首页|Applying Machine Learning Approach to Identifying Channels in MlMO Networks for Communications in 5G-Enabled Sustainable Smart Cities

Applying Machine Learning Approach to Identifying Channels in MlMO Networks for Communications in 5G-Enabled Sustainable Smart Cities

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  • NSTL
  • Springer Nature
During the process of sending a signal across a transmission channel that has a broad bandwidth, the Multiple-Input Multiple-Output, or MIMO, system will frequently require a larger quantity of energy and power than it would under normal circumstances. This is because the transmission channel will have a greater capacity to carry more data. When there are so many different sets of signals that have been found, it is necessary to be able to differentiate between them using one of the many different methods that have been developed. One of the methods that is now one of the most extensively employed in the process of discovery is called maximum likelihood detection. This method is also frequently referred to as machine learning (ML) detection. This is as a result of the very high throughput that ML detection has. On the other hand, the exponential growth in ideal throughput is far smaller than the complexity of the framework and the amount of energy that it consumes. In order to better simplify communication between the transmitter and the receiver, the major goal of this endeavor is to locate the shortest viable routing route for the physical data transmission layer and use that information to design the routing path. Because of this, both the quantity of energy that is used and the level of complexity that the system has will both drop. Improved Iterative Based Dijkstra Algorithm (IIBDA) to Gauge the Most Limited Course of the Channel with a Restricted Maximum Likelihood-Detection (RMLD) Design was a method that was presented to solve the problem. This was done in order to address the concerns that had been raised and resolved in the prior discussion. This was done in an effort to discover answers to problems such as these. The Execution Examination illustrates how the Complexity and Control Utilization in the Proposed Strategy may be Decreased by showing how these factors might be Addressed. An FPGA Virtex-6 was used for putting the suggested plan into action in order to accomplish this. When the recommended IIBDA-RMLD were put through their paces in terms of power consumption, area, time delay, and complexity, all of these characteristics exhibited improvements of up to 95.4%, 84.23%, 84.21%, 87.23%,90.14% respectively. These percentages represent the maximum levels of improvement that were observed. MIMO Transmission System is able to achieve a significantly higher Signal to Noise Ratio (Snr) demonstrating with reduced power usage of 0.538mw and range optimization accomplished 11093.13 m for Quadrature Phase Shift Keying (QPSK) balancing with the ML Location System using Feed Forward Neural Network (FFNN). This is in addition to the fact that the MIMO Transmission System can reduce the amount of power it uses by 0.538mw. This is accomplished with a reduced quantity of usage of electrical power. In conclusion, RMLD was employed on MIMO networks in order to enhance the transmission of information in military and other applications by making it more transportable and safer. This was done for a variety of reasons. This action was taken for a number of different reasons.

Improved Iterative Based Dijkstra Algorithm (IIBDA)Restricted Maximum Likelihood-Detection (RMLD)Quadrature Phase Shift Keying (QPSK)Multiple-Input Multiple-OutputFPGA (Field Programmable Gate Array)

Shalini Stalin、Manish Gupta、K. Makanyadevi、Amit Agrawal、Anupriya Jain、Shraddha V. Pandit、Ali Rizwan

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Department of Information Technology, International Institute of Information Technology, Bhopal, Madhya Pradesh 462003, India

Department of Computer Science & Engineering, Moradabad Institute of Technology, Moradabad, India

Department of Computer Science and Engineering, Kumarasamy College of Engineering, Karur, Tamil Nadu 639113, India

Department of Computer Science & Engineering & Director-T&P, Sreedattha Group of Educational Institutions, Hyderabad, Telangana, India

Manav Rachna International Institute of Research and Studies, Faridabad, India

Department of Artificial Intelligence and Data Science, PES Modern College of Engineering, Shivajinagar, Pune, Maharashtra, India

Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, 21589 Jeddah, Saudi Arabia

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2024

10.1007/s11036-023-02213-8

Mobile networks & applications

Mobile networks & applications

SCI
ISSN：1383-469X
年,卷(期)：2024.29(6)
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