Field Programmable Gate Array Implementation of Long Term Evolution Physical Downlink Control Channel Under Multiple Input Multiple Output Technique

2015 ◽  
Vol 21 (1) ◽  
pp. 29-35
Author(s):  
M. A. Mohamed ◽  
H. M. Abd-Elatty ◽  
M. E. A. Aboel-Seoud ◽  
W. M. Raslan
Keyword(s):  
Field Programmable Gate Array ◽  
Multiple Input Multiple Output ◽  
Long Term Evolution ◽  
Control Channel ◽  
Term Evolution ◽  
Multiple Input ◽  
Field Programmable ◽  
Input Multiple Output ◽  
Gate Array

scholarly journals ARREGLO DE ANTENAS CON BAJO ACOPLAMIENTO MUTUO PARA SISTEMAS DE COMUNICACIONES INALÁMBRICAS MIMO-LTE

2017 ◽  
Vol 1 (27) ◽  
Author(s):  
Eduardo Rodríguez Araque ◽  
Roberto G. Rojas-Teran
Keyword(s):  
Printed Circuit Board ◽  
Multiple Input Multiple Output ◽  
Long Term Evolution ◽  
Circuit Board ◽  
Printed Circuit ◽  
Term Evolution ◽  
Multiple Input ◽  
Input Multiple Output

Este trabajo trata de un arreglo de antenas MIMO (multiple-input-multiple-output) que opera en la banda de 2.6 GHz, una banda de Long Term Evolution (LTE), para sistemas de comunicaciones móviles inalámbricas 4G. El arreglo de antenas consiste de 4 antenas compactas tipo patch sobre un substrato dieléctrico (PCB) de 125 mm x 62.5 mm x 1.27 mm. Modificaciones del plano de tierra (GND) junto con la ubicación sistemática y orientación de cada antena en la cara posterior al plano de tierra del PCB (Printed Circuit Board) juegan un rol muy importante en la reducción sustancial del acoplamiento mutuo, esto generalmente afecta el desempeño de los arreglos MIMO.Palabras clave: Arreglo de antenas, modos característicos, correlación espacial, multiple-input-multiple-output (MIMO), acoplamiento mutuo.

Field Programmable Gate Array Based Testbed for Investigating Multiple Input Multiple Output Signal Transmission in Indoor Environments

2009 ◽  
pp. 474-499
Author(s):  
Konstanty Bialkowski ◽  
Adam Postula ◽  
Amin Abbosh ◽  
Marek Bialkowski
Keyword(s):  
Field Programmable Gate Array ◽  
Signal Transmission ◽  
Multiple Input Multiple Output ◽  
Indoor Environments ◽  
Front End ◽  
Rf Front End ◽  
Multiple Input ◽  
Field Programmable ◽  
Input Multiple Output ◽  
Gate Array

This chapter introduces the concept of Multiple Input Multiple Output (MIMO) wireless communication system and the necessity to use a testbed to evaluate its performance. A comprehensive review of different types of testbeds available in the literature is presented. Next, the design and development of a 2×2 MIMO testbed which uses in-house built antennas, commercially available RF chips for an RF front end and a Field Programmable Gate Array (FPGA) for based signal processing is described. The operation of the developed testbed is verified using a Channel Emulator. The testing is done for the case of a simple Alamouti QPSK based encoding and decoding scheme of baseband signals.

scholarly journals Artificial Intelligence Based LTE MIMO Antenna for 5th Generation Mobile Networks

2020 ◽  
Vol 2 (3) ◽  
pp. 155-162
Author(s):  
Dr. Abul Bashar
Keyword(s):  
Artificial Intelligence ◽  
Mobile Networks ◽  
Multiple Input Multiple Output ◽  
Long Term Evolution ◽  
Base Station ◽  
Fifth Generation ◽  
Term Evolution ◽  
Multiple Input ◽  
Input Multiple Output

Artificial intelligence based long term evolution multi in multi output antenna supporting the fifth generation mobile networks is put forth in the paper. The mechanism laid out in paper is devised using the monopole-antenna integrated with the switchable pattern. The long term evolution based multiple input and multiple output antenna is equipped with four antennas and capable of providing a four concurrent data streams quadrupling the theoretical maximum speed of data transfer allowing the base station to convey four diverse signals through four diverse transmit antennas for a single user equipment. The utilization of the long term evolution multiple input multiple output is capable of utilizing the multi-trial broadcasting to offer betterments in the signal performance as well as throughput and spectral efficiency when used along the fifth generation mobile networks. So the paper proposes the artificial intelligence based long term evolution multiple input multiple output four transmit antenna with four diverse signal transmission capacity that is operating in the frequency of 3.501 Gigahertz frequency. The laid out design is evaluated using the Multi-input Multi output signal analyzer to acquire the capacity of the passive conveyance of the various antennas with the diverse combination of patterns. The outcomes observed enables the artificial intelligence antenna to identify the choicest antenna to be integrated in the diverse environments for improving the throughput, signal performance and the data conveyance speed.

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