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类型 基础研究 预答辩日期 2017-11-19
开始(开题)日期 2014-05-28 论文结束日期 2017-09-12
地点 中国无线谷1319 论文选题来源 其他项目    论文字数 6.9 (万字)
题目 超密集无线网络能量效率关键技术研究
主题词 超密集网络,能量效率,小区关联,资源分配,回程链路优化
摘要 随着人们对于移动智能终端的依赖程度不断提高,越来越多的无线多媒体应用悄然走入人们的视野,人们对于无线移动数据业务的需求也有了质的飞跃。此外,据国际电信联盟统计显示,移动通信产业的能量消耗,以及CO2排放量比重逐年升高。因此,如何在保证用户需求的前提下,通过有效的利网络中现有的无线频谱资源,降低网络能量开销,提高网络能量效率成为下一代无线网络优化设计中亟待解决的问题。 为满足下一代无线网络数据容量提升1000倍和网络能量消耗降低10倍的目标,学术界和工业界投入了大量人力物力对其关键技术进行研究。超密集异构网络通过在现有宏小区覆盖范围内大量部署同频低功率小小区,达到缩短用户与关联小区之间的距离,提升用户服务质量和网络吞吐量的目的,成为下一代无线通信网络的重要组成部分。然而,大量部署小小区也使得网络中同频干扰更加复杂,网络能量开销显著增加,从而限制了网络能量效率的提升。因此,在超密集异构网络架构下,通过研究用户关联、资源分割、功率控制、小区开关、小区协作以及载波聚合等关键技术对于网络能量效率的影响具有重要的实用价值和意义。 本学位论文深入研究了超密集异构网络架构下的能量效率问题,论文主要研究内容如下: 1. 超密集异构网络中能量有效的资源分配和小区关联算法。针对目前研究中没有综合考虑小区关联的动态变化,只关注小小区的资源分配等问题以及忽略用户的速率需求。本章主要针对超密集异构网络中下行链路的能量效率问题展开研究。首先,在保证用户服务质量需求的前提下,将宏小区的资源分割系数,小小区的发射功率和小小区的小区关联偏置建模成网络能量效率最大化的联合优化问题。并证明了该问题属于NP-hard问题,在多项式时间内无法求解。其次,考虑到在优化过程用户的关联小区会动态变化和网络能量效率本身的非凸性,已有的资源分配和小区关联算法很难求得最优解集。本章提出一种基于改进粒子群的联合宏小区和小小区的资源分配和小区关联算法。其中,通过引入局部搜索以及多次初始化对传统粒子群算法进去改进,从而保证算法快速收敛,以及解的最优性。最后,仿真结果表明,相比于已有的资源分配和小区关联算法,提出的算法有较好的能量效率,较高的网络吞吐量性能,较低的能量消耗。 2. 异构网络中基于载波聚合的能量有效动态资源分配算法。针对处在正的小小区关联偏置值调整区域中的用户受到宏小区较强的干扰问题、载波分配过程中由于载波分配不当导致的系统性能下降以及网络能量消耗过大的问题。本节提出一个基于载波聚合的能量有效动态资源分配算法去缓解处在正的小小区关联偏置值调整区域内用户受到来自宏小区的强干扰,以及由于资源分配的不当导致的系统性能下降和能量消耗过大的问题。通过联合调整载波的配置方案,降低宏小区的在数据信道上的发射功率,达到降低处在小小区边界扩展区域用户所受干扰,提升网络吞吐量,降低网络能量消耗,提升网络能量效率的目的。通过仿真验证,所提算法相比于其他对比算法有较好的小小区边缘吞吐量性能,宏小区的吞吐量性能并没有因为对宏小区资源的消减而显著下降,网络总吞吐量性能也得到了提升。此外,由于采用合理分配载波以及动态功率控制,网络能量效率性能也有所提升。 3. 超密集异构网络中能量有效的小区关联偏置调整算法。针对传统的小区关联算法导致超密集网络中各个小区内的负载不均,网络吞吐量增长受限以及网络能量开销过大等问题,本章考虑在保证用户速率的基础上,通过优化每个小小区的小区关联偏置值改变用户的关联,关闭网络中没有用户的小小区,最终达到提升网络能量效率的目的。由于在网络能量效率的优化过程中,网络能量效率本身的非凸性,以及小小区的小区关联偏置值和用户关联存在复杂的耦合关系,导致无法直接求出网络最优的小区关联偏置解集。因此,本章首先提出一个基于吉布斯采样算法的集中式能量有效小区关联偏置调整算法。在该集中式小区关联偏置调整算法中,需要收集所有小小区与用户之间的信道增益、用户的关联信息以及网络的能量消耗信息。其次,考虑到收集以上全局信息的将导致网络中信息交互开销较大以及计算复杂度会随着网络规模的增大而增加。本章通过对小小区内的用户数和用户的信干噪比求解公式推导,提出一个基于吉布斯采样算法的分布式能量有效小区关联偏置调整算法。该算法具有较低信息开销和实施复杂度的优点。通过仿真实验表明,本章所提出的两种基于吉布斯采样算法的小区关联偏置均能较快的收敛到全局最优解,并且相比于已有的算法有较好的吞吐量和能量效率性能。此外,分析了用户速率限制在能量效率优化过程中的重要性,对比了不同算法随着小小区个数增加的能量效率性能以及不同算法中休眠小区个数随着网络小小区个数增长的性能。 4. 超密集网络中带有回程链路限制的能量有效小区关联调整算法。本章针对超密集网络中由于回程链路资源不足导致回程链路拥塞,假设网络前向链路速率低于回程链路速率,忽略回程链路资源分配以及没有考虑回程链路的能量消耗与回程链路速率之间的相关性等问题,在保证用户速率需求的基础之上,针对回程链路数据速率低于前向链路的情况,通过联合优化小区关联矩阵,小小区的开关系数以及回程链路数据速率达到提升网络能量效率的目的。因为小区关联,小小区开关和回程链路数据速率分配之间存在紧耦合关系,所以很难求得网络最优的资源分配闭式表达式。因此,本章将网络能量效率最大化问题分解为两个子问题,第一个子问题是在保证用户速率需求的情况下,通过优化小区关联矩阵和小小区开关系数最小化网络能量消耗的问题。该问题属于0-1非线性规划问题,由于网络规模较大,传统的小区关联算法很难求出最优解。因此,通过引入罚函数法将该问题转换为一个无约束问题,然后提出一个基于增强改进粒子群的优化算法进行求解,并依据获得的最优小区关联矩阵和小小区的开关系数,推导出每个小小区的回程链路速率上下限。第二个子问题是最大化最小可实现能量效率问题,其中,优化变量为回程链路数据速率,并考虑回程链路上的能量消耗,回程链路的能量消耗正比于回程链路数据速率,回程链路数据速率满足推导的上下限要求。该问题属于线性分式规划问题。由于得到的目标函数分母是正值,该问题属于拟凸优化问题。可以通过对该问题进一步转换得到一个线性规划问题,从而提出一个基于线性规划的回程链路速率控制算法进行求解。最后,由于网络能量效率是一个分式,以上两种化简并不能保证最终求得的网络能量效率是最优的。因此,本章提出一个小小区开关补偿算法,通过该算法将那些处于休眠状态的小小区打开进一步提升网络能量效率。仿真结果表明,与已有的小区关联和回程链路算法相比,本章所提出的算法有较好的网络能量效率性能。虽然协作多点传输增加了回程链路的能量消耗,但是网络总体能量消耗还是减少的。
英文题目 Investigations on key techniques of energy efficiency for ultra dense wireless network
英文主题词 ultra dense network, energy efficiency, cell association, resource allocation, backhaul link optimization
