A Survey Of Advanced Techniques For Spectrum Sharing In 5g Networks

[Carlos Beirise]

Allarticles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Abstract: 5G is the next mobile generation, already being deployed in some countries. It is expected to revolutionize our society, having extremely high target requirements. The use of spectrum is, therefore, tremendously important, as it is a limited and expensive resource. A solution for the spectrum efficiency consists of the use of dynamic spectrum sharing, where an operator can share the spectrum between two different technologies. In this paper, we studied the concept of dynamic spectrum sharing between LTE and 5G New Radio. We presented a solution that allows operators to offer both LTE and New Radio services using the same frequency bands, although in an interleaved mode. We evaluated the performance, in terms of throughput, of a communication system using the dynamic spectrum sharing feature. The results obtained led to the conclusion that using the dynamic spectrum sharing comes with a compromise of a maximum 25% loss on throughput. Nevertheless, the decrease is not that substantial, as the mobile network operator does not need to buy an additional 15 MHz of bandwidth, using the already existing bandwidth of LTE to offer 5G services, leading to cost reduction and an increase in spectrum efficiency. Keywords: dynamic spectrum sharing; LTE; 5G NR; throughput; spectrum efficiency

ISART 2010 laid the foundation with a comprehensive survey of spectrum sharing approaches from a Federal government perspective. Contemporary issues with radar sharing and the extensive allocations for radar systems motivated ISART 2011, which provided a comprehensive evaluation of radar spectrum management and usage. It mingled prominent radar and telecommunications engineers who explored the interactions between these distinctive technologies and contemplated enhanced spectrum engineering and sharing approaches.

Case Study 2 responded to progress in dynamic spectrum access (DSA) technology that highlighted the critical role of the spectrum sharing infrastructure, which is more the domain of computer scientists than radio scientists. This infrastructure is architected upon databases, information exchange languages, and business processes. More than any particular radio technology, computer science has spurred the advancements in technologies that enable new and innovative ways to share spectrum. Geolocation databases dictate channel availability in the TV white spaces, and the Department of Defense is developing capabilities to generate digital spectrum policy that would govern the operation of flexible spectrum use radios with and without spectrum sensing.

Future Federal spectrum sharing will require changes to the existing spectrum management infrastructure to expand spectrum availability through use of technologies like DSA. Widespread acceptance and adoption of spectrum sharing technologies will require well-defined business practices and sharing agreements, the development of supporting automation and management systems to generate coexistence rulesets, and secure networks that implement these rulesets.

ISART 2012 exposed radio scientists and computer scientists to a detailed view of each other's perspective of spectrum use and management and delineated the obstacles to development of spectrum sharing infrastructure for both Federal and commercial sharing.

The landscape of mobile communications has been witnessing a rapid and transformative evolution, especially with the advent of the fifth generation (5G) technologies. The 3rd Generation Partnership Project (3GPP), a crucial body in the mobile communications industry, has been at the forefront of this evolution, introducing pivotal enhancements in its successive releases. Each release by 3GPP not only brings forward technological advancements but also sets new standards for network efficiency, performance, and capabilities.

Release 16 laid down a solid foundation, introducing numerous enhancements and laying the groundwork for more advanced applications of 5G. It was a step towards realizing the full potential of 5G, with a focus on enabling new use cases and improving network performance.

As we progressed to Release 17, a notable shift was observed towards more efficient utilization of the spectrum. Dynamic Spectrum Sharing (DSS) emerged as a key feature, allowing operators to maximize their existing spectral resources by seamlessly sharing them between 4G LTE and 5G NR technologies. This enhancement was pivotal in driving the widespread adoption and deployment of 5G networks without necessitating the complete overhaul of existing infrastructures.

Now, as we anticipate the arrival of Release 18, a groundbreaking shift is expected with the integration of Artificial Intelligence (AI) and Machine Learning (ML) into the fabric of 5G networks. The potential of AI/ML in revolutionizing the way networks adapt to changing conditions and user demands is immense, suggesting a future where networks are not just faster and more reliable, but also smarter and more responsive.

The introduction of Dynamic Spectrum Sharing (DSS) in 3GPP Release 17 marked a significant advancement in the efficient utilization of spectral resources for 5G networks. DSS emerged as a strategic solution to one of the primary challenges in the deployment of 5G: the scarcity and cost of spectrum.

DSS technology enables mobile operators to use a single spectrum band simultaneously for both 4G LTE and 5G New Radio (NR) transmissions. This functionality is crucial, particularly for operators with limited spectrum resources, as it allows them to dynamically allocate spectrum based on real-time demand without the need for dedicated spectrum for each technology. This capability not only optimizes spectrum utilization but also facilitates a smoother and more cost-effective transition from 4G to 5G.

At its core, DSS operates by intelligently overlaying 5G NR signals on existing 4G LTE signals within the same frequency band. This overlay is managed dynamically, adjusting the allocation of spectrum between 4G and 5G based on user demand. One of the key advantages of DSS is its flexibility. It allows operators to ramp up 5G coverage and capacity without decommissioning their 4G services, ensuring continuous service provision across both network generations.

Implementing DSS requires certain upgrades to the existing network infrastructure, including software updates to base stations and a need for devices that are compatible with both 4G and 5G technologies. The complexity of DSS lies in its need to manage interference and maintain efficient signaling between the overlaid technologies. This requires sophisticated algorithms to ensure seamless coexistence and optimal performance of both 4G and 5G services within the shared spectrum.

The deployment of DSS in Release 17 has shown promising results in enhancing network performance. By leveraging existing infrastructure and spectrum, operators have been able to rapidly expand their 5G footprint while maintaining robust 4G services. Furthermore, DSS facilitates better management of network resources, adapting to varying traffic patterns and demands, thereby improving overall network efficiency and user experience.

In conclusion, DSS in Release 17 represents a strategic leap in spectrum management for 5G networks, offering a pragmatic and efficient path for operators to maximize their existing spectral assets while paving the way for widespread 5G deployment.

The 3GPP Release 18 is poised to bring about a transformative change in the world of 5G technology with the integration of Artificial Intelligence (AI) and Machine Learning (ML). This shift marks the beginning of an era where intelligent, data-driven decisions redefine network operations and performance.

The following figure illustrates the collaborative landscape of AI/ML integration within the 5G architecture, as per 3GPP Release 18, showcasing the pivotal roles of different study items and work groups. This integration promises to usher in a new era of network intelligence, efficiency, and user-centric services.

In Release 18, AI and ML are set to overhaul traditional network management approaches. These technologies promise to bring about a new level of efficiency and automation in network operations. By analyzing vast amounts of network data, AI-driven systems can predict network demands, optimize resource allocation, and even preemptively address potential issues before they impact users.

One of the critical areas where AI/ML will have a significant impact in Release 18 is the physical layer of the 5G network. Through advanced algorithms, AI/ML can optimize signal processing, modulation, and coding schemes, leading to improved spectral efficiency, higher data rates, and more reliable connections. This optimization is particularly crucial for supporting the ever-increasing demand for bandwidth and low-latency applications.

Network slicing, a key feature of 5G, stands to gain immensely from the integration of AI/ML. AI algorithms can dynamically manage and optimize network slices, ensuring that each slice meets its specific service level agreements (SLAs). Similarly, AI-driven load balancing can intelligently distribute network traffic, enhancing the overall user experience and network efficiency.

3a8082e126