Deep graph neural network optimized with fertile field algorithm based detection model for uplink multiuser massive multiple-input and multiple-output system,Transactions on Emerging Telecommunications Technologies

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Deep graph neural network optimized with fertile field algorithm based detection model for uplink multiuser massive multiple-input and multiple-output system
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2022-07-31 , DOI: 10.1002/ett.4614
R. Eswaramoorthi ₁ , M. Leeban Moses ₂ , Jennathu Beevi Sahul Hameed ₃ , Basanti Ghanti ₄

Affiliation

The core requirements are generated for sixth generation (6G) wireless communication with low-latency and ultra-high speeds to increase the count of ultra scale intelligent factors, like smart cars, mobile root users. The advancement of 6G communication can lead the interference exploitation. To manage the exploitation of uplink multiuser massive (UMM), the multiple-input and multiple-output (MIMO) is very difficult to detect the mechanisms, particularly, quadrature amplitude modulation (QAM) signals. To overcome these issues, a novel deep graph neural network optimized with fertile field algorithm based detection model (DGNNO-FFA) is proposed in this article for uplink multiuser massive MIMO System. The proposed DGNNO-FFA approach minimizes the channel estimation errors under low signal to noise ratio (SNR) with better bit error rate (BER). Finally, the proposed DGNNO-FFA approach attains 11.02%, 12.22%, and 25.27% lower BER value, 14.55%, 18.66%, and 29.49% higher energy efficiency, 15.59%, 19.06%, and 29.59% lower NMSE, and 15.59%, 19.06%, and 29.59% lower computational complexity compared with other existing approaches, like deep neural network based semi definite relaxation (DNN-SDR), QR based zero forcing algorithms (QR-ZF), and QAM based 2-dimensional double successive projection model (QAM-2D-DSP).

中文翻译：

上行链路多用户大规模多输入多输出系统检测模型基于沃土算法优化的深度图神经网络

第六代（6G）低时延、超高速无线通信产生核心需求，增加智能汽车、移动根用户等超大规模智能要素的数量。6G通信的进步可以引领干扰利用。为了管理上行链路多用户大规模 (UMM) 的利用，多输入多输出 (MIMO) 机制非常难以检测，尤其是正交幅度调制 (QAM) 信号。为了克服这些问题，本文针对上行链路多用户大规模 MIMO 系统提出了一种使用基于肥沃场算法的检测模型 (DGNNO-FFA) 进行优化的新型深度图神经网络。所提出的 DGNNO-FFA 方法最大限度地减少了低信噪比 (SNR) 下的信道估计误差，并具有更好的误码率 (BER)。

更新日期：2022-07-31

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