Generating Mimo Channels for 6G Virtual Worlds Using Ray-Tracing Simulations

Klautau, Aldebaro; Oliveira, Ailton; Trindade, Isabela; Alves, Wesin

doi:10.1109/ssp49050.2021.9513861

Cited by 5 publications

(5 citation statements)

References 19 publications

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“…Beam forming gain plus diversity gain adds up to the total SNR increase. And it is given as Equation (15).…”

Section: Snr Gainmentioning

confidence: 99%

“…For instance, it has been applied to the study of criminal networks, assisting in the comprehension of their composition and methods of functioning. 15 Additionally, it is essential to EEG signal analysis, which aids in the decoding of intricate brain signals for medical and scientific uses. Additionally, DL is progressively making its way into wireless communication's complex physical layer (PHY) to manage the optimization of numerous performance goals while abiding by a number of complex constraints.…”

Section: Introductionmentioning

confidence: 99%

“…Building on these achievements, deep learning applications keep growing into increasingly specialized domains. For instance, it has been applied to the study of criminal networks, assisting in the comprehension of their composition and methods of functioning 15 . Additionally, it is essential to EEG signal analysis, which aids in the decoding of intricate brain signals for medical and scientific uses.…”

Section: Introductionmentioning

confidence: 99%

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Optimized wireless communication for 6G signal processing using memory‐augmented deep unfolding network

Eswaramoorthi,

Prasad,

et al. 2024

Int J Communication

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SummaryIn this manuscript, optimized wireless communication for 6G signals processing using memory‐augmented deep unfolding network (WC‐6G‐SP‐MADUN) is proposed. The aim of this work is to give a memory‐augmented deep unfolding network approach with a focus on belief propagation decoding of error correction codes and multiple‐input multiple‐output (MIMO) wireless systems. In contrast, compares and tabulates the performance of memory‐augmented deep unfolding methodologies with classic principled procedures, which could potentially enable 6G. Here, the wireless communication for 6G signal processing methods was used for a variety of receiver functions, including self‐interference cancelation, signal estimation and detection using memory‐augmented deep unfolding Network (MADUN), and advanced error correction by clouded leopard optimization algorithm (CLOA). Furthermore, outline the general framework of memory‐augmented deep unfolding method for any kind of signal processing application and talk about cutting edge research paths that will make it possible to build communication networks in the future. The proposed approach is implemented in MATLAB; the performance is analyzed with performance metrics such as reliability, complexity, SNR gain, convergence rate, hardware efficiency, and quantization bit width. The proposed approach attains higher SNR gain 17.96%, 24.75%, and 30.09%; lower complexity 17.92%, 23.41%, and 30.13%; and higher convergence 18.01%, 25.41%, and 31.39% when analyzed with existing techniques such as redefined wireless communication for 6G: signal processing meets deep learning and deep unfolding (RWC‐6G‐SP‐DU), lower complication deep unfolded neural network receiver for MIMO under probability data association detector (LC‐DUNN‐MIMO‐PDAD), and deep learning in physical layer communications: evolution with prospects on 5G and 6G networks (DL‐PLC‐6G), respectively.

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“…Beam forming gain plus diversity gain adds up to the total SNR increase. And it is given as Equation (15).…”

Section: Snr Gainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimized wireless communication for 6G signal processing using memory‐augmented deep unfolding network

Eswaramoorthi,

Prasad,

et al. 2024

Int J Communication

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“…Raymobtime [3], presents a comprehensive approach for generating realistic datasets to simulate wireless communications. Several channel generation software provides support to mobility, but this is limited to moving the radio transceivers.…”

Section: Introductionmentioning

confidence: 99%

“…The rays parameters, in order, are the: received power (dBm), time of arrival (seconds), the elevation angle of departure (degrees), the azimuth angle of departure (degrees), the elevation angle of arrival (degrees), the azimuth angle of arrival (degrees). Unlike the number parameters of [3], some information has not been obtained, such as phase angle and line of sight (LOS) indicator.…”

Section: Introductionmentioning

confidence: 99%

Generation of 5G/6G Wireless Channels Using Raymobtime with Sionna's Ray-Tracing

Bastos,

Oliveira,

Suzuki

et al. 2023

Anais Do XLI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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It is challenging to generate wireless channels for 5G/6G scenarios with sophisticated mobility patterns. Raytracing (RT) is one of the most adopted techniques, especially when 5G/6G millimeter frequency bands are considered. However, most ray tracers, such as Wireless InSite and Sionna's, provide limited support to mobility: only the radio transceivers move over time, while all other objects are held in fixed positions. The Raymobtime methodology was proposed to fill this gap. Raymobtime supports repositioning all mobile objects, which enables simulating advanced mobility patterns. When the Raymobtime methodology was first implemented, its RT was solely based on the commercial Wireless InSite ray tracer. This work expands Raymobtime software to also support the recent NVIDIA Sionna's ray tracer, which is open source and mostly written in Python. The preliminary results indicate that Sionna's brings a fast RT tool, but it still needs to be systematically validated against measured channels.

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CAVIAR: Co-Simulation of 6G Communications, 3-D Scenarios, and AI for Digital Twins

Borges,

Bastos,

Correa

et al. 2024

IEEE Internet Things J.

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Generating Mimo Channels for 6G Virtual Worlds Using Ray-Tracing Simulations

Cited by 5 publications

References 19 publications

Optimized wireless communication for 6G signal processing using memory‐augmented deep unfolding network

Optimized wireless communication for 6G signal processing using memory‐augmented deep unfolding network

Generation of 5G/6G Wireless Channels Using Raymobtime with Sionna's Ray-Tracing

CAVIAR: Co-Simulation of 6G Communications, 3-D Scenarios, and AI for Digital Twins

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