基于能量效率优化的终端发送技术研究

发布时间：2018-03-24 22:40

本文选题：能量效率　切入点：链路自适应　出处：《中国科学技术大学》2014年博士论文

【摘要】：随着无线通信技术的高速发展,用户对于无线业务的需求也越来越高,这一切都集中反映出了日益增长的无线数据速率要求。这使得现有的无线通信系统受到频谱资源紧缺和能耗严重的双重挑战。随着3G甚至是4G通信网络的广泛架设,可以预见无线通信的能耗将会迅猛增长。作为无线通信系统中能耗的重要组成部分,提高发送端的能效对于降低通信系统的能耗有着重要的现实意义。同时由于移动终端便携性和移动性要求所带来的体积和重量限制,以及电池材料发展的相对缓慢,造成了现有移动通信终端的“电池瓶颈”。因此除了节能环保的意义,提高终端发送能效同样可以延长有效通信时间。因此如何通过终端发送技术,提高终端发送能效是一个亟待解决的问题。本文研究两个终端发送场景：点对点单用户链路和多点对点多用户发送。并分别提出了基于能效的链路自适应发送技术和多用户自适应发送策略,来根据系统的CSIT,干扰状态和目标频谱效率等自适应调整发送速率,发射功率,多天线模式,协作方式等来提高终端发送能效。首先,本文研究在非完美的发送端信道状态信息下,OSTBC-MIMO系统的能效链路自适应问题。之前基于完美信道状态信息的研究和基于统计信道状态信息的研究都可以归为本章研究问题的极限情况。同时,我们不仅仅考虑常值的放大器效率,还考虑MQAM调制时,由于非恒包络调制而造成的与调制阶数相关的放大器效率。尽管建模的问题是非凸的,通过广义凸优化的理论,我们推导出在即时BER约束下,能效最优的发送速率和发射功率的闭式表达结果。根据这个表达结果,我们进一步给出完美信道状态信息和统计信道状态信息下的极限结果。然后我们分别讨论了通过自适应的多天线选择和训练序列优化机制来提高点对点链路的能效。针对能效优化的多天线自适应选择,我们提出了一种基于能效优化的一般性天线选择合并机制,即EE-GSC机制。通过该机制,我们实现了发送端多天线分集增益和电路功耗的最优折中,来最大化链路的能效。基于顺序统计理论,我们推导出了在目标频谱效率下,平均发射天线数和平均发送能效。并基于推导出的理论结果,给出了一些特殊情况的分析。针对能效优化的训练序列自适应功率分配,我们分别分析了基于训练序列的有反馈MIMO系统和无反馈MIMO系统。首先,我们考虑了无反馈MIMO系统中,发送端发送功率在各个天线上平均分配的情况。当训练序列功率固定时,我们给出了能效最优的数据序列功率,同时证明了它的存在唯一性。当数据序列功率和训练序列功率都可变时,我们提出了一种收敛的交替优化算法,获得能效优化的数据序列功率和训练序列功率。接着,我们分析了有反馈的MIMO系统,发送功率在各个天线上注水分配的情况。当训练序列功率固定时,我们给出了最优的数据序列功率和其对应的有效发送天线数。尽管有效发送天线数目是离散的,我们仍证明了它们的存在唯一性。当数据序列功率和训练序列功率都可变时,我们提出了一种类似的收敛的交替优化算法,获得能效优化的数据序列功率和训练序列功率。通过这些算法的数值结果,我们讨论了多天线配置,电路功耗和块衰落长度对于发送端能效的影响。接着我们研究了多用户无用户间协作系统中,用户通过分布式功率控制,在实现目标SINR的约束下,最小化总的发射功耗,以提高用户能效的问题。对于系统中发射用户较少,每个用户都可以达到目标SINR,即系统可行时,我们证明了采用ZF和MMSE接收机,多用户采用标准功控算法时,可以收敛到最优的发射功率。当系统中发射用户较多,或者用户目标SINR较大时,不能保证每个用户都可以达到目标SINR,即系统不可行时,我们提出了具有用户软移除的分布式功控算法。通过广义标准的理论,我们证明了该算法的收敛性。通过数值结果,我们证明了该算法通过软移除不可行用户,不仅仅提高了用户的能效,而且降低了系统中用户的掉线概率。最后我们提出在有用户间协作的MU-SIMO系统中,目标频谱效率下,一种分布式的能效优化体制。我们将该体制分为两个部分。在第一个部分,我们回答这样一个问题,即：“在MU-SIMO协作集合内,用户间如何协作?”。我们给出目标频谱效率下,在MU-SIMO协作集合内,每个用户在各个RB上最优发送能效功率分配的闭式表达式。在第二个部分,我们回答“用户与谁协作,形成MU-SIMO协作集合?”。根据第一部分得到的闭式结果,我们提出了一种基于联盟形成的合作博弈算法,在用户间形成协作的MU-SIMO集合。该算法根据用户能效的Pareto特性,采用了一种融合分裂的收敛迭代操作。
[Abstract]:With the rapid development of wireless communication technology, users are more and more demand for wireless services, all of which reflects the growing demand for wireless data rate. This makes the existing wireless communication systems by the shortage of spectrum resources and energy challenges seriously. Even with the 3G 4G communication network is widely set up, energy consumption can foresee the rapid growth of wireless communication. As an important part of energy consumption in wireless communication system, improve energy efficiency and reduce the energy consumption of the transmitter for communication system has important practical significance. At the same time from the mobile terminal portability and mobility requirements of the size and weight limit, and the battery materials development is relatively slow, resulting in the existing mobile communication terminal "battery bottleneck". So in addition to the significance of energy saving and environmental protection, improving energy efficiency can also extend the terminal to send Effective communication time. So how to improve the terminal technology, terminal transmission efficiency is an urgent problem to be solved. This paper studies two terminal transmission scene: point-to-point single user and multi link point-to-point multiuser transmission. And then proposed link adaptive efficiency transmission technology and multi-user adaptive transmission strategy based on according to the system, CSIT, interference state and target spectral efficiency of adaptive adjust the transmission rate, transmission power, multi antenna mode, cooperative mode to improve the terminal energy efficiency.
First, this paper studies in the transmitter channel state information non perfect, efficient link adaptive OSTBC-MIMO system. Based on the research before the perfect channel state information and statistical research based on channel state information can be classified as the limit of this chapter studies the problem. At the same time, we not only consider the amplifier efficiency values, but also consider the MQAM modulation, the amplifier efficiency caused by the non constant envelope modulation and modulation order. Although the related modeling problem is non convex, the generalized convex optimization theory, we derive the instant BER constraints, transmission rate and power transmission efficiency optimal closed form results. According to this the expression of results, we further give the perfect channel state information and the statistical channel state information under the ultimate results.
