# 5G and 6G Networks: An Overview of Economic Interactions

Orland-Médy Saizonou, Patrick Maillé, Bruno Tuffin

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Orland-Médy Saizonou, Patrick Maillé, Bruno Tuffin. 5G and 6G Networks: An Overview of Economic Interactions. 2025. hal-05282366

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# 5G and 6G Networks: An Overview of Economic Interactions

Orland-Médy Saizonou

Inria, Univ. Rennes, CNRS, IRISA Rennes, France
orland-medy.saizonou@inria.fr

Patrick Maillé

IMT Atlantique, IRISA, UMR CNRS 6074 Rennes, France
patrick.maille@imt.fr

Bruno Tuffin

Inria, Univ. Rennes, CNRS, IRISA Rennes, France
bruno.tuffin@inria.fr

**Abstract**—Next-generation mobile networks—5G and the emerging 6G—introduce transformative capabilities in connectivity, latency, and massive device support. While their technological innovations and service applications are well studied, the economic interactions between network stakeholders remain underexplored. This paper presents a comprehensive analysis of 5G and 6G ecosystems from an economic perspective, including a classification of service classes, applications, and enabling technologies. We map all relevant stakeholders, examine their interactions, and discuss the business models and pricing strategies yielding revenue. It also highlights the regulatory frameworks helping to lead to expected societal outcomes. The study exposes insights on sustainable business strategies and policy development in next-generation communications.

**Index Terms**—5G, 6G, mobile networks, service classes, applications, enabling technologies, network ecosystem, stakeholders, business models, pricing strategies, network economics, regulation

## I. INTRODUCTION

Modern society is highly dependent on wireless communications. There is a constant evolution of networks, with each new generation significantly enhancing connectivity, data transmission, and application capabilities. 5G networks follow the same logic with a set of specifications defined by the International Telecommunication Union (ITU) under the name International Mobile Telecommunications-2020 (IMT-2020) [203]. According to [25], [203], 5G networks aim to offer three usage scenarios or service classes including enhanced mobile broadband (eMBB), ultra-low latency (uRLLC), massive device connectivity (mMTC). These features enable a wide range of applications, such as Augmented Reality (AR), Virtual Reality (VR) [179], Internet of things (IoT) [34], Autonomous driving [125], and rest on various technologies such as Network Slicing, Massive MIMO, Millimeter Wave, Ultra-densification, and many others [11]. Figure 1 illustrates some examples of emerging applications and anticipated service classes for 5G according to IMT-2020. To again go one step forward, 6G networks are expected in 2030 to be a game changer by providing more fluid and essential features that can be customized to meet diverse needs across environment, society, and various industries: ITU via its framework for

<figure><img src="image_3.png" /><figcaption>Fig. 1. Usage scenarios of 5G</figcaption></figure>

6G networks IMT-2030 [124] did announce six new service classes including immersive communication (IC) [56], [210], Hyper-Reliable and Low-Latency Communication (HRLLC) [200], [187], Ubiquitous Connectivity (UC) [134], Massive Communication (MC) [104], Artificial Intelligence (AI) and communication [117], and Integrated Sensing And Communication (ISAC) [167], [87] as enhancements with respect to 5G. Figure 2 depicts some examples of emerging applications and anticipated service classes for 6G (from IMT-2030 [124]). To fulfill the associated needs, the proposed solution is to

<figure><img src="image_4.png" /><figcaption>Fig. 2. Usage scenarios of 6G</figcaption></figure>
increase density and data rate thanks to terahertz (THz) communication technology, ultra-massive Multiple Input Multiple Output (umMIMO) technology. The framework will also enable non-terrestrial networks and Reconfigurable Intelligent Surfaces (RIS) for a global coverage, alongside quantum communication and blockchain for large data management and communications security.

A transition to the next generation network thus means new technologies, new stakeholders and new interactions which can impact others and society as a whole. It is typically true for 5G/B5G era, with the advent of players like non-terrestrial networks providers which are companies that provide internet, voice, or broadcast services using satellites or aerial technologies [145], network automation providers that offer cloud solutions with advanced technologies like AI and trust/security providers [181] that ensure the secure and reliable operation of the 6G ecosystem. These players are also called evolving service providers to highlight the new activities present in 6G environment in addition to the conventional roles already present in 5G networks such as Mobile Network Operators (MNOs), network infrastructure and equipment providers, software providers, regulatory bodies, content providers, or end users.

It is of primary importance to understand the whole 5G and 6G ecosystems for designing appropriate Business Models (BMs), economic relations and regulation rules. The purpose of this paper is therefore to review the actors, the network and service important characteristics from an economic point of view and describe the existing related business models. This will provide a valuable base for future activities on network economics to improve or propose new business models for increased revenues. Such propositions can then be through pricing or business models to one of the actors, or through regulation which can take different forms, either a telecom-specific one (e.g., net neutrality rules, spectrum attribution, enforced interconnection and roaming, etc) or a general market oriented one (e.g., on competition, trade and antitrust) [145].

There are numerous papers describing 5G and 6G networks specificities and capabilities. Some of them focus on classifying technologies. First, Ian F. Akyildiz et al. [11] present how specifications in terms of data rates, latency, energy consumption, reliability, scalability and security in 5G are fulfilled by ten technologies/applications designed to meet these requirements including network slicing, millimeter wave, massive MIMO, ultra-densification, edge computing, software-defined networking, network function virtualization. We will separate technologies from applications such as IoT or green communication. The authors of [123] discuss 5G by outlining its specifications, application trends and technological advancements. ITU in [203] uses a similar description adding a categorization of 5G service classes such as eMBB, uRLCC, mMTC. We will add this “service” classification in addition to technologies and applications since services make use of technologies but do not specifically tell for what exactly they are used. Similarly the author of [156] describes 5G technologies and applications, but do not consider service classes as us either. We can also mention that [156] adds an interesting historical part of network evolution in the

successive generations. [66] also details network evolution but focusing on its architecture, capabilities, applications and technologies, still without the classification of service types. The same type of innovation description, but for 6G, can be found in the following articles. The author of [124] analyzes 6G evolution by providing 6G service classes (IC, HRLLC, MC, ISAC, UC, AI) and key technologies. [187] has the same classification for 6G service classes but looks slightly differently at technologies, adding the network evolution from 1G to 5G and some challenges of 6G deployment. Our classification will be quite close to [187]. In [182], Afsoon Alidadi et al. explore only two specific 6G service classes (IC and HRLLC) with some applications related to them, key technologies proposed to provide them and relevant network architecture for them. [217] discusses 6G through its capabilities and applications: the author classifies 6G technologies into two groups aiming to meet the capabilities and applications. 6G additional capabilities are also described in [24], [52], [45], but still without the distinction we point out between services, technologies and applications. In [56], Jie Gao et al. perform an in-depth analysis of one of the six 6G service classes (IC) by describing the related applications, requirements, concerns and potential solutions; our paper additionally provides a description of the five remaining types of services.

But none of all the above works address the economic point of view, which will be our angle of analysis. Note though that some economic view of 5G and beyond networks is existing in the literature. In [145], the authors classify the existing players in the 5G ecosystem and the new ones which arrive with 6G, describe their roles, but without showing how they interact. They also discuss the business opportunities in 5G and 6G and the evolution of regulation policies, without mentioning any specific pricing model. Reference [144] follows the same logic but adds the 5G value chain to the discussion, showing the interaction between the actors. In line with previous works, [115] analyzes all the actors in 3G networks, their roles, and their interactions without mentioning any pricing model. [74] follows the same idea but for 5G mobile networks. So none of the above works tackles the pricing methods between two actors and analyses the ecosystem as a whole as we do. [85] on the other hand describes pricing schemes, but specifically for MNOs, explaining static or dynamic pricing methods. We do the same in our document but with all actors involving the ecosystem. [128] focuses on pricing strategies but for network security issues only. [129] is also restricted in the 5G actors studied in terms for the analysis of pricing strategies. Our paper improves all these works, trying to encompass all the actors of the ecosystem.

