Monte Carlo dropout may effectively capture model uncertainty in deep learning, where a measure of uncertainty is obtained by using multiple instances of dropout at test time. However, Monte Carlo dropout is applied across the whole network and thus significantly increases the computational complexity, proportional to the number of instances. To reduce the computational complexity, at test time we enable dropout layers only near the output of the neural network and reuse the computation from prior layers while keeping, if any, other dropout layers disabled. Additionally, we leverage the side information about the ideal distributions for various input samples to do `error correction' on the predictions. We apply these techniques to the radio frequency (RF) transmitter classification problem and show that the proposed algorithm is able to provide better prediction uncertainty than the simple ensemble average algorithm and can be used to effectively identify transmitters that are not in the training data set while correctly classifying transmitters it has been trained on.
This paper presents a system that sites (finds optimal locations for) thousands of radio transmitter towers on terrains of up to two billion elevation posts. Applications include cellphone towers, camera systems, or even mitigating environmental visual nuisances. The transmitters and receivers may be situated above the terrain. The system has been parallelized with OpenMP to run on a multicore CPU.
In this paper, we realize the transmitter of high frequency (HF) bidirectional transceiver radio. A bidirectional transceiver (Bitx) consists of a transmitter and receiver, in which some common circuits are used together by the transmitter and receiver. In addition, Bitx radio uses a single conversion superheterodyne transmitter, which only shifts frequency once in a signal generation process. The measurement result shows that it works at the frequency of 6.8 MHz with 10 MHz BFO, 3,2 MHz VFO, and 50,3 dBm transmit power. The transmitting frequency is obtained from the difference between BFO an…
Multi-modal Probabilistic Active Sensing (MMPAS) uses sensor fusion and probabilistic models to control the perception process of robotic sensing platforms. MMPAS is successfully employed in environmental exploration, collaborative mobile robotics, and target tracking, being fostered by the high performance guarantees on autonomous perception. In this context, we propose a bi-Radio-Visual PAS scheme to solve the transmitter discovery problem. Specifically, we firstly exploit the correlation between radio and visual measurements to learn a target detection model in a self-supervised manner. The…
In this paper, we apply the long short-term memory (LSTM), an advanced recurrent neural network based machine learning (ML) technique, to the problem of transmitter selection (TS) for secrecy in an underlay small-cell cognitive radio network with unreliable backhaul connections. The cognitive communication scenario under consideration has a secondary small-cell network that shares the same spectrum of the primary network with an agreement to always maintain a desired outage probability constraint in the primary network. Due to the interference from the secondary transmitter common to all prima…
Several different waveform processing techniques have been studied and proposed for the 5G new radio (NR) physical layer, to support new mixed numerology and asynchronous services. The evaluation and comparison of these different techniques is commonly based on matched waveform processing in the transmitter and receiver units. In this article, it is shown that different techniques can be flexibly mixed, allowing to separately optimize complexity-performance trade-offs for transmitter and receiver implementations. Mixing of different waveform processing techniques is possible by setting adequat…
Channel Knowledge Map Enabled Low-Complexity Dynamic Radio Environment Reconstruction
Nanjing University of Information Science and Technology（南京信息工程大学）；Sixty-Third Research Institute, National University of Defense Technology（国防科技大学第六十三研究所）
The most important problem in telecommunication is bandwidth limitation due to the uncontrolled growth of wireless technology. Deploying dynamic spectrum access techniques is one of the procedures provided for efficient use of bandwidth. In recent years, cognitive radio network introduced as a tool for efficient use of spectrum. These radios are able to use radio resources by recognizing surroundings via sensors and signal operations that means use these resources only when authorized users do not use their spectrum. Secondary users are unauthorized ones that must avoid from interferences with…
We study radio-frequency (RF) wireless power transfer (WPT) using a digital radio transmitter for applications where alternative analog transmit circuits are impractical. An important paramter for assessing the viability of an RF WPT system is its end-to-end efficiency. In this regard, we present a prototype test-bed comprising a software-defined radio (SDR) transmitter and an energy harvesting receiver with a low resistive load; employing an SDR makes our research meaningful for simultaneous wireless information and power transfer (SWIPT). We analyze the effect of clipping and non-linear ampl…
This paper describes the architecture and performance of ORACLE, an approach for detecting a unique radio from a large pool of bit-similar devices (same hardware, protocol, physical address, MAC ID) using only IQ samples at the physical layer. ORACLE trains a convolutional neural network (CNN) that balances computational time and accuracy, showing 99\% classification accuracy for a 16-node USRP X310 SDR testbed and an external database of $>$100 COTS WiFi devices. Our work makes the following contributions: (i) it studies the hardware-centric features within the transmitter chain that causes I…
LMU Munich（慕尼黑大学）；TU Berlin（柏林工业大学）；University of Tromsø（特罗姆瑟大学）
In this paper we propose a highly efficient and very accurate deep learning method for estimating the propagation pathloss from a point $x$ (transmitter location) to any point $y$ on a planar domain. For applications such as user-cell site association and device-to-device link scheduling, an accurate knowledge of the pathloss function for all pairs of transmitter-receiver locations is very important. Commonly used statistical models approximate the pathloss as a decaying function of the distance between transmitter and receiver. However, in realistic propagation environments characterized by t…
This paper investigates a distant proactive eavesdropping system in cooperative cognitive radio (CR) networks. Specifically, an amplify-and-forward (AF) full-duplex (FD) secondary transmitter assists to relay the received signal from suspicious users to legitimate monitor for wireless information surveillance. In return, the secondary transmitter is granted to share the spectrum belonging to the suspicious users for its own information transmission. To improve the eavesdropping, the transmitted secondary user's signal can also be used as a jamming signal to moderate the data rate of the suspic…
Univ Lyon（里昂大学）；Inria（法国国家信息与自动化研究所）；INSA Lyon（里昂国立应用科学学院）；Nokia Bell Labs（诺基亚贝尔实验室）；RTone
Hardware imperfections in RF transmitters introduce features that can be used to identify a specific transmitter amongst others. Supervised deep learning has shown good performance in this task but using datasets not applicable to real world situations where topologies evolve over time. To remedy this, the work rests on a series of datasets gathered in the Future Internet of Things / Cognitive Radio Testbed [4] (FIT/CorteXlab) to train a convolutional neural network (CNN), where focus has been given to reduce channel bias that has plagued previous works and constrained them to a constant envir…
This paper investigates a novel intelligent reflecting surface (IRS)-based symbiotic radio (SR) system architecture consisting of a transmitter, an IRS, and an information receiver (IR). The primary transmitter communicates with the IR and at the same time assists the IRS in forwarding information to the IR. Based on the IRS's symbol period, we distinguish two scenarios, namely, commensal SR (CSR) and parasitic SR (PSR), where two different techniques for decoding the IRS signals at the IR are employed. We formulate bit error rate (BER) minimization problems for both scenarios by jointly optim…