And then we discuss the adaptive multi antenna selection and training sequence optimization mechanism to improve the efficiency of a point-to-point link. For multi antenna adaptive optimization of energy efficiency, we propose a general antenna selection combining mechanism based on energy efficiency optimization, namely EE-GSC mechanism. Through this mechanism, we achieve the best compromise more the antenna diversity gain and power consumption of the circuit of the sending end, to maximize the link efficiency. Based on order statistics theory, we derive the spectral efficiency in the target, the average number of transmit antennas and the average transmission efficiency. Based on the theoretical results derived from the analysis is given in some special circumstances. For the training of adaptive power allocation efficiency sequence optimization, we analyzed based on training sequence feedback MIMO system and feedback MIMO system. Firstly, we consider the MIMO system without feedback,. The sending end transmission power in each antenna on the average distribution. When the training sequence power is fixed, we give the optimal energy efficiency of the power sequence data, and proved the existence and uniqueness of the data sequence. When the power and power variable training sequence, we propose an alternate optimization algorithm convergence, access to energy efficiency optimization of data sequence power and the training sequence power. Then, we analyzed the MIMO feedback system, distribution of water transmission power in each antenna case. When the training sequence power is fixed, we give the optimal number of transmit antennas according to the effective power sequence and its corresponding number. Although the number of transmit antennas is discrete and effective the US still proves their existence and uniqueness. When the data sequence power and power variable training sequence, we propose an alternating optimization algorithm with a similar convergence, by We get the data sequence power and training sequence power optimized by energy efficiency. Through the numerical results of these algorithms, we discuss the influence of multi antenna configuration, circuit power and block fading length on the energy efficiency of transmitter.
Then we studied the multi user cooperative system useless between households, the user through the control of distributed power, achieve the goal in the SINR under the constraint of minimizing the total transmit power, in order to improve the efficiency of user problems. For the launch of a few users in the system, each user can achieve the goal of SINR, the system is feasible, we prove that the use of ZF and MMSE receiver, multiuser using standard power control algorithm, can converge to the optimal transmit power. When transmitting more users in the system, or the user SINR is large, can not guarantee that each user can reach the goal of SINR, the system is not feasible, we propose a distributed power control software with user removed by using the generalized algorithm. The standard theory, we prove the convergence of the algorithm. The numerical results, we prove that the algorithm by soft remove infeasible users, not only enhance the user The energy efficiency of the system and the drop probability of the user in the system are reduced.
Finally, we put forward the MU-SIMO system has the collaboration between users in the target spectrum efficiency, energy efficiency optimization of a distributed system. The system is divided into two parts. In the first part, we answer such a question, namely: "in the MU-SIMO collaboration in the collection, the user how to work?" we give the target spectral efficiency, the MU-SIMO collaboration within each user in each RB optimal closed form sending energy-efficient power allocation. In the second part, we answer the user who form cooperation, MU-SIMO cooperation set? ". according to the first part of the closed form results, we propose a the algorithm of coalition formation based on cooperative game, formed in the collaboration between users of MU-SIMO set. The algorithm according to the characteristics of Pareto user efficiency, using a fusion fission iteration operation.

【学位授予单位】：中国科学技术大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TN929.5

【共引文献】