基于能量效率的自适应多天线传输技术研究
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摘要
随着移动互联网的快速发展,新兴业务和移动应用的爆炸性增长,移动用户对传输速率提出了更高的要求。多天线系统通过在收发端配置多根天线,利用复用/分集增益提高系统的频谱效率和传输可靠性,已经成为了未来移动通信系统的一项基本技术。传统多天线技术主要关注频谱效率,从信息论的角度探寻最大可达容量。与此同时,无线通信系统的高能耗成为了一个严重的问题,提高多天线系统的能量效率,对无线通信系统的绿色化有着重要的实用价值和意义。从频谱效率的角度,采用更大的发送功率、更多的发送天线和更高的带宽可以获得系统容量的提升。但从能量效率的角度,由于发送功率之外的功放线路功率、信号处理功率、馈线损耗等其它部分功率的存在,使得采用更大的发送功率、更多的发送天线和更高的带宽并不总是可以提高系统的能量效率,甚至会带来能量效率的恶化。因此,如何在保证频谱效率的同时提高多天线系统的能量效率是一个亟待解决的问题。本文关注下行多天线系统,对下行多入多出(MIMO)技术三种可能的应用方式：单用户MIMO、多用户MIMO和多小区MISO的能量效率问题进行了系统的研究,提出基于能量效率的自适应传输技术,在保证频谱效率的基础上提高能量效率。
首先,本文针对基于脏纸编码的下行多用户MIMO广播信道能效优化问题,提出了联合发送协方差矩阵优化和激活发送天线选择的能效优化基本框架,得到MIMO广播信道的能量效率理论上界,为能效优化的多用户MIMO传输指明了方向。在固定激活发送天线集合的情况下,利用MIMO广播信道和多址接入信道的对偶性,将MIMO广播信道的能效优化问题转化为凸分数优化问题,提出一种能效优化的迭代功率注水算法,并证明其收敛于全局最优点；在此基础上,提出激活发送天线选择来选取能量效率最优的激活发送天线集合,通过关闭非激活天线集合对应的功率放大器节省线路功率。
接下来,本文针对采用实用低复杂度的线性预编码技术、具有非完善发送端信道状态信息(CSIT)等实际约束的多用户MIMO系统的能效优化问题,提出多模传输技术来优化系统能量效率。由于非完善CSIT的存在将导致多用户干扰,多个用户复用以后,存在复用增益与多用户干扰之间的折中,因此考虑了两种传输策略——基于奇异值分解的单用户MIMO和基于块对角化的多用户MIMO。通过综合考虑发送功率、系统带宽、发送天线、接收天线/用户、传输策略等参数对能量效率的影响,针对以下两种场景,提出联合发送功率/带宽优化和模式选择的策略,提高多用户MIMO系统能量效率,具有很好的实用价值。
1)针对发送天线数目大于等于所有用户接收天线数目的场景,将模式定义为以下参数——传输策略、发送天线数目、接收天线数目和用户数目,进而提出基于各态历经容量的模式切换策略来提高能效。在固定的模式下,针对单用户MIMO和多用户MIMO,分别给出基于能效的发送功率/带宽联合优化方案,并提出一种对抗不完善CSIT的信道容量预测方法来帮助上述优化；在此基础上,进一步基于各态历经容量给出能效优化模式选择算法,为不同场景下最优能量效率对应的模式提供了指导意义。
2)针对发送天线数目小于所有用户接收天线数目的场景,将模式定义为以下参数——传输策略、激活发送天线集合、激活接收天线集合和激活用户集合,进而提出基于联合收发天线选择的模式切换策略来提高能效。在固定的模式下,把上述场景一的发送功率/带宽联合优化方案扩展到了基站总发送功率受限的情况；在此基础上,提出一种低复杂度的能效优化联合收发天线选择算法,并通过仿真结果验证,发现该算法可以获得与最优遍历搜索近似的性能；进一步将算法应用于非实时业务场景下,通过利用延时-能耗折中关系,降低系统的能耗。
最后,本文针对多小区MISO系统的能量效率问题,定义新的多小区能效度量准则,提出不同协作等级的传输策略和一种新的协同静默技术,在保证用户服务质量的同时提高多小区MISO系统的能量效率。通过利用网络能量效率作为多小区能效度量准则,提出了采用线性预编码方式下,三种基于不同协作等级的传输策略,并根据不同协作等级分别得到基于能效的功率分配方案,在保证频谱效率的情况下最大化网络能量效率。在此基础上,创新性的提出了协同静默技术,通过基站间协作利用基站短时睡眠,协同关闭基站的功率放大器,进一步提高网络能量效率。
As the continuous development of the mobile Internet, explosive growth of new services and mobile applications, the mobile users require higher and higher data rates. Through deploying multiple antennas at the transmitters and receivers, multi-antenna systems can highly increase the spectral efficiency (SE) and transmission reliability through employing the multiplexing/diversity gain. Therefore, multi-antenna technology has become one of the key technologies for the future mobile communication systems. The research on the conventional multi-antenna technology mainly focuss on the spectral efficiency and tries to derive the achievable capacity from the information theory point of view. However, the high energy consumption is becoming a serious problem for the mobile networks and green mobile networks have drawn increasing attentions recently. Thus, how to increase the energy efficiency (EE) of the multi-antenna systems is significant in practice. From the standpoint of SE, employing higher transmit power, more transmit antennas and higher bandwidth can increase the SE. However, it is not the case from the standpoint of EE. Due to the existence of power consumed by circuit, signal processing and feeder etc. except for the transmit power, increasing the transmit power, transmit antenna number and bandwidth cannot always increase the EE and would even decrease it. Therefore, a big challenge for the future green mobile networks is how to increase the EE with guaranteed SE. This thesis focuses on the EE of the downlink multi-antenna systems and studies the EE of single user (SU) multiple input multiple output (MIMO), multiuser (MU) MIMO and multi-cell MIMO from a system level. Through EE based adaptive transmission technologies, the EE can be improved significantly with some SE constraints.
