基于低秩张量补全的多声道音频信号恢复方法

多声道音频信号在采集、压缩、传输过程中可能造成音频数据丢失,为了确保给听众带来更真实的听觉感受,该文提出一种基于低秩张量补全的音频丢失数据恢复方法。首先,把多声道音频信号表示为一个张量;其次,把张量补全作为一个凸优化问题建模,利用松弛技术和变量分离技术得到闭合的增强拉格朗日函数;最后,通过交替迭代方法求解得到恢复的音频张量。在不同数据丢失率的实验中,通过与线性预测、加权优化的CANDECOMP /PARAFAC分解方法进行对比分析,表明利用张量补全方法具有更高的音频信号恢复精度,隐藏参考和基准的多激励测试结果也显示低秩张量补全方法能够有效地恢复多声道音频的丢失数据,从而获得更好的听觉效果。     

Joint Optimization of Capacity and Flow Assignment in a Packet-Switched Communications Network

This paper presents a global optimal solution technique for the joint optimization of link capacity and flow assignment (CFA) in a packet-switched communications network,m-M/M/1queueing systems to model existing networks are considered. A continuous lower bound of the average packet delay is used in the formulation of the cost objective function for the capacity and flow assignment. The cost objective function thus formulated is shown to be convex with respect to the network multicommodity flow. This convexity ensures the global optimal solution to the CFA problem via the flow deviation (FD) method. Refinement of the CFA optimization techhique to allow optimal discretization of the continuous solutions to discrete solutions is presented based on dynamic programming and iterations of Flow Optimization and Capacity Optimization. Application examples of these optimization procedures are illustrated.     

Utility Optimization Based on MSE for Parallel Broadcast Channels: The Square Root Law

In order to design efficient online resource allocation algorithms, convexity of the underlying optimization problem is an important prerequisite. This paper covers two resource allocation problems: the sum-power constrained utility maximization and the sum-power minimization for minimum utility requirements for parallel broadcast channels. We derive a new class of utility functions for which both optimization problems can be transformed into convex representations and give necessary and sufficient conditions for the optimum solution of the original non-convex problems with regard to power. We thereby extend the known log-convexity class for which convex representations can be found and, by introducing the square-root criteria, present a straight forward test to check whether arbitrary utility functions belong to our class. For the new class of utility functions we present simple algorithms which operate in the non-convex domain, prove convergence to the global optimum and evaluate their performance by simulations. Besides, the paper reveals some insights on the general structure of the mean square error region and thereby disproves a former result.     

Efficient design method of cosine-modulated QMF banks satisfying PR property

In this paper the design problem of perfect-reconstruction cosine-modulated QMF banks has been formulated as a quadratic-constrained least-squares (QCLS) minimization problem in which all constrained matrices of the QCLS optimization problem are symmetric and positive definite. A cost function which is a convex function of desired prototype filter coefficients is constructed so that this kind of QCLS optimization problems can be efficiently solved. So a global minimizer of this problem can be easily obtained. Results of two design examples are presented to support the derivations and analyses.     

Formulas useful for the synthesis and optimization of general,uniform contradirectional couplers

Explicit formulas allowing the synthesis and optimization of general, uniform, contradirectional couplers are derived. The well-known method of compensating for different phase velocities of the coupler modes with shunting capacitors is also mathematically formulated in a stringent form so that a lossless coupler of the general class mentioned above can be made ideal at any chosen frequency. Previously, only specialized cases have been treated in closed mathematical form. Compensated and uncompensated microstrip coupler designs are compared under the influence of realistic losses. It is shown that a considerably better directivity is attainable with capacitive compensation, even over a frequency range as large as one octave. The value of the formulas is confirmed by application to previously published experimental results     

Low complexity robust linear transceiver design for MIMO interference channel

This paper focuses on the linear transceiver design for multiple input multiple output (MIMO) interference channel (IC), in which a bounded channel error model is assumed. Two optimization problems are formulated as minimizing maximum per-user mean square error (MSE) and sum MSE with the per-transmitter power constraint. Since these optimization problems are not jointly convex on their variable matrices, the transmitter and receiver can be optimized alternately respectively. For each matrix, an approximated approach is presented where the upper bound of constraint is derived so that it has less semidefinite, thus the problem can be viewed as second-order-cone programming (SOCP) and gets less computational complexity. Compared with the conventional S-procedure method, the proposed approach achieves similar performance, but reduces the complexity significantly, especially for the system with large scale number of antennas.     