英文摘要 With the increasing dependence on the mobile intelligent terminal, more and more wireless multimedia application has come into people’s perspective, and the requirement of the wireless mobile data services has increased tremendously. In addition, according to the statistics from the International Telecommunication Union, the energy consumption and the proportion of CO2 emissions of the mobile communication industry are increasing year by year. Therefore, how to guarantee the users’ demand, decrease the network energy consumption and improve the network energy efficiency through the effective utilization of the existing wireless spectrum resource, have become key problems needed to be solved of the next generation of wireless network optimization design. In order to meet the targets of 1000 times data rate improvement and 10 times energy consumption decrease of the next generation of wireless network, academia and industry have devoted much effort on investigating the key technologies. For the ultra dense heterogeneous network (UDNs), deployed with a large number of Co-Channel and low power small cells under the coverage of macrocell, shorten the distance between the user and its associated base station, improve the users’ quality of service and the network throughput, has become an important component of the next generation wireless network. However, because of the large-scale deployment of small cells, the Co-Channel interference becomes more and more complexity and the energy consumption of the network increases tremendously, which limits the improvement of energy efficiency. In this dissertation, the energy efficiency problem of UDN is deeply studied. The main contributions of the dissertation are listed as follows: 1. Energy-efficient resource allocation and cell association algorithm for ultra dense heterogeneous networks. Aiming at improving the current studies which didn’t consider the alteration of cell association, the resource allocation of macrocell and the users’ data rate requirement, this chapter investigates the downlink energy efficiency problem in ultra dense heterogeneous network. Under the constraint of the users’ quality of service, a network energy efficiency maximization problem by jointly optimizing the frequency resource partitioning of macrocells, transmission power and cell association bias of small cells is formulated. This problem is proven to be an NP-hard problem which cannot be worked out by direct methods in polynomial time. And then, considering the alteration of users’ serving cell in the optimization process and the characteristic of energy efficiency, it is difficult for the existing resource allocation and cell association algorithms to find the optimal solution set. An modified particle swarm optimization based resource allocation and cell association algorithm is employed to solve the proposed joint optimization problem. In modified particle swarm optimization, local search and multi-restart are introduced to improve the performance of tranditional particle swarm optimization and guarantee the convergence and the optimality of the proposed algorithm. At last, numerical simulations are conducted and the simulation results reveal that, compare to the existing resource allocation algorithms, the proposed algorithm can obtain better energy efficiency and system throughput performance with lower energy consumption. 2. Carrier aggregation based energy-efficient dynamic resource allocation algorithm for heterogeneous network. Aiming at handling the strong interference problem caused by macrocell to users in the small cell edge extension region, and the performance degradation and excessive energy consumption caused by the poorly arranged carrier configurations, in this chapter, a novel dynamic resource allocation scheme based on carrier aggregation to alleviate the downlink interference from macrocells to users located in the cell range expansion area, the performance degradation and the excessive energy consumption is proposed. By jointly adjusting the carrier configurations and transmission power of macrocell on the signal channel, the proposed algorithm can avoid the interference of users in the small cell edge extension region, increase