考虑LTE信道干扰物理下行链路多目标功率优化

该模型针对控制通道的小区间干扰协调对于区域控制资源消耗分析的重要性,重点改进了小区间干扰协调所产生的峰值信噪比平均分布的尾部,减少了控制资源的平均消耗.同时,引入多目标差分进化算法,采用可变参数动态调整策略进行多目标优化算法的性能改进,并对长期演进的蜂窝网络的下行链路进行优化,实现了模型功率优化性能提升.最后,通过在标...     

WCDMA系统功率控制研究

文章首先从干扰因素上得出WCDMA通信技术进行功率控制的必要性,然后介绍了WCDMA上行功率控制和下行功率控制原理,为实际应用中改善系统功率控制,提高系统的容量提供了参考依据。     

雷达对无人机系统上行链路的干扰仿真研究

基于对雷达干扰无人机测控系统的情况的分析,提出了基于Simulink的干扰仿真分析系统的建立方法.主要分析了雷达干扰无人机测控系统上行链路的过程,给出了雷达系统、无人机测控系统上行链路模型,并提出了信噪比、干噪比计算的仿真方法.通过全过程仿真和相关实地测试,确保了仿真系统的准确性.仿真结果说明该仿真系统适合模拟雷达干扰无人机测控系统全过程,结果真实、可信.为电磁频谱管理机构进行系统间干扰分析提供了直观、准确的仿真手段,为作战指挥员合理运用战术提供了理论依据.     

多用户MIMO下行链路中基于SLNR的联合用户-天线功率分配算法

在多用户MIMO系统中,在天线间或用户间进行发射功率分配,可以显著提高系统总容量.本文针对多用户MIMO系统中的下行链路,在多用户迭代注水算法的基础上,提出一种联合用户-天线功率分配算法,在进行多用户迭代注水之前,首先基于信漏噪比(SLNR)进行用户间功率分配.仿真结果显示,与多用户迭代注水算法及仅进行用户选择的多用户迭代注水算法相比,本文提出的算法大大提高了系统总容量.     

潜铺型卫星认知通信中上行链路功率控制

针对卫星通信中存在有效信道远小于注册频率的情况,提出了以潜铺型认知无线电为技术依靠的卫星上行链路功率控制算法。该算法以次要用户所获吞吐量与付出代价之差为效用函数,通过次要用户作为参与者建立的博弈模型进行纳什均衡求解,得到最优功率分配策略。该策略可满足次要用户自身需求,亦不影响主要用户系统正常通信,能有效提高频带使用率。在性能方面,指出了次要用户系统容量和预留信噪比的关系。仿真结果表明,在主要用户系统容许范围内次要用户数量越多则其系统吞吐量和系统收益越大,最后讨论了算法的实现复杂度。     

OFDMA中继系统下行链路公平资源分配算法研究*

为了公平有效地在OFDMA中继系统下行链路进行资源分配,提出了基于效用函数的资源分配算法,并根据凸优化问题的原始分解理论,提出了相应的分布式求解算法,得到了相关问题的全局最优解。理论分析和数值仿真结果表明,提出的资源分配算法具有较低的系统复杂度,可以使系统效用函数最大,从而在保证用户公平性的前提下,使系统的和速率容量最大。     

链路分布式数据采集系统中时间同步技术研究

在高速数据传输的分布式数据采集系统中,各个组成单元间的时钟同步是保证系统正常工作的关键.本文对于时延的测量采用改进的线性规划算法来进行测量,利用前向和反向两个单向测量可以估计出消除收发时钟频差和初始偏差的端到端的时延.提出了在采用Sue B.Moon的方法基础上进行改进实现收发时钟的同步技术,提高了测试的精度和准确性.     

无线系统中Bluetooth链路的有效性及可靠性研究

本文通过蓝牙通信链路信道模型的自适应性研究,采用层次式编码方案和Turbo码编码技术,在蓝牙协议固有的编码基础上增加另一编码层,构成跨层的双重自适应结构,自适应于时变的信道特征和不同媒体业务的服务质量(QoS),最大限度地提高蓝牙无线链路的有效性和可靠性。     

LTE系统多小区上行联合功控算法

针对传统的小区内开环功率控制算法通常以提升本小区的吞吐量性能为目标，忽略了当前小区用户对邻小区用户同频干扰的问题，为提升边缘用户性能的同时兼顾系统整体性能，提出了一种LTE系统小区间上行联合功率控制（UJPC）算法。该算法采用单基站三扇区为系统模型，以最优化系统吞吐量比例公平函数为目标，首先根据最小信干噪比（SINR）约束值和用户最大发射功率这两个约束条件得到相应的数学优化模型，然后采用连续凸近似的方法求解优化问题得出各个基站所管辖的小区内所有用户的最优发射功率。仿真结果表明，与基准的开环功控方案相比，联合功控方案在保证系统平均频谱利用率的情况下能够较大幅度地提高小区边缘频谱利用率，其最佳性能增益能达到50%。     

