Cell-Coverage-Area Optimization Based on Particle Swarm Optimization (PSO) for Green Macro Long-Term Evolution (LTE) Cellular Networks

2015 ◽  
pp. 245-262
Author(s):  
Mohammed H. Alsharif ◽  
Rosdiadee Nordin ◽  
Mahamod Ismail
Keyword(s):  
Particle Swarm Optimization ◽  
Cellular Networks ◽  
Particle Swarm ◽  
Long Term Evolution ◽  
Coverage Area ◽  
Swarm Optimization ◽  
Cell Coverage ◽  
Term Evolution ◽  
Area Optimization

scholarly journals Implementation of Particle Swarm Optimization Technique for Enhanced Outdoor Network Coverage in Long Term Evolution Network in Port Harcourt, Nigeria

2017 ◽  
Vol 2 (5) ◽  
pp. 36 ◽  
Author(s):  
Olukunle Akinyinka Akande ◽  
Onyebuchi Chikezie Nosiri ◽  
Agubor Cosmas Kemdirim ◽  
Okpara Chinedu Reginald
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Long Term Evolution ◽  
Urban Region ◽  
Swarm Optimization ◽  
Modified Model ◽  
Term Evolution ◽  
Port Harcourt ◽  
Optimized Model

This paper describes the development of optimized model for urban outdoor coverage in Long Term Evolution (LTE) network at 2300 MHz frequency band in Port Harcourt urban region, Nigeria. Signal attenuation and fluctuation remain amongst the major channel impairments for mobile radio communication systems. This arises as a result of model incompatibility with terrain and Line of Sight (LOS) obstruction of the channel signals. Some path loss models such as Okumura-Hata, COST 231, Ericsson 999, Egli and ECC-33 models were evaluated for suitability and compared with the modified model for the environments. The models were based on data collected from LTE base stations at three geographical locations in Port Harcourt namely- Rumuokoro, Eneka and Ikwerre roads respectively. The simulation was implemented using MATLAB R2014a software. The modified model was further optimized with some selected parameters such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) using Particle Swarm Optimization (PSO) technique. The results obtained gave rise to 3.030dB for RMSE and 0.00162dB for MAE respectively. The results obtained from the PSO optimized model demonstrated a better performance which is suitable for cell coverage planning and smooth handoff processes.

Long-Term Evolution (LTE)

Data Mining ◽  
2013 ◽  
pp. 336-365
Author(s):  
Bing He ◽  
Bin Xie ◽  
Sanjuli Agrawal ◽  
David Zhao ◽  
Ranga Reddy
Keyword(s):  
Cellular Networks ◽  
Orthogonal Frequency Division Multiplexing ◽  
New Technologies ◽  
Network Capacity ◽  
Long Term Evolution ◽  
High Data ◽  
Term Evolution ◽  
Input Multiple Output ◽  
Speed Up

With the ever growing demand on high throughput for mobile users, 3G cellular networks are limited in their network capacity for offering high data services to a large number of users. Consequently, many Internet services such as on-demand video and mobile TV are hard to be satisfactorily supported by the current 3G cellular networks. 3GPP Long Term Evolution (LTE) is a recently proposed 4G standard, representing a significant advance of 3G cellular technology. Attractively, LTE would offer an uplink data speed up to 50 Mbps and a downlink speed up to 100 Mbps for various services such as traditional voice, high-speed data, multimedia unicast, and multimedia broadcasting. In such a short time, it has been broadly accepted by major wireless vendors such as Verizon-Vodafone, AT&T, NTT-Docomo, KDDI, T-Mobile, and China Mobile. In order for high data link speed, LTE adapts new technologies that are new to 3G network such as Orthogonal Frequency Division Multiplexing (OFDM) and Multiple-Input Multiple-Output (MIMO). MIMO allows the use of more than one antenna at the transmitter and receiver for higher data transmission. The LTE bandwidth can be scalable from 1.25 to 20 MHz, satisfying the need of different network operators that may have different bandwidth allocations for services, based on its managed spectrum. In this chapter, we discuss the major advance of the LTE and its recent research efforts in improving its performance. Our illustration of LTE is comprehensive, spanning from the LTE physical layer to link layer. In addition, the LTE security is also discussed.

Time and Frequency Analysis of Particle Swarm Trajectories for Cognitive Machines

2011 ◽  
Vol 5 (1) ◽  
pp. 18-42 ◽  
Author(s):  
Dario Schor ◽  
Witold Kinsner
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Space ◽  
Particle Swarm ◽  
Local Area ◽  
Complexity Measures ◽  
Swarm Optimization ◽  
Frequency Domains ◽  
Frequency Domain Techniques ◽  
And Control

This paper examines the inherited persistent behavior of particle swarm optimization and its implications to cognitive machines. The performance of the algorithm is studied through an average particle’s trajectory through the parameter space of the Sphere and Rastrigin function. The trajectories are decomposed into position and velocity along each dimension optimized. A threshold is defined to separate the transient period, where the particle is moving towards a solution using information about the position of its best neighbors, from the steady state reached when the particles explore the local area surrounding the solution to the system. Using a combination of time and frequency domain techniques, the inherited long-term dependencies that drive the algorithm are discerned. Experimental results show the particles balance exploration of the parameter space with the correlated goal oriented trajectory driven by their social interactions. The information learned from this analysis can be used to extract complexity measures to classify the behavior and control of particle swarm optimization, and make proper decisions on what to do next. This novel analysis of a particle trajectory in the time and frequency domains presents clear advantages of particle swarm optimization and inherent properties that make this optimization algorithm a suitable choice for use in cognitive machines.

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