5G凭借快速、高质量的资源分配和系统的高度灵活性，使其可以通过自适应的调整自身性能来面对外界的变化和发展趋势。为了实现对频谱资源的快速和高质量分配，同时考虑降低信道预留资源计算过程中的复杂度，通过信道拟合建立信道预留资源和等效容量之间的理论关系，可以使得系统所提供的服务满足用户业务的时延服务质量（Quality of Service，QoS）约束。为了进一步提升系统资源分配的灵活度，考虑到不同小区内不同的用户业务类型和用户不平衡的上下行业务需求，可以凭借时分双工下的基于滤波器组的正交频分复用（Filtered- Orthogonal Frequency Division Multiplexing，F-OFDM）技术，使得每个小区能够通过获得不同参数的资源块以适配不同类型的业务，并且小区内部能够通过动态调整上下行子帧配置因子以达到用户上下行业务接入率的适配，从而使得整体系统在资源分配时具有高度灵活性。

依据以上系统优化思路，本文以提升多小区多用户多输入多输出（Multiple Input Multiple Output，MIMO）系统的频谱利用率和业务接入率为目的，针对不同小区内不同的用户业务类型和不平衡的上下行业务需求，研究了面向时分双工的多小区F-OFDM动态资源分配技术，具体工作如下：

首先，利用有限状态马尔科夫模型，进行信道状态预测，具体包括利用有限状态马尔科夫模型预测MIMO空间信道状态矩阵和利用有限状态马尔科夫信道模型计算一个调度时隙内的信道预留资源。考虑到有限状态马尔科夫信道模型状态数过多的问题，提出了该模型的状态压缩方法，压缩和合并信道状态数，重建信道模型，因而，在求解具体用户和资源块间信道预留资源时，相关计算的复杂度得到下降。

然后，针对分析在业务时延QoS约束下的资源分配问题，采用混合高斯函数拟合的方法实现对信道容量概率分布的逼近，搭建物理层中的信道预留资源与MAC层中基于业务时延QoS指数的等效容量之间的关系，使得系统所提供的服务可以满足用户业务的时延QoS约束，为后续研究系统资源分配提供理论支撑。

最后，研究了在多小区多用户MIMO系统中如何利用时分双工技术联合上行链路和下行链路，动态的实现系统预留资源与每个小区内用户业务需求的适配，小区内部完成物理层和MAC层的资源跨层迭代过程，即各小区灵活的设置各自的子带参数和上下行子帧配置因子，利用虚拟MIMO技术、F-OFDM技术和时分双工技术，以均衡用户上行和下行业务接入率为目标，对小区内用户上行和下行业务需求进行资源适配，各小区基站间通过非合作博弈竞争系统预留资源，并针对所提出的资源分配模型进行了相应的算法求解。

本文通过仿真验证了压缩后的马尔科夫信道模型的优越性和利用混合高斯拟合函数表征信道预留资源的可行性，之后也验证了所提出资源分配模型在保证计算速度和服务质量的前提下，其频谱效率、业务接入率、计算复杂度和上下行业务接入率适配等方面的优越性和稳定性。

5G has fast, high-quality resource allocation and a highly flexible system, allowing it to adjust its performance adaptively to face external changes and development trends. In order to achieve fast and high-quality allocation of spectrum resources, it can be considered to reduce the complexity in the calculation process of channel reserved resources. Then, by establishing a theoretical relationship between the channel reserved resources and the effective capacity through channel fitting, the services provided by the system can meet the delay-bounded QoS requirements of user traffic flows. In order to improve the flexibility of system resource allocation, considering the different types of user traffic flows in different cells and the asymmetric uplink and downlink traffic flow requirements of users, we used the F-OFDM technology and the dynamic time division duplex technology in the resource allocation process, thus the resource block with different parameters can be obtained in each cell to adapt to different types of traffic flows, and the cell can adjust the uplink and downlink sub-frame configuration factor dynamically to achieve the adaptation of the user's uplink and downlink traffic flow access ratios. This makes the overall system flexible in resource allocation highly.

 

Based on the above system optimization ideas, the multi-cell F-OFDM dynamic resource allocation technology is studied for time division duplex in this paper to improve the spectrum utilization and the user traffic flow access ratio of the multi-cell multi-user MIMO system. The specific work is as follows:

 

First, the finite-state Markov model is used to perform channel state prediction, which specifically includes using the finite-state Markov model to predict the MIMO spatial channel state matrix and using the finite-state Markov channel model to calculate channel reservation resources in a scheduling slot. Considering the problem of the large number of states of the channel state, a state compression method of the model is proposed, including compressing and merging the number of channel states, and reconstructing the channel model. Therefore, when calculating the channel reserved resources between users and resource blocks, the complexity of related calculations can be reduced.

 

Then, to analyze the resource allocation problem under the delay-bounded QoS constraint, the method of mixed Gaussian function fitting is used to achieve the best approximation of the channel capacity probability distribution. We establish the relationship between the channel reserved resources in the physical layer and the effective capacity based on the delay-bounded QoS index in the MAC layer. It makes the services provided by the system meet the delay QoS constraints of user traffic flows, and provides theoretical support for subsequent research on system resource allocation.

 

Finally, we studied how to use the time division duplex technology to combine uplink and downlink in the multi-cell multi-user MIMO system to achieve the best adaptation dynamically of the system resources to the traffic flow requirements in each cell. Each cell completes the resource cross-layer iterative process between the physical layer and the MAC layer. During this process, each cell sets its own sub-band parameters and the uplink and downlink configuration factor flexibly. We use the virtual MIMO technology, the F-OFDM technology and the time division duplex technology to balance the user's uplink and downlink traffic flow access ratio and to complete the resource adaptation. In the system, the base stations compete for system reservation resources through non-cooperative games. Then, we solve the proposed resource allocation model with corresponding algorithms.

 

In this paper, the superiority of the compressed Markov channel model and the feasibility of using the mixed Gaussian fitting function to characterize the channel reserved resources are verified through simulation, meanwhile, the advantages and the stability of the proposed model in the spectrum efficiency, the traffic flow access ratio, the computational complexity and the adaptation of access ratio of uplink and downlink traffic flows are also verified by simulation.

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