Cognition and Learning for Robot Communications and Vision

This problem with AHT occurs due to an asymmetry in the energy detection threshold setting for LTE-LAA and Wi-Fi. In this dissertation, I focus on the problem of asymmetric hidden terminals between LTE license-assisted access (LTE-LAA) and Wi-Fi.

Main Contributions

This system effectively anonymizes all faces in the video frames and thus can protect personal privacy while maintaining the visual perception of the drone. Using various test datasets and implementing ORB-SLAM2, I confirm that our facial anonymization system effectively removes personal privacy while maintaining visible drone detection.

Organization of the Dissertation

Privacy Protection Patrol Drone System Using Face Anonymization Networks: For privacy protection patrol drone system, I propose Face Anonymization Networks to remove the privacy of people in video frames.

Introduction

Development of the Wi-Fi Frame Detection for LAA-LTE

II Spatial Spectral Reuse of LTE-LAA: Development of an LAA-LTE Transceiver with Lightweight Wi-Fi Frame Detection. The challenge of Wi-Fi frame detection using off-the-shelf LAA-LTE equipment lies in the difference in sampling frequency.

Figure 1: Structure of a Wi-Fi Frame

Results in the UNIST CAMPUS

Conclusion and Future Work

Introduction

As a result, LAA-LTE should experience intermittent transmissions, leading to time-varying network capacity. In addition, LAA-LTE should also consider using channel access priority class 4, which is currently unavailable, and carefully adjust the value of K.

Related Work

System Model

• How often would the asymmetry be encountered?
• Why is the capture effect negligible?

This implies that the asymmetric hidden area occupies 68% of the eNB's service area and therefore there. DL,DH Channel access delay of the L node and an H node TM COT The duration of an MCOT period.

Table 2: Channel access priority classes in LAA-LTE Channel Access

Performance Analysis

• Markov Chain Analysis of the L Node
• Markov Chain of an H Node
• Joint Markov Chain Analysis
• Throughput and Access Delay Analysis

To do this, we introduce a simplified MC that selectively groups the states of the L-node's MC in Fig. Unless otherwise specified, Lt will henceforth imply the state of the L-node's simplified MC at slot t.

Figure 10: Markov Chain of the L Node (LAA-LTE eNB)

Performance Evaluation

• Performance with Default Parameter Set
• Performance with Varying CW max L
• Performance with Varying RSF
• Performance with Varying K

18 value of CWmaxL on the throughput and channel access delay of LAA-LTE and Wi-Fi. 22 and 23 present the throughput and channel access delay of LAA-LTE and Wi-Fi.

Figure 14: Per-node transmit probability of a node L and H nodes: τ L and τ H

Conclusion

According to the aforementioned results, larger K penalizes LAA-LTE in terms of channel access delay, but improves Wi-Fi throughput without degrading SL. Unfortunately, however, adoption of largerKalone cannot fully restore fair coexistence, as LAA-LTE almost always overwhelms Wi-Fi in terms of throughput and channel access delay regardless of the chosen K.

LAA-LTE vs. WLAN: Asymmetric Hidden Terminal Problem in Unli-

• Introduction
• Channel Access Mechanism of LAA-LTE
• Fundamental Operation of LAA-LTE
• The Asymmetric Hidden Terminal (AHT) Problem
• What is the AHT problem?
• Three Types of Regions around LAA-LTE
• Technical Challenges in LTE-WLAN Coexistence due
• Throughput and Channel Access Delay of LAA-LTE
• The Way Forward for Alleviating the AHT Problem
• Conclusion

Therefore, Wi-Fi's CCA must use energy detection in the presence of LAA-LTE signals. 24, which divides the coexistence area of ​​LAA-LTE and Wi-Fi into three regions as in Fig. We measure two performance metrics, normalized throughput and channel access delay, for the LAA-LTE eNB and different types of Wi-Fi APs.

Table 5: Channel access priority classes in LAA-LTE Channel Access Traffic

Downlink Interference Control of LAA-LTE for Coexistence with Asym-

Introduction

We confirm that the asymmetric hidden terminals seriously degrade LAA-LTE's throughput and delay performance. In addition, the formulations of these studies cannot capture the impact due to the difference in the number of subcarriers between LAA-LTE and Wi-Fi. In addition, the older Wi-Fi devices cannot receive or transmit synchronously with LAA-LTE.

System Model

An eNB equipped with a Wi-Fi module can easily acquire time-domain channel impulse responses from access points by listening to access point beacon signals [63–65]. We also assume that the quality of the channel estimation is good enough using time domain interpolation [63–65]. Additionally, for each channel connection, channel reciprocity is assumed for the channel impulse response, as assumed in most of the literature.

Problem Statement

• Asymmetric Hidden Terminal
• Exposure Boundary APs

We consider the block fading with low mobility for channel impulse responses, i.e. nomadic use cases where the channel impulse responses remain constant during the coherence time. As the channel between a transmitter and a receiver undergoes small-scale fading caused by multiple paths between them, the received signal strength at the receiver fluctuates around the signal strength spread by the large-scale fading. In short, the channel fluctuation changes exposure limit APs around an eNB into AHAPs, and then such AHAPs critically degrade the throughput and delay performance of the eNB by causing collision during the eNB's transmission.