Therefore, none of the above explores how the economic interactions between the various actors are structured and designed in 5G and 6G ecosystems, nor specify what pricing models are used in those interactions when providing an overview of the innovations introduced by 5G and 6G for mobile communication, with an comprehensive representation of technologies, services and applications. The purpose of this paper is to fill this gap. We first propose a classification of service classes and technologies of 5G and 6G, then we present the stakeholders involved the ecosystem to provide a
good representation of the economic interactions at stake in the environment, and a comprehensive view of the applied pricing models. This will later help to investigate which areas could need further research and what regulation could bring towards a better network overall usage.

The rest of the paper is organized as follows. Section II presents 5G and 6G networks by describing the novelty they bring in terms of service classes, service applications, and key technologies. Section III then introduces the set of networking actors, with a particular focus on the new ones in 5G and 6G networks. Section IV describes the economic interactions among all key players, their complex relationships and the associated business models to make them profitable; the key issue addressed by the paper. Section V presents regulations and policies aspects put in place at different levels which impact all players actions and interactions between them, in order to maintain order, and promote fairness. Section VI discusses what we believe to be important future activities on network economics perspectives for 5G, 6G and B6G, and conclusions are presented in Section VII.

## II. 5G AND 6G NETWORKS TECHNOLOGICAL INNOVATIONS

Initially, mobile networks were focusing on communication through voice and data. However, with 5G and 6G, new markets have emerged, requiring tailored technical solutions. Their requirements turn around three service classes for 5G networks according to IMT-2020, eMBB, uRLLC, mMTC, and six new ones for 6G networks according to IMT-2030: IC, HRLLC, UC, MC, AI and Communication, ISAC. Based on those service classes, several technological solutions have been proposed to expand the boundaries of bandwidth, latency, and applications in 5G and 6G networks, remaining the base of their deployment. Finally, some applications are designed by industries for companies or end-users, implementing the service classes and technologies, and then meet the associated demands for high data rate, hyper reliability and very low latency. In the following subsections, we discuss service classes for 5G and 6G networks, and discuss some key technologies and applications. They will be used to specify the roles of actors and explain how they interact and make money.

### A. Key service classes

We specify in this subsection the service classes introduced by 5G and then by 6G. A *service class* is defined by its performance characteristics to be fulfilled. It will be implemented thanks to *technologies* to serve *applications*.

#### 1) 5G service classes:

* **Enhanced Mobile Broadband (eMBB):** eMBB is often considered the expansion of 4G broadband [25] and is the service class designed and focusing on high data rate transmissions up to 10 Gbps or more focuses on supporting applications that demand high bandwidth, on high user experienced data rate up to 50Mbps for assessing the QoS when they use internet and on high user mobility either 500km/h ensuring seamless connectivity and service continuity without degradation of performance. eMBB

achieves spectral efficiency up to 15bits/Hz, enabling enhanced user experiences across diverse environments [203].

* **Massive Machine-Type Communications (mMTC):** This service is designed specifically to support a vast number of connected devices, typically situated in a confined space, often transmitting data intermittently, as seen in IoT scenarios [25]. According to the IMT-2020 vision, mMTC is configured to support 1 million wireless devices per km2, 10 years battery lifespan and 10Mbits/s/m² for area traffic capacity [203].

* **Ultra-Reliable Low Latency Communications (uRLLC):** The goal of this service class is to deliver low latency (as low as 1 millisecond) [11], to ensure a very high reliability $10^{-5}$ and high security [25] to comply with the emergence of critical applications.

Figure 3 summarizes the service classes borught by 5G and their performance characteristics. Building upon the foundation

<figure><img src="image_5.png" /><figcaption>Fig. 3. 5G key performance indicators</figcaption></figure>

of 5G service classes, the evolution to 6G introduces an enhanced set of service categories, designed to address the demands of future applications.

#### 2) 6G service classes:

* **Immersive Communication (IC):** IC will be one of the key service classes of 6G networks, ensuring users to have realistic experiences in the physical, virtual, or hybrid worlds [56]. According to IMT-2030 plan, the peak data rate to handle immersive communication is in the order of 1 Tbps, which greatly surpasses what 5G can handle [169]. It ensures user experience rate of 1Gbps, area traffic capacity of 1Gbps/m², 100-1000x spectrum efficiency more than 5G and 1000km/h for user mobility [202].

* **Hyper Reliable and Low-Latency Communication (HRLLC):** The HRLLC service class, which is an advanced version of URLLC in 5G, ensures data delivery with reliability from the $10^{-5}$ of URLLC but up to $10^{-7}$, and a minimal delay $25\mu s \sim 1ms$ [169]. To achieve these goals, several key factors must be improved such as network management strategies employed in the core network, signal processing methods like modulation and coding, and the signal propagation delay within the network [182].

* **Massive Communications (MC):** MC is the advanced version of mMTC from 5G in 6G networks, with the
ability to manage real-time interactions for million devices, along with a large network of devices that can communicate with central or distributed servers, or directly with another one [79]. MC is configured to support 10 million wireless devices per km2, 1Gbps/m² for area traffic capacity and 100x network energy efficiency [202].

* **Integrated Sensing And Communication (ISAC):** As future networks aim to connect a large amount of intelligent devices (nodes) thanks to MC, these nodes need to sense each other and communicate to exchange information. The ISAC system will deliver high capabilities in augmented human sensing, localization and tracking, posture and gesture recognition, as well as imaging and environmental reconstruction, to enhance communication performance [167] by covering very large areas (~ 200m), allowing to grasp details with high resolution (0.5mm ~ 0.5m), and the correct length of objects with high accuracy (0.5mm ~ 0.1m), high availability (99.9999%) and high refresh rate (1ms ~ 1s).

* **Ubiquitous Connectivity (UC):** UC represents a novel service class for future networks aiming to enhance network access and users experience regardless of where they are located, using a variety of devices [187]. It focuses on providing coverage to areas that are currently underserved, such as rural, isolated, and sparsely populated locations, by collaborating with various access technologies [124].

* **Artificial Intelligence (AI) integrated Communication:** The integration of AI in future networks will allow to optimize network operations, and to ensure the management of complex networks infrastructure autonomously. AI enhances predictive maintenance, intelligent resource allocation, personalized user experiences, but also extends virtualization [45].

Figure 4 summarizes the service classes brought in 6G and their characteristics.

<figure><img src="image_6.png" /><figcaption>Fig. 4. 6G key performance indicators</figcaption></figure>

But for 5G and 6G networks to meet these service requirements, specific technologies need to be developed. It is the purpose of the next subsection.

## B. Key service technologies

Service technologies, here focusing on 5G or 6G networks, are technical solutions enabling the networks' functionalities and providing the infrastructure and capabilities to operate the previous service classes. They range from those that reduce latency, improve data rates, ensure million devices connected to those that offer the possibility of increasing coverage.

### 1) 5G technologies:

* **Network Slicing:** Network Slicing is a key technology which enables 5G networks to create distinct and virtualized networks within a single physical infrastructure [73], [120]. It divides the network into independent portions logically separated and called slices, customized to meet specific requirements such as high data rates, high reliability, ultra low latency [55].

* **Massive MIMO (Multiple Input Multiple Output):** Massive MIMO is a solution proposed for 5G to enhance data throughput, optimizing spectral efficiency by employing several antennas on the transmitter and the receiver ends [11]. This approach significantly boosts network capacity, improves coverage, provides low latency, and offers a high data rate by exploiting advanced antenna technologies [38].

* **Millimeter Wave (mmWave):** mmWave technology is another advancement of 5G, using high-frequency radio waves to enhance network capacity, improve user experiences and allow higher data transmission speeds [12], [19]. The frequency waves used, operate in two categories range. The first one covers frequencies below 6 GHz and the second one covers approximately frequencies from 24 GHz to 100 GHz [25].

* **Ultra-Densification-Networks (UDNs):** In the context of high data demands in the 5G, UDNs are a strategy consisting of deploying numerous **small cells** in dense areas to increase network capacity and then providing better coverage [197]. Small cells include **femtocells** (covering 10–50 meters), **picocells** (spanning 100–300 meters), and **microcells** (reaching 250–1000 meters) [25].

* **Software-Defined Networking (SDN) & Network Function Virtualization (NFV):** A key challenge for 5G communication networks is developing adaptable network architectures, which can be achieved using SDN approach [10], allowing to separate the control plane (decision-making) from the data plane (packet forwarding) in multiple network devices [25]. NFV stands as a vital technology for evolving 5G networks, providing a platform that efficiently optimizes resource usage and involves transferring tasks like load balancing or intrusion detection from traditional hardware systems to virtual network functions (VNFs) [28], [192]. These technologies control latency overhead, manage connections from millions of network devices and facilitate dynamic network traffic and on-demand resource allocation to optimize network performance.

