scholarly journals A Novel Subcarrier-Level Spectrum Sensing Method by Utilizing Fine-Grained Channel State Information in Wireless Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zheng Lu ◽  
Handong Wang ◽  
Hongyu Sun ◽  
Chin-Ling Chen ◽  
Zhenjiang Tan
Keyword(s):  
Spectrum Sensing ◽  
Channel State Information ◽  
Utilization Efficiency ◽  
Spectrum Utilization ◽  
Channel State ◽  
State Information ◽  
Wireless Technologies ◽  
Ism Band ◽  
Fine Grained ◽  
Level Spectrum

Traditionally, the channelization structures of wireless technologies (802.11/ZigBee/BLE) have been fixed. Each node content for the spectrum is assigned one channel with a specific bandwidth. However, classical channel-based spectrum sensing and sharing algorithms have great limitations to further optimize spectrum utilization when multiple IoT with different wireless technologies coexisting in the same environment. Therefore, exploring the fine-grained spectrum sensing algorithm becomes an essential work to further improve the spectrum utilization efficiency, especially in the Industrial Scientific Medical (ISM) band. This paper proposes Subcarrier-Sniffer, a novel subcarrier-level spectrum sensing and sharing method, which utilizes channel state information (CSI) to sense the fine-grained status of each subcarrier of the traditional channel. To evaluate the performance of Subcarrier-Sniffer, we implemented Subcarrier-Sniffer by USRP B200min, and the experimental results show that the accuracy of subcarrier-level spectrum sensing could achieve 100% in our settings that the distance between Subcarrier-Sniffer and the monitor is not greater than 7 m. Subcarrier-Sniffer could be applied in WiFi and ZigBee, WiFi and BLE, and WiFi and LTE-U coexisted environments for better spectrum utilization.

scholarly journals Subcarrier-Sniffer：CSI-based Subcarrier-Level Spectrum Sensing Algorithm

2019 ◽  
Author(s):  
Zhenjiang Tan ◽  
Zheng Lu ◽  
Hongyu Sun
Keyword(s):  
Spectrum Sensing ◽  
Channel State Information ◽  
Spectrum Sharing ◽  
Smart Homes ◽  
Experimental Results ◽  
Spectrum Utilization ◽  
Channel State ◽  
State Information ◽  
Level Spectrum ◽  
Iot Devices

Abstract: As the massive deployment of the heterogeneous IoT devices in the coexisting environment such as smart homes，Traditional channel-based spectrum sharing algorithms such as CSMA has great limitations to further optimize spectrum utilization. Therefore, exploring more efficient spectrum sensing algorithm becomes hot topic these years. This paper proposes Subcarrier-Sniffer, which utilizes Channel State Information (CSI) to sense the subcarrier-level detailed status of the spectrum. In order to evaluate the performance of Subcarrier-Sniffer, we implemented Subcarrier-Sniffer by USRP B200min, and the experimental results show that when the distance between Subcarrier-Sniffer and the monitored devices is not great than 7 m, the accuracy of subcarrier-level spectrum sensing could achieve 100% in our settings.

scholarly journals Wi-SL: Contactless Fine-Grained Gesture Recognition Uses Channel State Information

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4025
Author(s):  
Zhanjun Hao ◽  
Yu Duan ◽  
Xiaochao Dang ◽  
Yang Liu ◽  
Daiyang Zhang
Keyword(s):  
Sign Language ◽  
Gesture Recognition ◽  
Channel State Information ◽  
Computational Cost ◽  
Support Vector ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Channel State ◽  
State Information ◽  
Fine Grained

In recent years, with the development of wireless sensing technology and the widespread popularity of WiFi devices, human perception based on WiFi has become possible, and gesture recognition has become an active topic in the field of human-computer interaction. As a kind of gesture, sign language is widely used in life. The establishment of an effective sign language recognition system can help people with aphasia and hearing impairment to better interact with the computer and facilitate their daily life. For this reason, this paper proposes a contactless fine-grained gesture recognition method using Channel State Information (CSI), namely Wi-SL. This method uses a commercial WiFi device to establish the correlation mapping between the amplitude and phase difference information of the subcarrier level in the wireless signal and the sign language action, without requiring the user to wear any device. We combine an efficient denoising method to filter environmental interference with an effective selection of optimal subcarriers to reduce the computational cost of the system. We also use K-means combined with a Bagging algorithm to optimize the Support Vector Machine (SVM) classification (KSB) model to enhance the classification of sign language action data. We implemented the algorithms and evaluated them for three different scenarios. The experimental results show that the average accuracy of Wi-SL gesture recognition can reach 95.8%, which realizes device-free, non-invasive, high-precision sign language gesture recognition.

scholarly journals Channel State Information (CSI) based Sparse Reconstruction for Biomedical Applications Using hybrid mm-WAVE MIMO System

