Cognitive Radios to Mitigate Interference in Macro/femto Heterogeneous Networks

Cheng, Shin‐Ming; Chen, Kwang‐Cheng

doi:10.1002/9781118555262.ch6

Cited by 2 publications

(2 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[5] of the Wiener filter, SEGAN performs slightly worse in the subjective voice quality evaluation PESQ, and has a better effect than the Wiener method in eliminating noise CBAK and SSNR and voice distortion CSIG, and is also better in subjective evaluation. In this paper, the noisy speech database used to train the TTS model [6] for training speech augmentation algorithms is proposed as the training and test data set, which is from Valentini-Botinghau [7] of the Faculty of Information, School of Information, University of Edinburgh. The dataset contains a total of 30 people, and the ratio of the amount of data from the training set to the test set is 14:1.…”

Section: Gan-based Speech-enhanced Network Algorithmmentioning

confidence: 99%

Research on audio enhancement algorithm based on generative adversarial network

Kang¹,

Chen²,

Chen³

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

View full text Add to dashboard Cite

In this paper, based on the theoretical foundation of generative adversarial networks and knowledge related to audio enhancement and denoising, a generative adversarial network model and framework are established，and the function of SEGAN audio enhancement algorithm is selected to complete audio noise reduction, improve the purity of audio and realize the audio enhancement algorithm function. At the same time, the training data of the loss function of GAN is optimized from the mathematical principle and the audio enhancement algorithm is evaluated by the speech quality assessment index.This paper replicates SEGAN's audio enhancement generative adversarial network with good practical effects in noise containing frequency denoising and pure audio enhancement, and the enhanced speech of this research method has better auditory quality and intelligibility, and also has better stability compared with the original SEGAN network.

show abstract

Section: Gan-based Speech-enhanced Network Algorithmmentioning

confidence: 99%

Research on audio enhancement algorithm based on generative adversarial network

Kang¹,

Chen²,

Chen³

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

View full text Add to dashboard Cite

show abstract

“…In Cognitive-based approaches, the network utilizes the distributed sensing scheme to sense the number of active Femtocells per cluster by an HeNB and adjusts its initial power setting correspondingly. Based on the information collected by applying cognitive-based approach, the Gibbs sampler, game-theoretic and compressive sensing approaches have been explored more for interference mitigation (both cross-tier and co-tier) in a Macro-Femto HetNet [58]. The cognitive-based interference mitigation technique is basically used to control the cross-tier and co-tier interference in OFDMA based Femtocell HetNets.…”

Section: Cognitive-based Approachmentioning

confidence: 99%

Interference Management Techniques in Small Cells Overlaid Heterogeneous Cellular Networks

Samal

2018

JMM

View full text Add to dashboard Cite

Now-a-days because of the rapid increase in mobile users mobile data has also raised. Due to the heavy demand of data rates for advanced applications service providers are forced to adopt different technological advancements in conventional networks. Looking toward this context heterogeneous networks (HetNets) play a vital role in future 5G wireless cellular network deployment. In Heterogeneous Cellular Networks (HCNs) low power small cells are overlaid with the existing macro-only cell which increases the complexity of the network. Some of the emerging network technologies for this cellular evolution are the Femtocell, Picocell and Metrocell networks. Due to the deployment of these small cells in HCNs there are wide varieties of network structures and different transmitting powers which lead to interference in the cellular network. In addition to this, due to the spatiotemporal distribution of the mobile users and their mobility, hotspots also lead to increase of the possibilities of interference generation in wireless cellular network systems. The major technical issues associated with the mass deployment of smaller cells are related to the interference management between smallcells i.e. Femtocells and other serving cells in the same spectrum. Since Femtocells and Macrocells share the same frequency bands, it is necessary to develop efficient interference management techniques to increase the capacity and throughput of HCNs. So in order to meet the user demands in an efficient way

show abstract

Cognitive Radios to Mitigate Interference in Macro/femto Heterogeneous Networks

Cited by 2 publications

References 48 publications

Research on audio enhancement algorithm based on generative adversarial network

Research on audio enhancement algorithm based on generative adversarial network

Interference Management Techniques in Small Cells Overlaid Heterogeneous Cellular Networks

Contact Info

Product

Resources

About