Wireless Interference Mitigation for Emerging Applications and Systems
- Publication Type:
- Thesis
- Issue Date:
- 2023
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Wireless jamming is one of the critical threats to emerging wireless applications, e.g., Ultra-reliable low-latency communication (URLLC). The problem becomes even more challenging when the jamming signals come simultaneously from multiple sources. This thesis aims to investigate and mitigate by leveraging signal beam-forming and machine learning (ML) techniques. It is observed that by varying the correlation coefficients, jammers can ``virtually change'' the jamming channels hence their nullspace even when these channels do not physically change. To tackle the problem, we propose techniques to monitor the jamming residual and effectively update the beam-forming matrix.
However, such a jamming residual monitoring process incurs additional system overhead, thus significantly reducing the spectral efficiency. Even ignoring the unknown strategy of the jammers and the challenging nullspace estimation process, the resulting problem is an integer programming problem, hence intractable to obtain its optimal solution. To deal with such uncertainty and incomplete information, we reformulate it using a partially observable semi-Markov decision process (POSMDP). We then design a deep dueling Q-learning-based technique to quickly obtain the optimal policy for legitimate devices.
Next, we study the methods to deal with jamming signals in the joint radar and communication (JRC) systems. Specifically, novel modulation and demodulation schemes were proposed for a frequency-hopping (FH) JRC system with robustness against jamming. In these schemes, both sub-pulse frequencies and durations are used for information modulation, leading to higher communication data rates. For information demodulation, a novel scheme was proposed by using the time-frequency analysis (TFA) technique and a `you only look once' (YOLO)-based detection system. Simulation results demonstrate the effectiveness of the proposed scheme.
Finally, we study jamming mitigation in joint communication and radar (JCR) systems. Specifically, we study how to optimize the durations of the jamming nullspace estimation, the preamble, and the data transmission phases. Increasing the duration of the nullspace estimation and the preamble phases can increase the radar's performance. However, such an increase also reduces the effective spectral efficiency of the communication function, because the data transmission phase fraction is decreased. Moreover, the surrounding radio environments of the JRC systems are typically dynamic with high uncertainties due to their high mobility, making the duration optimization problem even more challenging. To deal with such uncertainty, we reformulate the problem using a Markov decision process (MDP). Then, we design a deep dueling Q-learning-based technique to quickly obtain the optimal policy.
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