异构网络下干扰管理与资源分配算法研究
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摘要
异构网络技术被普遍认为是第三代伙伴计划(3GPP)制定增强型长期演进(LTE-Advanced)标准的关键候选技术之一。异构网络技术融合了多种不同类型的基站,包括宏蜂窝基站、微蜂窝基站、微微蜂窝基站、毫微微蜂窝基站、中继基站和射频单元节点。异构网络技术在传统宏蜂窝基站的覆盖区域中部署了多种类型的低功率基站,用于消除传统宏蜂窝基站的覆盖“盲区”和“忙区”,并大幅提升蜂窝网络的总容量。相比于传统的宏蜂窝基站,低功率基站(如微微蜂窝基站、家庭基站和中继基站)是一种更经济的方式来提高网络的容量。但是,在异构网络中同时部署多种类型基站,不可避免的出现不同基站覆盖区域重叠的现象。因此,异构网络需要解决复杂的小区间干扰。
本文重点研究了异构网络小区间干扰协调算法、基于功率消耗和干扰抑制的资源优化算法。异构网络小区间干扰协调算法可以有效降低小区间干扰,提高小区边缘用户的信道质量,进一步提高网络的容量和用户的性能。资源优化算法可以在满足用户需求的前提下,降低异构网络的总功率损耗,并抑制网络总干扰功率。主要研究工作和贡献如下：
提出了一种异构网络小区间干扰协调算法。根据服务用户和相邻基站的干扰关系,建立由基站节点和干扰关系连线组成的干扰图。研究了一种基于图节点染色算法的正交频带分配算法,为干扰图中相邻基站节点分配互不重叠的频带资源,在分配的正交频带完全消除了来自相邻小区的强干扰。正交频带分配方案是根据边缘用户需要满足的信噪比要求分配频带资源,可以有效控制频带内的干扰并保证边缘用户的信道质量。
为了进一步提高频谱利用率,在正交频带分配算法的基础上,提出了一种基于用户信道差异的自适应频带分配算法。自适应频带分配算法是根据本基站用户和相邻基站用户的信道质量信息与信噪比门限的比较结果,采用“申请-反馈-判决”三个连续步骤,每个基站利用相邻基站反馈的共用信息独立判断频带是否可以共用。自适应频带分配算法只需要基站间交换一定数量的频带申请、反馈信息,就可以实现资源的快速分配,提高了频谱利用率。
提出一种基于功率优化的资源分配算法。该算法在保证用户最小传输速率的限制条件下,优化异构网络中基站的功率资源和频率资源的分配。基于功率优化的资源分配算法包括集中式算法和分布式算法。区别于传统的优化算法,集中式算法综合考虑了基站负载因子、用户满意度、基站调度优先级和基站发射功率等因素,利用效用函数矩阵确定基站和用户使用频率资源的顺序,并根据已分配资源的用户的受限矩阵限制存在较强干扰的相邻基站使用相同的资源。最后,联合计算相同频带上所有基站的发射功率。集中式算法可以协调分配所有基站的频率和功率资源,并降低异构网络内所有基站消耗的总功率。而分布式算法是根据用户类型把基站使用的频带划分为正交频带和非正交频带。小区边缘用户优先使用正交频带资源可以消除来自相邻小区的干扰,而小区中心用户在所有频带内都满足信噪比门限要求。结合功率控制算法和用户频带扩展可以进一步降低基站下行链路需要的发射功率。分布式算法只需要相邻基站之间的信道信息和干扰信息就可以优化分配基站的频率资源和功率资源。
最后一部分提出了一种基于干扰抑制的分布式资源分配算法。由于自适应调制与编码技术的应用,基站根据用户的信道条件指定用户的调制与编码方式(MCS)。但是,用户的调制与编码方式也会影响基站的功率消耗、网络内总干扰功率。首先,分布式算法根据每个基站服务用户可能采用的调制与编码方式组成所有用户的调制与编码方式搜索空间。随后,分布式算法按照搜索空间中用户的调制与编码方式的组合,根据网络总干扰功率增量最小准则,确定频率资源和功率资源的分配结果。最后,根据异构网络总干扰功率最小的原则,确定用户的调制与编码方式、频率资源和功率资源的最优分配结果。
Heterogeneous networks (HetNets) technique has been widely considered to be accepted by3rd Generation Partnership Project (3GPP) as one of the key candidate technologies of LTE-Advanced standards. HetNets consist of several types of base stations, such as macrocell, microcell, picocell, femtocell, relay and remote radio head (RRH). HetNets deploy multi-type of low power base stations in the coverage area of traditional cellular system, in order to eliminate the areas of both blind-zone and hot-zone, which will tremendously increase the network capacity. Compared with traditional macro cellular base stations, low power base stations, such as picocell, femtocell and relay, are a more economical approach to enhance the network capacity. However, a phenomenon cannot be avoided that the coverage areas of different base stations deployed in the same area simultaneously would be overlapping. Therefore, the complicated inter-cell interference is required to be tackled.
