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类型 基础研究 预答辩日期 2018-01-28
开始(开题)日期 2016-09-02 论文结束日期 2017-12-04
地点 四牌楼校区李文正楼北512室 论文选题来源 973、863项目     论文字数 9 (万字)
题目 分布式天线系统中的传输设计
主题词 能量效率,有效容量,多播传输,延迟需求
摘要 分布式天线是5G通信系统的一种具有潜力的技术。在分布式天线系统构架下,所有的信号处理任务都在中央控制器,远程端口只负责简单信号传输和接收。于是,系统更易于集中式的信号处理,比如多点协作传输技术(CoMP),联合用户调度和数据流控制等。同时,由于远程端口的低功能特性,它的尺寸比传统微型基站更小,更加易于密集摆放在小区中不同的位置。因此,分布式天线系统可以大大提高系统中容量和小区边缘覆盖率。另外,随着云计算技术的发展,分布式天线系统架构显得尤为重要,因为它可以支持一些新兴技术,例如NFV,SDN和AI。 本文侧重研究分布式天线系统的传输设计问题,进而提高系统传输设计中的频谱效率和能量效率两个指标。主要工作如下: 一、提出了一种优化传输协方差矩阵的方法来最大化单用户分布式天线场景下系统的能量效率。在该系统中,用户和每个分布式接入点都配置多根天线。与现有相关工作不同的地方是,本文同时考虑了用户速率需求和分布式接入点选择问题。这里系统总的链路消耗与激活的分布式接入点数目有关系。在这种情况下,首先,在给定激活分布式接入点集合情况下,提出了一种优化传输协方差矩阵的方案来最大化系统的能量效率。具体来讲将这个问题分为三个子问题:速率最大化问题,没有速率约束下的能效最大化问题,和功率最小化问题,每个问题都能够得到有效解决。接着提出一种低复杂度的基于距离的分布式接入点选择方案来决定分布式接入点被激活的集合。仿真结果表明,提出的分布式接入点选择算法与最优的穷尽搜索方法在性能上非常接近,但是复杂度得到了大大的降低。同时,本文提出的算法与已有的能效最大化方案相比在能效上有显著的提高。 二、提出了一种联合优化前端链路选择和发送协方差矩阵的方法来优化多用户MIMO DAS系统中能效的方法。前端链路的功耗假设为与激活的前向链路数目成正比,其中激活的前端链路可以通过指示函数来表征。同时该优化问题考虑了各个用户的速率需求以及每个RAU 的功率约束。由于RAU 功率约束条件,一些用户的速率可能不会得到满足。因此,本文建模了一个两阶段的优化问题。在第一阶段,本文提出了一种新型的用户选择算法用来决定最大接入的用户数。在第二阶段,求解能效最大化问题。首先,用一个平滑的凹函数对指示函数进行近似。接着,本文提出了一种三层迭代算法来求解近似的能效优化问题,证明了这种算法可以收敛到平滑后的能效优化问题的KKT点。为了进一步减小复杂度,本文提出了一种能保证收敛的单层迭代算法。仿真结果表明提出的用户选择算法逼近穷尽搜索算法的性能。最后,仿真结果表明,提出的算法较原有的不考虑链路选择的能效算法在能效性能上高出一个数量级。 三、研究了考虑延迟约束下分布式系统中的资源分配问题。具体优化问题为最大化用户的有效容量,同时保证每个RAU的平均功率约束和峰值功率约束,其中有效速率定义为在保证延迟需求下,系统所能支撑的最大达到数据流速率。首先将有效速率最大化问题转化为一个等效的凸优化问题。通过使用拉格朗日分解方法和求解KKT条件,可以得到每个RAU上的最优发送功率的闭合表达式。为了更新拉格朗日因子,提出了一种在线的追踪算法来近似计算每个RAU的平均功率。对于两个RAU 的情况,每个RAU上的平均功率的表达式可以写为显式形式,这样可以通过数值计算得到。因此,在这种特殊情况下,拉格朗日对偶变量可以提前计算出而不需要在线追踪。仿真结果表明提出的算法对于所有考虑到的场景都能够快速收敛,并且比每个RAU单独优化的方法提高20\%的有效容量。 四、研究了Nakagami-$m$信道下的单用户分布式天线系统中的能效最大化问题,其中能效定义为平均频谱效率与平均总功率的比值。该优化问题除了考虑传统的每个RAU上的峰值功率约束,也考虑了每个RAU上的平均功率约束。首先采用分式规划方法将原始的分式目标函数转化为一个更加容易求解的形式。接下来,引入与平均功率约束相对应的对偶变量来将原问题分解为多个独立的子问题,通过分析KKT条件可以得到每个子问题的闭合解。然后利用次梯度算法来更新对偶变量。为了计算次梯度,本文采用在线跟踪的算法来追踪每个RAU的平均功率值。对于两个RAU的特殊情况,得到了平均功率的闭合表达式,这将使得该场景下不需要通过训练就可以直接得到平均功率。仿真结果表明提出的算法可以很快收敛,并且能效性能相比已有的算法高出了约40\%。 五、研究了多播DAS系统中的能效最大化问题,其中每个RAU配置多根天线。提出了一种新颖的迭代算法,每次迭代求解两个子问题:功率分配问题和波束方向优化问题。第一个子问题可以转化为一维的伪凸问题,可以通过黄金搜索的方法进行求解。第二个子问题可以使用现有的算法进行求解。仿真结果表明所提的算法比现有的速率最大化算法有着更好的能效性能。另外,当前端链路功耗较低时,分布式天线系统的能效性能要优于集中式天线系统。
英文题目 Transmission Design for Distributed Antenna Systems
英文主题词 Energy Efficiency, Effective Capacity, Multicast Transmission, Delay requirement
英文摘要 DAS is a promising architecture towards 5G networks. Under the DAS architecture, all the signal processing tasks are executed in the centralized processing unit (CPU), and the remote access units (RAUs) are only responsible for simple signal transmission and reception. Hence, the system is more amenable for centralized signal processing, such as Coordinated Multiple Points Transmission/Reception (CoMP), joint user scheduling and flow control, etc. In addition, due to its simple functionality, its size is smaller than that of the conventional small cell BSs and more densely deployed in the cell with low operation cost. Furthermore, due to the recent development of cloud computing, DAS architecture is an ideal platform to support some emerging information technologies, such as NFV, SDN and AI. This dissertation mainly focuses on the transmission design for DAS, with the aim of improving the spectral efficiency and energy efficiency of DAS. Firstly, a transmit covariance optimization method is proposed to maximize the energy efficiency for a single-user distributed antenna system, where both RAUs and the user are equipped with multiple antennas. Unlike previous related work, both the rate requirement and RAU selection are taken into