* **Edge Computing (MEC – Multi-access Edge Computing):** Another technology developed for 5G is Multi-access Edge Computing (MEC) which brings computa-
tional resources closer to the user or device, reducing latency and allowing data to be processed locally rather than in a distant cloud [11]. Edge Computing is a technology that reduces the burden on centralized fog nodes, leading to faster response times, better bandwidth availability and enhanced operational efficiency when compared to cloud and fog computing [71], [151].

Figure 5 highlights how 5G service classes utilize different enabling technologies. While some technologies like SDN and network slicing are universal across all services, others like Massive MIMO and Edge Computing are selectively applied based on the specific demands of eMBB, URLLC, or mMTC.

<figure><img src="image_7.png" /><figcaption>Fig. 5. Relationship between updated 5G key technologies and service classes</figcaption></figure>

2) 6G technologies: Since 6G introduces new service classes with respect to 5G, new technologies are also needed to fulfill the associated qualities of service. We list now these advertised and expected technologies.

* **Terahertz (THz):** THz is an advanced wireless communication technology that operates in the Thz frequency band, typically ranging from 0.1 to 3 THz [182], offering high data rates due to their wide bandwidth, and enabling multi-terabit-per-second connections. These frequencies make THz technologies ideal for bandwidth-intensive applications. Ultra massive Multiple Input Multiple Output (umMIMO) [67], and Reconfigurable Intelligent Surfaces (RIS) [13], [37], [69] are the main THz technologies designed into for 6G to improve the signal gain toward users and maintain connection with the base station.

- **ultra-massive Multiple Input Multiple Output (umMIMO):** umMIMO is the extension of MIMO technology from 5G networks, where Thz frequencies enable the deployment of a significantly greater number of antenna elements and the formation of narrow beams customized for individual users [68], [182].

- **Reconfigurable Intelligent Surfaces (RIS):** RIS technology offers a novel approach to signal propa-

gation and control. It involves using arrays of small, programmable reflectors that can manipulate radio waves in real time to enhance signal quality, extend coverage, and mitigate interference [13]. As the deployment of additional base stations increases to ensure reliable connections in 6G networks, leading to higher energy consumption, as well as increased deployment and maintenance costs, RIS can be strategically positioned at various locations at a comparatively low cost [182].

* **Quantum Communication (QC):** QC extends all previous computing technologies of 5G by processing multidimensional, and large-scale data in parallel for quick, dependable, smart, and secure communication [90]. QC technology can also encrypt sensitive data using Quantum Key Distribution (QKD) [155] and enhance intrusion detection thanks to quantum mechanics.

* **Non-Terrestrial Networks (NTSs):** NTSs include space satellites and aerial platforms like High-Altitude Platform Stations (HAPS) and Unmanned Aerial Vehicles (UAVs), which present a hopeful architecture for 6G networks, and then are a sustainable solutions for extending wireless coverage to rural and remote areas. However, in response to the growing capacity demands of future wireless communication systems and the necessity for enhanced resilience and reliability in network architecture, the focus is shifting towards integrating NTNs with urban Terrestrial Networks (TNs). Authors in [31], [148], [209], [222] have developed several techniques with AI to provide the combination of both.

* **Orbital Angular Momentum (OAM) technology:** The use of OAM promises to be an important advance in wireless modulation by enhancing channel capacity and optimizing spectrum efficiency. OAM uses wave-fronts with helical phases, these modes facilitate multiplexing and demultiplexing, potentially offering significant capacity increases for future networks [24]. This technology is preferred in line-Of-Sight (LOS) environments more than Non-Line-Of-Sight (NLOS) environments because of obstacles like buildings, trees, or other objects [46].

* **Blockchain:** Another important technology for future networks (6G) is blockchain which provides secure, reliable, decentralized, and distributed networks in order to enhance privacy and security for users. Blockchain allows multiple users to utilize the same spectrum efficiently, addressing the challenge of large spectrum demands in future networks [24]. For privacy protection, several works have designed a model for AI integration in blockchain with Distributed Machine Learning (DML) like [75], [108].

Figure 6 provides a comprehensive overview of the alignment between key 6G enabling technologies and their relevance across six service classes. Among these technologies, some are aligned with all service classes, while others have limited roles in certain domains.

Nevertheless, the previous innovations are not only reshaping the way networks operate but also unlocking new possi-
<table><caption>Fig. 6. Relationship between updated 6G key technologies and service classes</caption><thead><tr><th></th><th>IC</th><th>HRLLC</th><th>MC</th><th>AI</th><th>UC</th><th>ISAC</th></tr></thead><tbody><tr><th>umMIMO</th><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><th>RIS</th><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><th>QC</th><td>✓</td><td>✓</td><td>X</td><td>✓</td><td>X</td><td>X</td></tr><tr><th>NTNs</th><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><th>OAM</th><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><th>Blockchain</th><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>X</td><td>X</td></tr></tbody></table>

bilities across a variety of applications. In the next subsection, we explore how these innovations are applied in real-world scenarios, driving significant transformations across industries and improving user experiences.

### C. Key applications

The ecosystems of 5G and 6G networks are therefore built to enable new sets of applications in various industries. While some need high data rates, ultra low latency, high reliability, others need high bandwidth, security, automation. We list below the main anticipated new usage of technologies.

#### 1) 5G applications:

* **Augmented Reality (AR) and Virtual Reality (VR):** The high data rates and reliability, low latency offer by 5G through technologies like edge computing, and network slicing allow AR/VR experiences, from immersive gaming to remote training and virtual tours [17].

* **Autonomous Vehicles:** 5G enables low-latency and high data rates, supporting real-time data transfer for autonomous driving, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. With edge computing, 5G processes data close to the vehicle, network slicing also can create high-priority channels for safety-critical vehicle communications [180].

* **Smart Cities:** 5G powers smart cities by connecting a vast number of IoT devices for citizen-centric services involving access to public services, healthcare, and education [142]. With massive MIMO, smart cities cover a large area of public facilities, when network slicing customs slice for needs like surveillance, traffic control, etc.

* **Remote Healthcare and Telemedicine:** With low latency, 5G facilitates high-quality video consultations, remote surgeries and real-time health monitoring for patients [17], [142].

* **Smart Homes:** 5G improves home systems and devices like connected appliances, wearables, security systems, by providing high data rates and high reliability [142]. Edge computing processes data near the devices, reducing latency and bandwidth use.

The evolution of wireless networks from 5G to 6G was also observed on the applications side. In 6G, some 5G applications will be improved and new ones will take place.

#### 2) 6G applications:

* **Extended Reality (XR):** IC ensures ultra-high bandwidth for XR experiences like immersive virtual travel, digital twins for real-world simulations and real-time collaborative spaces [183].

* **Tactile Internet:** 6G with ISAC allows the remote control of physical devices with a sense of touch in areas like robotics, haptic feedback and industrial automation [170].

* **Super-Smart Society:** With a billion of devices connected, 6G makes our society ultra smart with smart homes, smart devices, smart cities, fully automated driving and so on [82]. These are possible thanks to technologies like QC, Thz, AI, NTSs, RIS or umMIMO.

* **Brain-Computer Interactions (BCI):** BCI which is a direct communication between the brain and external devices necessitates high-data-rates, ultra-low-latency and ultra-high-reliability from 6G [62]. With these requirements and thanks to Thz technologies or RIS, BCI can retrieve signals from brain and transmit them to a digital device for analysis and interpretation.

* **Industrial Automation:** 6G supports advanced AI and ML algorithms for real-time monitoring, predictive maintenance and automated operations in industries like manufacturing and logistics [106].

* **Space-Air-Ground:** The revolution brought back by 6G integrating terrestrial, aerial and space-based systems into a unified network leads to the emergence of applications like maritime and aviation navigation, earth observation [77].

