in many communication channels. Unfortunately, there exists no analytic expression for the Probability Density Function (PDF) of Symmetric Alpha Stable (SαS) distribution. In order to approximate the PDF of SαS, we propose a bi-region curve approximation algorithm with the bi-region separated by the triple divergence. Specially, within the triple divergence, we propose a penalty function with two special parameters and adopt a very simple and effective exponential function for approximation. Different from the existing algorithms using the series expansion, our model avoids the problem of selecting the number of the series items and the risk of series expansion divergence. Compared with the conventional Cauchy-Gaussian mixture approximation, our derivation emploits the simple bi-region approximation and yields a very simple and closed-form expression. Numerical results verify that our approximation is very close to the actual PDF of SαS.
  Qionghua Zhu , Yuan Wu , Peng Hong , Liping Qian , Jingyu Hua , Zhijiang Xu and Limin Meng
  This study investigates the joint sensing assignment and resource allocations for Cognitive Radio (CR) systems where multiple Secondary Users (SUs) opportunistically share the channels of Primary System (PS) through cooperative sensing. The key questions concerned here are how to assign different SUs to sense different channels and how to allocate the transmission power and bandwidth for SUs such that the detected idle channels of PS are fully utilized. Specifically, the cooperative spectrum sensing improves the detection accuracy and reduces the potential interference to PS due to mis-detection. However, the cooperative spectrum sensing and the consequent reports of sensing results consume additional energy which should be taken account of in the SU resource allocations. Hence, the coupling effect between the spectrum sensing and the consequent resource allocations is critical for CR to optimize its performance. Based on these considerations, the problem of joint sensing assignment and resource allocations for CR is formulated as a mixed binary nonlinear programming problem and a two-layer procedure is proposed to obtain the optimal solutions. The extensive results show that due to the energy overhead for sensing and reporting, the optimal number of SUs assigned to sense the PU channel does not increase in the idle probability of the channel monotonically. Meanwhile, to maximize the SUs throughput, the consequent optimal transmission power and bandwidth allocations heavily depend on the sensing accuracy determined by the optimal sensing assignment.
  Zhi-Jiang Xu , Shui-Qin Chu , Yuan Wu and Li-Min Meng
  TFRC (TCP (Transmission Control Protocol) Friendly Rate Control) has been widely used in wired networks for its enhanced friendliness and fairness. However, TFRC cannot distinguish between packet losses due to network congestion and those due to wireless link error in wireless networks. Thus, in this study an improved TFRC scheme that is able to differentiate between congestion losses and wireless link error losses for wired/wireless hybrid network is proposed. Specifically, the improved TFRC scheme utilizes the information of the one-way delay to regulate the transmission rate at the sender and simulation experimental results show this strategy is effective and performs much better than the traditional TFRC.
  Wankun Kuang , Jingyu Hua , Chengfeng Ruan , Zheng Gao and Yuan Wu
  The minimax optimization is widely used in wireless communications to design the equiripple lowpass filter, such as the Linear Programming (LP) method. However, the conventional LP method suffered from its large computation loads. Hence, this study investigates an iterative LP method, in which constraints are iteratively thrown on the non-uniformly distributed frequency grid to reduce the problem scale as much as possible, resulting in much lower computations. Moreover, since the non-uniform frequency grid allows us to precisely control the ripple, the proposed method also yields a better equiripple result compared to the conventional LP method and the Particle Swarm Optimization (PSO) algorithm.
  Xiaojie Sun , Yuan Wu , Xiao Liang , Yi Yang and Limin Meng
  Future smart grid has been conceived to be able to improve efficiency and stability of the grid operations. Based on the smart meter and advanced mechanism of two-way communications, Energy-Users (EUs) are able to receive real-time signalling (e.g., the electricity price) from the grid and schedule their energy consumption to optimize their objectives of interest correspondingly. Besides the conventional fuel-based energy supply, renewable energy supplies, e.g., solar and wind power, are expected to play important roles in smart grid. Despite their advantages in lowering the electricity-provisioning cost and being environment-friendly, renewable supplies usually suffer from uncontrollable and volatile generations, which result in great fluctuations in their provisioning. Therefore, it is indispensable for EUs equipped with renewable energy suppliers to take a careful tradeoff between exploiting the benefit from the renewable energy and controlling the adversary impact due to its volatility. Based on this motivation, this study aims at jointly optimizing the EU's average energy-acquisition cost as well as its fluctuation. This problem is formulated as a nonconvex optimization problem and this study proposes an efficient Layered Particle Swarm Optimization (L-PSO) algorithm to determine the EU’s optimal scheduling of energy consumptions. Our numerical results show how EU can trade off between benefiting from the renewable supplies and suffering from the associated fluctuation through tuning the weighting-factors.
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