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A d2d resource allocation method based on multi-agent deep reinforcement learning

A technology of reinforcement learning and resource allocation, applied in the field of D2D resource allocation based on multi-agent deep reinforcement learning, can solve problems such as same-layer interference, D2D communication interference management, and cross-layer interference of cellular communication links

Active Publication Date: 2020-08-18
BEIJING UNIV OF POSTS & TELECOMM
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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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  • A d2d resource allocation method based on multi-agent deep reinforcement learning
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Embodiment Construction

[0071] In order to make the technical principles of the present invention more clearly understood, the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0072] A D2D resource allocation method based on multi-agent deep reinforcement learning (MADRL, Multi-Agent Deep Reinforcement Learning based Device-to-Device Resource Allocation Method) is applied to the heterogeneous network where the cellular network and D2D communication coexist; first establish the D2D respectively The expression of signal-to-interference-noise ratio and communication rate per unit bandwidth of receiving users and cellular users, with the optimization goal of maximizing system capacity, taking the SINR of cellular users greater than the minimum SINR threshold, D2D link spectrum allocation constraints and the emission of D2D transmitting users The power is less than the maximum transmit power threshold as the optimization condition, and ...

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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 ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W16/14H04W24/02H04W76/14
CPCY02D30/70
Inventor 郭彩丽李政宣一荻冯春燕
Owner BEIJING UNIV OF POSTS & TELECOMM
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