基于MIMO OFDM无线资源管理技术的研究
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摘要
第四代移动系统包含多种关键性的技术,其中多入多出(MIMO)和正交频分复用(OFDM)是最有前景的两项物理层技术。MIMO技术能够极大的提高系统容量,而OFDM技术能够抵抗多径干扰,因此MIMO OFDM技术能够为未来移动通信网络提供最强劲的空中接口支撑。然而,频谱资源的日趋紧张,使得运营商提供无线多业务服务需要付出昂贵的频谱费用。因此,频谱效率是决定采用一种技术的非常重要的因素,对B3G系统也是如此。无线资源管理的目标就是在满足用户QoS需求的情况下,在有限的带宽上最大限度地提高频谱效率和系统容量,同时避免网络拥塞的发生。因此,本文主要研究基于MIMO OFDM系统的无线资源管理技术。
系统容量对于无线资源管理的研究具有指导意义,因此本文首先研究了MIMO OFDM系统的容量。利用MIMO OFDM信道在空时频三维衰落的特性,提出了空时频三维注水算法,即在空时频三维进行功率分配。因此,同空频注水算法和等功率分配算法相比,该算法可以获得最大的各态历经信道容量。同时,相对于空频注水算法来说,由于空时频注水水平的计算是基于信道的统计状态信息,因此其需要反馈的信道状态信息量少,而且对于平坦衰落和频率选择性衰落信道都适用。另外,由于不需要每个功率分配间隔都计算注水水平,因此,空时频注水水平的计算复杂度也要低很多。
按照七层协议的定义标准,目前,MIMO OFDM无线资源管理技术的研究成果主要集中在物理层以及PHY-MAC跨层分配上。本文首先在物理层对MIMO OFDM波束成型系统的无线资源管理技术进行了研究。通过对几种MIMO OFDM波束成型技术进行性能分析,考虑到各种因素,本文采用直接共享的波束成型方式,其实质上是选定了空分多址(SDMA)的方式。接下来,建立了OFDMA-SDMA MIMO OFDM波束成型系统无线资源管理的数学模型。该模型不仅考虑了空间上的多用户分集,还考虑到每个子载波的多个空间信道同时进行并行传输以最大限度的提高资源利用率。在这里,每个子载波和它的空间子信道的组合被定义为一组空频子信道。比较遗憾的是,由于存在同信道干扰,该问题在数学上是个NP-hard的问题。为了解决这个问题,我们提出了次优的空频子信道分配方案。该方案分为三个步骤:第一,假设不存在子载波共享,经过推导,我们获得最佳的空频子信道分配方案;第二,基于第一步的子载波分配结果,在空间相关性足够小的用户之间进行子载波共享;第三步,逐个用户在所有的空频子信道上进行比特加载和功率分配。为了使得空频子信道分配算法更为实用化,本文又提出了一种实用的空频子信道分配算法。该算法对于每个子载波基于次优算法中推导出的子载波分配表达式给出新的子载波分配因子,利用该子载波分配因子进行子载波分配,简化了计算复杂度。同时,结合自身的特点和反馈信息量降低的要求,又提出了一种插值波束成型反馈方案,以使得实用化算法在限制反馈的信道条件下也能使用。
跨层资源分配算法既考虑了上层的业务QoS特性,又考虑底层信道状态,成为无线通信系统中比较有效的资源调度方式。本文研究了基于MIMO OFDM技术的IEEE 802.16系统中的跨层资源调度算法。跨层设计有两种理念:第一,自下而上,将下层信道信息传递到上层,在上层进行资源调度;第二,自上而下,将上层业务的QoS要求映射到下层,在下层进行资源分配。在本文中,QoS保证的公平性算法属于自下而上的方式,即将等效信道增益映射到上层来代表信道状态。然后综合考虑业务QoS需求、调度器的状态、业务的公平性以及信道状态,定义子信道分配因子,利用该子信道分配因子进行资源块的分配,最后对每个连接进行比特加载和功率分配。而QoS保证的最优化调度算法则是基于自上而下的思想,即将上层实时业务对时延的要求转换为物理层对实时业务的最低调度速率的要求,从而将资源调度问题放到物理层来做。以最大化系统容量为目标,通过在物理层求解,获得了最优化的子载波、比特和功率分配方案。仿真结果表明,这两种方案,QoS保证的公平性算法的系统吞吐量要小于QoS保证的最优化算法,但是前者的公平性要好于后者。
The 4~(th) wireless communication system proposed many novel technologies, the most important two of which are multi-input multi-output (MIMO) and orthogonal frequency division multiplexing (OFDM). MIMO can provide a huge amount of the system capacity and OFDM can well defeat the inter-symbol interference (ISI), hence the combination of MIMO and OFDM must be the strongest air interface technology for the 4~(th) wireless communication. However, the frequency resource will become more and more emergent for guaranteed QoS requirements of various services. The frequency efficiency always decides the commercialization of a technology and The 4~(th) wireless communication system is no expectation. Radio resource management (RRM) is just a technology devoting to improve the frequency efficiency and providing QoS guarantees for any service. Hence, in this dissertation, we mainly focus on researches of the radio resource management in MIMO OFDM systems.
