scholarly journals SDR-Fi: Deep-Learning-Based Indoor Positioning via Software-Defined Radio

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 145784-145797 ◽  
Author(s):  
Erick Schmidt ◽  
Devasena Inupakutika ◽  
Rahul Mundlamuri ◽  
David Akopian
Keyword(s):  
Deep Learning ◽  
Software Defined Radio ◽  
Indoor Positioning

scholarly journals Hybrid Deep Learning Model Based Indoor Positioning Using Wi-Fi RSSI Heat Maps for Autonomous Applications

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 2
Author(s):  
Alwin Poulose ◽  
Dong Seog Han
Keyword(s):  
Deep Learning ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Learning Model ◽  
Signal Interference ◽  
Positioning System ◽  
Heat Maps ◽  
Positioning Systems ◽  
Localization Performance ◽  
Deep Learning Model

Positioning using Wi-Fi received signal strength indication (RSSI) signals is an effective method for identifying the user positions in an indoor scenario. Wi-Fi RSSI signals in an autonomous system can be easily used for vehicle tracking in underground parking. In Wi-Fi RSSI signal based positioning, the positioning system estimates the signal strength of the access points (APs) to the receiver and identifies the user’s indoor positions. The existing Wi-Fi RSSI based positioning systems use raw RSSI signals obtained from APs and estimate the user positions. These raw RSSI signals can easily fluctuate and be interfered with by the indoor channel conditions. This signal interference in the indoor channel condition reduces localization performance of these existing Wi-Fi RSSI signal based positioning systems. To enhance their performance and reduce the positioning error, we propose a hybrid deep learning model (HDLM) based indoor positioning system. The proposed HDLM based positioning system uses RSSI heat maps instead of raw RSSI signals from APs. This results in better localization performance for Wi-Fi RSSI signal based positioning systems. When compared to the existing Wi-Fi RSSI based positioning technologies such as fingerprint, trilateration, and Wi-Fi fusion approaches, the proposed approach achieves reasonably better positioning results for indoor localization. The experiment results show that a combination of convolutional neural network and long short-term memory network (CNN-LSTM) used in the proposed HDLM outperforms other deep learning models and gives a smaller localization error than conventional Wi-Fi RSSI signal based localization approaches. From the experiment result analysis, the proposed system can be easily implemented for autonomous applications.

Toward Polymorphic Internet of Things Receivers Through Real-Time Waveform-Level Deep Learning

2022 ◽  
Vol 25 (3) ◽  
pp. 28-33
Author(s):  
Francesco Restuccia ◽  
Tommaso Melodia
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Software Defined Radio ◽  
Wireless Systems ◽  
Physical World ◽  
Wireless Protocols ◽  
Key Innovation ◽  
Custom Made ◽  
Time Waveform

Wireless systems such as the Internet of Things (IoT) are changing the way we interact with the cyber and the physical world. As IoT systems become more and more pervasive, it is imperative to design wireless protocols that can effectively and efficiently support IoT devices and operations. On the other hand, today's IoT wireless systems are based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. In this paper, we introduce the new notion of a deep learning-based polymorphic IoT receiver, able to reconfigure its waveform demodulation strategy itself in real time, based on the inferred waveform parameters. Our key innovation is the introduction of a novel embedded deep learning architecture that enables the solution of waveform inference problems, which is then integrated into a generalized hardware/software architecture with radio components and signal processing. Our polymorphic wireless receiver is prototyped on a custom-made software-defined radio platform. We show through extensive over-the-air experiments that the system achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.

scholarly journals SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1323 ◽  
Author(s):  
Donald L. Hall ◽  
Ram M. Narayanan ◽  
David M. Jenkins
Keyword(s):  
Deep Learning ◽  
Software Defined Radio ◽  
High Performance ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Signal Propagation ◽  
Multipath Channel ◽  
K Nearest Neighbor ◽  
Positioning Systems ◽  
Channel Characteristics

Wireless indoor positioning systems (IPS) are ever-growing as traditional global positioning systems (GPS) are ineffective due to non-line-of-sight (NLoS) signal propagation. In this paper, we present a novel approach to learning three-dimensional (3D) multipath channel characteristics in a probabilistic manner for providing high performance indoor localization of wireless beacons. The proposed system employs a single triad dipole vector sensor (TDVS) for polarization diversity, a deep learning model deemed the denoising autoencoder to extract unique fingerprints from 3D multipath channel information, and a probabilistic k-nearest-neighbor (PkNN) to exploit the 3D multipath characteristics. The proposed system is the first to exploit 3D multipath channel characteristics for indoor wireless beacon localization via vector sensing methodologies, a software defined radio (SDR) platform, and multipath channel estimation.