基于统计信道信息的智能反射面辅助物理层广播算法设计

智能反射面(Intelligent Reflective Surface,IRS)作为一种非常有前景的新型无线中继技术,可以改变无线传输环境,有效提升无线系统的频谱和能量效率.提出了一种基于统计信道的IRS辅助物理层广播系统的传输方案,在仅已知统计信道时,联合设计基站波束成形和IRS反射相位,最大化最小用户的各态历经容量.目标函数非连续,IRS的模一约束导致对应的优化问题非凸,对此首先得到了近似问题目标函数的闭式表达式,随后将其解耦为两个子问题:子问题1对应基站波束成形设计,可直接求得闭式解;子问题2对应IRS反射相位设计,该问题依然非凸,采用基于软件无线电的方法获得了该问题的近似最优解.仿真结果表明,基于统计信道信息的传输方案在降低系统开销和算法复杂度的同时能够有效提升IRS辅助的物理层广播系统性能.     

Joint Optimization of Transmit Power-Time and Bit Energy Efficiency in CDMA Wireless Sensor Networks

In this paper, we address the problem of minimizing energy consumption in a CDMA-based wireless sensor network (WSN). A comprehensive energy consumption model is proposed, which accounts for both the transmit and circuit energies. Energy consumption is minimized by jointly optimizing the transmit power and transmission time for each active node in the network. The problem is formulated as a non-convex optimization. Numerical as well as closed-form approximate solutions are provided. For the numerical solution, we show that the formulation can be transformed into a convex geometric programming (GP), for which fast algorithms, such as interior point method, can be applied. For the closed-form solution, we prove that the joint power/time optimization can be decoupled into two sequential sub-problems: optimization of transmit power with transmission time serving as a parameter, and then optimization of the transmission time. We show that the first sub-problem is a linear program while the second one can be well approximated as a convex programming problem. Taking advantage of these analytical results, we further derive the per-bit energy efficiency. Our results are verified through numerical examples and simulations     

一种余弦调制正交镜象滤波器组的设计方法

本文将具有精确重建特性的余弦调制正交镜象滤波器的设计问题转化为一种带二次约束的最小二乘（ＱＣＬＳ）优化问题,其中所有的约束矩阵都是对称正定的,为了有效地求解该类ＱＣＬＳ优化问题,文中构造了一个代价函数,从而很容易地获得了全局最优解,最后给出的两个设计实例验证了本文方法的正确性和有效性。     

Subgradient-Based Neural Networks for Nonsmooth Convex Optimization Problems

This paper develops a neural network for solving the general nonsmooth convex optimization problems. The proposed neural network is modeled by a differential inclusion. Compared with the existing neural networks for solving nonsmooth convex optimization problems, this neural network has a wider domain for implementation. Under a suitable assumption on the constraint set, it is proved that for a given nonsmooth convex optimization problem and sufficiently large penalty parameters, any trajectory of the neural network can reach the feasible region in finite time and stays there thereafter. Moreover, we can prove that the trajectory of the neural network constructed by a differential inclusion and with arbitrarily given initial value, converges to the set consisting of the equilibrium points of the neural network, whose elements are all the optimal solutions of the primal constrained optimization problem. In particular, we give the condition that the equilibrium point set of the neural network coincides with the optimal solution set of the primal constrained optimization problem and the condition ensuring convergence to the optimal solution set in finite time. Furthermore, illustrative examples show the correctness of the results in this paper, and the good performance of the proposed neural network.      

Closed form solutions for water-filling problems in?optimization and?game frameworks

We study power control in optimization and game frameworks. In the optimization framework there is a single decision maker who assigns network resources and in the game framework users share the network resources according to Nash equilibrium. The solution of these problems is based on so-called water-filling technique, which in turn uses bisection method for solution of non-linear equations for Lagrange multipliers. Here we provide a closed form solution to the water-filling problem, which allows us to solve it in a finite number of operations. Also, we produce a closed form solution for the Nash equilibrium in symmetric Gaussian interference game with an arbitrary number of users. Even though the game is symmetric, there is an intrinsic hierarchical structure induced by the quantity of the resources available to the users. We use this hierarchical structure to perform a successive reduction of the game. In addition to its mathematical beauty, the explicit solution allows one to study limiting cases when the crosstalk coefficient is either small or large. We provide an alternative simple proof of the convergence of the Iterative Water Filling Algorithm. Furthermore, it turns out that the convergence of Iterative Water Filling Algorithm slows down when the crosstalk coefficient is large. Using the closed form solution, we can avoid this problem. Finally, we compare the non-cooperative approach with the cooperative approach and show that the non-cooperative approach results in a more fair resource distribution.     

Sensing Transmission Tradeoff Over Penalty for Miss Detection in Cognitive Radio Network

In this paper, a challenge issue ‘sensing and data transmission trade-off’ is addressed. Different from existing efforts, penalty is taken as a rule of regulation for spectrum sensing and access in order to possibly protect primary’s activity. Specifically, secondary user needs to pay rental fee to primary user for opportunistic access. However, penalty will be charged from secondary user as well if it has transmitted on the miss estimated spectrum, in which, an negative secondary raw utility will be generated when the penalty value is properly decided. Thus the scheme will further force secondary user to improve its sensing accuracy and select the best transmission strategy when compared with conventional sensing access model. Based on the scheme, in this research, the bounds for proper rental and penalty prices are derived. Further, within both proper prices region, an optimization problem for setting sensing duration and transmission power is formulated. Through investigation, convexity of the problem is hard to be obtained at global view. But thanks to the special property that the problem is local convex when fixing any one of targeted variables, a Lagrange method based iterative algorithm is presented to find solution within a decent convergence of no more than 10 iterations.     