The channel capacity can give some instruments to RRM. Hence, we first discuss the ergodic capacity of MIMO OFDM systems. The space-time-frequency waterfilling scheme (STFWF) is proposed to maximize the ergodic capacity through allocating power in the space, time and frequency domains. It keeps the long-term power constant and allows the short-term power fluctuating. Compared with space-frequency waterfilling scheme, STFWF scheme has the following merits. Since the waterfilling level is calculated based on long-term channel information in the STFWF scheme, the channel statistic information is only needed which can reduce the feedback information and computational complexity. Furthermore, it can suit both flat and frequency selective fading channel.
At present, RRM schemes in MIMO OFDM systems are mainly focused on PHY layer and PHY-MAC cross layer. We first make some researches on RRM in MIMO OFDM beamforming systems at PHY layer. After doing some performance analyses on several kinds of beamforming technologies, the subcarrier shared directly one is chosen to provide SDMA among multiuser. Then, the RRM mathematical module is set up in OFDMA-SDMA MIMO OFDM systems. It is different from the conventional ones by considering both space multiuser diversity and all the space subchannels for each subcarrier. Hence, it improves the frequency efficiency to the largest extent. Here, each subcarrier and its space subchannels are defined as a group of space-frequency subchannels. Unfortunately, it is a NP-hard problem from mathematical viewpoint. Hence, the suboptimal space-frequency subchannel allocation and adaptive modulation (SSFSA) is proposed which divides the RRM problem into three steps. First, subcarriers are not shared among users and the optimal space-frequency subchannel allocation outcome is obtained by mathematical deduction. Then, based on the subcarrier allocation in the first step, the subcarriers sharing scheme is proposed to share subcarriers among users with low spatial correlation at the premise that negligible co-channel interferences are introduced. Bit loading and power allocation are made at the third step for each user on its all space-frequency subchannels. Considering the computational complexity of SSFSA and feedback information reduction, a practical space-frequency subchannel allocation and adaptive modulation (PSFSA) is also proposed. This scheme defines an initial subcarrier allocation factor based on the first step subcarrier allocation outcome of SSFSA. It can reduce the computational complaexity to init subcarrier allocation according to the factor. An improved interpolation-based beamforming algorithm is also proposed to reduce the feedback information in PSFSA.
Since considering both the QoS characteristics at the upper layer and the channel information at the PHY layer, cross layer RRM is an efficient method to manage resources in wireless networks. We make some researches on cross layer RRM in IEEE 802.16 MIMO OFDM systems. There are two ways to realize cross layer RRM. The first one is mapping channel information from PHY layer to upper layer and executing RRM at the upper layer. The other one is mapping QoS parameters to PHY layer and allocate resources at the PHY layer. The proposed QoS-guaranteed fairness scheme is the first type which mapping the channel fading information to the MAC layer. The subchannel allocation untility function is defined considering QoS requirements of different services, the status of scheduler, the fairness among connections and the channel fading status. The subchannel is allocated to the connection with the largest subchannel allocation unility and then bit and power allocations are made for each connection. While the optimal QoS-guaranteed resource allocation scheme belongs to the second one which maps the upper rtPS delay requirements to scheduling rate requirements. And then, the capacity-maximized resource allocation problem with scheduling and power constraints is solved by mathematical deduction. From the simulation results in Matlab, the system throughput of QoS-guaranteed fairness scheme is less than that of the optimal QoS-guaranteed resource allocation scheme, while the fairness of the former is better than the later.