Table I provides a comprehensive mapping between emerging applications and their corresponding service classes, alongside the enabling technologies necessary for their realization. The applications are demanding distinct network capabilities. Service classes including uRLLC, HRLLC, ISAC and others have been identified according to application-specific requirements. Enabling technologies such as edge computing, network slicing, THz communications, RIS, QC, and umMIMO are aligned with these service classes to ensure performance, scalability, reliability and low latency. This mapping shows the evolving landscape of 5G and 6G networks, emphasizing the critical role of advanced technologies in supporting next-generation intelligent and immersive applications.

As 5G and 6G networks are built on decades of cellular innovation, they not only introduce new technical features but also lead to important changes in how players competing and operating in the market. The transformative capabilities of 6G extend far beyond the existing actors by influencing the nature of the services they provide. Below, we review the market players in the evolving 5G ecosystem and those expected with 6G.

### III. THE SET OF ECONOMIC ACTORS AND ITS EVOLUTION

In the traditional telecommunication ecosystem, multiple activities and processes are realized by some companies which produce and distribute value to their customers in the form of products and services. The main established actors are Infrastructure and Network Equipment Providers (INEPs),
<table><caption>TABLE I
MAPPING OF APPLICATIONS TO SERVICE CLASSES AND ENABLING TECHNOLOGIES.</caption><thead><tr><td>Applications</td><td>Service Classes</td><td>Enabling Technologies</td></tr></thead><tbody><tr><td>Remote Healthcare and Telemedicine</td><td>uRLLC (5G), HRLLC (6G)</td><td>Edge Computing, Network Slicing (5G), THz Communications, RIS (6G)</td></tr><tr><td>Autonomous Vehicles</td><td>uRLLC, mMTC (5G), HRLLC, MC (6G)</td><td>Network slicing, mmWave, Massive MIMO, Ultra Dense Networks, edge computing (5G), THz, umMIMO, QC (6G)</td></tr><tr><td>Brain-Computer Interface (BCI)</td><td>HRLLC, ISAC, AI/ML</td><td>THz, QC, RIS</td></tr><tr><td>Tactile Internet</td><td>HRLLC, ISAC</td><td>THz Communications, RIS, OAM</td></tr><tr><td>XR (Extended Reality)</td><td>HRLLC, IC</td><td>umMIMO, QC</td></tr><tr><td>Space-Air-Ground</td><td>HRLLC, UC</td><td>NTNs, THz, RIS</td></tr><tr><td>VR/AR</td><td>URLLC, eMBB</td><td>Network slicing, edge computing</td></tr><tr><td>Super-Smart Society</td><td>HRLLC, UC, MC, AI/ML</td><td>QC, THz, RIS, umMIMO</td></tr><tr><td>Full-Sensory Digital Sensing and Reality</td><td>ISAC</td><td>OAM</td></tr></tbody></table>

Regulatory Bodies (RBs), Mobile Virtual Network Operators (MVNOs), End-Users (EUs), etc which are organized around the Mobile Network Operators (MNOs). MNOs play a central role in telecommunication ecosystem. As 5G/B5G era is expected to provide more capabilities than previous generations, leading to new actors that we describe in this section.

### A. Traditional network stakeholders

* **Mobile Network Operators (MNOs):** In 5G networks and beyond, MNOs play a pivotal role by offering network access to enterprises, developers, and EUs. MNOs manage spectrum licenses and diverse infrastructure elements essential for enhancing mobile coverage, optimizing network operations and improving real-time capabilities and QoS [144]. They also offer diverse services like voice calls, messaging, data, TV and develop complex interactions with other players in the telecommunication landscape [219]. Typical examples of MNOs include Orange, China Mobile, Deutsche Telekom, or Verizon.

* **Infrastructure and Network Equipment Providers (INEPs):** In the 2G/3G era, network infrastructure and equipment were owned and operated by MNOs, but with the advent of 4G, 5G, and beyond, ownership has progressively evolved into a set of players. According to [144], INEPs deliver network and radio equipment such as routers, switches, servers, Radio Access Network (RAN) and base stations, along with mobile devices directly to EUs. They build and maintain the physical infrastructure for MNOs that enable network coverage and connectivity with cell towers, small cells, fiber backhaul, rooftop. Finally, they also offer data centers to support data

storage, processing and management for CCPs. INEPs include companies like Huawei, ZTE, Ericsson, Cellnex Telecom, American Tower Corporation, Equinix, Nokia.

* **Regulatory Bodies (RBs):** Implementing regulation procedures, that is, the application by governmental agencies of action laws and policies established by legislation, may significantly influence the interactions and activities among all stakeholders within the 5G networks and beyond ecosystem [145]. Regulatory Bodies ensure fair competition, consumer protection, spectrum management and compliance at three different levels [105]: national with Telecom Regulatory Authority of India (TRAI), Of-com, ARCEP, continental with European Telecommunications Standards Institute (ETSI) and international with International Telecommunication Union (ITU), World Trade Organization (WTO).

* **Mobile Virtual Network Operators (MVNOs):** Due to the limited availability of radio spectrum, which restricts the number of MNOs, MVNOs have emerged as an effective solution for infrastructure sharing, facilitated by network virtualization [65]. MVNOs are presented as companies that rent access to spectrum resources from MNOs, provide specific wireless services like voice calls, messaging, data [119] directly to EUs and allow MNOs to expand their revenue streams. MVNOs include companies like Lebara Mobile, Lycamobile, Tracfone.

* **Cloud Computing Providers (CCPs):** These entities establish public clouds, manage private clouds, and offer on-demand resources such as computing power, storage and platforms via cloud computing offerings [219] including Infrastructure as a service (IaaS) [218], Platform as a Service (PaaS) [164] and Software as a Service (SaaS) [114]. These services are offered to various organizations like CPs and InCs or individuals over the internet by configuring servers according to their specific requirements. They may develop partnership with MNOs for cloud services delivering and have to carefully evaluate security considerations associated with storing information in the cloud. For instance, Microsoft Azure implement industry-recommended access controls and compliance management configurations to ensure data security and regulatory compliance. CCPs companies category include AWS, Google Cloud, Microsoft Azure, Alibaba Cloud, Tencent Cloud, IBM Cloud.

* **Industrials Companies (InCs):** InCs are emerging as some of the most transformative actors in the 5G ecosystem. With the rise of Industry 4.0, sectors like manufacturing, energy, mining, logistics, and construction [109] are leveraging 5G to drive automation, enhance productivity, and enable previously impossible use cases through ultra-low latency, massive IoT connectivity, and high reliability. Global industrial leaders such as Siemens, Bosch, ABB, Schneider Electric, and Honeywell partner with MNOs for deploying private 5G networks in factories to support connected robots, digital twins, predictive maintenance, and real-time quality control [8], [161] and then deliver specialized services to EUs.

* **Content Providers (CPs):** CPs emerge as key companies
which develop and provide digital content through various mediums like streaming, online publications, games, social media content, audiobooks [144] directly to EUs over internet with several resources acquired from other providers like MNOs and CCPs. They include Streaming Service Platforms like Netflix, Spotify, News and Information Providers like The New York Times, BBC, Social Media Platforms like Facebook, Instagram and Gaming Platforms like Steam, Epic Games.

* **End-Users (EUs):** EUs are the ultimate beneficiaries of technological advancements. They represent the individuals or entities which use digital content services from CPs, mobile connectivity from MNOs or MVNOs, mobile devices from INEPs or cloud resources from CCPs [145]. EU satisfaction and service adoption are key drivers of success and growth of telecommunication ecosystem [144].

As the 5G/B5G landscape evolves, emerging technologies, capabilities and innovative players are redefining industry dynamics. The next subsection explores the impact of these changes by highlighting the new actors in the ecosystem.

## B. Emerging stakeholders

* **Non-Terrestrial Networks Providers (NTNPs):** In 5G/B5G era, Non-Terrestrial Networks Providers represent entities which deliver communication services using non-terrestrial platforms, such as satellites, High-Altitude Platforms (HAPs), and even drones. Through these services, NTNPs provide faster data speeds, greater network capacity, lower latency, and enhanced reliability [97] to EUs, and differ fundamentally from MNOs in terms of the infrastructure they deploy, the coverage they deliver, and the performance they can guarantee [83], [171]<fnref n="1" />. However, they operate also in collaboration with MNOs to enhance global communication coverage, especially in underserved or remote areas and include companies like SpaceX's Starlink, Eutelsat OneWeb, AST SpaceMobile. To illustrate relations with traditional MNOs, T-Mobile US and AT&T partnered with SpaceX's Starlink and AST SpaceMobile respectively to expand mobile coverage in remote areas using Low Earth Orbit (LEO) satellites.