Firstly, we study the EE maximization problem for the MIMO broadcasting channels under dirty paper coding (DPC). We propose a new optimization framework, in which transmit covariance optimization and active transmit antenna selection (ATAS) are designed jointly. This framework can reach the EE upper bound and point out the directions of energy efficient multiuser MIMO transmission. To optimize the EE under a fixed transmit antenna set, we propose an energy efficient iterative waterfilling scheme, through transforming the problem into a concave fractional optimization via uplink-downlink duality. It is proved that the proposed scheme converges to the global optimality. After that, ATAS is employed to determine the active transmit antenna set and to turn off the rest inactive antennas to save the circuit power.
After that, this thesis addresses EE optimization for the downlink MIMO systems with linear precoding and imperfect channel state information at the transmitter (CSIT), and proposes multimode transmission technology to solve this problem. Due to the existence of imperfect CSIT, there exists a tradeoff between the interuser interference and the multiplexing gain, so two transmission schemes including SU-MIMO with singular value decomposition (SVD) and MU-MIMO with block diagonalization (BD) are considered here. Considering the effect of transmit power, bandwidth, transmission schemes, transmit antennas and receive antennas in a comprehensive manner, we propose joint bandwidth/power adaptation and mode switching to improve the EE for the following two scenarios, which is valuable in practice.
1) Over the scenario when the transmit antenna number is larger than or equal to the total receive antenna number, the mode is defined as the following parameters, i.e. transmission schemes (SVD or BD), transmitter antenna number, receiver antenna number and user number. Under a fixed mode, we develop a joint transmit power and bandwidth adaptation scheme for both SU-MIMO and MU-MIMO, and employ capacity prediction schemes to combat the effect of the imperfect CSIT. After that, we propose a mode selection scheme based on the ergodic capacity, which provides guidelines on the preferred mode under different scenarios.
2) Over the scenario when the transmit antenna number is smaller than the total receive antenna number, the mode is defined as the following parameters, i.e. transmission schemes (SVD or BD), active transmitter antenna set, active receiver antenna set and active user set. Under a fixed mode, we extend the above joint transmit power and bandwidth adaptation scheme in scenario1) to the case with maximum transmit power constraints. After that, we propose a low complexity joint active transmit and receive antenna selection scheme, which can achieve performance close to the optimal exhaust search based on the simulation results. Finally, we apply the algorithms in non-real time sessions and we can see the energy is saved significantly through employing the delay tolerance based on the energy-delay tradeoff.
