Proposed APs Distribution Optimization Algorithm: Aware of Interference (APD-AI)

Mohammed, Rawaa Akram; Salim, Omar Nameer Mohammed; Al-Nakkash, Aseel H.; Alabdullah, Ali A. S.

doi:10.1088/1757-899x/745/1/012040

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…Rational planning (i.e., minimizing the number of APs) and effective planning of the wireless network (i.e., maximizing the throughput) for indoor/outdoor operation usually requires the consecutive evaluation of many candidate solutions using optimization algorithms. Among the optimization algorithms commonly used in network planning, we can mention: e.g., heuristics [12,[22][23][24], GA [25][26][27][28], or Particle Swarm Optimization (PSO) [29][30][31].…”

Section: Network Planning Algorithmsmentioning

confidence: 99%

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Indoor Genetic Algorithm-Based 5G Network Planning Using a Machine Learning Model for Path Loss Estimation

Santana

Alonso

Nieto³

et al. 2022

Applied Sciences

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Accurate wireless network planning is crucial for the deployment of new wireless services. This usually requires the consecutive evaluation of many candidate solutions, which is only feasible for simple path loss models, such as one-slope models or multi-wall models. However, such path loss models are quite straightforward and often do not deliver satisfactory estimations, eventually impacting the quality of the proposed network deployment. More advanced models, such as Indoor Dominant Path Loss models, are usually more accurate, but as their path loss calculation is much more time-consuming, it is no longer possible to evaluate a large set of candidate deployment solutions. Out of necessity, a heuristic network planning algorithm is then typically used, but the outcomes heavily depend on the quality of the heuristic. Therefore, this paper investigates the use of Machine Learning to approximate a complex 5G path loss model. The much lower calculation time allows using this model in a Genetic Algorithm-based network planning algorithm. The Machine Learning model is trained for two buildings and is validated on three other buildings, with a Mean Absolute Error below 3 dB. It is shown that the new approach is able to find a wireless network deployment solution with an equal, or smaller, amount of access points, while still providing the required coverage for at least 99.4% of the receiver locations and it does this 15 times faster. Unlike a heuristic approach, the proposed one also allows accounting for additional design criteria, such as maximal average received power throughout the building, or minimal exposure to radiofrequency signals in certain rooms.

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Section: Network Planning Algorithmsmentioning

confidence: 99%

“…A multi-objective algorithm to optimize the coverage and maximize the Signal-to-Interference Ratio (SIR), based on Binary Particle Swarm Optimization (BPSO), was proposed in [29]. The proposal was applied to the existing network, which consists of four APs.…”

Section: Network Planning Algorithmsmentioning

confidence: 99%

Indoor Genetic Algorithm-Based 5G Network Planning Using a Machine Learning Model for Path Loss Estimation

Santana

Alonso

Nieto³

et al. 2022

Applied Sciences

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“…The same rear authors in 16 have presented an optimization method by using BPSO algorithm based on the use of a multi-objective function. In their study, a small part of the target area used to identify and suggest AP deployment locations based on two different objectives.…”

Section: Related Workmentioning

confidence: 99%

WOAIP: Wireless Optimization Algorithm for Indoor Placement Based on Binary Particle Swarm Optimization (BPSO)

Naif

Mohammed

2022

Baghdad Sci.J

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Optimizing the Access Point (AP) deployment has a great role in wireless applications due to the need for providing an efficient communication with low deployment costs. Quality of Service (QoS), is a major significant parameter and objective to be considered along with AP placement as well the overall deployment cost. This study proposes and investigates a multi-level optimization algorithm called Wireless Optimization Algorithm for Indoor Placement (WOAIP) based on Binary Particle Swarm Optimization (BPSO). WOAIP aims to obtain the optimum AP multi-floor placement with effective coverage that makes it more capable of supporting QoS and cost-effectiveness. Five pairs (coverage, AP deployment) of weights, signal thresholds and received signal strength (RSS) measurements simulated using Wireless InSite (WI) software were considered in the test case study by comparing the results collected from WI with the present wireless simulated physical AP deployment of the targeted building - Computer Science Department at University of Baghdad. The performance evaluation of WOAIP shows an increase in terms of AP placement and optimization distinguished in order to increase the wireless coverage ratio to 92.93% compared to 58.5% of present AP coverage (or 24.5% coverage enhancement on average).

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“…Training and learning methods are used to compute all of the processes that are involved in ANN models, such as collecting data and analyzing it, weights and biasing trade-offs, designing networks structure, and simulation the networks [19,26]. The effectiveness of an ANN model is largely dependent on its architecture, where the algorithm that is utilized for training, and the selection of the s tructure utilized during the process of training are very important factors in ANN performance [27,28]. The ANN model could also be combined with many other optimization algorithms, such as an adaptive neural-fuzzy inference system [29].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Based Transmission Power Control for Underwater Wireless Optical Communication System

Salim

Adnan

Mutlag

2023

ETJ

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 A 450 nm Underwater Wireless OpticalCommunication System was implemented in this study.  Bit error rate was measured in the tap water channel for 2M bit/s,10M bit/s, and 20M bit/s.  The optimization for striking a balance between low power consumption and reliable data transfer in underwater ambient was investigated.  A FFBP-ANN model-based transmission power control for the UWOC system has been adopted.  power needed in multiple scenarios.Several scientists have proposed using an underwater wireless optical communication system (UWOC) to deliver high-speed data services using the abundant optical spectrum. However, wireless optical signal propagation faces an antagonistic environment when using undersea channels due to various factors like scattering, absorption, turbulence, and optical link misalignment between the transmitter and the receiver. These factors will attenuate the optical signal and lead to degrading system performance. To reduce these factors impact on the communication system's performance, transmitted optical power (OTP) should be increased. Since the UWOC system is battery -powered, increasing OTP will consume more electrical power. Therefore, it is necessary to adjust OTP to a value commensurate with the underwater channel changes. So, an ANN model is proposed in this article for link adaptation, which can adjust the OTP level in tandem with the underwater channel conditions. Data for training, testing, and validation of the proposed system reliability was collected experimentally, and tap water was used as a transmission medium. Evaluation of the proposed model outcome demonstrates that reliable performance is achieved in predicting OTP needed in multiple scenarios. The M SE of the predicted OTP is(9.5×10-3,1.5×10-2, and 1.7×10-2) dBm in the training, testing, and validation stages, respectively. The regression values of the training, testing and validation sets are (0.9997,0.9990, and 0.9996). The results achieved by the prop osed model prove it is reliable to be applied in UWOC systems.

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Proposed APs Distribution Optimization Algorithm: Aware of Interference (APD-AI)

Cited by 4 publications

References 8 publications

Indoor Genetic Algorithm-Based 5G Network Planning Using a Machine Learning Model for Path Loss Estimation

Indoor Genetic Algorithm-Based 5G Network Planning Using a Machine Learning Model for Path Loss Estimation

WOAIP: Wireless Optimization Algorithm for Indoor Placement Based on Binary Particle Swarm Optimization (BPSO)

Artificial Neural Network-Based Transmission Power Control for Underwater Wireless Optical Communication System

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