Analysis of PAPR on NOMA Waveforms Using Hybrid Algorithm

Hop plant is a plant used as a raw material for brewing beer, commonly grown in temperate climates. Some diseases, such as Downy Mildew and Powdery Mildew, can interfere with the cultivation process. The efforts that farmers have made have not gotten maximum results in this problem. Therefore, a more accurate and efficient detector is needed to reduce this problem. With the help of computer vision to identify diseases and machine learning to make correct decisions, we propose a disease detection system in hops. In this experiment, we compare the value of classification accuracy using feature selection and without feature selection. Based on these 2 methods, the Binary Particle Swarm Optimization (BPSO) feature selection and the Support Vector Machine (SVM) classifier resulted in a higher accuracy value of 95%, followed by the SVM classifier results without feature selection of 93%. 

Hops Plants Disease Detection using Feature Selection based BPSO-SVM

Hop plant is a plant used as a raw material for brewing beer, commonly grown in temperate climates. Some diseases, such as Downy Mildew and Powdery Mildew, can interfere with the cultivation process. The efforts that farmers have made have not gotten maximum results in this problem. Therefore, a more accurate and efficient detector is needed to reduce this problem. With the help of computer vision to identify diseases and machine learning to make correct decisions, we propose a disease detection system in hops. In this experiment, we compare the value of classification accuracy using feature selection and without feature selection. Based on these 2 methods, the Binary Particle Swarm Optimization (BPSO) feature selection and the Support Vector Machine (SVM) classifier resulted in a higher accuracy value of 95%, followed by the SVM classifier results without feature selection of 93%.

The Pattern division multiple access (PDMA) technique is proposed to meet diverse demands for the 5G system. The pattern matrix of PDMA with unequal diversity is designed to reduce error propagation, which relies on the joint design of transmission and receiver. In this paper, it forms a discrete optimization problem based on the characteristics of PDMA. We form the fitness function to maximize the average mutual information (AMI) of the PDMA system, and binary particle swarm optimization (BPSO) algorithm is introduced to design the pattern matrix of the PDMA system. It demonstrated that the optimized PDMA scheme could obtain better coding gain and diversity gain. The numerical results indicate that the optimized PDMA scheme brings about 0.5 dB-1.2 dB performance gains.

Optimized design pattern matrix of PDMA based on binary particle swarm optimization for 5G

Non-orthogonal multiple access (NOMA) scheme is a promising multiple access technique for the fifth generation (5G) New Radio (NR) due to its high spectral efficiency, massive connectivity and low latency. In this paper, a Low Density Superposition Modulation (LDSM) using discrete-cosine transform (DCT) scheme with 5G-NR low-density parity-check (LDPC) channel code is proposed for the 5G scenario. Moreover, we adopt a low-complexity elementary signal estimator (ESE) detection algorithm for the multi-user detection. Simulation results show that our proposed scheme has the 3–5 dB peak-to-average power ratio (PAPR) reduction as compared with the conventional sparse code multiple access (SCMA) and pattern division multiple access (PDMA). Besides, our scheme brings about 0.6 dB-2.0 dB performance gains. Therefore, the DCT-LDSM scheme is efficient and suitable for 5G scenario.

Low Density Superposition Modulation using DCT for 5G NOMA scheme

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Kun Lu

Author Tools

Chat about Author

Papers

Optimized design pattern matrix of PDMA based on binary particle swarm optimization for 5G

Low Density Superposition Modulation using DCT for 5G NOMA scheme