scholarly journals Research and Analysis of Electromagnetic Trojan Detection Based on Deep Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jiazhong Lu ◽  
Xiaolei Liu ◽  
Shibin Zhang ◽  
Yan Chang
Keyword(s):  
Deep Learning ◽  
Software Defined Radio ◽  
Time Window ◽  
Rate Of Change ◽  
Network Resources ◽  
Trojan Detection ◽  
White List ◽  
Electromagnetic Signals ◽  
Manual Testing ◽  
The Cost

The electromagnetic Trojan attack can break through the physical isolation to attack, and the leaked channel does not use the system network resources, which makes the traditional firewall and other intrusion detection devices unable to effectively prevent. Based on the existing research results, this paper proposes an electromagnetic Trojan detection method based on deep learning, which makes the work of electromagnetic Trojan analysis more intelligent. First, the electromagnetic wave signal is captured using software-defined radio technology, and then the signal is initially filtered in combination with a white list, a demodulated signal, and a rate of change in intensity. Secondly, the signal in the frequency domain is divided into blocks in a time-window mode, and the electromagnetic signals are represented by features such as time, information amount, and energy. Finally, the serialized signal feature vector is further extracted using the LSTM algorithm to identify the electromagnetic Trojan. This experiment uses the electromagnetic Trojan data published by Gurion University to test. And it can effectively defend electromagnetic Trojans, improve the participation of computers in electromagnetic Trojan detection, and reduce the cost of manual testing.

scholarly journals Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of Trajectories

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5776
Author(s):  
Zhongfeng Zhang ◽  
Minjae Lee ◽  
Seungwon Choi
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Software Radio ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Training Dataset ◽  
Positioning System ◽  
Indoor Environments ◽  
Generative Adversarial Network ◽  
Indoor Positioning System

In a Wi-Fi indoor positioning system (IPS), the performance of the IPS depends on the channel state information (CSI), which is often limited due to the multipath fading effect, especially in indoor environments involving multiple non-line-of-sight propagation paths. In this paper, we propose a novel IPS utilizing trajectory CSI observed from predetermined trajectories instead of the CSI collected at each stationary location; thus, the proposed method enables all the CSI along each route to be continuously encountered in the observation. Further, by using a generative adversarial network (GAN), which helps enlarge the training dataset, the cost of trajectory CSI collection can be significantly reduced. To fully exploit the trajectory CSI’s spatial and temporal information, the proposed IPS employs a deep learning network of a one-dimensional convolutional neural network–long short-term memory (1DCNN-LSTM). The proposed IPS was hardware-implemented, where digital signal processors and a universal software radio peripheral were used as a modem and radio frequency transceiver, respectively, for both access point and mobile device of Wi-Fi. We verified that the proposed IPS based on the trajectory CSI far outperforms the state-of-the-art IPS based on the CSI collected from stationary locations through extensive experimental tests and computer simulations.

scholarly journals Deep Learning Based Antenna Selection for MIMO SDR System

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6987
Author(s):  
Shida Zhong ◽  
Haogang Feng ◽  
Peichang Zhang ◽  
Jiajun Xu ◽  
Huancong Luo ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Software Defined Radio ◽  
Antenna Selection ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Input Multiple Output ◽  
Set Up ◽  
Time Division

In this paper, we propose and implement a novel framework of deep learning based antenna selection (DLBAS)-aided multiple-input–multiple-output (MIMO) software defined radio (SDR) system. The system is constructed with the following three steps: (1) a MIMO SDR communication platform is first constructed, which is capable of achieving uplink communication from users to the base station via time division duplex (TDD); (2) we use the deep neural network (DNN) from our previous work to construct a deep learning decision server to assist the MIMO SDR platform for making intelligent decision for antenna selection, which transforms the optimization-driven decision making method into a data-driven decision making method; and (3) we set up the deep learning decision server as a multithreading server to improve the resource utilization ratio. To evaluate the performance of the DLBAS-aided MIMO SDR system, a norm-based antenna selection (NBAS) scheme is selected for comparison. The results show that the proposed DLBAS scheme performed equally to the NBAS scheme in real-time and out-performed the MIMO system without AS with up to 53% improvement on average channel capacity gain.

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