系统容量对于无线资源管理的研究具有指导意义,因此本文首先研究了MIMO OFDM系统的容量。利用MIMO OFDM信道在空时频三维衰落的特性,提出了空时频三维注水算法,即在空时频三维进行功率分配。因此,同空频注水算法和等功率分配算法相比,该算法可以获得最大的各态历经信道容量。同时,相对于空频注水算法来说,由于空时频注水水平的计算是基于信道的统计状态信息,因此其需要反馈的信道状态信息量少,而且对于平坦衰落和频率选择性衰落信道都适用。另外,由于不需要每个功率分配间隔都计算注水水平,因此,空时频注水水平的计算复杂度也要低很多。
按照七层协议的定义标准,目前,MIMO OFDM无线资源管理技术的研究成果主要集中在物理层以及PHY-MAC跨层分配上。本文首先在物理层对MIMO OFDM波束成型系统的无线资源管理技术进行了研究。通过对几种MIMO OFDM波束成型技术进行性能分析,考虑到各种因素,本文采用直接共享的波束成型方式,其实质上是选定了空分多址(SDMA)的方式。接下来,建立了OFDMA-SDMA MIMO OFDM波束成型系统无线资源管理的数学模型。该模型不仅考虑了空间上的多用户分集,还考虑到每个子载波的多个空间信道同时进行并行传输以最大限度的提高资源利用率。在这里,每个子载波和它的空间子信道的组合被定义为一组空频子信道。比较遗憾的是,由于存在同信道干扰,该问题在数学上是个NP-hard的问题。为了解决这个问题,我们提出了次优的空频子信道分配方案。该方案分为三个步骤:第一,假设不存在子载波共享,经过推导,我们获得最佳的空频子信道分配方案;第二,基于第一步的子载波分配结果,在空间相关性足够小的用户之间进行子载波共享;第三步,逐个用户在所有的空频子信道上进行比特加载和功率分配。为了使得空频子信道分配算法更为实用化,本文又提出了一种实用的空频子信道分配算法。该算法对于每个子载波基于次优算法中推导出的子载波分配表达式给出新的子载波分配因子,利用该子载波分配因子进行子载波分配,简化了计算复杂度。同时,结合自身的特点和反馈信息量降低的要求,又提出了一种插值波束成型反馈方案,以使得实用化算法在限制反馈的信道条件下也能使用。
跨层资源分配算法既考虑了上层的业务QoS特性,又考虑底层信道状态,成为无线通信系统中比较有效的资源调度方式。本文研究了基于MIMO OFDM技术的IEEE 802.16系统中的跨层资源调度算法。跨层设计有两种理念:第一,自下而上,将下层信道信息传递到上层,在上层进行资源调度;第二,自上而下,将上层业务的QoS要求映射到下层,在下层进行资源分配。在本文中,QoS保证的公平性算法属于自下而上的方式,即将等效信道增益映射到上层来代表信道状态。然后综合考虑业务QoS需求、调度器的状态、业务的公平性以及信道状态,定义子信道分配因子,利用该子信道分配因子进行资源块的分配,最后对每个连接进行比特加载和功率分配。而QoS保证的最优化调度算法则是基于自上而下的思想,即将上层实时业务对时延的要求转换为物理层对实时业务的最低调度速率的要求,从而将资源调度问题放到物理层来做。以最大化系统容量为目标,通过在物理层求解,获得了最优化的子载波、比特和功率分配方案。仿真结果表明,这两种方案,QoS保证的公平性算法的系统吞吐量要小于QoS保证的最优化算法,但是前者的公平性要好于后者。
The 4~(th) wireless communication system proposed many novel technologies, the most important two of which are multi-input multi-output (MIMO) and orthogonal frequency division multiplexing (OFDM). MIMO can provide a huge amount of the system capacity and OFDM can well defeat the inter-symbol interference (ISI), hence the combination of MIMO and OFDM must be the strongest air interface technology for the 4~(th) wireless communication. However, the frequency resource will become more and more emergent for guaranteed QoS requirements of various services. The frequency efficiency always decides the commercialization of a technology and The 4~(th) wireless communication system is no expectation. Radio resource management (RRM) is just a technology devoting to improve the frequency efficiency and providing QoS guarantees for any service. Hence, in this dissertation, we mainly focus on researches of the radio resource management in MIMO OFDM systems.