consideration. Here, the total circuit power consumption is related to the number of active RAUs. Given this setup, we first propose an optimal transmit covariance optimization method to solve the EE optimization problem under a fixed set of active RAUs. More specifically, we split this problem into three subproblems, i.e., the rate maximization problem, the EE maximization problem without rate constraint, and the power minimization problem, and each subproblem can be efficiently solved. Then, a novel distance-based RAU selection method is proposed to determine the optimal set of active RAUs. Simulation results show that the performance of the proposed RAU selection is almost identical to the optimal exhaustive search method with significantly reduced computational complexity, and the performance of the proposed algorithm significantly outperforms the existing EE optimization methods. Secondly, a jointly selecting the fronthaul links and optimizing the transmit precoding matrices method aiming at maximizing the energy efficiency of a multiuser multiple-input multipleoutput aided distributed antenna system is proposed. The fronthaul link’s power consumption is taken into consideration, which is assumed to be proportional to the number of active fronthaul links quantified by using indicator functions. Both the rate requirements and the power constraints of the remote access units are considered. Under realistic power constraints some ofthe users cannot be admitted. Hence, we formulate a two-stage optimization problem. In Stage I, a novel user selection method is proposed for determining the maximum number of admitted users. In Stage II, we deal with the energy efficiency optimization problem. First, the indicator function is approximated by a smooth concave logarithmic function. Then, a triple-layer iterative algorithm is proposed for solving the approximated energy efficiency optimization problem, which is proved to converge to the Karush-Kuhn-Tucker conditions of the smoothened energy efficiency optimization problem. To further reduce the complexity, a single-layer iterative algorithm is conceived, which guarantees convergence. Our simulation results show that the proposed user selection algorithm approaches the performance of the exhaustive search method. Finally, the proposed algorithms is capable of achieving an order of magnitude higher energy efficiency than its conventional counterpart operating without considering link selection. Thirdly, a QoS driven power-and rate-adaptation scheme aiming at maximizing the effective capacity of the user subject to both per-RAU average-and peak-power constraints is proposed, where the EC is defined as the tele-traffic maximum arrival rate that can be supported by DAS under the statistical delay-QoS requirement. We first transform the EC maximization problem into an equivalent convex optimization problem. By using the Lagrange dual decomposition method and satisfying the KKT conditions, the optimal transmission power of each RAU can be obtained in closed form. Furthermore, an online tracking method is provided for approximating the average power of each RAU for the sake of updating the Lagrange dual variables. For the special case of two RAUs, the expression of the average power to be assigned to each RAU can be calculated in explicit form, which can be numerically evaluated. Hence, the Lagrange dual variables can be computed in advance in this special case. Our simulation results show that the proposed scheme converges rapidly for all the scenarios considered and achieves 20% higher EC than the optimization method, where each RAU