Effective Channel Model for LAA-WLAN Coexistence

• Motivation of a New Derivation on the Effective Channels 58
• Effective Channel Model from an eNB to an AP

There are M whole Wi-Fi symbols in the data part of the LAA-LTE symbol, where M =b(TdataL −τ1)/TtotalW c. Thus, we derive the LAA-LTE signal received at the AP for the duration of one OFDM Wi-Fi symbol. Specifically, in the right image, an LAA-LTE subcarrier in transmission has an effect on one or two Wi-Fi subcarriers in reception.

Table 8: OFDM parameters of LAA-LTE and Wi-Fi for a 20 MHz channel

Interference Control via AP Suppression and Power Allocation . 70

• Problem Formulation
• AHAP Selection Module
• Power Allocation Module

Our interference control scheme aims to control the current on each subcarrier of the eNB for eNB. Each element of the list consists of table index i, AHAP index set Bi and the sum of the interference from the AHAPs in Bi to the UE associated with the eNB. Through the AHAP selection module, the eNB can select as many AHAPs as possible, which can be suppressed along with considering the interference of AHAPs with the UE.

Figure 33: Depending on the channel on each subcarrier, the examples for the existence of the polyhedra are shown when two subcarriers are used in LAA-LTE.

Simulation Results

• Single AP Deployment
• Three APs Deployment
• Many APs Deployments

99% guaranteed delay of eNB and each AP (D99% [msec]): the successful delay guaranteed with probability 99%, where the successful delay represents the elapsed time between two successful subframe transmissions of the eNB (or packet transmissions) of an AP ). Since the proposed scheme increases the interference to the exposure limit AP, the AP becomes able to detect transmissions of the eNB. Therefore, the SAP of exposure limit APs can be improved due to the increased number of successful transmissions.

Figure 34: Node droppings for the simulations.

Discussion

• Convergence and Computational Complexity
• Trade off between the throughput of EAPs and AHAPs 84

43(b) presents the throughput of an eNB and WLAN and sum throughput of the eNB and WLAN. Therefore, the sum throughput of eNB and WLAN is rather reduced by introducing positive ED margin. The throughput of the eNB also decreases, leading to reduced sum throughput of the eNB and WLAN.

Figure 42: Percentage of the number of searches performed in the proposed AHAP selection algorithm for various scenarios.

Conclusion

The lesson from the simulation results is that if the eNB needs to transmit in the presence of Wi-Fi, it should try its best to transmit successfully as soon as possible without collision; that is, the eNB should suppress as many AHAPs as possible, as in the proposed formulation. This seemingly selfish strategy will ultimately be best for Wi-Fi as well due to minimized collisions.

Downlink MU-MIMO LTE-LAA for Coexistence with Asymmetric Hidden

Introduction

Recently, several researchers have addressed issues due to the difference in the CCA thresholds in LTE-LAA and Wi-Fi [55–57]. We also verify that the asymmetric hidden terminal problem is indeed unavoidable and caused by the difference in the CCA thresholds of LTE-LAA and Wi-Fi. This paper proposes a Multi-User MIMO (MU-MIMO) transmission precoding and energy control technique for an LTE-LAA eNodeB (eNB) to mitigate the performance degradation due to the CCA threshold difference between LTE-LAA and Wi-Fi.

System Model

• LAA-WLAN Coexistence Scenario
• Two Types of APs around an eNB

The exponential drift parameters of LTE-LAA and Wi-Fi are defined differently in their standards. In the perspective of LAA-WLAN coexistence, the hidden access point Wi-Fi AP metric is briefly reviewed. Due to the asymmetry in the threshold, the Wi-Fi AP ignores the current LTE-LAA eNB signals weaker than −62 dBm and decides that the channel is busy.

Preliminary

51(a), the full range result indicates that a transmitting LTE-LAA subcarrier affects several Wi-Fi subcarriers around it in reception. Specifically, from the right figure, one LTE-LAA subcarrier in transmission has an effect on one or two Wi-Fi subcarriers in standby. 51(b) represents that a Wi-Fi subcarrier in transmission interferes with some LTE-LAA subcarriers in standby.

Figure 51: Interference between LTE-LAA’s subcarriers and Wi-Fi’s subcarriers.

Proposed MU-MIMO LTE-LAA System

• Notations
• Problem Statement
• Problem Formulation
• Transmission Probability of an AHAP based on Stochas-
• Beamforming Vector Design
• Power Allocation

In addition, the power vectors for eNB and α-th AP are denoted by pL∈RNUEN. Therefore, we need to carefully choose B so that the problem is feasible while the SINRs of the UEs can be further improved. As in the section to find a possible B', the eNB constructs the AHAP's combination list and evaluates the feasibility of P1 for each case of the list.