The channel capacity can give some instruments to RRM. Hence, we first discuss the ergodic capacity of MIMO OFDM systems. The space-time-frequency waterfilling scheme (STFWF) is proposed to maximize the ergodic capacity through allocating power in the space, time and frequency domains. It keeps the long-term power constant and allows the short-term power fluctuating. Compared with space-frequency waterfilling scheme, STFWF scheme has the following merits. Since the waterfilling level is calculated based on long-term channel information in the STFWF scheme, the channel statistic information is only needed which can reduce the feedback information and computational complexity. Furthermore, it can suit both flat and frequency selective fading channel.
At present, RRM schemes in MIMO OFDM systems are mainly focused on PHY layer and PHY-MAC cross layer. We first make some researches on RRM in MIMO OFDM beamforming systems at PHY layer. After doing some performance analyses on several kinds of beamforming technologies, the subcarrier shared directly one is chosen to provide SDMA among multiuser. Then, the RRM mathematical module is set up in OFDMA-SDMA MIMO OFDM systems. It is different from the conventional ones by considering both space multiuser diversity and all the space subchannels for each subcarrier. Hence, it improves the frequency efficiency to the largest extent. Here, each subcarrier and its space subchannels are defined as a group of space-frequency subchannels. Unfortunately, it is a NP-hard problem from mathematical viewpoint. Hence, the suboptimal space-frequency subchannel allocation and adaptive modulation (SSFSA) is proposed which divides the RRM problem into three steps. First, subcarriers are not shared among users and the optimal space-frequency subchannel allocation outcome is obtained by mathematical deduction. Then, based on the subcarrier allocation in the first step, the subcarriers sharing scheme is proposed to share subcarriers among users with low spatial correlation at the premise that negligible co-channel interferences are introduced. Bit loading and power allocation are made at the third step for each user on its all space-frequency subchannels. Considering the computational complexity of SSFSA and feedback information reduction, a practical space-frequency subchannel allocation and adaptive modulation (PSFSA) is also proposed. This scheme defines an initial subcarrier allocation factor based on the first step subcarrier allocation outcome of SSFSA. It can reduce the computational complaexity to init subcarrier allocation according to the factor. An improved interpolation-based beamforming algorithm is also proposed to reduce the feedback information in PSFSA.
Since considering both the QoS characteristics at the upper layer and the channel information at the PHY layer, cross layer RRM is an efficient method to manage resources in wireless networks. We make some researches on cross layer RRM in IEEE 802.16 MIMO OFDM systems. There are two ways to realize cross layer RRM. The first one is mapping channel information from PHY layer to upper layer and executing RRM at the upper layer. The other one is mapping QoS parameters to PHY layer and allocate resources at the PHY layer. The proposed QoS-guaranteed fairness scheme is the first type which mapping the channel fading information to the MAC layer. The subchannel allocation untility function is defined considering QoS requirements of different services, the status of scheduler, the fairness among connections and the channel fading status. The subchannel is allocated to the connection with the largest subchannel allocation unility and then bit and power allocations are made for each connection. While the optimal QoS-guaranteed resource allocation scheme belongs to the second one which maps the upper rtPS delay requirements to scheduling rate requirements. And then, the capacity-maximized resource allocation problem with scheduling and power constraints is solved by mathematical deduction. From the simulation results in Matlab, the system throughput of QoS-guaranteed fairness scheme is less than that of the optimal QoS-guaranteed resource allocation scheme, while the fairness of the former is better than the later.
引文
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