The inter-cell interference coordination algorithm, the resource optimization algorithms to minimize the total power consumption and the network interference power level, are researched in the thesis. The inter-cell interference coordination algorithm could effectively decrease the inter-cell interference power level and improve the channel qualities of cell-edge users, which would further enhance the network capacity and users'performance. Resource optimization algorithms reduce the power consumption and suppress the total interference power level of the network, while satisfy the users demands. The main research work and contributions are summarized as follows:
A novel algorithm to coordinate the inter-cell interference in HetNets is firstly proposed. On the basis of the interference relationship of users being served and neighbor base stations, an interference graph is constructed by the nodes and edges, which represent the base stations and the interference relationships respectively. An orthogonal spectrum allocation algorithm is studied, which is based on the coloring algorithm according to the vertex degree to assign the adjacent base stations in the graph with non-overlapping frequency bandwidth. The algorithm eliminates the strong interference from adjacent base stations is eliminated. The orthogonal frequency bandwidth is assigned to the base stations, which effectively controls the interference power level in the bandwidth and satisfies the signal to interference plus noise ratio (SINR) requirements of cell-edge users.
To further improve the spectrum utilization ratio than the study above, an adaptive frequency resource allocation algorithm is proposed to explore the variations of the users'channel qualities. Based on the comparison results of the SINR threshold and the channel quality information from both the users being served and nearby users of the adjacent base stations, the adaptive frequency allocation algorithm is consisted of three consecutive steps as'request-reply-decision', in which each base station independently determines whether the spectrum could be shared according to the feedback spectrum sharing information. The adaptive frequency resource allocation algorithm only needs to exchange limited information including the bandwidth request and feedback spectrum sharing information, and quickly assigns the frequency resource to improve the spectrum utilization ratio.
The resource allocation algorithms are well researched in the thesis, which optimize the power allocation in HetNets scenario and guarantee the users' minimum transmission rate. The power optimization algorithms consist of the centralized algorithm and the distributed algorithm. Different from other optimization methods, several factors including the load of base stations, users' dissatisfactory ratio, scheduling priority and the downlink transmission power, are jointly considered to design the centralized algorithm. The scheduling priorities of base stations and users are determined according to the utility values in the centralized algorithm. But the base stations in the restricted matrix are forbidden to share the same bandwidth to eliminate the strong co-channel interference received by the users of the adjacent base stations. The transmission power levels of all base stations, which are selected as the values of the utility matrix in ascending order, within the same bandwidth are jointly calculated. The power and frequency resource allocation of all the base stations are coordinated to minimize the total power consumption of all the base station in the centralized algorithm. However, the total bandwidth is classified into the orthogonal bandwidth and non-orthogonal bandwidth according to users' types in the distributed algorithm. The cell-edge users are permitted to occupy the orthogonal bandwidth with higher priority to eliminate the interference from neighboring base stations, while the cell-center users could satisfy the SINR threshold requirements in the whole bandwidth. With the power control algorithm and user bandwidth extension, the downlink transmission power consumption of each base station is further reduced to a lower level. The channel quality and interference information, which is used to perform the deployment optimization of both the frequency resource and power allocation, is just required to exchange among the adjacent base stations in the distributed algorithm.
A distributed resource allocation algorithm is proposed in the last section, which is designed to suppress the interference power level. With the application of adaptive modulation and coding (AMC) technique, the modulation and coding scheme (MCS) of the users are determined by the serving base stations according to the channel qualities. However, the MCS levels of users have great influence on the power consumption of base stations and the total interference power level of the network. The distributed algorithm firstly constructs the MCS search space according to all the possible combinations of the MCS levels of all users. Then, the distributed algorithm determines the frequency and power resource allocation results depending on the selected MCS combination of users, which is in accordance with the rule of the minimum increment of total network interference power. Finally, the optimal assignment results of the MCS levels of all users, frequency resource and power level are determined according to the criterion of minimizing interference power level of the HetNets.