2021 ◽  
Vol 12 (3) ◽  
pp. 1557-1568
Author(s):  
Sunil Kumar M Et.al
Keyword(s):  
Channel State Information ◽  
Mimo System ◽  
Signal Reconstruction ◽  
Effective Bandwidth ◽  
Sparse Reconstruction ◽  
Spectrum Utilization ◽  
Channel State ◽  
State Information ◽  
Low Snr ◽  
Multiple Input

The significance of Channel State Information (CSI) is very essential in a hybrid mm-WAVE Multiple Input Multiple Input (MIMO) System due to its direct dependency on medium capacity and energy efficiency of a network. Therefore, a Channel State Information (CSI)-based Sparse Reconstruction (CSISR) technique is adopted for effective evaluation of CSI for future 5G cellular network implementation. A hybrid mm-WAVE MIMO communication system is also employed for effective bandwidth spectrum utilization. Furthermore, a joint sparse coding algorithm is introduced to study the channel matrices of hybrid mm-WAVE MIMO system. The proposed CSISR technique ensure proficient signal reconstruction, signal compression and resource reduction by exploiting sparsity of channel matrix. The proposed CSISR technique under low SNR conditions as well for hybrid mm-WAVE MIMO system with optimization of pre-processors and combiners. The performance throughput of proposed CSISR technique is measured against conventional algorithms considering power consumption, Normalized Mean Square Error (NMSE) and spectral efficiency of the mm-Wave MIMO system. The superiority of proposed CSISR technique is concluded based on simulations considering different system configurations and performance matrices.

scholarly journals Enhancing Packet-Level Wi-Fi Device Authentication Protocol Leveraging Channel State Information

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yubo Song ◽  
Bing Chen ◽  
Tianqi Wu ◽  
Tianyu Zheng ◽  
Hongyuan Chen ◽  
...  
Keyword(s):  
Channel State Information ◽  
Dispersion Analysis ◽  
Authentication Protocol ◽  
Small Scale ◽  
Channel State ◽  
State Information ◽  
Fine Grained ◽  
Long Time ◽  
Work First

Wi-Fi device authentication is crucial for defending against impersonation attacks and information forgery attacks. Most of the existing authentication technologies rely on complex cryptographic algorithms. However, they cannot be supported well on the devices with limited hardware resources. A fine-grained device authentication technology based on channel state information (CSI) provides a noncryptographic method, which uses the CSI fingerprints for authentication since CSI can uniquely identify the devices. But long-term authentication based on CSI fingerprints is a challenging work. First, the CSI fingerprints are environment-sensitive, which means that the local authenticator should be updated to adapt to the changing channel state. Second, the local authenticator trained with old CSI fingerprints is outdated when users reconnect to the network after being offline for a long time, thus, it needs to be retrained in the access phase with new fingerprints. To tackle these challenges, we propose a CSI-based enhancing Wi-Fi device authentication protocol and an authentication framework. The protocol helps to collect new CSI fingerprints for authenticator’s training in access phase and performs the fingerprints’ dispersion analysis for authentication. In the association phase, it provides packet-level authentication and updates the authenticator with valid CSI fingerprints. The authenticator consists of an ensemble of small-scale autoencoders, which has high enough time efficiency for packet-level authentication and authenticator’s update. Experiments show that the accuracy of the framework is up to 98.7%, and the authenticator updating method can help the framework maintains high accuracy.

scholarly journals A Novel Spectrum Sensing Without Channel State Information Using Estimated Parameters

2010 ◽  
Author(s):  
Pham Duy Phong ◽  
Dang Trung Chinh ◽  
Vu Van Yem
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Channel State Information ◽  
Multiple Antennas ◽  
The Other ◽  
Multiple Antenna ◽  
Channel State ◽  
State Information ◽  
Estimated Parameters ◽  
A New Technique

this  paper  develops  a  spectrum  sensing technique using multiple antenna and energy detector in cognitive  radio.  The  conventional  spectrum  sensing techniques  using  multiple  antennas  such  as  maximum ratio  processing  (MRP),  Equal  Gain  Combining (EGC)…  require  channel  state  information  (CSI)  to combine  received signal at each antenna. In practice, it is complicate and requires time to obtain CSI. Recently, some  methods  performing  spectrum  sensing  without CSI  have  been  proposed.  However,  these  methods  do not  bring  desired  results  compared  with  the conventional techniques using CSI. In our research, we propose  a  new  technique  without  requiring  CSI  to combine  signals from  multiple  antennas.  The  proposed technique  brings  good  results  compared  to  the  other conventional  techniques  requiring  CSI  like  EGC.  In addition,  we  do  not  assume  exact  parameters  of  signal and noise in our simulation. The samples at the receiver are used for two purposes: estimating these parameters of noise and signal and performing spectrum sensing

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