5,317 research outputs found
Deep generative models for network data synthesis and monitoring
Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network.
Although networks inherently
have abundant amounts of monitoring data, its access and effective measurement is
another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset
without leaking commercial sensitive information. Second, it could be very expensive
to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of
flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources
in the network element that can be applied to support the measurement function are
too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex
structure. Various emerging optimization-based solutions (e.g., compressive sensing)
or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet
meet the current network requirements.
The contributions made in this thesis significantly advance the state of the art in
the domain of network measurement and monitoring techniques. Overall, we leverage
cutting-edge machine learning technology, deep generative modeling, throughout the
entire thesis. First, we design and realize APPSHOT , an efficient city-scale network
traffic sharing with a conditional generative model, which only requires open-source
contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we
design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time
network telemetry system with latent GANs and spectral-temporal networks. Finally,
we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through
this research are summarized, and interesting topics are discussed for future work in
this domain. All proposed solutions have been evaluated with real-world datasets and
applied to support different applications in real systems
Securing NextG networks with physical-layer key generation: A survey
As the development of next-generation (NextG) communication networks continues, tremendous devices are accessing the network and the amount of information is exploding. However, with the increase of sensitive data that requires confidentiality to be transmitted and stored in the network, wireless network security risks are further amplified. Physical-layer key generation (PKG) has received extensive attention in security research due to its solid information-theoretic security proof, ease of implementation, and low cost. Nevertheless, the applications of PKG in the NextG networks are still in the preliminary exploration stage. Therefore, we survey existing research and discuss (1) the performance advantages of PKG compared to cryptography schemes, (2) the principles and processes of PKG, as well as research progresses in previous network environments, and (3) new application scenarios and development potential for PKG in NextG communication networks, particularly analyzing the effect and prospects of PKG in massive multiple-input multiple-output (MIMO), reconfigurable intelligent surfaces (RISs), artificial intelligence (AI) enabled networks, integrated space-air-ground network, and quantum communication. Moreover, we summarize open issues and provide new insights into the development trends of PKG in NextG networks
Authentication enhancement in command and control networks: (a study in Vehicular Ad-Hoc Networks)
Intelligent transportation systems contribute to improved traffic safety by facilitating real time communication between vehicles. By using wireless channels for communication, vehicular networks are susceptible to a wide range of attacks, such as impersonation, modification, and replay. In this context, securing data exchange between intercommunicating terminals, e.g., vehicle-to-everything (V2X) communication, constitutes a technological challenge that needs to be addressed. Hence, message authentication is crucial to safeguard vehicular ad-hoc networks (VANETs) from malicious attacks. The current state-of-the-art for authentication in VANETs relies on conventional cryptographic primitives, introducing significant computation and communication overheads. In this challenging scenario, physical (PHY)-layer authentication has gained popularity, which involves leveraging the inherent characteristics of wireless channels and the hardware imperfections to discriminate between wireless devices. However, PHY-layerbased authentication cannot be an alternative to crypto-based methods as the initial legitimacy detection must be conducted using cryptographic methods to extract the communicating terminal secret features. Nevertheless, it can be a promising complementary solution for the reauthentication problem in VANETs, introducing what is known as “cross-layer authentication.” This thesis focuses on designing efficient cross-layer authentication schemes for VANETs, reducing the communication and computation overheads associated with transmitting and verifying a crypto-based signature for each transmission. The following provides an overview of the proposed methodologies employed in various contributions presented in this thesis.
1. The first cross-layer authentication scheme: A four-step process represents this approach: initial crypto-based authentication, shared key extraction, re-authentication via a PHY challenge-response algorithm, and adaptive adjustments based on channel conditions. Simulation results validate its efficacy, especially in low signal-to-noise ratio (SNR) scenarios while proving its resilience against active and passive attacks.
2. The second cross-layer authentication scheme: Leveraging the spatially and temporally correlated wireless channel features, this scheme extracts high entropy shared keys that can be used to create dynamic PHY-layer signatures for authentication. A 3-Dimensional (3D) scattering Doppler emulator is designed to investigate the scheme’s performance at different speeds of a moving vehicle and SNRs. Theoretical and hardware implementation analyses prove the scheme’s capability to support high detection probability for an acceptable false alarm value ≤ 0.1 at SNR ≥ 0 dB and speed ≤ 45 m/s.
3. The third proposal: Reconfigurable intelligent surfaces (RIS) integration for improved authentication: Focusing on enhancing PHY-layer re-authentication, this proposal explores integrating RIS technology to improve SNR directed at designated vehicles. Theoretical analysis and practical implementation of the proposed scheme are conducted using a 1-bit RIS, consisting of 64 × 64 reflective units. Experimental results show a significant improvement in the Pd, increasing from 0.82 to 0.96 at SNR = − 6 dB for multicarrier communications.