Finally, we address the EE optimization problem for the multi-cell MISO systems, and introduce a novel EE metric of network EE (NEE) for the multi-cell systems. We propose three transmission schemes with different cooperative levels and further propose a cooperative idling (CI) scheme to improve the NEE with each user's SE constraints. Based on the NEE metric, we develop energy efficient power control strategies for three schemes with linear precoders. After that, CI is proposed. Through employing the micro-sleep cooperatively among different base stations and switching off the power amplifiers (PA) cooperatively, the NEE can be further improved.
首先,本文针对基于脏纸编码的下行多用户MIMO广播信道能效优化问题,提出了联合发送协方差矩阵优化和激活发送天线选择的能效优化基本框架,得到MIMO广播信道的能量效率理论上界,为能效优化的多用户MIMO传输指明了方向。在固定激活发送天线集合的情况下,利用MIMO广播信道和多址接入信道的对偶性,将MIMO广播信道的能效优化问题转化为凸分数优化问题,提出一种能效优化的迭代功率注水算法,并证明其收敛于全局最优点；在此基础上,提出激活发送天线选择来选取能量效率最优的激活发送天线集合,通过关闭非激活天线集合对应的功率放大器节省线路功率。
接下来,本文针对采用实用低复杂度的线性预编码技术、具有非完善发送端信道状态信息(CSIT)等实际约束的多用户MIMO系统的能效优化问题,提出多模传输技术来优化系统能量效率。由于非完善CSIT的存在将导致多用户干扰,多个用户复用以后,存在复用增益与多用户干扰之间的折中,因此考虑了两种传输策略——基于奇异值分解的单用户MIMO和基于块对角化的多用户MIMO。通过综合考虑发送功率、系统带宽、发送天线、接收天线/用户、传输策略等参数对能量效率的影响,针对以下两种场景,提出联合发送功率/带宽优化和模式选择的策略,提高多用户MIMO系统能量效率,具有很好的实用价值。
1)针对发送天线数目大于等于所有用户接收天线数目的场景,将模式定义为以下参数——传输策略、发送天线数目、接收天线数目和用户数目,进而提出基于各态历经容量的模式切换策略来提高能效。在固定的模式下,针对单用户MIMO和多用户MIMO,分别给出基于能效的发送功率/带宽联合优化方案,并提出一种对抗不完善CSIT的信道容量预测方法来帮助上述优化；在此基础上,进一步基于各态历经容量给出能效优化模式选择算法,为不同场景下最优能量效率对应的模式提供了指导意义。
2)针对发送天线数目小于所有用户接收天线数目的场景,将模式定义为以下参数——传输策略、激活发送天线集合、激活接收天线集合和激活用户集合,进而提出基于联合收发天线选择的模式切换策略来提高能效。在固定的模式下,把上述场景一的发送功率/带宽联合优化方案扩展到了基站总发送功率受限的情况；在此基础上,提出一种低复杂度的能效优化联合收发天线选择算法,并通过仿真结果验证,发现该算法可以获得与最优遍历搜索近似的性能；进一步将算法应用于非实时业务场景下,通过利用延时-能耗折中关系,降低系统的能耗。
最后,本文针对多小区MISO系统的能量效率问题,定义新的多小区能效度量准则,提出不同协作等级的传输策略和一种新的协同静默技术,在保证用户服务质量的同时提高多小区MISO系统的能量效率。通过利用网络能量效率作为多小区能效度量准则,提出了采用线性预编码方式下,三种基于不同协作等级的传输策略,并根据不同协作等级分别得到基于能效的功率分配方案,在保证频谱效率的情况下最大化网络能量效率。在此基础上,创新性的提出了协同静默技术,通过基站间协作利用基站短时睡眠,协同关闭基站的功率放大器,进一步提高网络能量效率。
As the continuous development of the mobile Internet, explosive growth of new services and mobile applications, the mobile users require higher and higher data rates. Through deploying multiple antennas at the transmitters and receivers, multi-antenna systems can highly increase the spectral efficiency (SE) and transmission reliability through employing the multiplexing/diversity gain. Therefore, multi-antenna technology has become one of the key technologies for the future mobile communication systems. The research on the conventional multi-antenna technology mainly focuss on the spectral efficiency and tries to derive the achievable capacity from the information theory point of view. However, the high energy consumption is becoming a serious problem for the mobile networks and green mobile networks have drawn increasing attentions recently. Thus, how to increase the energy efficiency (EE) of the multi-antenna systems is significant in practice. From the standpoint of SE, employing higher transmit power, more transmit antennas and higher bandwidth can increase the SE. However, it is not the case from the standpoint of EE. Due to the existence of power consumed by circuit, signal processing and feeder etc. except for the transmit power, increasing the transmit power, transmit antenna number and bandwidth cannot always increase the EE and would even decrease it. Therefore, a big challenge for the future green mobile networks is how to increase the EE with guaranteed SE. This thesis focuses on the EE of the downlink multi-antenna systems and studies the EE of single user (SU) multiple input multiple output (MIMO), multiuser (MU) MIMO and multi-cell MIMO from a system level. Through EE based adaptive transmission technologies, the EE can be improved significantly with some SE constraints.
