Wan Nur Suryani Firuz Wan Ariffin
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Preferred name
Wan Nur Suryani Firuz Wan Ariffin
Official Name
Wan Nur Suryani Firuz, Wan Ariffin
Alternative Name
Wan Ariffin, W. N.F.
Ariffin, Wan Suryani Firuz Wan
Ariffin, W. N.F.W.
Main Affiliation
Scopus Author ID
57191614329
Researcher ID
HNR-7804-2023

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Performance analysis of coordinated multi-point (CoMP) with energy trading and management in green cloud RAN

2024-02-08 , Mohd Asri L.I. , Wan Nur Suryani Firuz Wan Ariffin , `Aini Syuhada Md Zain , Nazatul Syima Saad

Traditional cellular network architectures are facing considerable issues as a result of tremendous rise in high-power consumption, limited frequency accessibility, and wireless data traffic. Given that both research groups and industry are continually searching for fundamental breakthroughs in the creation of new network architectures that may fulfil expanding consumer demand while minimizing network operators' operating and capital expenses. In order to achieve these needs, many creative areas are being investigated for potential inclusion in new radio access networks, with Coordinated Multi-Point (CoMP) being one of the most researched alternatives. This study analyzes the CoMP technique and its performance in dealing with interference in cloud radio access networks (C-RAN) to address the issue. This study's analysis analyzed two distinct situations. Scenario 1 evaluates the effectiveness of CoMP C-RAN to traditional techniques, whereas scenario 2 assesses system performance as the number of users grows. The proposed network architecture is proven by numerical findings, which show that CoMP C-RAN outperforms the typical cellular network.

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Optimization of Beamforming Matrix Design for Multi-Cell MIMO with SWIPT Systems

2020-12-11 , Wan Nur Suryani Firuz Wan Ariffin , Nakhai M.R. , Hasneeza Liza Zakaria , Nur Baya Mohd Hashim , `Aini Syuhada Md Zain

Energy harvesting is emerging as a promising new solution to provide continuous energy supplies to wireless rechargeable devices. This paper investigates wireless information and energy transfer in multi-cell multiple-input-multiple-output (MIMO) systems. An optimisation problem that designs the beamforming matrix is introduced to maximise the sum of total harvested energy collected from all energy receiving terminals while guaranteeing the desired data rate for each information receiving terminal remains above a certain level. Total power constraint at each transmitting terminal that was generated from the renewable energy and purchased from the grid is also considered. The channel capacity that requires the use of slower and less reliable CVX's successive approximation heuristic is formulated, and then, the non-convex problem is transformed into a convex problem form modelled by a semidefinite relaxation (SDR). The results show the effectiveness of the multiple antennas used in all cells provide better performance and also maximise the total harvested energy at all energy receiving terminals.

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Online learning approach for predictive real-time energy trading in cloud-rans

2021-04-01 , Wan Nur Suryani Firuz Wan Ariffin , Zhang X. , Nakhai M.R. , Hasliza A Rahim @ Samsuddin , R Badlishah Ahmad

Constantly changing electricity demand has made variability and uncertainty inherent characteristics of both electric generation and cellular communication systems. This paper develops an online learning algorithm as a prescheduling mechanism to manage the variability and uncertainty to maintain cost-aware and reliable operation in cloud radio access networks (Cloud-RANs). The proposed algorithm employs a combinatorial multi-armed bandit model and minimizes the long-term energy cost at remote radio heads. The algorithm preschedules a set of cost-efficient energy packages to be purchased from an ancillary energy market for the future time slots by learning both from cooperative energy trading at previous time slots and by exploring new energy scheduling strategies at the current time slot. The simulation results confirm a significant performance gain of the proposed scheme in controlling the available power budgets and minimizing the overall energy cost compared with recently proposed approaches for real-time energy resources and energy trading in Cloud-RANs.

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