A D2D resource allocation method based on multi-agent deep reinforcement learning

Abstract

The invention discloses a D2D resource allocation method based on multi-agent deep reinforcement learning, and belongs to the field of wireless communication. The method comprises the following steps:firstly, constructing a heterogeneous network model of a cellular network and D2D communication shared spectrum; establishing a signal to interference plus noise ratio (SINR) of a D2D receiving userand an SINR of a cellular user based on the existing interference, respectively calculating unit bandwidth communication rates of a cellular link and a D2D link, and constructing a D2D resource allocation optimization model in a heterogeneous network by taking the maximum system capacity as an optimization target; For the time slot t, constructing a deep reinforcement learning model of each D2D communication pair on the basis of the D2D resource allocation optimization model; And respectively extracting respective state feature vectors from each D2D communication pair in the subsequent time slot, and inputting the state feature vectors into the trained deep reinforcement learning model to obtain a resource allocation scheme of each D2D communication pair. According to the invention, spectrum allocation and transmission power are optimized, the system capacity is maximized, and a low-complexity resource allocation algorithm is provided.

Description

technical field [0001] The invention belongs to the field of wireless communication, relates to a heterogeneous cellular network system, and specifically relates to a D2D resource allocation method based on multi-agent deep reinforcement learning. Background technique [0002] The popularity of smart terminals and the explosive development of mobile Internet services have put forward higher requirements for the data transmission capabilities of wireless communication networks. Under the current general trend, existing cellular networks have problems such as a shortage of spectrum resources and overloaded base stations, which cannot meet the transmission requirements of future wireless networks. [0003] Device-to-Device (D2D, Device-to-Device) communication allows adjacent users to establish direct links for communication, because it has the advantages of improving spectral efficiency, saving energy consumption, and unloading base station loads, it has become a very popular ...

AI Technical Summary

Problems solved by technology

[0004] However, D2D communication multiplexing the frequency spectrum of the cellular network will cause cross-layer interference to the cellular communication link, and the communication quality of the cellular user as the primary user of the cellular frequency band should be guaranteed. Multiplexing the same spectrum will cause the same layer interference between each other, so the interference management problem when the cellular network and D2D communication coexist is an urgent problem to be solved

However, when multiple agents are learning and training, the strategy of each agent is changing, which will cause the training environment to be unstable and the training is not easy to converge.

Therefore, it is necessary to study a distributed resource allocation algorithm with good convergence and low complexity to solve the interference management problem of D2D communication in cellular networks.

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