5G全双工D2D通信系统中的最优功率控制方案

作为5G的关键技术,设备直通技术(Device-to-Device Communication,D2D通信)和全双工可以提升5G系统的频谱效益和吞吐量,两者结合可以进一步提升性能增益。但是全双工节点的自干扰和资源复用产生的同频干扰会很大程度影响系统的性能。基于此提出了功率控制方案,通过凸规划的拉格朗日对偶方法得到最优的发射功率,来最大化全双工D2D的链路速率并同时满足蜂窝用户的服务质量(quality-of-service,QoS)需求。通过仿真,观察了相比于半双工D2D,全双工D2D的增益随着蜂窝用户的需求和自干扰消除量变化的影响,并且对比了全双工D2D复用不同数目信道时的性能。     

Enhanced handoff scheme based on efficient uplink quality estimation in LTE-Advanced system

Asymmetric traffics cause downlink–uplink asymmetric interference. It can lead to a critical unbalance between a downlink and an uplink channel qualities in cell edge areas. This paper proposes an enhanced handoff scheme including an efficient uplink channel estimation method. The proposed handoff scheme determines an appropriate handoff-timing and handoff-direction according to an estimated uplink channel quality and a measured downlink channel quality. In the proposed scheme, an uplink or downlink, whose quality dominantly affects a link failure, becomes the main handoff-criterion. An efficient uplink channel estimation method is also proposed to exploit an uplink channel quality in handoff. The proposed method estimates an uplink signal strength from the measured downlink signal strength and predicts an uplink interference based on the interference-level information from neighbor base stations. We propose two different uplink estimation modes such as the simple and the precise modes. The simple estimation mode calculates the uplink channel quality for the overall bandwidth for a general handoff process. The precise estimation mode finds the best uplink band for the handoff user who wants an elaborate handoff process. In the elaborate handoff, a target base station allocates the best uplink band to a handoff user to provide the better uplink channel quality. Simulation results show that the proposed uplink estimation method can accurately compute the uplink channel quality of neighbor cells where the estimation error rate is less than 0.7% The simulation results also show that the proposed handoff scheme reduces handoff-call-dropping probability by up to 69% compared to LTE-Advanced system. In addition, the end-to-end delay of the proposed scheme can be better than that of LTE-Advanced system by 26%.     

Capacity and outage analysis of a dual-hop decode-and-forward relay-aided NOMA scheme

Non-orthogonal multiple access (NOMA) is regarded as a candidate radio access technique for the next generation wireless networks because of its manifold spectral gains. A two-phase cooperative relaying strategy (CRS) is proposed in this paper by exploiting the concept of both downlink and uplink NOMA (termed as DU-CNOMA). In the proposed protocol, a transmitter considered as a source transmits a NOMA composite signal consisting of two symbols to the destination and relay during the first phase, following the principle of downlink NOMA. In the second phase, the relay forwards the symbol decoded by successive interference cancellation to the destination, whereas the source transmits a new symbol to the destination in parallel with the relay, following the principle of uplink NOMA. The ergodic sum capacity, outage probability, outage sum capacity, and energy efficiency are investigated comprehensively along with analytical derivations, under both perfect and imperfect successive interference cancellation. To inquire more insight into the system outage performance, diversity order for each symbol in the proposed DU-NOMA is also demonstrated. The performance improvement of the proposed DU-CNOMA over the conventional CRS using NOMA is proved through analysis and computer simulation. Furthermore, the correctness of the author's analysis is proved through a strong agreement between simulation and analytical results.     

A dual to Lyapunov's stability theorem

Lyapunov's second theorem is a standard tool for stability analysis of ordinary differential equations. Here we introduce a theorem which can be viewed as a dual to Lyapunov's result. From existence of a scalar function satisfying certain inequalities it follows that “almost all trajectories” of the system tend to zero. The scalar function has a physical interpretation as the stationary density of a substance that is generated in all points of the state space and flows along the system trajectories. If the stationary density is bounded everywhere except at a singularity in the origin, then almost all trajectories tend towards the origin. The weaker notion of stability allows for applications also in situations where Lyapunov's theorem cannot be used. Moreover, the new criterion has a striking convexity property related to control synthesis.     

