AN IMPROVED CHANNEL DETECTION METHOD FOR IEEE 802.11N MIMO SYSTEM

Coexistence and interoperability between 20 MHz and 40 MHz device and modes of op-erations are stressed in standard IEEE 802.11n system.It is mandate to report the both sub-channels states to Medium Access Control(MAC) at receiver,since for 40 MHz device,it should serve not only 20 MHz but also 40 MHz signals receiving.Both energy detection and carrier sense are employed to detect channel state.In the case of 20/40 M mode,the power difference between the two sub-channels is also detected in order to report the channel state accurately.The simulation results demonstrate that the performance of the proposed methods are much better than the methods which just employ energy detection.Besides,the simulation results show that the proposed methods ensure that the channel sensing is not a roadblock of IEEE 802.11n system design.

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources.Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs.However,manual channel picking is both time consuming and tedious.Moreover,similar to any other process dependent on human intervention,manual channel picking is error prone and inconsistent.To address these issues,automatic channel detection is both necessary and important for efficient and accurate seismic interpretation.Modern systems make use of real-time image processing techniques for different tasks.Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies.In this paper,we propose an innovative automatic channel detection algorithm based on machine learning techniques.The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process.The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches.We provide a field data example to demonstrate the performance of the new algorithm.The training phase gave a maximum accuracy of 84.6%for the classifier and it performed even better in the testing phase,giving a maximum accuracy of 90%.

A LoRaWAN Access Technology Based on Channel Adaptive Adjustment

Low-power wide area network(LPWAN)has developed rapidly in recent years and is widely used in various Internet of Things(IoT)services.In order to reduce cost and power consumption,wide coverage,LPWAN tends to use simple channel access control protocols,such as the Aloha protocol.This protocol is simple with poor extension capability.In high-density environment,Aloha protocol will lead to low channel utilization,prolonged access and high conflict probability.Therefore,in order to solve the above problems,we propose an enhanced channel access control mechanism based on the existing LoRaWAN protocol,that is,a dynamic listening backoff mechanism.We combine the improved“listen first and then talk”(LBT)mechanism with the current state of the channel to adaptively adjust the size of the backoff window.The theoretical analysis and simulation results show that the proposed mechanism have a better performance than the existing mechanism,it can reduce conflicts in dense environments.By comparison,the packet transmission success rate is increased by 17%.

Joint Channel and Multi-User Detection Empowered with Machine Learning

The numbers of multimedia applications and their users increase with each passing day.Different multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network systems.In this article,a fuzzy logic empowered adaptive backpropagation neural network(FLeABPNN)algorithm is proposed for joint channel and multi-user detection(CMD).FLeABPNN has two stages.The first stage estimates the channel parameters,and the second performsmulti-user detection.The proposed approach capitalizes on a neuro-fuzzy hybrid systemthat combines the competencies of both fuzzy logic and neural networks.This study analyzes the results of using FLeABPNN based on a multiple-input andmultiple-output(MIMO)receiver with conventional partial oppositemutant particle swarmoptimization(POMPSO),total-OMPSO(TOMPSO),fuzzy logic empowered POMPSO(FL-POMPSO),and FL-TOMPSO-based MIMO receivers.The FLeABPNN-based receiver renders better results than other techniques in terms of minimum mean square error,minimum mean channel error,and bit error rate.

A NEW METHOD OF FREQUENCY-HOPPING(FH) SIGNAL DETECTION

A new Frequency-Hopping(FH) signal detection method is proposed.Different from pre-vious methods which need to monitor the total band,it can monitor part of the band and decrease the range of the bandwidth.According to this method,a new detection model is set and the computation formulas of the detection probability and false-alarm probability are given.The parameters of a VHF radio are used to prove the validity of the method.Simulation results show that this method can de-crease the range of the bandwidth and detect the FH signal with some penalty on the SNR and signal loss.

A PILOT-ASSISTED NON-CYCLIC PREFIXED MIMO-SCFDE SYSTEM

A non-Cyclic Prefixed Multiple-Input Multiple-Output Single-Carrier Frequency-Domain Equalization (non-CP MIMO-SCFDE) system based on a recursive algorithm of Joint Channel Es- timation and Data Detection (recursive-JCEDD) is proposed in this paper. Unlike the traditional CP MIMO-SCFDE system, the transmitted block of the proposed system is designed in the way that block-type pilot sequences and Single-Carrier (SC) information sequences have been arranged alter- nately without any cyclic prefix before each SC information sequence. Moreover, a recursive-JCEDD algorithm based on interference cancellation is proposed for the corresponding receivers. Simulation results show that the Bit Error Rate (BER) of the proposed system based on the recursive-JCEDD algorithm is lower than traditional CP MIMO-SCFDE or MIMO-OFDM with channel estimation for more than 0.5 dB.

Robust and Passive Motion Detection with COTS WiFi Devices

Device-free Passive（DfP） detection has received increasing attention for its ability to support various pervasive applications. Instead of relying on variable Received Signal Strength（RSS）, most recent studies rely on finer-grained Channel State Information（CSI）. However, existing methods have some limitations, in that they are effective only in the Line-Of-Sight（LOS） or for more than one moving individual. In this paper, we analyze the human motion effect on CSI and propose a novel scheme for Robust Passive Motion Detection（R-PMD）. Since traditional low-pass filtering has a number of limitations with respect to data denoising, we adopt a novel Principal Component Analysis（PCA）-based filtering technique to capture the representative signals of human motion and extract the variance profile as the sensitive metric for human detection. In addition, existing schemes simply aggregate CSI values over all the antennas in MIMO systems. Instead, we investigate the sensing quality of each antenna and aggregate the best combination of antennas to achieve more accurate and robust detection. The R-PMD prototype uses off-the-shelf WiFi devices and the experimental results demonstrate that R-PMD achieves an average detection rate of 96.33% with a false alarm rate of 3.67%.