Massive MIMO Systems In Wireless Communication

Varshney, Ruchi; Jain, Parag; Vijay, Sandip

doi:10.1109/icmete.2018.00022

Cited by 5 publications

(1 citation statement)

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“…Multi-layer DNN training algorithms, such as traingdx and traincgp, play a vital role in shaping the learning process and influencing the DNN's performance [20,21].…”

Section: Impact Of Training Algorithmsmentioning

confidence: 99%

A Comparative Analysis of DNN and Conventional Signal Detection Techniques in SISO and MIMO Communication Systems

Shoukat,

Khurshid,

Daha

et al. 2024

Telecom

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This paper investigates the performance of deep neural network (DNN)-based signal detection in multiple input, multiple output (MIMO), communication systems. MIMO technology plays a critical role in achieving high data rates and improved capacity in modern wireless communication standards like 5G. However, signal detection in MIMO systems presents significant challenges due to channel complexities. This study conducts a comparative analysis of signal detection techniques within both the single input, single output (SISO), and MIMO frameworks. The analysis focuses on the entire transmission chain, encompassing transmitters, channels, and receivers. The effectiveness of three traditional methods—maximum likelihood detection (MLD), minimum mean square error (MMSE), and zero-forcing (ZF)—is meticulously evaluated alongside a novel DNN-based approach. The proposed study presents a novel DNN-based signal detection model. While this model demonstrates superior computational efficiency and symbol error rate (SER) performance compared to more conventional techniques like MLD, MMSE, and ZF in the context of a SISO system, MIMO systems face some challenges in outperforming the conventional techniques specifically in terms of computation times. This complexity of MIMO systems presents challenges that the current DNN design has yet to fully address, indicating the need for further developments in wireless communication technology. The observed performance difference between SISO and MIMO systems underscores the need for further research on the adaptability and limitations of DNN architectures in MIMO contexts. These findings pave the way for future explorations of advanced neural network architectures and algorithms specifically designed for MIMO signal-processing tasks. By overcoming the performance gap observed in this work, such advancements hold significant promise for enhancing the effectiveness of DNN-based signal detection in MIMO communication systems.

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“…Multi-layer DNN training algorithms, such as traingdx and traincgp, play a vital role in shaping the learning process and influencing the DNN's performance [20,21].…”

Section: Impact Of Training Algorithmsmentioning

confidence: 99%