Wan Nur Suryani Firuz Wan Ariffin
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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.
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Scopus Author ID
57191614329
Researcher ID
HNR-7804-2023

Research Output

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  • Publication
    Online learning approach for predictive Real-Time energy trading in Cloud-RANs
    ( 2021)
    Wan Nur Suryani Firuz Wan Ariffin
    ;
    Xinruo Zhang
    ;
    Mohammad Reza Nakhai
    ;
    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.
      2  11
  • Publication
    A hybrid modified sine cosine algorithm using inverse filtering and clipping methods for low autocorrelation binary sequences
    ( 2022-01-01)
    Rosli S.J.
    ;
    Hasliza A Rahim @ Samsuddin
    ;
    Khairul Najmy Abdul Rani
    ;
    Ruzelita Ngadiran
    ;
    Wan Azani Wan Mustafa
    ;
    Abdulmalek M.
    ;
    Mohd Najib Mohd Yasin
    ;
    Muzammil Jusoh
    ;
    Thennarasan Sabapathy
    ;
    Wan Nur Suryani Firuz Wan Ariffin
    ;
    Alkhayyat A.
    The essential purpose of radar is to detect a target of interest and provide information concerning the target's location, motion, size, and other parameters. The knowledge about the pulse trains' properties shows that a class of signals is mainlywell suited to digital processing of increasing practical importance. A low autocorrelation binary sequence (LABS) is a complex combinatorial problem. The main problems of LABS are low Merit Factor (MF) and shorter length sequences. Besides, the maximumpossibleMF equals 12.3248 as infinity length is unable to be achieved. Therefore, this study implemented two techniques to propose a new metaheuristic algorithm based on Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA) using Inverse Filtering (IF) and clipping method to achieve better results. The proposed algorithms, LABS-IF and HMSCACSA-IF, achieved better results with two large MFs equal to 12.12 and 12.6678 for lengths 231 and 237, respectively, where the optimal solutions belong to the skew-symmetric sequences. TheMFoutperformed up to 24.335% and 2.708% against the state-of-the-art LABS heuristic algorithm, xLastovka, and Golay, respectively. These results indicated that the proposed algorithm's simulation had quality solutions in terms of fast convergence curve with better optimal means, and standard deviation.
      2  21
  • Publication
    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.
      1  25
  • Publication
    SWIPT in Rician MIMO Interference Channels with Spatial Antenna Correlation
    ( 2020-12-11)
    Wan Nur Suryani Firuz Wan Ariffin
    ;
    Mohammad Reza Nakhai
    ;
    Hasneeza Liza Zakaria
    ;
    Nur Baya Mohd Hashim
    ;
    `Aini Syuhada Md Zain
    ;
    Hasliza A Rahim @ Samsuddin
    In this paper, we investigate simultaneous wireless information and power transfer (SWIPT) under spatial correlation in multiuser multiple-input-multiple-output (MU-MIMO) fading channels where the line-of-sight (LOS) path between the transmitter and the receiver is present. While the energy receiving terminals are harvesting energy, the information receiving terminals are receiving their desired signal-to-interference-plus-noise ratio (SINR) under the total power constraint at the transmitting terminal of the proposed SWIPT system. We use the Kronecker model to study the impact of MIMO antenna correlation and the Rician K-factor as the main parameter to set up the different fading conditions. We use semi-definite programming (SDP) to formulate the proposed SWIPT system. Our numerical results confirm that the performance of the proposed design improves in the absence of LOS component, i.e., with a zero K-factor or pure Rayleigh fading channel, and deteriorates as the LOS channel component grows. We show that the total harvested energy monotonically decreases as the Rician K-factor increases.
      1  24
  • Publication
    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.
      1
  • Publication
    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.
      1