* **Network Automation Providers (NAPs):** B5G or 6G is expected to make networking tasks including management, monitoring, and configuration fully autonomous. This introduced new set of actors named NAPs, which offers tools and platforms that deliver cloud services integrated with AI and data analytics capabilities such as machine learning, natural language processing, computer vision and predictive analytics [145] for network optimization, predictive maintenance, security enhancement, and operational efficiency. NAPs include companies such as VMware, Juniper, Puppet and their targeted customers are MNOs, and NTNPs for network infrastructure automation, CCPs for cloud resources automation, InCs for private 6G [177] automation, and CPs for video/audio editing, transcription.

* **Trust/Security Providers (TSPs):** B5G/6G connectivity is expected to also come with safety, reliability, and privacy. TSPs act as companies which enable secure communication through end-to-end encryption [146] and post-quantum cryptography [27], protecting data privacy with federated learning [58] and consent management [33], and detecting threats using AI-driven analytics to various businesses like InCs, CPs, in areas such as data treatment, financial transactions, digital contents, etc. TSPs may collaborate with MNOs, and use blockchain [18], adopt dynamic policies [189] and zero trust architecture [64] to deliver trust services. TSPs include companies such as Hyperledger Foundation, Palo Alto Networks, CrowdStrike, European Union Agency for Cybersecurity (ENISA).

After classifying service classes and technologies of 5G and 6G networks in Section II, with a view on the involved players we have just presented, we describe in the next section the economic interactions between actors, including the associated BMs.

## IV. ECONOMIC ECOSYSTEM

<figure><img src="image_8.png" /><figcaption>Fig. 7. Layered Evolution of Markets from 5G to 6G</figcaption></figure>

Figure 7 describes the markets 6G brings (in green) in addition to the already existing 5G markets (in blue) to 6G markets (green), and the relationships between the markets represented by the resources used by each market from others. The markets existing in 5G are composed of the radio resource market, the physical infrastructure market, the device market, the computing resource market and the network slicing market which were interacting as presented. 6G introduces new ones: the satellite connectivity market, the AI-based market and the trust and security market. The markets may be seen as organized in layers, where the basic layer, composed of the devices, radio resources, and physical infrastructure markets, forms the

<footnote n="1">¹https://www.3gpp.org/technologies/ntn-overview</footnote>
indispensable basis on which subsequent developments depend. The intermediate layer includes the computing resources market which benefits from the physical infrastructure and device markets by enabling scalable processing capabilities, and the network slicing market, enabling flexible resource allocation. Finally, the top layer, with trust and security, AI-based, and satellite connectivity markets, represents the domains of differentiation and added value expected with 6G.

Figure 8 describes the actors involved in each market of Figure 7. It illustrates that the MNOs as central and present in all markets. It also shows that 5G actors are also present in new 6G markets.

<figure><img src="image_9.png" /><figcaption>Fig. 8. 5G and 6G Value Chain: Markets, and Actors</figcaption></figure>

In what follows, we will describe for each market the roles, interactions and services exchanged between the various involved stakeholders. We will differentiate traditional actors (light blue nodes) and emerging actors (light green nodes) and the BMs with which these entities collaborate to deliver a wide range of services in order to make profit.

The 5G ecosystem and the emerging 6G feature complex interdependencies between several types of actors, with the MNO playing a central role. The MNO typically controls the physical infrastructure, spectrum, and core services. Around the MNO, various actors interact through different business models depending on the type of resource exchanged: network capacity, radio access, computing resources [131], providing different levels of quality of service [3], [135]. We can distinguish economic interactions across several markets in 5G and 6G ecosystem.

## A. Radio Resources Market

First, in radio resource markets, RB organizes frequently auction processes, to assign specific frequency bands to MNOs reducing the risk of interference during their usage. The main auctions used by the RB include Simultaneous Multiple Round Auction (SMRA) [30], [191], as used in the UK 5G auction 2018 and India 5G Auction 2024 and Combinatorial Clock Auction (CCA) [21], [54], [99], as used in Ofcom 5G auction 2021. In the first format, the RB offers multiple spectrum lots simultaneously, and the auction proceeds in a sequence of discrete bidding rounds. In each round, MNOs can place new bids on any number of individual spectrum lots, provided the bids exceed the standing high bids by a predefined increment. At the end of each round, the current highest bid for each lot becomes the new standing bid, and this process continues until a round concludes with no new bids on any lot. The SMRA allows first-price rule, where MNOs pay the highest bid submitted on the spectrum block [1] to RB. In the second format, the RB initially conducts a series of clock rounds where bidders submit their demand for packages of spectrum at announced prices. Prices increase in subsequent rounds for items that are in excess demand, and bidders must adjust their bids accordingly. Once the clock phase concludes, a supplementary round is held where bidders may submit additional package bids, subject to activity and pricing constraints derived from their behavior in the clock rounds. The final allocation is determined through a Winner Determination Problem (WDP) algorithm [21] that selects the combination of bids maximizing total value, while ensuring no overlapping allocations. The CCA allows second-price or Vickrey–Clarke–Groves (VCG)-like pricing [107], [129], where the winning MNOs pay to RB the highest losing bid or the second-highest bid for the block. The relationship between RB and MNOs is characterized by a combination of oversight, negotiation, and sometimes conflict, with RB acting as referees and enablers, and MNOs as strategic actors navigating to shape the regulatory environment.

Thanks to spectrum licenses from RB, MNOs acquire exclusive control of the spectrum, allowing them to operate with a high degree of autonomy. However, 5G introduces flexible spectrum usage model, which opens the door to 5G Spectrum Sharing (SS) models. SS can take multiple forms, each with distinct economic and strategic implications. First, SS among MNOs can be implemented through Co-Primary Spectrum Sharing (CoPSS), where via semi-static, proactive, or reactive way [5], MNOs with underutilized spectrum bands sharing spectrum to enhance coverage, balance load, or reduce cost. Their relationship is one of co-opetition [6], where MNOs simultaneously cooperate over spectrum but compete over EUs for providing high-speed mobile data and voice services. This approach is illustrated in Sweden, where Telia and Tele2 operate a joint venture, **Net4Mobility**, to share spectrum, while maintaining separate customer relationships. Second, SS may occur between MNOs and MVNOs. In this model, MVNOs obtain access to spectrum and network infrastructure through wholesale access [40] from MNOs. MVNOs pay MNOs per traffic unit sent on their network (e.g access to
data at a price per unit) [86] and differentiate themselves at the service layer by offering unique pricing models, data and voice services plans to EUs. The relationship is economically asymmetric and hierarchical, since MNOs act as a spectrum gatekeeper and lead resource allocation decisions and MVNOs as a price-taker. This model is exhibited in France, where Lycamobile pays Bouygues Telecom per minute of voice call or per MB/GB of data used by their customers after accessing Bouygues Telecom radio infrastructure.

The relationship between MNOs and EUs and that between MVNOs and EUs in radio resource markets are characterized by direct service provisioning and pricing strategies [47], [48], [126]. EUs typically access the network via subscription fee, where they pay a monthly fee to MNOs or MVNOs for connectivity, often bundled with voice, SMS, and MMS. Since EUs are the ultimate consumers of data services, their demand behavior significantly influences MNOs' spectrum acquisition strategies from regulators during auctions, as well as MVNOs' spectrum access decisions from MNOs. In this relationship, MNOs or MVNOs are the leader, while EUs are price-taker with limited control over spectrum usage.

<figure><img src="image_10.png" /><figcaption>Fig. 9. Radio resources market</figcaption></figure>

Figure 9 illustrates the market interactions among RBs, MNOs, MVNOs, and EUs in the radio resources environment. The diagram shows how spectrum is allocated from RBs to MNOs and how these resources are shared among MNOs to enhance global coverage. MNOs provide wholesale access to MVNOs, and both MNOs and MVNOs offer data, voice, and SMS services directly to EUs. Arrows labeled with the (€) symbol represent monetary transactions between entities, while the remaining arrows indicate the flow of services.

