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Attention-based deep convolutional neural network for spectral efficiency optimization in MIMO systems

  • S.I. : Deep Social Computing
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Abstract

Spectral efficiency (SE) optimization in massive multiple input multiple output (MIMO) antenna cognitive systems is a challenge originated from the coexistence restrictions. Traditional power allocation can optimize the SE; however, involving deep learning can meet real-time and fairness processing requirements. In unfair allocation problem, all power is possibly assigned to one or few antennas of a particular user. In this paper, we build a mathematical optimization model considering the fairness problem such that SE is optimized for all users. To implement the model, we propose an attention-based convolutional neural network (Att-CNN), where \(h_0\) and \(h_k\) (i.e., cross-interference and direct channels) attention mechanisms are used to improve the SE. The convolutional neural network is applied to decrease the floating point operations (FLOPs) and number of network parameters. We conducted experiments from these aspects: Fair antenna power allocation, power allocation performance and computational performance. Heat maps with different interference thresholds show the fair allocation for all users. Analyses of SE validate the superiority of the Att-CNN compared with the equal power allocation and fully connected neural network (FNN) schemes. The analyses of the FLOPs and number of parameters show the superiority of the Att-CNN over the FNN.

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Notes

  1. Basic Qos in this sense means the ability to get the communication service with a data rate proportional to channel conditions. Satisfactory QoS via securing a minimum data rate goes along with the CR spectral efficiency enhancement goal only when good channel conditions are witnessed.

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Acknowledgements

This work was supported by a grant from the National Natural Science Foundation of China (No.U1609211).

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Authors and Affiliations

  1. Institute of Industrial Internet, Hangzhou Dianzi University, Hangzhou, 310018, China

    Danfeng Sun, Jiachen Qiu & Huifeng Wu

  2. Institut f. Automation und Kommunikation, Magdeburg, 39106, Germany

    Danfeng Sun, Abdullah Yaqot, Lutz Rauchhaupt & Ulrich Jumar

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  1. Danfeng Sun
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  2. Abdullah Yaqot
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  3. Jiachen Qiu
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  4. Lutz Rauchhaupt
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Correspondence to Huifeng Wu.

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Sun, D., Yaqot, A., Qiu, J. et al. Attention-based deep convolutional neural network for spectral efficiency optimization in MIMO systems. Neural Comput & Applic 35, 12967–12978 (2023). https://doi.org/10.1007/s00521-020-05142-9

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  • DOI: https://doi.org/10.1007/s00521-020-05142-9

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