4. The fourth proposal: RIS-enhanced vehicular communication security: Tailored for challenging SNR in non-line-of-sight (NLoS) scenarios, this proposal optimises key extraction and defends against denial-of-service (DoS) attacks through selective signal strengthening. Hardware implementation studies prove its effectiveness, showcasing improved key extraction performance and resilience against potential threats.
5. The fifth cross-layer authentication scheme: Integrating PKI-based initial legitimacy detection and blockchain-based reconciliation techniques, this scheme ensures secure data exchange. Rigorous security analyses and performance evaluations using network simulators and computation metrics showcase its effectiveness, ensuring its resistance against common attacks and time efficiency in message verification.
6. The final proposal: Group key distribution: Employing smart contract-based blockchain technology alongside PKI-based authentication, this proposal distributes group session keys securely. Its lightweight symmetric key cryptography-based method maintains privacy in VANETs, validated via Ethereum’s main network (MainNet) and comprehensive computation and communication evaluations.
The analysis shows that the proposed methods yield a noteworthy reduction, approximately ranging from 70% to 99%, in both computation and communication overheads, as compared to the conventional approaches. This reduction pertains to the verification and transmission of 1000 messages in total
Best sum-throughput evaluation of cooperative downlink transmission nonorthogonal multiple access system
In cooperative simultaneous wireless information and power transfer (SWIPT) nonorthogonal multiple access (NOMA) downlink situations, the current research investigates the total throughput of users in center and edge of cell. We focus on creating ways to solve these problems because the fair transmission rate of users located in cell edge and outage performance are significant hurdles at NOMA schemes. To enhance the functionality of cell-edge users, we examine a two-user NOMA scheme whereby the cell-center user functions as a SWIPT relay using power splitting (PS) with a multiple-input single-output. We calculated the probability of an outage for both center and edge cell users, using closed-form approximation formulas and evaluate the system efficacy. The usability of cell edge users is maximized by downlink transmission NOMA (CDT-NOMA) employing a SWIPT relay that employs PS. The suggested approach calculates the ideal value of the PS coefficient to optimize the sum throughput. Compared to the noncooperative and single-input single-output NOMA systems, the best SWIPT-NOMA system provides the cell-edge user with a significant throughput gain. Applying SWIPT-based relaying transmission has no impact on the framework’s overall throughput
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Finite SNR diversity-multiplexing trade-off in hybrid ABCom/RCom-assisted NOMA networks
The upcoming sixth generation (6G) driven Internetof- Things (IoT) will face the great challenges of extremely low power demand, high transmission reliability and massive connectivities. To meet these requirements, we propose a novel hybrid ambient backscatter communication (ABCom) or relay communication (RCom) assisted non-orthogonal multiple access (NOMA) network, which simultaneously enables traditional relay networks and ABCom-assisted IoT networks. Specifically, we investigate the reliability and the finite signal-to-noise ratio (SNR) diversity-multiplexing trade-off (f-DMT) of the proposed system to characterize the outage performance of the proposed system in the non-asymptotic SNR region. We derive the outage probability (OP) and the finite SNR diversity gain when two sources aim to communicate through either ABCom or RCom. On the basis that the results of Monte Carlo simulation and analysis are in perfect agreement, we discover that in the high SNR regime, the OP for ABCom tends to be a constant, leading to a zero diversity gain and an error floor, while the OP for RCom is monotone decreasing with respect to the SNR. Also, compared with the imperfect successive interference cancellation (ipSIC) mode, the reliability of the system under the ideal condition is significantly improved; Moreover, in the lower multiplexing gain regime, for both ABCom and RCom, the higher finite SNR diversity gain results in better system reliability, which provides good opportunities for ABCom to adapt f-DMT and improve relevant performance metrics by adapting the reflection parameter
Programming Wireless Security through Learning-Aided Spatiotemporal Digital Coding Metamaterial Antenna
The advancement of future large-scale wireless networks necessitates the
development of cost-effective and scalable security solutions. Conventional
cryptographic methods, due to their computational and key management
complexity, are unable to fulfill the low-latency and scalability requirements
of these networks. Physical layer (PHY) security has been put forth as a
cost-effective alternative to cryptographic mechanisms that can circumvent the
need for explicit key exchange between communication devices, owing to the fact
that PHY security relies on the physics of the signal transmission for
providing security. In this work, a space-time-modulated digitally-coded
metamaterial (MTM) leaky wave antenna (LWA) is proposed that can enable PHY
security by achieving the functionalities of directional modulation (DM) using
a machine learning-aided branch and bound (B&B) optimized coding sequence. From
the theoretical perspective, it is first shown that the proposed space-time MTM
antenna architecture can achieve DM through both the spatial and spectral
manipulation of the orthogonal frequency division multiplexing (OFDM) signal
received by a user equipment. Simulation results are then provided as
proof-of-principle, demonstrating the applicability of our approach for
achieving DM in various communication settings. To further validate our
simulation results, a prototype of the proposed architecture controlled by a
field-programmable gate array (FPGA) is realized, which achieves DM via an
optimized coding sequence carried out by the learning-aided branch-and-bound
algorithm corresponding to the states of the MTM LWA's unit cells. Experimental
results confirm the theory behind the space-time-modulated MTM LWA in achieving
