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Heterogeneous communication modes in 5G demand integrated device connections, resource availability, and high capacity for meeting user demands. The radio resource allocation and usage for massive users results in interference between the device-to-device (D2D) uplink channels. This issue is addressed using a Non-orthogonal Convex Optimization Problem (NCOP) that identifies the chances of self-interference cancellations. This technique classifies interference and non-interference allocations in the rate of uplink communications. The channel reassignment is addressed as an NCOP based on the available interference levels. The interference levels before and after allocation and reallocation are analyzed under convex optimization. The interference cancellation convergence is computed for both channels wherein the transfer switching is performed. The convergence rate is estimated using the interference level and the number of channels reassigned for the uplink devices. Hence, the self-interference cancellation relies on non-convex channel allocations across various switching in this case. This feature is revisited if the D2D channels exceed their capacity for communication. Therefore, the 5G communication features coexist with the D2D uplinks for interference cancellations to improve channel allocation. For the SNR = 45dBm, the proposed NCOP reduces 12.4% of channel reassignment by augmenting 9.24% of interference cancellation.
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http://dx.doi.org/10.1038/s41598-025-92026-4 | DOI Listing |
Sci Rep
September 2025
Department of Computer Engineering, Faculty of Engineering, Pharos University, Canal El Mahmoudia Street, Beside Green Plaza Complex 21648, Alexandria, Egypt.
This paper introduces the enhancement of Visible Light Communications (VLC) for V2V using artificial intelligence models. Different V2V scenarios are simulated. The first scenario considers a specific longitudinal separation and a variable lateral shift between vehicles.
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August 2025
Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia.
The Multiple-Input Multiple-Output (MIMO) system can provide improved spectral efficiency and energy performance. However, the computational demand faced by conventional signal recognition techniques has significantly increased due to the growing number of antennas and higher-order modulations. To overcome these challenges, deep learning approaches are adopted as they offer versatility, nonlinear modelling capabilities, and parallel computation efficiency for large-scale MIMO detection.
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August 2025
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
In the realm of optical wireless communication (OWC), augmented spectral efficiency discrete multitone (ASE-DMT) has been widely recognized as a promising modulation due to its outstanding spectral efficiency and high power efficiency. However, ASE-DMT exhibits an inherently high peak-to-average power ratio (PAPR), which exacerbates error propagation and leads to a substantial transmission performance degradation in the successive interference cancellation (SIC) receiver of ASE-DMT. Therefore, a novel low-PAPR ASE-DMT scheme (LP-ASE-DMT) is proposed in the paper.
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August 2025
QTF Centre of Excellence, Department of Applied Physics, Aalto University, FI-00076 Aalto, Finland.
Carrier noise in coherent tones limits sensitivity and causes heating in many experimental systems, such as force sensors, time-keeping, and studies of macroscopic quantum phenomena. Much progress has been made to reduce carrier noise using phase noise cancellation techniques; however, in systems where amplitude noise dominates, these methods are ineffective. Here, we present a technique to reduce amplitude noise from microwave generators using feedback cancellation.
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August 2025
School of Cyber Engineering, Xidian University, Xi'an 710126, China.
The demand for high data rates and large system capacity has posed significant challenges for medium access control (MAC) methods. Successive interference cancellation (SIC) is a classical multi-user detection (MUD) method; however, it suffers from an error propagation problem. To address this deficiency, we propose a method called .
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