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In response to the evolving landscape of wireless communication networks and the escalating demand for unprecedented wireless connectivity performance in the forthcoming 6G era, this paper proposes a new 6G architecture to enhance the wireless network's sum rate performance. Therefore, we introduce an aerial base station (ABS) network with reconfigurable intelligent surfaces (RISs) while leveraging the multi-users multiple-input single-output (MU-MISO) antenna technology. The motivation behind our proposal stems from the imperative to address critical challenges in contemporary wireless networks and harness emerging technologies for substantial performance gains. We employ deep reinforcement learning (DRL) to jointly optimize the ABS trajectories, the active beamforming weights, and the RIS phase shifts. Simulation results show that this joint optimization effectively improves the system's sum rate while meeting minimum quality of service (QoS) requirements for diverse mobile users. 

As we progress from 5G to emerging 6G wireless, the spectrum of cellular communication services is set to broaden significantly, encompassing real-time remote healthcare applications and sophisticated smart infrastructure solutions, among others. This expansion brings to the forefront a diverse set of service requirements, underscoring the challenges and complexities inherent in next-generation networks. In the realm of 5G, Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) have been pivotal service categories. As we venture into the 6G era, these foundational use cases will evolve and embody additional performance criteria, further diversifying the network service portfolio. This evolution amplifies the necessity for dynamic and efficient resource allocation strategies capable of balancing the diverse service demands. In response to this need, we introduce the Intelligent Dynamic Resource Allocation and Puncturing (IDRAP) framework. Leveraging Deep Reinforcement Learning (DRL), IDRAP is designed to balance between the bandwidth-intensive requirements of eMBB services and the latency and reliability needs of URLLC users. The performance of IDRAP is evaluated and compared against other resource management solutions, including Intelligent Dynamic Resource Slicing (IDRS), Policy Gradient Actor-Critic Learning (PGACL), System-Wide Tradeoff Scheduling (SWTS), Sum-Log, and Sum-Rate.The results show an improved Service Satisfaction Level (SSL) for eMBB users while maintaining the essential SSL threshold for URLLC services. 

Emerging communication services and satellite system deployments pose heightened interference challenges for crucial passive radiometer sensors used in environmental and atmospheric sensing. Therefore, there is an urgent necessity to develop effective approaches for detecting, mitigating, and characterizing the influence of anthropogenic sources, commonly referred to as radio frequency interference (RFI) on passive Earth-observing microwave radiometers. Experimenting the co-existence of active communication and passive sensing systems would greatly benefit from a thorough and realistic dataset covering a wide range of scenarios. The insufficient availability of extensive datasets in the radio frequency (RF) domain, particularly in the context of active/passive coexistence, poses a significant obstacle to progress. This limitation is particularly notable in the context of comprehending the effectiveness of conventional model-based RFI detection approaches when applied to advanced 5th-generation (5G) wireless communication signals. This study first shows the development of an experimental passive radiometer and 5G testbed system and aims to assess the efficacy of the widely employed spectral kurtosis RFI detection approach within controlled anechoic chamber experiments. Our experimental setup comprises a fully calibrated SDR-based L-band radiometer subjected to diverse 5 G wireless signals, varying in power levels, frequency resource block group allocation, and modulation techniques. Significantly, our testbed facilitates the concurrent recording of ground truth temperatures while subjecting the radiometer to 5 G signal transmission which helps to understand the overall effect in the radiometer. This distinctive configuration provides insights into the effectiveness of traditional RFI detection models, offering valuable perspectives on the associated challenges in RFI detection. 

The Third Generation Partnership Project (3GPP) introduced the fifth generation new radio (5G NR) specifications which offer much higher flexibility than legacy cellular communications standards to better handle the heterogeneous service and performance requirements of the emerging use cases. This flexibility, however, makes the resources management more complex. This paper therefore designs a data driven resource allocation method based on the deep Q-network (DQN). The objective of the proposed model is to maximize the 5G NR cell throughput while providing a fair resource allocation across all users. Numerical results using a 3GPP compliant 5G NR simulator demonstrate that the DQN scheduler better balances the cell throughput and user fairness than existing schedulers. 

The main resource for providing wireless services is radio frequency (RF) spectrum. In order to explore new uses of spectrum shared among radio systems and services, field data needs to be collected. In this paper we design a testbed that can generate different 5G New Radio (NR) downlink transmission frames using the MATLAB 5G Toolbox, software-defined radio (SDR) hardware and GNU Radio Companion. This system will be used as a part of a testbed to study the RF interference caused by 5G transmissions to remote sensing receivers and evaluate different mechanisms for co-channel coexistence. 

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.