DM, which is observed via both the spectral harmonic patterns and bit error
rate (BER) measurements
Potential of machine learning/Artificial Intelligence (ML/AI) for verifying configurations of 5G multi Radio Access Technology (RAT) base station
Abstract. The enhancements in mobile networks from 1G to 5G have greatly increased data transmission reliability and speed. However, concerns with 5G must be addressed. As system performance and reliability improve, ML and AI integration in products and services become more common. The integration teams in cellular network equipment creation test devices from beginning to end to ensure hardware and software parts function correctly. Radio unit integration is typically the first integration phase, where the radio is tested independently without additional network components like the BBU and UE. 5G architecture and the technology that it is using are explained further. The architecture defined by 3GPP for 5G differs from previous generations, using Network Functions (NFs) instead of network entities. This service-based architecture offers NF reusability to reduce costs and modularity, allowing for the best vendor options for customer radio products. 5G introduced the O-RAN concept to decompose the RAN architecture, allowing for increased speed, flexibility, and innovation. NG-RAN provided this solution to speed up the development and implementation process of 5G. The O-RAN concept aims to improve the efficiency of RAN by breaking it down into components, allowing for more agility and customization. The four protocols, the eCPRI interface, and the functionalities of fronthaul that NGRAN follows are expressed further. Additionally, the significance of NR is described with an explanation of its benefits. Some benefits are high data rates, lower latency, improved spectral efficiency, increased network flexibility, and improved energy efficiency. The timeline for 5G development is provided along with different 3GPP releases. Stand-alone and non-stand-alone architecture is integral while developing the 5G architecture; hence, it is also defined with illustrations. The two frequency bands that NR utilizes, FR1 and FR2, are expressed further. FR1 is a sub-6 GHz frequency band. It contains frequencies of low and high values; on the other hand, FR2 contains frequencies above 6GHz, comprising high frequencies. FR2 is commonly known as the mmWave band. Data collection for implementing the ML approaches is expressed that contains the test setup, data collection, data description, and data visualization part of the thesis work. The Test PC runs tests, executes test cases using test libraries, and collects data from various logs to analyze the system’s performance. The logs contain information about the test results, which can be used to identify issues and evaluate the system’s performance. The data collection part describes that the data was initially present in JSON files and extracted from there. The extraction took place using the Python code script and was then fed into an Excel sheet for further analysis. The data description explains the parameters that are taken while training the models. Jupyter notebook has been used for visualizing the data, and the visualization is carried out with the help of graphs. Moreover, the ML techniques used for analyzing the data are described. In total, three methods are used here. All the techniques come under the category of supervised learning. The explained models are random forest, XG Boost, and LSTM. These three models form the basis of ML techniques applied in the thesis. The results and discussion section explains the outcomes of the ML models and discusses how the thesis will be used in the future. The results include the parameters that are considered to apply the ML models to them. SINR, noise power, rxPower, and RSSI are the metrics that are being monitored. These parameters have variance, which is essential in evaluating the quality of the product test setup, the quality of the software being tested, and the state of the test environment. The discussion section of the thesis explains why the following parameters are taken, which ML model is most appropriate for the data being analyzed, and what the next steps are in implementation
Optical ground receivers for satellite based quantum communications
Cryptography has always been a key technology in security, privacy and defence.
From ancient Roman times, where messages were sent cyphered with simple encoding techniques, to modern times and the complex security protocols of the Internet.
During the last decades, security of information has been assumed, since classical
computers do not have the power to break the passwords used every day (if they are
generated properly). However, in 1984, a new threat emerged when Peter Shor presented the Shor’s algorithm, an algorithm that could be used in quantum computers
to break many of the secure communication protocols nowadays. Current quantum
computers are still in their early stages, with not enough qubits to perform this
algorithm in reasonable times. However, the threat is present, not future, since the
messages that are being sent by important institutions can be stored, and decoded
in the future once quantum computers are available.
Quantum key distribution (QKD) is one of the solutions proposed for this threat,
and the only one mathematically proven to be secure with no assumptions on the
eavesdropper power. This optical technology has recently gained interest to be performed with satellite communications, the main reason being the relative ease to
deploy a global network in this way. In satellite QKD, the parameter space and
available technology to optimise are very big, so there is still a lot of work to be
done to understand which is the optimal way to exploit this technology.
This dissertation investigates one of these parameters, the encoding scheme.
Most satellite QKD systems use polarisation schemes nowadays. This thesis presents
for the first time an experimental work of a time-bin encoding scheme for free-space
receivers within a full QKD system in the second chapter. The third and fourth
chapter explore the advantages of having multi-protocol free-space receivers that
can boost the interoperability between systems, polarisation filtering techniques to
reduce background. Finally, the last chapter presents a new technology that can
help increase communications rates
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