本文重点研究了异构网络小区间干扰协调算法、基于功率消耗和干扰抑制的资源优化算法。异构网络小区间干扰协调算法可以有效降低小区间干扰,提高小区边缘用户的信道质量,进一步提高网络的容量和用户的性能。资源优化算法可以在满足用户需求的前提下,降低异构网络的总功率损耗,并抑制网络总干扰功率。主要研究工作和贡献如下：
提出了一种异构网络小区间干扰协调算法。根据服务用户和相邻基站的干扰关系,建立由基站节点和干扰关系连线组成的干扰图。研究了一种基于图节点染色算法的正交频带分配算法,为干扰图中相邻基站节点分配互不重叠的频带资源,在分配的正交频带完全消除了来自相邻小区的强干扰。正交频带分配方案是根据边缘用户需要满足的信噪比要求分配频带资源,可以有效控制频带内的干扰并保证边缘用户的信道质量。
为了进一步提高频谱利用率,在正交频带分配算法的基础上,提出了一种基于用户信道差异的自适应频带分配算法。自适应频带分配算法是根据本基站用户和相邻基站用户的信道质量信息与信噪比门限的比较结果,采用“申请-反馈-判决”三个连续步骤,每个基站利用相邻基站反馈的共用信息独立判断频带是否可以共用。自适应频带分配算法只需要基站间交换一定数量的频带申请、反馈信息,就可以实现资源的快速分配,提高了频谱利用率。
提出一种基于功率优化的资源分配算法。该算法在保证用户最小传输速率的限制条件下,优化异构网络中基站的功率资源和频率资源的分配。基于功率优化的资源分配算法包括集中式算法和分布式算法。区别于传统的优化算法,集中式算法综合考虑了基站负载因子、用户满意度、基站调度优先级和基站发射功率等因素,利用效用函数矩阵确定基站和用户使用频率资源的顺序,并根据已分配资源的用户的受限矩阵限制存在较强干扰的相邻基站使用相同的资源。最后,联合计算相同频带上所有基站的发射功率。集中式算法可以协调分配所有基站的频率和功率资源,并降低异构网络内所有基站消耗的总功率。而分布式算法是根据用户类型把基站使用的频带划分为正交频带和非正交频带。小区边缘用户优先使用正交频带资源可以消除来自相邻小区的干扰,而小区中心用户在所有频带内都满足信噪比门限要求。结合功率控制算法和用户频带扩展可以进一步降低基站下行链路需要的发射功率。分布式算法只需要相邻基站之间的信道信息和干扰信息就可以优化分配基站的频率资源和功率资源。
最后一部分提出了一种基于干扰抑制的分布式资源分配算法。由于自适应调制与编码技术的应用,基站根据用户的信道条件指定用户的调制与编码方式(MCS)。但是,用户的调制与编码方式也会影响基站的功率消耗、网络内总干扰功率。首先,分布式算法根据每个基站服务用户可能采用的调制与编码方式组成所有用户的调制与编码方式搜索空间。随后,分布式算法按照搜索空间中用户的调制与编码方式的组合,根据网络总干扰功率增量最小准则,确定频率资源和功率资源的分配结果。最后,根据异构网络总干扰功率最小的原则,确定用户的调制与编码方式、频率资源和功率资源的最优分配结果。
Heterogeneous networks (HetNets) technique has been widely considered to be accepted by3rd Generation Partnership Project (3GPP) as one of the key candidate technologies of LTE-Advanced standards. HetNets consist of several types of base stations, such as macrocell, microcell, picocell, femtocell, relay and remote radio head (RRH). HetNets deploy multi-type of low power base stations in the coverage area of traditional cellular system, in order to eliminate the areas of both blind-zone and hot-zone, which will tremendously increase the network capacity. Compared with traditional macro cellular base stations, low power base stations, such as picocell, femtocell and relay, are a more economical approach to enhance the network capacity. However, a phenomenon cannot be avoided that the coverage areas of different base stations deployed in the same area simultaneously would be overlapping. Therefore, the complicated inter-cell interference is required to be tackled.
The inter-cell interference coordination algorithm, the resource optimization algorithms to minimize the total power consumption and the network interference power level, are researched in the thesis. The inter-cell interference coordination algorithm could effectively decrease the inter-cell interference power level and improve the channel qualities of cell-edge users, which would further enhance the network capacity and users'performance. Resource optimization algorithms reduce the power consumption and suppress the total interference power level of the network, while satisfy the users demands. The main research work and contributions are summarized as follows:
A novel algorithm to coordinate the inter-cell interference in HetNets is firstly proposed. On the basis of the interference relationship of users being served and neighbor base stations, an interference graph is constructed by the nodes and edges, which represent the base stations and the interference relationships respectively. An orthogonal spectrum allocation algorithm is studied, which is based on the coloring algorithm according to the vertex degree to assign the adjacent base stations in the graph with non-overlapping frequency bandwidth. The algorithm eliminates the strong interference from adjacent base stations is eliminated. The orthogonal frequency bandwidth is assigned to the base stations, which effectively controls the interference power level in the bandwidth and satisfies the signal to interference plus noise ratio (SINR) requirements of cell-edge users.
