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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. 

Internet of Things (IoT) is becoming increasingly popular due to its ability to connect machines and enable an ecosystem for new applications and use cases. One such use case is industrial loT (1IoT) that refers to the application of loT in industrial settings especially engaging instrumentation and control of sensors and machines with Cloud technologies. Industries are counting on the fifth generation (5G) of mobile communications to provide seamless, ubiquitous and flexible connectivity among machines, people and sensors. The open radio access network (O-RAN) architecture adds additional interfaces and RAN intelligent controllers that can be leveraged to meet the IIoT service requirements. In this paper, we examine the connectivity requirements for IIoT that are dominated by two industrial applications: control and monitoring. We present the strength, weakness, opportunity, and threat (SWOT) analysis of O-RAN for IIoT and provide a use case example which illustrates how O-RAN can support diverse and changing IIoT network services. We conclude that the flexibility of the O-RAN architecture, which supports the latest cellular network standards and services, provides a path forward for next generation IIoT network design, deployment, customization, and maintenance. It offers more control but still lacks products-hardware and software-that are exhaustively tested in production like environments. 

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. 

Receiver nonlinearity gives rise to intermodulation products that are caused by two strong adjacent channel signals called blockers. The nonlinear distortion effects are significantly higher for multiple antenna wideband systems in dispersive environments because third order intermodulation products decreases the signal-to-noise ratio (SNR) at the output of the equalization process. This complicates the demodulation process and increases the bit error rate. This paper considers such nonlinear distortion in the context of space-time shift keying (STSK)-enabled wideband single-carrier systems and proposes an iterative space-time block equalization (ISTBE) framework for frequency domain equalization. We present our design of a practical ISTBE receiver based on the turbo principle and numerically demonstrate that it effectively removes the residual inter-symbol interference while suppressing high-power blockers and the in-band intermodulation distortion that they cause. The proposed system is thus suitable for simple wideband radio frequency front ends operating in the weak nonlinear region and enables adjacent channel spectrum coexistence with heterogeneous transmitters and receivers of different qualities. 

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.