## B. Physical Infrastructure Market

Second, as mobile networks evolve from centralized, hardware-defined architectures toward disaggregated, cloud-native, and software-driven deployments, the division of infrastructure ownership between MNOs and INEPs gives rise

to a second type of market in 5G: *the physical infrastructures market*. In this dense environment, INEPs supply the passive physical assets, like towers, rooftops, ducts, power, and back-haul [133], leasing them through a *neutral-host model* to multiple MNOs [130]. In this model, MNOs share the infrastructure, made of base stations, small cells, RAN basebands and antennas, and pay to the INEP fixed fees per tower used [89]. On the other hand, MNOs use these infrastructures to deliver network slices to various companies like InCs, MVNOs, and CPs in network slice market and also internet services directly to EUs. The relationship between INEP and the MNO is hierarchical, with INEP acting as the leader by setting tower prices. The MNO, as the follower, adjusts its CAPEX and OPEX [141], [174] accordingly, leading to lower prices aimed at attracting more EUs. It is illustrated in the USA, where American Tower rents space on its towers to Verizon through lease agreement, facilitating Verizon's 5G network deployment. All details here.

With 5G, the demand for ultra-low latency and distributed computing is in constant acceleration, INEPs rent rack space, power, and cross-connect capacity (dark-fiber paths) to CCPs also through a *co-location model*, for deploying edge servers, data centers, and cloud outposts close to EUs [150]. In this model, CCPs maintain their own separate infrastructure for providing cloud-based services to many businesses like CPs, and InCs in computing resources market, and pay co-location rent, based on rack space, power consumption (per kW), or per cross-connect fees to INEPs, who benefit from higher utilization of their edge infrastructure while remaining application-neutral. This model is also hierarchical, as INEPs act as leaders by setting co-location costs, while CCPs are price takers who must adapt their deployment and pricing strategies accordingly to remain competitive. For example, Cellnex Telecom through partnership with Vapor IO deploys AWS Outposts inside Kinetic Edge Grid modules.

<figure><img src="image_11.png" /><figcaption>Fig. 10. Physical infrastructures market</figcaption></figure>

Figure 10 shows relationships between INEPS, MNOs,
CCPs, CPs, InCs, MVNOs, and EUs in the 5G physical infrastructure ecosystem. INEPs supply essential resources such as data centers, rack space, towers, rooftops, ducts, and power, offered through co-location or neutral-host models. MNOs and CCPs use these resources to deliver respectively network slices under auction model to CPs, InCs, MVNOs, and cloud resources through on-demand, with per-resource usage pricing to CPs, InCs, and in turn they provide several services to EUs via subscription fee. The arrows indicate flows of services and infrastructure dependencies, while the euro (€) symbols highlight the monetary exchanges underpinning these interactions.

### C. Devices Market

Third, device provisioning plays a critical supporting role in the 5G ecosystem. INEPs like Samsung and Huawei provide 5G-enabled mobile and IoT devices directly to EUs, shaping how consumers interact with the network layer [70] in the *devices market*. This model is characterized by one-time purchase corresponding to the price of each device. While EUs demand influences how INEPs design and optimize their devices, the relationship remains asymmetric, with INEPs taking the lead by setting device prices, and EUs following by adapting their preferences to the available offerings. MNOs also engage in the device market as reseller by bundling mobile devices with mobile data, voice and SMS directly to EUs through subscription fee after buying them in bulk from INEPs at wholesale prices in *resale distribution model*. In France, Orange offering a Galaxy S24 for €1 + monthly fee (with data plan).

<figure><img src="image_12.png" /><figcaption>Fig. 11. Devices market</figcaption></figure>

Figure 11 illustrates the devices market within the telecommunications ecosystem, involving INEPs, MNOs, and EUs. INEPs supply mobile, network, and IoT devices to MNOs using the resale model at wholesale prices, allowing MNOs to purchase equipment at reduced costs. These MNOs then resell the equipment to EUs bundled with services such as data, SMS, and MMS through subscription fee. Alternatively, INEPs may also directly provide devices to EUs via a one-time payment model, bypassing MNOs entirely. The diagram shows the flow of goods and services alongside the corresponding monetary transactions (€), highlighting the dual-channel approach (resale through MNOs and direct provision from INEPs) and the value chain from suppliers to the end consumer.

### D. Computing Resources Market

Fourth, 5G networks must handle a massive volume of service requests with highly strict performance requirements, necessitating substantial computing and storage resources for service delivery. This growing demand is driving the emergence of a new market: *the computing resources market*. In this environment, CCPs deliver cloud services, such as IaaS, PaaS, SaaS, IoT platforms [60], analytics [188], and orchestration tools [224] to InCs, CPs through *on-demand model* [207], [145] with data centers co-located on INEP's space. In this model, InCs use cloud resources to design private 5G networks for delivering customized services, while CPs use them to deliver high video contents to EUs through direct service provisioning model and pay CCPs for what they actually use, such as number of servers, of transcoding functions, instead of buying a product outright. On the other hand, EUs access content via fixed subscription fee, where they pay monthly to InCs or CPs for the right to temporarily consume digital content or services without obtaining ownership. A notable example of applied BM is Netflix using AWS services including online storage, video transcoding, databases, and analytics, and is billed based on the number of servers it utilizes, either over 100,000 server instances and on the number of Amazon Kinesis<fnref n="2" /> used, either over 1,000 Amazon Kinesis used in parallel with the servers. Details on Amazon Kinesis can be found at AWS's official site. Netflix offers three main subscription plans in France for EUs. The standard one with ads plan costs €7.99/month, includes Full HD streaming on two devices, and features limited ads. The plan at €14.99/month removes ads, allows the same two-device streaming and downloads. The Premium plan, priced at €21.99/month, offers Ultra HD (4K) quality, immersive audio, and access on up to four screens with downloads on six devices. All details are on Netflix's official site. CCPs also collaborate with MNOs through a *resource sharing model* to jointly manage network, computing, and storage resources [116], for delivering cloud services, analytics, and orchestration tools to InCs, and CPs still through an on-demand model, where CCPs provision computing resources to InCs or CPs for a given service [207] and with data centers from

<footnote n="2">²managed service for processing and analyzing real-time streaming data at a large scale.</footnote>
INEPs, CCPs and MNOs mutually benefit by sharing revenue from their collaboration internally. A concrete example of this model is Verizon and AWS joining Verizon 5G edge and AWS Wavelength for AR/VR experiences companies, video conferencing and streaming companies. All details are on Verizon's site.

<figure><img src="image_13.png" /><figcaption>Fig. 12. Computing Resources market</figcaption></figure>

Figure 12 illustrates the computing resources ecosystem where various entities interact through the exchange of services and resources, along with monetary transactions. At the center of the ecosystem are CCPs, who provide cloud services and resources to CPs, InPs, and EUs. These services are delivered via on-demand model, and in return, the CPs, InPs, and EUs pay for the services per usage, as indicated by € symbols. In the model, CPs and InCs use cloud resources to deliver services to EUs via subscription fee. Additionally, MNOs and CCPs maintain a collaborative relationship by exchanging real-time information about their resource availability, including network and computing resources. This interaction ensures efficient resource utilization and service delivery across the ecosystem. The diagram highlights the complex but organized flow of services, resources, and financial transactions that sustain the cloud-based infrastructure.

