Interference Mitigation in RIS-assisted 6G Systems for Indoor Industrial IoT Networks

Rubab, Naila; Zeb, Shah; Mahmood, Aamir; Hassan, Syed Ali; Ashraf, Muhammad Ikram; Gidlund, Mikael

doi:10.1109/sam53842.2022.9827809

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…6G networks will make extensive use of the space dimension in addition to high-frequency bands. These MADUN receiver techniques' intelligent deep unfolding approaches serve as a foundation for other multiantenna techniques, so the detection in signal estimation of wireless 6G communication can be given as Equation (9).…”

Section: Signal Estimation and Detection Using Memory-augmented Deep ...mentioning

confidence: 99%

“…For example, deep learning models provide deep analysis of pictures in computer vision, 8 enabling tasks like object detection, semantic segmentation, and facial recognition. 9 DL has made important progress in the field of speech recognition, allowing for the use of voice assistants and speech-to-text capabilities, which are indispensable in our day-to-day existence. 10 Large language models (LLM) such as generative pretraining transformer (GPT) use deep learning to fully comprehend natural language.…”

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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Section: Signal Estimation and Detection Using Memory-augmented Deep ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…However, the increasing demand for cutting-edge services in innovative industries, like augmented reality (AR)/virtual reality (VR) maintenance, automatic guided vehicles, and holographic control display systems, will pose substantial communication challenges for industrial wireless communications [2]. The large-scale IIoT networks, being extremely efficient, are intended to provide extremely high-speed communication of up to terabits-per-second (Tbps) with low latency, ultra-high reliability, and minimal power consumption [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Joint Devices and IRSs Association for Terahertz Communications in Industrial IoT Networks

Rahim,

Kaddoum,

2024

IEEE Trans. on Green Commun. Netw.

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The Industrial Internet of Things (IIoT) enables industries to build large interconnected systems utilizing various technologies that require high data rates. Terahertz (THz) communication is envisioned as a candidate technology for achieving data rates of several terabits-per-second (Tbps). Despite this, establishing a reliable communication link at THz frequencies remains a challenge due to high pathloss and molecular absorption. To overcome these limitations, this paper proposes using intelligent reconfigurable surfaces (IRSs) with THz communications to enable future smart factories for the IIoT. In this paper, we formulate the power allocation and joint IIoT device and IRS association (JIIA) problem, which is a mixed-integer nonlinear programming (MINLP) problem. Furthermore, the JIIA problem aims to maximize the sum rate with imperfect channel state information (CSI). To address this non-deterministic polynomial-time hard (NP-hard) problem, we decompose the problem into multiple sub-problems, which we solve iteratively. Specifically, we propose a Gale-Shapley algorithm-based JIIA solution to obtain stable matching between uplink and downlink IRSs. We validate the proposed solution by comparing the Gale-Shapley-based JIIA algorithm with exhaustive search (ES), greedy search (GS), and random association (RA) with imperfect CSI. The complexity analysis shows that our algorithm is more efficient than the ES.

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“…In areas where direct channel between transmitter or receiver is not available, IRS can act as a reflector and improve the multipath reception. In addition to these, IRS provides advantages such as improved indoor reception [10], [11], increased energy efficiency [3], [12] and resistance against eavesdropping [13], [14]. The passive nature of IRS along with the ease of deployment makes it a viable solution for the performance enhancement of wireless channels.…”

Section: Introductionmentioning

confidence: 99%

Approximate Composite Channel Statistics and Performance Analysis of IRS-Aided Wireless System Under Nakagami-m Fading

Ajayan,

Dash,

Ramkumar

2023

IEEE Access

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An intelligent reflecting surface (IRS) enhances the performance of a wireless channel by controlling the phase of signals reflected by them. This paper investigates an IRS-aided single input single output wireless communication system. Owing to the closely spaced placement of the IRS elements, the channels between the IRS and the transmitter, as well as the channels between the IRS and the receiver, are considered to be spatially correlated. Assuming optimal phase configuration at the IRS, correlated Nakagami-m random vectors are used to model the magnitude of the channel coefficient vectors between the IRS and the transmitter as well as between the IRS and the receiver. A statistical approximation of the distribution of composite channel is obtained as a Gamma distribution. The parameters of the approximated Gamma distribution are derived in terms of the scale parameter and spread parameter of Nakagami-m random vectors. The approximated Gamma distribution is validated using Monte-Carlo simulations and found to be closely following the actual distribution of the composite channel coefficient. Using the approximation, closed form expressions of the performance metrics such as symbol error probability (SEP), outage probability and average channel capacity are derived and validated.INDEX TERMS Average channel capacity, IRS, Nakagami-m fading, outage probability, SEP, spatial correlation.

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Interference Mitigation in RIS-assisted 6G Systems for Indoor Industrial IoT Networks

Cited by 9 publications

References 13 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

Joint Devices and IRSs Association for Terahertz Communications in Industrial IoT Networks

Approximate Composite Channel Statistics and Performance Analysis of IRS-Aided Wireless System Under Nakagami-m Fading

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