Simulation Results

• Stochastic Geometry
• Single AP Deployment
• Two APs Deployment
• Many APs Deployment
• Fairness in WLAN

The term “with IC” in the figure legend implies that the results were obtained under our interference control (IC) scheme. As a result, the proposed scheme effectively reduces the long delay of both the APs and the eNB. 58(a), D99% of the APs are downsized because the proposed scheme suppresses the abrupt increase in the CW size of the APs.

Figure 52: Node droppings for the simulations.

Discussion

• Is the Asymmetric Hidden Terminal Problem Really Se-
• Changing the energy detection threshold or the trans-
• Tradeoff between WLAN’s throughput and LTE-LAA’s

Is the problem of asymmetric hidden terminal really serious?: In [57], to show that the problem of asymmetric hidden terminal can really affect the performance of LTE-LAA and Wi-Fi, we investigated the ratio of the eNB (or AP) communication area to the area coverage. Therefore, it is not possible to further increase the transmit power of the eNB to solve the asymmetric hidden terminal problem. So the XED cannot be set to −62dBm like Wi-Fi to easily solve the hidden asymmetric terminal problem.

Figure 63: Communicatable area around an eNB.

Conclusion

Therefore, the eNB with XED =−62 dBm can initiate its new transmission during transmissions from the AP within the donut-like region between the −62 dBm and −72 dBm lines while an eNB with XED =−72 dBm guarantees the transmissions of the AP- ve postponing his new broadcast. As a result, if an eNB sets XED =−62dBm, the eNB and AP cannot detect each other's signals, and thus will suffer more collisions, leading to more severe degradation in their performance. It is always possible to sacrifice LTE-LAA throughput to further improve WLAN performance by performing the proposed beamforming and power distribution scheme not in every MCOT.

Introduction

We propose a face-anonymizing approach where a face image is transformed into an intermediate image by eliminating the privacy-sensitive information, and then the intermediate image is converted into a photorealistic face image. We conduct extensive evaluation to verify our face anonymization framework through various face image and video datasets. We are building a drone consisting of a zed camera and a companion computer to demonstrate our drone patrol system.

Related Work

Removal of Privacy Sensitive Information

In other words, the problem is that the generator does not learn how to draw a face that is not in the training dataset. Furthermore, the generator observes pixels of a face for adjustment, implying that an adjusted face is generated from the original face. However, in the case where the faces are large in a frame, an intimate can recognize who the person in the frame is even if the face is blurred.

Generative Adversarial Networks

The framework detects faces from ultra-low-resolution images through a proposed deep learning-based algorithm. Therefore, only faces are blurred in the resulting images, protecting the privacy-sensitive parts while maintaining the robot's detection performance. In addition, it would be possible not to detect a face in a low-resolution image, and then this scheme would not be able to protect a person's privacy.

SLAM

Privacy-Protection Drone Patrol System

The accompanying computer receives video frames from the camera and then anonymizes the faces in the received frames by running our face anonymizing networks. In addition, the accompanying computer receives a drive command and passes it on to the engine control computer. By anonymizing faces, our system effectively protects a person's privacy while maintaining the robot's perception performance.

Figure 65: Composition of the developed patrol robot system with privacy preserving face detection.

Approach, Modification, and Algorithm

• Face-Anonymizing Approach
• Training Architecture for Segmentation and Synthesis Networks 130
• Well-Known Basic Definitions
• Our Modifications for the Anonymization System
• Modification on Segmentation Generator’s Loss
• Modifications of the Loss of the Synthesis Generator
• Training Procedure and Details
• Face-Anonymizing Algorithm
• Drone Patrol System

LGs is the loss of a synthesis generator Gs and will be explained in detail in section 5.4.3.2. Challenge in VGG perceptual loss: In the synthesis learning part, the loss (116) needs to be minimized to train the generator Gs. In Algorithm 4, we repeat this to run the face detector with detected faces until the size of a face is infDoccupies Rthr×100of size offD. The reason for this repeated detection is that our generators have an input image filled with .

Figure 67: Training Architecture for Segmentation and Synthesis Networks

Evaluation

• Face-Anonymizing Generators Evaluation
• SLAM Results
• Drone Hardware Setup
• Our Face-Anonymizing System Result on Drone

The anonymized images are sent back to the ground station via the wireless chipset, and thus we can immediately check the results on the ground station's screen. The extracted features of our anonymization scheme are almost the same as in the original video. In our anonymization scheme, the extracted feature points are almost the same as in the original video.

Figure 69: Test Results on CelebA-HQ Dataset Table 13: Average Euclidean distance for each test dataset

Conclusion

Han, "Coexistence of Wi-Fi and Cellular With Listen-Before-Talk in Unlicensed Spectrum," IEEE Communications Letters, vol. Maaref, "Operation of massive MIMO in unlicensed bands for improved coexistence and spatial reuse," IEEE Trans. Zhang, “Modeling and Analysis of the Coexistence of Wi-Fi and LTE in Unlicensed Spectrum Access,” IEEE Trans.

Figure 74: SLAM results on drone