Designing Future Wireless Networks (FWN)s With Net Zero (NZ) and Zero Touch (ZT) Perspective

Abbas, Waqas Bin; Ahmed, Qasim Zeeshan; Khan, Fahd Ahmed; Mian, Naeem S.; Lazaridis, Pavlos I.; Sureephong, Pradorn

doi:10.1109/access.2023.3301849

Cited by 2 publications

(2 citation statements)

References 137 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For simulations, M T = 64, M Sel = 16 and M RF T = 4. For GSA, two options, i.e., (3,1) and (2, 1, 1) have been considered, where (3, 1) means 3 RF chains are connected to a single sub-array whereas 1 is connected to a different sub-array as mentioned in [49]. Figures 9 and 10 compare different hybrid architectures in terms of SE and power consumption, respectively.…”

Section: G Comparison Of Different Hybrid Architecturesmentioning

confidence: 99%

“…Our planet has started witnessing the adverse effects of climate change and immediate efforts from all industry vertical are required to minimize the carbon emission [1], [2]. To reduce the carbon footprint of the information and communication technology (ICT), one of the crucial objectives for future sixth generation (6G) networks is to design them with energy efficiency (EE) as a central criterion [3]- [5]. To address this, a hybrid architecture, that comprises of both the analog and digital counter parts, provides a favourable compromise between the spectral efficiency (SE) and EE [6], [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Heuristic Antenna Selection and Precoding for a Massive MIMO System

Abbas,

Khalid,

Ahmed

et al. 2024

IEEE Open J. Commun. Soc.

Self Cite

View full text Add to dashboard Cite

Sixth Generation (6G) transceivers are envisioned to feature massively large antenna arrays compared to its predecessor. This will result in even higher spectral efficiency (SE) and multiplexing gains. However, immense concerns remain about the energy efficiency (EE) of such transceivers. This work focuses on partially connected hybrid architectures, with the primary aim of enhancing the EE of the system. To achieve this objective, the study proposes a combined approach of joint antenna selection and precoding, which holds the potential to further optimize the system's EE while maintaining a satisfactory SE performance levels. The proposed approach incorporates antenna selection based on a meta-heuristic cyclic binary particle swarm optimization algorithm along with successive interference cancellation-based precoding. The results indicate that the proposed solution, in terms of SE and EE, performs very close to the optimal exhaustive search algorithm. This study also investigates the trade-off between SE and EE in a low and high signal-to-noise ratio (SNR) regimes. The robustness of the proposed scheme is also demonstrated when the channel state information is imperfect. In conclusion, this work presents a lower complexity approach to enhance EE in 6G transceivers while maintaining SE performance and along with a reduction in power consumption.

show abstract

Section: G Comparison Of Different Hybrid Architecturesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heuristic Antenna Selection and Precoding for a Massive MIMO System

Abbas,

Khalid,

Ahmed

et al. 2024

IEEE Open J. Commun. Soc.

Self Cite

View full text Add to dashboard Cite

show abstract

Foreign Object Debris Detection on Wireless Electric Vehicle Charging Pad Using Machine Learning Approach

Rajamanickam,

Abraham,

Alroobaea

et al. 2024

Processes

View full text Add to dashboard Cite

Foreign object debris (FOD) includes any unwanted and unintentional material lying on the charging lane or parking lots, posing a risk to the wireless charging system, the vehicle, or the people inside. FOD in an Electric Vehicle (EV) wireless charging system can cause problems, including decreased charging efficiency, safety risks, charging system damage, communication issues, and health risks. To address this problem, this paper proposes the deep learning object detection network approach of using YOLOv4 (You Only Look Once), which is a single-shot detector. Additionally, for real-time implementation, YOLOv4-Tiny is suggested, which is a compressed version of YOLOv4 designed for devices with low computational power. YOLOv4-Tiny enables faster inferences and facilitates the deployment of FOD detectors on edge devices. The algorithm is trained using the FOD dataset, consisting of images of common debris on runways or taxiways. Furthermore, utilizing the concept of transfer learning, the last few layers of the pre-trained YOLOv4 model are modified using the COCO (Common Objects in Context) dataset to transfer features to the new network and retrain the model on the FOD dataset. The results obtained using this YOLOv4 model yielded a precision rate of 99.05%, while the results from YOLOv4-Tiny achieved a precision rate of 97.74%, with an average inference time of 150 ms under the ambient light and weather conditions.

show abstract

Designing Future Wireless Networks (FWN)s With Net Zero (NZ) and Zero Touch (ZT) Perspective

Cited by 2 publications

References 137 publications

Heuristic Antenna Selection and Precoding for a Massive MIMO System

Heuristic Antenna Selection and Precoding for a Massive MIMO System

Foreign Object Debris Detection on Wireless Electric Vehicle Charging Pad Using Machine Learning Approach

Contact Info

Product

Resources

About