Deep graph neural network optimized with fertile field algorithm based detection model for uplink multiuser massive multiple‐input and multiple‐output system

Abstract

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).

Joint delay and energy aware dragonfly optimization‐based uplink resource allocation scheme for LTE‐A networks in a cross‐layer environment

“…These algorithms are beneficial for wireless cellular networks aiming to provide uninterrupted services to UEs. Channel dependent scheduling algorithms are specifically designed for applications with guaranteed bit rate (GBR) and those sensitive to delays [10]. The eNodeB's packet scheduler employs specific strategies to allocate the shared spectrum among users.…”

“…The constraint developed in ( 8) and ( 9) assures the assumed condition, that each uplink channel of UE can be shared only by one spectrum of M2M users in the same channel. The constraint developed in (10) assures that the M2M pair can share only one spectrum in the uplink channel of UE. The next section describes the energy-efficient resource allocation algorithm according to the developed constraints.…”

“…LTE-A is a cellular communication standard that provides high bandwidth, mobility, and flexibility to meet the different needs of UEs, but MTC involves low-bandwidth data bursts and has different QoS requirements compared to H2H communication. MTC data transmission primarily depends on the uplink direction and competes with uplink H2H traffic [10]. There are various critical applications of MTC communication that require prioritization over H2H communication, and managing this competition is a challenge for the uplink scheduler.…”