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The coexistence of active 5G communication signals and passive sensors in shared spectrum environments presents significant challenges due to radio frequency interference (RFI). This paper introduces a novel two-stage successive interference cancellation (SIC) architecture leveraging artificial intelligence (AI) to mitigate interference and preserve the integrity of passive sensing. The first stage employs a deep multilayer perceptron (D-MLP) trained with the Levenberg-Marquardt (LM) algorithm to reconstruct dominant active signals. The second stage, powered by a Bayesian Regularization (BR)-trained D-MLP, addresses residual non-linear and weak interference. Together, these stages achieve superior interference cancellation, ensuring robust separation of active and passive signals. The proposed architecture is evaluated in scenarios with varying interference complexities, including single and multiple active sources. Results demonstrate that the AI-assisted SIC framework significantly outperforms conventional methods, effectively reconstructing and removing 5G signals even under challenging conditions, such as low signal-to-noise ratios. The system also showcases adaptability, maintaining high performance when trained on one gain level and tested on another. This research advances the field by providing a scalable and robust solution for enabling reliable spectrum coexistence, particularly for Earth observation and environmental monitoring. 

The advent of 5G technology introduces significant advancements in speed, latency, and device connectivity, but also poses complex security challenges. Among typical denial-of-service (DoS) attacks, jamming attack can severely degrade network performance by interfering critical communication channels. To address this issue, we propose a novel security solution utilizing multipath communication, which distributes message segments across multiple paths to ensure message recovery even when some paths are compromised. This strategy enhances network resilience and aligns with zero-trust architecture principles. Moreover, the proposed scheme has been implemented in our testbed to validate the concept and assess the network performance under jamming attacks. Our findings demonstrate that this method eliminates the negative impacts caused by DoS attacks, maintaining the integrity and availability of critical network services. The results highlight the robustness of multipath communication in securing 5G networks against sophisticated attacks, thereby safeguarding essential communications in dynamic and potentially hostile environments. 

Research has shown that communications systems and receivers suffer from high power adjacent channel signals, called blockers, that drive the radio frequency (RF) front end into nonlinear operation. Since simple systems, such as the Internet of Things (IoT), will coexist with sophisticated communications transceivers, radars and other spectrum consumers, these need to be protected employing a simple, yet adaptive solution to RF nonlinearity. This paper therefore proposes a flexible data driven approach that uses a simple artificial neural network (ANN) to aid in the removal of the third order intermodulation distortion (IMD) as part of the demodulation process. We introduce and numerically evaluate two artificial intelligence (AI)-enhanced receivers—ANN as the IMD canceler and ANN as the demodulator. Our results show that a simple ANN structure can significantly improve the bit error rate (BER) performance of nonlinear receivers with strong blockers and that the ANN architecture and configuration depends mainly on the RF front end characteristics, such as the third order intercept point (IP3). We therefore recommend that receivers have hardware tags and ways to monitor those over time so that the AI and software radio processing stack can be effectively customized and automatically updated to deal with changing operating conditions. 

With the advances in wireless communications towards beyond 5G (B5G) and 6G networks, new signal processing and resource management methods need to be explored to overcome the channel impairments and other radio and computing obstacles. In contrast to the conventional methods which are based on classic digital communications structures, B5G and 6G will leverage artificial intelligence (AI) to configure or adapt the radios and networks to the operational context. This requires the ability to reformulate legacy transceiver structures and drive research, development and standardization that can leverage the amount of data that is available and that can be processed with the available computing technology. This paper describes this vision and discusses successful research that justifies it as well as the remaining challenges. We numerically analyze some of the tradeoffs when replacing the physical layer receiver processing with an artificial neural network (ANN).