## E. Network Slicing Market

Fifth, in the 5G ecosystem, industries increasingly develop applications like autonomous vehicles, and HD streaming, driving demand for networks with specific performance needs. Network slicing meets this demand by enabling MNOs to create tailored virtual networks on a shared infrastructure, making it a key driver of the network slicing market and a crucial enabler of 5G monetization. As a result, MNOs

provision distinct slices of radio resources (e.g., RAN spectrum blocks) [63] and computing resources (storage, compute power, bandwidth) [57], which they lease to MVNOs, InCs, and CPs through an on-demand model with static or dynamic resource allocation. In this model, MNOs provision the resources for a given service from MVNOs, InCs, and CPs or for himself delivering internet to EUs. MNOs allocate resources (customized end-to-end network slices) to tenants (MVNOs, InCs, CPs) through an *auction model*. With the slices obtained from MNOs, the tenants deliver services to EUs also through an auction or fixed subscription price, depending on the service type. Since there is a relation between the money that tenants get from the EUs and the bid they submit to MNOs for the resources, we naturally end up with Stackelberg game [102], [185], [199], [223]. A practical situation is in France where Orange and Nokia deployed a 4G/5G private network with network slicing functionality at Schneider Electric's factory in Le Vaudreuil, offering a full portfolio of solutions: Mobile Private Networks (MPNs), virtual MPNs, and hybrid models, designed to meet industrial customers' needs regarding security, performance, and cost. Public sources do not specify explicit price structures, but given typical enterprise arrangements in private industrial connectivity. Another example where EUs as actual slice customers is Ludium Lab operating under a direct-to-consumer subscription-based on Sora Stream platform available on Android or via web browser on iOS and desktop, across multiple devices. EUs access to the full Sora Stream game library (more than 100 games) at €9.99/month using Deutsche Telekom 5G+ slice. All details are on Deutsche Telekom's site. In this environment, EUs are the ultimate consumers of all tenants' services; their demand behavior significantly influences the tenants' resource acquisition strategies from MNOs. The relationships (MNOs-Tenants and Tenants-EUs) are all hierarchical, where MNOs act as market leaders, setting resource prices and availability based on network slicing policies, while tenants operate as price takers, adjusting their service strategies according to both MNO conditions and EU demand. This leader-follower dynamic forms a multi-level market, where MNOs control the supply of essential infrastructure, tenants mediate service delivery, and EUs ultimately shape the ecosystem through their consumption choices.

Figure 13 illustrates the ecosystem of network slice allocation and monetization within a 5G network infrastructure. It features the five previously mentioned key entities: MNOs, InCs, CPs, MVNOs, and EUs. MNOs acquire radio and cloud resources with network slices via auction and supply these resources to MVNOs, InCs, and CPs for a fee (denoted by the € symbol). MVNOs then offer data and voice services directly to EUs using these slices, while CPs provide digital content, and InCs deliver customized services, both through auction or subscription fee. Each of these providers earns revenue from EUs.

## F. Satellite Connectivity Market

With the advent of 6G, the ecosystem becomes significantly more complex and dynamic, since we have more actors.
<figure><img src="image_14.png" /><figcaption>Fig. 13. Network slicing market</figcaption></figure>

The architecture becomes more open, programmable, and AI-
native, giving rise to a more distributed and dynamic economic
structure. NTNPs as operator gain more power thanks to their
role in *the satellite connectivity market*, complementing MNOs
by providing coverage in remote or high-mobility areas [83]
through satellite connectivity. NTNPs may provide residential
internet services or roaming to EUs through direct service
provisioning model with a physical asset like satellite. EUs
access connectivity via a retail subscription contract, where
they pay a monthly fee to NTNPs for the right to temporarily
consume satellite services. For example, Starlink, operated by
SpaceX, offers high-speed satellite internet in the EU through
its Starlink Residential or Roam plans. After a one-time satel-
lite reception equipment purchase, users can subscribe monthly
to either a 50 GB regional plan or an unlimited data plan for
roam services, with prices starting around €40/month. EUs
can also access residential services monthly from €29/MB
depending of their location. The roaming services work across
the country of registration and supports temporary roaming in
other EU countries for up to 2 months. Plans are flexible,
cancelable anytime, and ideal for travelers, nomads, or remote
users. More details can be found on Starlink’s official site.

NTNPs may develop partnership with MNOs, to jointly
manage spectrum band and Low Earth Orbit (LEO)/ Medium
Earth Orbit (MEO)/ Geostationary Earth Orbit (GEO) satellites
under *spectrum sharing model* [49], [111], [136] and *satellite
sharing model* to provide Direct-to-Cell (D2C) services to EUs
without owning satellite equipment. Note that customers are
handled by MNOs in this model. A relatable example of this
model is in USA where T-Mobile US contributes its mid-band
spectrum and handles the retail relationship with customers
and Space X provides LEO satellites equipped with a cellular
payload to offer direct to phone services to EUs at $15 per
month. More details can be found on T-Mobile’s official site.
There are no publicly available details or official documen-
tation describing payments between Starlink and T-Mobile.
Instead, the model appears to be revenue shared benefits. The

relationship between NTNPs and MNOs is coopetition since
their collaborate over spectrum and satellite assets for D2C
services but also compete over EUs for residential internet
services at all.

<figure><img src="image_15.png" /><figcaption>Fig. 14. Satellite connectivity market</figcaption></figure>

Figure 14 illustrates the relationship between NTNPs, MNOs, and EUs in the delivery of internet services in satellite connectivity market. NTNPs, enabled by satellite, provide residential connectivity, and D2C, directly to EUs through a direct-to-customer model with monthly subscription, competing head-to-head with MNOs, who also offer home internet services to the same customer base. This creates a competitive landscape where both NTNPs and MNOs seek to capture revenue streams from EUs. At the same time, the relationship is not purely adversarial: NTNPs and MNOs also establish partnership agreements through resource-sharing arrangements (spectrum, satellites), enabling them to complement each other’s strengths for delivering D2C services. As a result, the ecosystem is characterized by both competition and collaboration, with EUs ultimately benefiting from increased choice and potentially improved service coverage.

G. AI-based Market

6G networks also marks a shift toward ultra-intelligent,
hyper-connected systems where AI drives real-time decision-
making across routing, orchestration, resources allocation and
optimization. In the *AI-based market*, NAPs provide AI-ML-
based platforms and tools that automate critical tasks such as
network slicing, dynamic resource allocation, traffic routing,
predictive analytics & Quality of Experience (QoE), and
fault detection. NAPs serve through *on-demand pricing model*
MNOs, who rely on efficient network deployment and slice
allocation [35], [113] for MVNOs, InCs or CPs, or internet ser-
vices to EUs; NTNPs for optimizing Non Terrestrial Networks
(NTNs) [101], [152] to deliver satellite connectivity to EUs;
CCPs, who rely on cloud-based services [36] for InCs, and
CPs; InCs for managing private 6G and providing customized
services to EUs; and CPs for analyzing contents to provide
better QoS to EUs. All customers may pay NAPs, typically
under an annual subscription fee + a node-based licensing
where the pricing is per managed node. A node is a network
element that is automated by the platform, such as network slices, devices, cloud elements, physical infrastructure. For example, AT&T uses ONAP (Open Network Automation Platform) [212], backed by the Linux Foundation to deploy network services efficiently through network slices. All details here. Public sources don't specify explicit price structures.

<figure><img src="image_16.png" /><figcaption>Fig. 15. AI-based market</figcaption></figure>

Figure 15 illustrates how NAPs serve as central hubs delivering AI/ML-based platforms and tools to various entities, such as MNOs, CCPs, InCs, MVNOs, CPs, and NTNPs, through an on-demand pricing model, based on annual subscriptions and per-network element charges. The previously mentioned entities also use the network elements automated on the platforms to deliver optimized services to EUs, often monetized via subscription fees or per-resource usage pricing. The figure highlights the interdependencies across this ecosystem, with monetary flows (depicted by € symbols) reinforcing the service and value exchanges between stakeholders, ultimately converging on delivering optimized and flexible services to EUs.

## H. Trust and Security Market

Simultaneously, TSPs become essential in addressing the new security and trust challenges of software-defined architecture. Networks move from centralized to distributed, with multiple providers contributing to infrastructure, computing, and application layers, risks multiply: from unauthorized access to data leaks, identity spoofing, or slice-level breaches. TSPs serve CPs, and InCs several solutions like APIs, blockchain, smart contracts, that span Identity and Access Management (IAM), encryption, network isolation, and compliance assurance in the trust and security market through an on-demand model. In this model, CPs use trust services to deliver digital contents to EUs. At the same time, InCs who rely on IoT security, use trust services to deliver secured specialized services to EUs. CPs or InCs typically pay TSPs either via a monthly or yearly enterprise subscription to access e-signatures, seals, timestamps, encryption certificate, API calls

or per each service usage based. For example, Siemens adopted Microsoft 365 E5, a suite of advanced security and identity tools, as part of its Zero Trust strategy. This includes Azure AD, Defender for Endpoint, Conditional Access, and more. Siemens pays Microsoft through enterprise licensing fees for this security stack per Siemens's employees and per month. You will find all details here.