the network throughput, and decrease the network energy consumption. Simulation results show that, compared to the existing resource allocation schemes, the proposed scheme can significantly boost the picocell throughput and increase the throughput of macrocell while save the transmission power of macrocell. At the same time of the overall throughput promotion, the energy consumption of the network is reduced, and hence the energy efficiency performance is also be improved. The improvement of the energy efficiency is attributable to the rational allocation of component carrier and the dynamic adjustment of the transmission power. 3. Energy efficient cell-association bias adjustment algorithm for UDN. Aiming at the problems of the network load imbalance, network throughput limitation and the network energy cost, caused by traditional cell association algorithms, in this chapter, under the constraint of users’ data rate, we propose a novel cell association adjustment scheme by optimizing the cell-association bias configuration which finally alters the cell association relationship, increases network throughput, turns off the extra small cells that have no users, and hence improve the network energy efficiency. Considering the non-convexity of the energy efficiency optimization problem and the coupled relationship between cell association and scheduling during the optimization process, it is difficult to achieve an optimal cell-association bias solution. In this chapter, we first propose an energy-efficient centralized Gibbs Sampling based cell-association bias adjustment (CGSCA) algorithm. In CGSCA, global information such as channel state information, cell association information, and network energy consumption information need to be collected. Then, considering the overhead of the message exchange and the implementation complexity for CGSCA to obtain the global information in UDNs, we propose an energy-efficient distributed Gibbs Sampling based cell-association bias adjustment (DGSCA) algorithm with a lower message-exchange overhead and implementation complexity. At last, we analyze the implementation complexities (e.g., computation complexity and communication complexity) of the proposed two algorithms and other existing algorithms. Simulations are conducted and the results show that, compared to other existing algorithms, CGSCA and DGSCA have faster convergence speed, and higher performance gain of the energy efficiency and throughput. In addition, the importance of the users’ data rate constraint in optimizing the energy efficiency is analyzed. The energy efficiency performance of different algorithms with different number of small cell and the number of sleeping small cells as the increasing number of small cells are compared. 4. Energy-efficient cell association algorithm with backhaul link constraint for cooperative UDN. Different from existing works which assume the data rate of fronthaul link is higher than that of the backhaul link, ignore the resource allocation and energy consumption of backhaul link, aiming at the problem of backhaul link congestion caused by coordinated multiple points (CoMP) joint transmission (JT), in this chapter, we focus on the scenario of the constrained backhaul link data rate in UDN and handle the network energy efficiency optimization problem under CoMP JT. Considering the scenario of the constrained backhaul link data rate where the backhaul link data rate cannot meet the forward link data rate, this chapter propose a network energy efficiency optimization algorithm under CoMP JT by jointly optimize the cell association and backhaul link resource allocation of the network. To be specific, the