Femtocell heterogeneous network power adaptive optimization method based on deep reinforcement learning
A heterogeneous network and reinforcement learning technology, applied in neural learning methods, specific mathematical models, constraint-based CAD, etc., to improve network communication capacity and ensure network quality.
Pending Publication Date: 2021-12-14
马鞍山学院
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[0008] The above-mentioned patents are all researches to improve network quality, but further improvement and enhancement are still needed
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Embodiment 1
[0036] Step S1, establish Femtocell heterogeneous network system model, and carry out problem modeling: such as figure 1 As shown in the Femtocell heterogeneous network system model shown, the main body designed in the whole system model includes a macro base station MBS (Macro BaseStation) and N Femtocell base stations FBS deployed at the same frequency. At the same time, the MBS serves one active user MUE within the coverage area, and the FBS provides information services for M users FUE within the coverage area.
[0037] Among them, MBS--Macro Base Station Macrocell base station;
[0038] MUE--Macro User Equipment Macrocell base station user;
[0039] FBS--Femto Base Station Femtocell base station;
[0040] FUE--Femto User Equipment Femtocell base station user.
[0041] In this system model, since Femtocell and Macrocell are deployed at the same frequency, MBS has inter-network interference to FUE, FBS has inter-network interference to MUE, and FBS has same-network interfe...
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The invention discloses a Femtocell heterogeneous network power adaptive optimization method based on deep reinforcement learning. The method comprises the following steps of: S1, establishing a Femtocell heterogeneous network system model, and carrying out problem modeling; S2, modeling a power adaptive optimization problem based on a Femtocell heterogeneous network into a Markov decision process MDP, and designing a state space, an action space and a reward function of the MDP; S3, constructing a deep reinforcement learning algorithm DDPG; and S4, carrying out model training. The deep reinforcement learning utilizes continuous interaction between an intelligent agent and the environment, self-optimization is carried out from feedback of the environment, the self-strategy is improved, self-adaptive learning and decision making in a complex network environment can be rapidly achieved, and the problem of interference suppression in the Femtocell heterogeneous network is solved.
Description
technical field [0001] The invention relates to the field of self-adaptive optimization of wireless communication network resources, in particular to a resource optimization method using a machine learning method. Background technique [0002] With the rapid development of wireless mobile communication technology, the number of mobile users and mobile devices is increasing rapidly. As a small mobile base station, or home base station, Femtocell can provide indoor users with high-speed wireless signals within the effective signal coverage range due to its small size, plug and play, low cost, and low power consumption. Access is an important means to solve the indoor coverage problem of wireless signals. Femtocell heterogeneous network is composed of traditional Macrocell layer and Femtocell layer. Due to the scarcity of spectrum resources, the Macrocell layer and the Femtocell layer are often deployed in the same frequency band, which will cause serious interference in the ...
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IPC IPC(8): H04W52/04G06F30/18G06F30/27G06N3/04G06N3/08G06N7/00G06F111/02G06F111/04G06F111/08G06F119/06
CPCH04W52/04G06F30/18G06F30/27G06N3/04G06N3/084G06F2111/02G06F2111/04G06F2111/08G06F2119/06G06N7/01
Inventor 郭伟汪玉冰郭晓明张丰丰徐煜
Owner 马鞍山学院