power is independently optimized. Fourthly, we consider the energy efficiency maximization problem for a single-user distributed antenna system over the Nakagami-m fading channels, where EE is defined as the ratio of the average SE to the average total power consumption. In addition to the conventional peak power constraints for each RAU, the average power constraint for each RAU is also taken into account in the optimization problem due to the limited power budget. We first adopt the fractional programming method to transform the original fractional objective function into a more tractable subtractive form. Then, the dual variables associated with average power constraints are introduced to decompose several independent subproblems, the solution of which can be obtained in closed form by analyzing the KKT conditions. The subgradient method is used to update the dual variables, and the online algorithm is adopted to track the average power in order to calculate the subgradient. For the special case of two RAUs, the closed-form expression of the average power is derived, which facilitates direct applications without the need for training. Simulation results demonstrate the fast convergence of our proposed algorithm and the performance gain of nearly 40% over the the existing algorithms in terms of the EE performance. Fifthly, we solve the energy efficiency maximization problem for multicast services in a MISO distributed antenna system. A novel iterative algorithm is proposed, which consists of solving two subproblems iteratively: the power allocation problem and the beam direction updating problem. The former subproblem can be equivalently transformed into a one-dimension quasi-concave problem that is solved by the golden search method. The latter problem can be efficiently solved by the existing method. Simulation results show that the proposed algorithm achieves significant EE performance gains over the existing rate maximization method. In addition, when the backhaul power consumption is low, the EE performance of the DAS is better than that of the centralized antenna system.
学术讨论
主办单位时间地点报告人报告主题
东南大学移动通信国家重点实验室 2015.4 江宁无线谷1号楼会议室 Yu Henhu Big Data analytics, Internet of Things, and Everything in Between
东南大学移动通信国家重点实验室 2016.6 江宁无线谷1号楼会议室 Prof. Koichi Asatani Network Science and its Applications to Future Networking
东南大学移动通信国家重点实验室 2016.10 江宁无线谷1号楼会议室 Prof. Rui Zhang Mobile Communication Surveillance: A New Wireless Security Paradigm
东南大学移动通信国家重点实验室 2016.10 江宁无线谷1号楼会议室 Dr. Tony Q.S. Quek Fundamentals and Recent Advances in 5G Wireless Systems
东南大学移动通信国家重点实验室 2015.06 无线谷1319会议室 任红 Optimal Precoding Design and Port Selection for Energy Efficiency Maximization in MIMO Distributed Antenna
东南大学移动通信国家重点实验室 2015.12 无线谷1319会议室 任红 Joint Fronthaul Link Selection and Transmit Precoding for Energy Efficiency Maximization of multiuser MIMO-Aided Distributed Antenna Systems
东南大学移动通信国家重点实验室 2016.01 无线谷1319会议室 任红 Energy Efficient Transmission for Multicast Services in MISO Distributed Antenna Systems
东南大学移动通信国家重点实验室 2016.05 无线谷1319会议室 任红 凸优化理论
     
学术会议
会议名称时间地点本人报告本人报告题目
IEEE Globecom 2017 Workshop 2015.12 美国 圣地亚哥 Energy Efficiency Optimization for MIMO Distributed Antenna Systems
能效973项目研讨会 2015.07 江苏 南京 Energy Efficiency of Distributed Massive MIMO
     
代表作
论文名称
Energy Efficient Transmission for Multicast Services in MISO Distributed Antenna Systems
Energy Efficiency Optimization for MIMO Distributed Antenna Systems
 
答辩委员会组成信息
姓名职称导师类别工作单位是否主席备注
束峰 正高 博导 南京理工大学
梁涛 正高 博导 总参第63研究所
陈明 正高 教授 博导 东南大学
盛彬 正高 教授 博导 东南大学
潘志文 正高 教授 博导 东南大学
      
答辩秘书信息
姓名职称工作单位备注
康维 副高 副教授 东南大学