Firstly, we study the EE maximization problem for the MIMO broadcasting channels under dirty paper coding (DPC). We propose a new optimization framework, in which transmit covariance optimization and active transmit antenna selection (ATAS) are designed jointly. This framework can reach the EE upper bound and point out the directions of energy efficient multiuser MIMO transmission. To optimize the EE under a fixed transmit antenna set, we propose an energy efficient iterative waterfilling scheme, through transforming the problem into a concave fractional optimization via uplink-downlink duality. It is proved that the proposed scheme converges to the global optimality. After that, ATAS is employed to determine the active transmit antenna set and to turn off the rest inactive antennas to save the circuit power.
After that, this thesis addresses EE optimization for the downlink MIMO systems with linear precoding and imperfect channel state information at the transmitter (CSIT), and proposes multimode transmission technology to solve this problem. Due to the existence of imperfect CSIT, there exists a tradeoff between the interuser interference and the multiplexing gain, so two transmission schemes including SU-MIMO with singular value decomposition (SVD) and MU-MIMO with block diagonalization (BD) are considered here. Considering the effect of transmit power, bandwidth, transmission schemes, transmit antennas and receive antennas in a comprehensive manner, we propose joint bandwidth/power adaptation and mode switching to improve the EE for the following two scenarios, which is valuable in practice.
1) Over the scenario when the transmit antenna number is larger than or equal to the total receive antenna number, the mode is defined as the following parameters, i.e. transmission schemes (SVD or BD), transmitter antenna number, receiver antenna number and user number. Under a fixed mode, we develop a joint transmit power and bandwidth adaptation scheme for both SU-MIMO and MU-MIMO, and employ capacity prediction schemes to combat the effect of the imperfect CSIT. After that, we propose a mode selection scheme based on the ergodic capacity, which provides guidelines on the preferred mode under different scenarios.
2) Over the scenario when the transmit antenna number is smaller than the total receive antenna number, the mode is defined as the following parameters, i.e. transmission schemes (SVD or BD), active transmitter antenna set, active receiver antenna set and active user set. Under a fixed mode, we extend the above joint transmit power and bandwidth adaptation scheme in scenario1) to the case with maximum transmit power constraints. After that, we propose a low complexity joint active transmit and receive antenna selection scheme, which can achieve performance close to the optimal exhaust search based on the simulation results. Finally, we apply the algorithms in non-real time sessions and we can see the energy is saved significantly through employing the delay tolerance based on the energy-delay tradeoff.
Finally, we address the EE optimization problem for the multi-cell MISO systems, and introduce a novel EE metric of network EE (NEE) for the multi-cell systems. We propose three transmission schemes with different cooperative levels and further propose a cooperative idling (CI) scheme to improve the NEE with each user's SE constraints. Based on the NEE metric, we develop energy efficient power control strategies for three schemes with linear precoders. After that, CI is proposed. Through employing the micro-sleep cooperatively among different base stations and switching off the power amplifiers (PA) cooperatively, the NEE can be further improved.
引文
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