Deep Learning-Based Resource Allocation for Device-to-Device Communication

doi:10.1109/TWC.2021.3138733

Deep Learning-Based Resource Allocation for Device-to-Device Communication

2022-07-01

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DOI：10.1109/TWC.2021.3138733

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In this paper, a deep learning (DL) framework for the optimization of the resource allocation in multi-channel cellular systems with device-to-device (D2D) communication is proposed. Thereby, the channel assignment and discrete transmit power levels of the D2D users, which are both integer variables, are optimized for maximization of the overall spectral efficiency whilst maintaining the quality-of-service (QoS) of the cellular users. Depending on the availability of channel state information (CSI), two different configurations are considered, namely 1) centralized operation with full CSI and 2) distributed operation with partial CSI, where in the latter case, the CSI is encoded according to the capacity of the feedback channel. Instead of solving the resulting resource allocation problem for each channel realization, a DL framework is proposed, where the optimal resource allocation strategy for arbitrary channel conditions is approximated by deep neural network (DNN) models. Furthermore, we propose a new training strategy that combines supervised and unsupervised learning methods and a local CSI sharing strategy to achieve near-optimal performance while enforcing the QoS constraints of the cellular users and efficiently handling the integer optimization variables based on a few ground-truth labels. Our simulation results confirm that near-optimal performance can be attained with low computation time, which underlines the real-time capability of the proposed scheme. Moreover, our results show that not only the resource allocation strategy but also the CSI encoding strategy can be efficiently determined using a DNN. Furthermore, we show that the proposed DL framework can be easily extended to communication systems with different design objectives.

Keywords:

Resource management

Wireless communication

Device-to-device communication

Training

Quality of service

Optimization

Communication systems

Multi-channel wireless communication systems

interference channel

D2D transmission

deep learning

distributed operation

resource allocation

CSI sharing

Deep Learning-Based Resource Allocation for Device-to-Device Communication

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