Comparative investigation and optimization of turning process parameters using Taguchi technique in turning of Inconel 718

Bohidar, Shailendra Kumar; Gupta, Vikram Kumar; Surakasi, Raviteja; Gift, M. D. Mohan; Madhavarao, S.; Janardhana, Kedri

doi:10.1016/j.matpr.2022.02.397

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…12 Various DL-based techniques are devised in Dörner et al 13 and O'Shea et al 14 for managing interference in signals. These techniques 15,16 have imperative impacts on the enhancement of performance, and it utilized DL-based AE, which is a most emerging technique for interference-free channels. Many techniques are devised that offered a brief outline of physical layer DL and previous models based on 5G systems.…”

Section: Introductionmentioning

confidence: 99%

Hybrid optimization‐based deep neuro‐fuzzy network for designing m‐user multiple‐input multiple‐output interference channel

Reddy,

Ravi Kumar

2023

Int J Communication

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SummaryMultiple‐input multiple‐outputs (MIMOs) have eminent quality in maximizing the throughput of wireless communication models. In MIMO, the antenna arrays can be utilized for fulfilling the needs of 5G by utilizing various spatial signatures of users. Even though 5G communication is imminent, there exist issues, like network interference that arise due to reused frequency spectrum resources. This delving presents an optimized deep model for suppressing interference occurring in the Rayleigh channel in the multiple‐user MIMO (MU‐MIMO) model. Here, an MU‐MIMO model is employed with correlated interference wherein there exist various users around the base station (BS) with several antennas at the transmitter and receiver. Here, a deep neuro‐fuzzy network (DNFN) is used to upgrade the performance of detectors underneath correlated interference. Here, the model comprises zero forcing‐maximum likelihood detection (ZF‐MLD) that assists to generate an initial estimate of broadcasted signals in a particular time slot. The DNFN is used to capture latent correlation among several symbols. Here, the DNFN training is performed using developed autoregressive Henry gas spider monkey optimization (RHGSMO), which is the combination of conditional autoregressive value at risk (CAViaR), Henry gas solubility optimization (HGSO), and spider monkey optimization (SMO). With the lowest symbol error rate (SER), bit error rate (BER), and signal to interference and noise ratio (SINR), the suggested RHGSMO‐based DNFN performed better than existing approaches.

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