International Journal of Computer Networks and Communications Security

Volume 8, Issue 8, August 2020

 

 

Energy Efficient Linear and Non-Linear Precoders for Massive MIMO Systems
 

Energy Efficient Linear and Non-Linear Precoders for Massive MIMO Systems
Pages: 59-66 (8) | [Full Text] PDF (1.26 MB)
SH Sackey, MK Ansong, SN Kofie, AK Armahy
College of Internet of Things, Hohai University, Changzhou, 213022, China
School of Managerial Engineering, Zhengzhou University, Zhengzhou, 450001, China

https://doi.org/10.47277/IJCNCS/8(8)1

Abstract - The term Massive MIMO means, Massive multiple input multiple output also known as (large-scale antenna system, very large MIMO). Massive Multiple-Input-MultipleOutput (MIMO) is the major key technique for the future Fifth Generation (5G) of mobile wireless communication network due to its characteristics, elements and advantages. Massive MIMO will be comprised of five major elements; antennas, electronic components, network architectures, protocols and signal processing. We realize that precoding technique is a processing technique that utilizes Channel State Information Technique (CSIT) by operating on the signals before transmitting them. This technique varies base on the type of CSIT and performance criterion. Precoding technique is the last digital processing block at the transmitting side. In this paper, linear and non-linear Precoding technique was reviewed and we proposed two techniques under each that is Minimum Mean Square Error (MMSE), Block Diagonalization (BD), Tomlinson-Harashima (TH) and Dirty paper coding (DPC). Four Precoding techniques: MMSE, BD, DPC and TH were used in the studies to power consumption, energy efficiency and area throughput for single-cell and multi-cell scenarios. In comparing the proposed techniques, in terms of energy efficiency and area throughput, reuse factor (Reuse 4) performs better than other techniques when there is an imperfect CSI is used.
 
Index Terms - Massive MIMO, Precoding, CSI, antennas, base station, signals, linear/non-linear precoders

Citation - SH Sackey, MK Ansong, SN Kofie, AK Armahy. "Energy Efficient Linear and Non-Linear Precoders for Massive MIMO Systems." International Journal of Computer Networks and Communications Security 8, no. 8 (2020): 59-66.
Threat Detection using Machine/Deep Learning in IOT Environments
 

Threat Detection using Machine/Deep Learning in IOT Environments
Pages: 67-73 (7) | [Full Text] PDF (530 KB)
D Javeed, U MohammedBadamasi, T Iqbal, Al Umar, CO Ndubuisi
Northeastern University, Shenyang, Liaoning province, China
Changchun University of Science and Technology, China
Jigawa State Institute of Information Technology Kazaure, Nigeria

https://doi.org/10.47277/IJCNCS/8(8)2

Abstract - The quality of human life is improving day by day and IOT plays a very important role in this improvement. Everything related to internet have some security concerns. This paper aims to improve the security in IOT environments. In any of the IOT networks the unknown and knows flaws can be a backdoor for any adversary. The increase use of such environment results in the increase of zero day cyber-attacks. This paper aims to focus on different models of DL in order to predict the attacks in IOT environments. The main aim of this research is to provide a very best solution for the detection of threats in order to improve the infrastructures of IOT. In this paper different experiments has been conducted and its results has been discussed in order to provide an effective solution.
 
Index Terms - IOT, Machine Learning, Threat Detection, Deep Learning

Citation - D Javeed, U MohammedBadamasi, T Iqbal, Al Umar, CO Ndubuisi. "Threat Detection using Machine/Deep Learning in IOT Environments." International Journal of Computer Networks and Communications Security 8, no. 8 (2020): 67-73.