Learning Convex Optimization Models

A convex optimization model predicts an output from an input by solving a convex optimization problem. The class of convex optimization models is large, and includes as special cases many well-known models like linear and logistic regression. We propose a heuristic for learning the parameters in a convex optimization model given a dataset of input-output pairs, using recently developed methods for differentiating the solution of a convex optimization problem with respect to its parameters. We describe three general classes of convex optimization models, maximum a posteriori (MAP) models, utility maximization models, and agent models, and present a numerical experiment for each.      

The CCUBE Constraint Object-Oriented Database System

Constraints provide a flexible and uniform way to represent diverse data capturing spatio-temporal behavior, complex modeling requirements, partial and incomplete information etc, and have been used in a wide variety of application domains. Constraint databases have recently emerged to deeply integrate data captured by constraints in databases. This paper reports on the development of the first constraint object-oriented database system, CCUBE, and describes its specification, design and implementation. The CCUBE system is designed to be used for the implementation and optimization of high-level constraint object-oriented query languages as well as for directly building software systems requiring extensible use of constraint database features. The CCUBE data manipulation language, Constraint Comprehension Calculus, is an integration of a constraint calculus for extensible constraint domains within monoid comprehensions, which serve as an optimization-level language for object-oriented queries. The data model for the constraint calculus is based on constraint spatio-temporal (CST) objects that may hold spatial, temporal or constraint data, conceptually represented by constraints. New CST objects are constructed, manipulated and queried by means of the constraint calculus. The model for the monoid comprehensions, in turn, is based on the notion of monoids, which is a generalization of collection and aggregation types. The focal point of our work is achieving the right balance between the expressiveness, complexity and representation usefulness, without which the practical use of the system would not be possible. To that end, CCUBE constraint calculus guarantees polynomial time data complexity, and, furthermore, is tightly integrated with the monoid comprehensions to allow deeply interleaved global optimization.     

基于聚合等级的5G通信下行信道传输控制系统设计

为提高5G通信下行信道的传输稳定性,提升传输控制系统存在的控制效果,设计并开发了基于聚合等级的5G通信下行信道传输控制系统。改装DSP协处理器、通信数据采集器以及信道传输控制器,完成硬件系统的优化。根据信道的空间结构和工作原理,构建通信下行信道模型,判定通信下行信道实时状态。在该模型下,采集通信下行信道的实时传输数据,根据信道的聚合等级,分配信道传输资源。最终从信道增强、发送/接收端、传输干扰、传输速度等方面,实现系统的传输控制功能。通过系统测试实验得出结论,设计系统的传输速率控制误差、信道传输丢包量和信道拥塞概率均有所降低,即设计系统在传输控制功能方面更具优势。     

Optimal allocation of heterogeneous resources in cooperative control scenarios

This paper introduces a mathematical framework for the study of resource allocation problems involving the deployment of heterogeneous agents to different teams. In this context, the term heterogeneous is used to describe classes of agents that differ in the basic functions they can perform (e.g., one type of agent searches for targets while another type engages those targets). The problem is addressed in terms of optimization using concepts from economic theory and the proposed algorithm was designed to ensure asymptotic stability of the optimal solution.     

A memetic algorithm for the Minimum Sum Coloring Problem

Given an undirected graph G, the Minimum Sum Coloring Problem (MSCP) is to find a legal assignment of colors (represented by natural numbers) to each vertex of G such that the total sum of the colors assigned to the vertices is minimized. This paper presents a memetic algorithm for MSCP based on a tabu search procedure with two neighborhoods and a multi-parent crossover operator. Experiments on a set of 77 well-known DIMACS and COLOR 2002–2004 benchmark instances show that the proposed algorithm achieves highly competitive results in comparison with five state-of-the-art algorithms. In particular, the proposed algorithm can improve the best known results for 15 instances.     

分布式约束优化方法研究进展

多agent系统作为分布式人工智能研究领域的重要分支,已被广泛应用于多个领域中复杂系统的建模.而分布式约束优化作为一种多agent系统求解的关键技术,已成为约束推理研究的热点.首先对其适用性进行分析,并基于对已有算法的研究,总结出采用该方法解决问题的基本流程,在此基础上,从解的质量保证、求解策略等角度对算法进行了完整的分类;其次,根据算法分类结果以及执行机制,对大量经典以及近年来的分布式约束优化算法进行了深入分析,并从通信、求解质量、求解效率等方面对典型算法进行了实验对比;最后,结合分布式约束优化技术的求解优势给出了分布式约束优化问题的实际应用特征,总结了目前存在的一些问题,并对下一步工作进行了展望.