formulated system energy efficiency optimization problem is solved by jointly adjusting the sleep/on indicator, the cell association matrix and the backhaul link data rate. The sleep/on indicator, cell association and backhaul link data rate allocation are tightly coupled with the resource consumption of small cell and the backhaul link, which makes the closed-form of the optimal solution difficult to be obtained, under such circumstance, decomposition method is utilized to convert the original mixed integrate nonlinear fraction programming problem into two subproblems. The first subproblem is an energy consumption minimization problem accomplished by optimizing sleep/on indicator and cell association matrix under the users’ data rate constraint. This problem is a 0-1 nonlinear programming problem and the traditional cell association algorithm is difficult to be applied due to the large network scale. So we convert it by means of the penalty function and propose an improved modified particle swarm optimization algorithm to solve it. Based on the obtained optimal sleep/on indicator and cell association matrix, the upper and lower bound of the data rate constraint of each small cell are derived. The second subproblem is maximizing the minimum achievable energy efficiency optimization problem which is a linear fractional programming by optimizing backhaul link data rate under the derived upper bound and lower bound. Due to the positive denominator of the second subproblem, it is actually a quasiconvex optimization problem which can be transformed into an equivalent liner programming problem. Then a linear programming algorithm is proposed to solve it. Considering the fractional form of energy efficiency, the decomposition and transformation are not always the best. So finally we propose a complementary small cell turned on algorithm to converge the result to the optimal solution by turning on the sleeping cell to achieve the best energy efficiency. Numerical simulations are conducted and the results show that the proposed algorithm has a better energy efficiency and throughput than existing algorithms with the increasing number of small cells and users, respectively. Although the backhaul link energy consumption is higher than the existing algorithms due to CoMP JT, the overall energy consumption of the network is lower.
学术讨论
主办单位时间地点报告人报告主题
东南大学 2016.6.6 中国无线谷1319 Prof. Koichi Asatani Network Science and its Applications to Future Networking
东南大学--索尼中国 2014.8.19 中国无线谷1319 朱文祥 Energy-efficient resource allocation algorithm
东南大学--索尼中国 2014.9.25 中国无线谷1319 朱文祥 A user switch method on the license and unlicensed bands based on energy efficiency per bandwidth--scenario and system model
东南大学--索尼中国 2014.10.23 中国无线谷1319 朱文祥 Energy-efficient based on LAA users switch algorithm
东南大学--索尼中国 2014.12.21 中国无线谷1319 朱文祥 Energy-efficient based on LAA users switch algorithm--simulation results
东南大学 2014.1.17 中国无线谷1319 党建 IDMA
东南大学 2017.4.25 中国无线谷1319 Prof. Julian Chen Energy-efficient resource allocation for Non-Orthogonal Multiple Access(NOMA) wireless networks
东南大学 2015.4.25 中国无线谷1319 Yu Henhu Big Data analytics, Internet of Things, and Everything in Between
     
学术会议
会议名称时间地点本人报告本人报告题目
International Conference on Body Area Networks 2015.9.28 澳大利亚悉尼 Transmission Policies for Energy Harvesting Sensors
PIMRC 2015.9.2 中国香港 Energy Efficient and Low-latency Data Collection in TDMA-based WSN
     
代表作
论文名称
Energy efficient and low-latency data collection in TDMA-based WSN
 
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
蔡跃明 正高 教授 博导 解放军理工大学
束锋 正高 教授 博导 南京理工大学
潘志文 正高 教授 博导 东南大学
赵新胜 正高 教授 博导 东南大学
许威 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
吴亮 其他 讲师 东南大学