Optimal aggregation factor and clustering under delay constraints in aggregate sequential group paging schemes

This paper considers several optimization problems of sequential paging with aggregation mechanism which has been shown to reduce significantly the paging cost of a wireless communication system. An important problem is to find the optimal aggregation factor subject to a constraint on the average paging delay. Another problem is, given a cost function that depends on both paging cost and paging delay, how to find the optimal aggregation factor to minimize that cost function. We have formulated and shown that these can be solved nicely due to the monotonicity and convexity of the average paging cost function and paging delay function. We demonstrate that the optimization problems of the aggregate factor and subnet clustering are not separable. This leads to joint optimization problems of aggregation factor and clustering that are investigated in this paper. The paper presents different algorithms to solve these joint optimization problems using the monotonicity in the aggregation factor and the number of clusters of the average paging cost and delay with the unconstrained optimal clustering and the structures of the constrained optimal clustering.     

A recurrent neural network for nonlinear convex optimization subject to nonlinear inequality constraints

This paper presents a novel recurrent neural network for solving nonlinear convex programming problems subject to nonlinear inequality constraints. Under the condition that the objective function is convex and all constraint functions are strictly convex or that the objective function is strictly convex and the constraint function is convex, the proposed neural network is proved to be stable in the sense of Lyapunov and globally convergent to an exact optimal solution. Compared with the existing neural networks for solving such nonlinear optimization problems, the proposed neural network has two major advantages. One is that it can solve convex programming problems with general convex inequality constraints. Another is that it does not require a Lipschitz condition on the objective function and constraint function. Simulation results are given to illustrate further the global convergence and performance of the proposed neural network for constrained nonlinear optimization.     

Optimal Resource Allocation Scheme in a Multirate Overlapped Optical CDMA System

This paper addresses the problem of resource allocation in a multiservice optical network based on an overlapped code-division-multiple-access system. A joint transmission power and overlapping coefficient (transmission rate) allocation strategy is provided via the solution of a constrained convex quadratic optimization problem. The solution of this problem maximizes the aggregate throughput subject to peak laser transmission power constraints. The optimization problem is solved in a closed form, and the resource allocation strategy is simple to implement in an optical network. Simulation results are presented, showing a total agreement between the derived analytical solution and the one obtained using a numerical search method. In addition, analytical and numerical results show that the proposed resource allocation strategy can offer substantial improvement in the system throughput.     

最小功耗的中继选择与功率分配的联合优化

研究采用放大转发协议的单向和双向中继网络中的中继选择与功率分配的联合优化策略。该策略以一定服务质量(QoS)要求为约束条件,以最小化中继网络中各节点的总功率为目标,建立优化问题。运用凸优化技术,得到了该优化问题的闭合解。仿真结果表明在满足相同的QoS要求下,该最优联合策略较传统的随机选择与等功率分配策略具有较低的功率消耗。     

Optimizing content routers deployment in large‐scale information centric core‐edge separation Internet

In this paper, we investigate the optimization of content router deployment in large‐scale information centric core‐edge separation Internet. We formulate the target cost function, which captures the trade‐off between the cost for traffic transmission and cost due to additional deployment of content routers. To solve the problem, we first derive the analytical results of traffic ratio and its closed‐form approximation. We then construct a convex optimization problem, from which the optimal deployment probability can be solved. By extensive numerical results, the accuracy of the closed‐form approximation of traffic ratio is demonstrated. And, it is found that the optimal deployment probability is decided by the average number of hops for obtaining the content, the cost for traffic transmission, and the cost of extending router to content router. Copyright © 2013 John Wiley & Sons, Ltd.     

凸表面上天线间互耦的射线分析法

本文给出了在电尺寸很大的理想导体光滑凸表面上,无限小磁矩或电矩在空间产生的电磁场的近似渐近解,利用该解可以准确有效地计算凸表面上天线间的互耦。此解中,表面塌沿Keller的表面射线路经传播,且在阴影边界过渡区域,包括对于源的邻城,它们仍然一致适用。除此之外,在给出的解中,通过一个系数T/K给出了关于表面场的表面射线绕曲的影响,其中,T表示表面射线的绕曲,K表示射线方向上的表面曲率。该解是由简单的典型问题的近似解导出的。给出了圆柱面和圆锥面上裂缝间互耦的数字结果,它与实验结果相吻合。