To further improve the spectrum utilization ratio than the study above, an adaptive frequency resource allocation algorithm is proposed to explore the variations of the users'channel qualities. Based on the comparison results of the SINR threshold and the channel quality information from both the users being served and nearby users of the adjacent base stations, the adaptive frequency allocation algorithm is consisted of three consecutive steps as'request-reply-decision', in which each base station independently determines whether the spectrum could be shared according to the feedback spectrum sharing information. The adaptive frequency resource allocation algorithm only needs to exchange limited information including the bandwidth request and feedback spectrum sharing information, and quickly assigns the frequency resource to improve the spectrum utilization ratio.
The resource allocation algorithms are well researched in the thesis, which optimize the power allocation in HetNets scenario and guarantee the users' minimum transmission rate. The power optimization algorithms consist of the centralized algorithm and the distributed algorithm. Different from other optimization methods, several factors including the load of base stations, users' dissatisfactory ratio, scheduling priority and the downlink transmission power, are jointly considered to design the centralized algorithm. The scheduling priorities of base stations and users are determined according to the utility values in the centralized algorithm. But the base stations in the restricted matrix are forbidden to share the same bandwidth to eliminate the strong co-channel interference received by the users of the adjacent base stations. The transmission power levels of all base stations, which are selected as the values of the utility matrix in ascending order, within the same bandwidth are jointly calculated. The power and frequency resource allocation of all the base stations are coordinated to minimize the total power consumption of all the base station in the centralized algorithm. However, the total bandwidth is classified into the orthogonal bandwidth and non-orthogonal bandwidth according to users' types in the distributed algorithm. The cell-edge users are permitted to occupy the orthogonal bandwidth with higher priority to eliminate the interference from neighboring base stations, while the cell-center users could satisfy the SINR threshold requirements in the whole bandwidth. With the power control algorithm and user bandwidth extension, the downlink transmission power consumption of each base station is further reduced to a lower level. The channel quality and interference information, which is used to perform the deployment optimization of both the frequency resource and power allocation, is just required to exchange among the adjacent base stations in the distributed algorithm.
A distributed resource allocation algorithm is proposed in the last section, which is designed to suppress the interference power level. With the application of adaptive modulation and coding (AMC) technique, the modulation and coding scheme (MCS) of the users are determined by the serving base stations according to the channel qualities. However, the MCS levels of users have great influence on the power consumption of base stations and the total interference power level of the network. The distributed algorithm firstly constructs the MCS search space according to all the possible combinations of the MCS levels of all users. Then, the distributed algorithm determines the frequency and power resource allocation results depending on the selected MCS combination of users, which is in accordance with the rule of the minimum increment of total network interference power. Finally, the optimal assignment results of the MCS levels of all users, frequency resource and power level are determined according to the criterion of minimizing interference power level of the HetNets.
引文
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[30]李慧霸,王凤芹,图论简明教程,清华大学出版社,2004年。
[1]Lopez-Perez, D.; Guvenc, I.; de la Roche, G.; Kountouris, M.; Quek, T.Q.S.; Jie Zhang, "Enhanced intercell interference coordination challenges in heterogeneous networks," Wireless Communications, IEEE, vol.18, no.3, pp.22-30, June 2011.
[2]Chandrasekhar, V.; Andrews, J.G.; Muharemovic, T.; Zukang Shen; Gatherer, A., "Power control in two-tier femtocell networks," Wireless Communications, IEEE Transactions on, vol.8, no.8, pp.4316-4328, August 2009.
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[1]Damnjanovic, A.; Montojo, J.; Yongbin Wei; Tingfang Ji; Tao Luo; Vajapeyam, M.; Taesang Yoo; Osok Song; Malladi, D., "A survey on 3GPP heterogeneous networks," Wireless Communications, IEEE, vol.18, no.3, pp.10-21, June 2011.
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[30]李慧霸,王凤芹,图论简明教程,清华大学出版社,2004年。
[1]Lopez-Perez, D.; Guvenc, I.; de la Roche, G.; Kountouris, M.; Quek, T.Q.S.; Jie Zhang, "Enhanced intercell interference coordination challenges in heterogeneous networks," Wireless Communications, IEEE, vol.18, no.3, pp.22-30, June 2011.
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[23]3GPP TS 36.213 v 10.2.0, "Evolved Universal Terrestrial Radio Access (E-UTRA) Physical layer procedures," June 2011.
[24]3GPP TR 36.814 (V9.0.0), "Evolved Universal Terrestrial Radio Access (E-UTRA) Further advancements for E-UTRA physical layer aspects (Release 9), " 2010.