TSPs also may collaborate with MNOs through a resource sharing model to jointly manage APIs for security and network elements for delivering trust services to InCs, and CPs. TSPs and MNOs mutually benefit by sharing revenue from their collaboration internally. For example, Singtel (MNO) with “Security-as-a-Slice” (SecaaS) launched in collaboration with Palo Alto (TSP), offering ML-powered Next-Generation Firewalls (NGFWs) [162] embedded in network slices. All details here. There are no publicly available details or official documentation describing payments between Singtel and Palo Alto. Instead, the model appears to be based on revenue shared benefits.

<figure><img src="image_17.png" /><figcaption>Fig. 16. Trust and Security market</figcaption></figure>

Figure 16 summarizes the role of TSPs in the digital ecosystem, delivering security and trust services to CPs, InCs, through on-demand pricing model, based on fixed subscription fee or per trust services used, and indirectly to EUs. CPs and InCs offer secured digital content and specialized services through fixed retail subscription fee. Additionally, TSPs collaborate with MNOs via resource sharing involving, APIs for security, network elements, and share their revenue internally. Monetary flows (€) highlight how trust services enable secure, value-added offerings across the ecosystem.

Modeling the multi-actor interactions in 5G and emerging 6G ecosystems requires going beyond static resource allocation frameworks, adopting dynamic and strategic formulations.

In summary, the economic organization of 5G networks revolves around the MNO and its structured, though increasingly flexible, interactions with known entities. With 6G, the ecosystem becomes significantly more complex, introducing new actors, relationships, and forms of control, leading to
new markets build over the existing ones. Future research must not only track how resources are traded and priced but also how decision rights are distributed, how roles evolve dynamically, and how economic and regulatory mechanisms can adapt to ensure efficiency, innovation, and fairness in this rapidly shifting landscape. In the next section, we will analyze the different rules which impact on the evolution of BMs.

## V. REGULATORY IMPLICATIONS OF 5G AND 6G

The ability of 5G and 6G networks to realize their ambitious potential in transforming our society largely depends on the regulatory rules implemented. These rules operate at various levels—national, regional, and international [145], including spectrum regulations; pricing regulations; security, privacy and AI regulations; spectrum regulations; innovation regulations; roaming regulations; and net neutrality regulations. The goal of regulation is (except for authoritarian states) to establish rules towards a network design beneficial for society, as fair as possible for all actors. But, it is important for them to be simultaneously profitable for all actors and encouraging competition and innovation. We give more details on the list of possible regulations.

* **Spectrum Regulation:** Spectrum regulation is designed to ensure the efficient, and fair use of the radio-frequency spectrum without interference. At the international level, the ITU provides the rights and obligations in spectrum use through the Radio Regulations (RRs), which are revised at the World Radiocommunication Conferences (WRCs), to optimize radio spectrum management. Regional bodies, like the European Conference of Postal and Telecommunications Administrations (CEPT) harmonize allocations and technical standards to facilitate interoperability, while at national level, regulatory authorities, like ARCEP establish frequency allocation tables, assign licenses through auctions, monitor spectrum use, and enforce compliance to prevent harmful interference. Spectrum regulation is essential for enabling 5G, which relies on a mix of licensed, unlicensed, and shared bands to support diverse services like eMBB, URLLC, and mMTC [137]. For example, in the US, the Federal Communications Commission (FCC) approved Citizens Broadband Radio Service (CBRS), for licensed and unlicensed spectrum access [4], while the European Union adopted Licensed Shared Access (LSA) allowing to share spectrum between MNOs and non-MNOs such as MVNOs, InCs or CPs [139]. At international level, 5G New Radio (NR) has been extended to unlicensed bands as NR-Unlicensed (NR-U) [184]. 5G spectrum policies in general improve utilization but raise concerns around coordination and fairness. 6G will amplify these issues by introducing THz bands, NTNs, and AI-driven spectrum decisions [84], [103]. These trends challenge existing regulatory paradigms and call for forward-looking policies that allow the development of new licensed spectrum for Thz bands and NTNs, and also open the availability of more spectrum offered without licenses to fuel open innovation for non-traditional actors.

* **Pricing Regulation:** Pricing regulations in mobile networks aim to ensure services remain affordable without distorting competition. In the European Union, regulators actively cap retail and wholesale prices for services such as roaming (e.g., Roam Like at Home (RLAH) in 2017 [41], which abolishes retail surcharges for intra European Union roaming and imposes wholesale pricing limits to prevent market failures. All details here) and spectrum access (e.g., Wholesale Access Regulation to MVNOs [88]) to protect consumers and promote fair competition. In the United States, pricing is largely market-driven, with regulators intervening only to prevent anti-competitive practices, allowing operators greater flexibility in setting wholesale and retail charges. In 5G, high deployment and spectrum costs challenge regulators to prevent excessive pricing while avoiding underpricing that could harm market sustainability [96]. 5G also introduces new pricing challenges through network slicing, shared infrastructure, and diverse QoS levels [7], [93]. With 6G, the rise of terahertz spectrum, AI-native networks, and non-terrestrial platforms will require real-time, dynamic pricing frameworks that reflect usage, spectrum value, and service differentiation. To address these challenges, regulators may adopt mechanisms such as cost-based pricing models [198] that prevent exaggerated profits, spectrum auction rules with long-term investment incentives [158] that allow MNOs to not only pay for spectrum access but also commit to service obligations [157], transparency for slice allocation [91] that helps avoid hidden favoritism or price manipulation that could deform competition.

* **Net Neutrality (NN):** NN mandates equal treatment of all internet traffic [61], [112], [132], [166], MNOs should provide equal and unrestricted internet access, ensuring no alterations based on content, user, platform, application, or devices. For example, MNOs can not slow down your connection to video streaming applications such as Amazon Prime video or speed up a connection to its own favored streaming sites like Netflix or Disney+. Most regulators recognize NN as a guiding principle, its implementation varies across jurisdictions. In the European Union, the Body of European Regulators for Electronic Communications (BEREC) enforces strict rules preventing blocking, throttling, or paid prioritization, whereas in the United States the FCC has shifted between stricter and lighter approaches depending on the regulatory cycle, but in early 2025, a federal court invalidated the FCC's NN rules previously restored in 2024. All details here. 5G challenges NN with technologies like network slicing and QoS differentiation, which enable tailored services for industries [94], [95], [216]. These capabilities risk creating “fast lanes” that could undermine neutrality principles if not transparently regulated. 6G will intensify these issues with AI-based traffic management, semantic-aware networking, and non-terrestrial systems that obscure transparency and accountability [9], [32]. A recent work by some co-authors [95] proposes also an initial step toward conciling 5G network slicing capabilities with net neutrality principles. Their framework suggests
classifying traffic flows into categories based on QoE requirements, assigning slices accordingly, and ensuring neutrality within each slice while allowing objective differentiation across slices. This “class-based neutrality” approach seeks to balance fairness, user experience, and operator flexibility, provided it is accompanied by strong transparency and regulatory oversight.

*   **Roaming Regulation:** The Roaming regulation defines the rules applied by MNOs for charging their customers when they use their mobile devices in visiting country. At the international level, roaming depends on spectrum harmonization coordinated by the ITU, which facilitates device interoperability and cross-border frequency use. The European Union has pioneered strong roaming regulation through the “RLAH” policy. By contrast, in the United States, roaming agreements are left largely to commercial negotiation, subject only to competition and consumer protection oversight. All details here. 5G complicates regulation with the introduction of ultra-low latency services, which require accurate service mapping and quality guarantees across borders [122]. With 6G, roaming regulation must address even more complexity, like dynamic user-service associations across countries and platforms [44], [205]. This increased complexity will require regulators to promote federated learning [165], [220] that enables MNOs to share their anonymized performance insights, without sharing their customers sensitive data, and define SLAs for latency-sensitive applications across NTNs. These approaches seek to dissolve interoperability barriers, balance user experiences, and avoid conflicts between MNOs.

*   **Innovation Regulation:** Innovation Regulation in telecommunications reflects different regulatory philosophies across jurisdictions, balancing the need to foster new technologies with safeguards for competition, security, and equitable access. Ensuring open innovation while preventing market concentration and vendor lock-in has become a key concern for bodies like the FCC and BEREC [26], [163]. In the European Union, regulators often adopt proactive and principle-based frameworks [39] that encourage innovation and experimentation rather than prescribing only rigid compliance rules. One example is the use of regulatory sandboxes [20], which allow MNOs and InCs to test 5G-enabled applications like Industry 4.0. Another illustration is the European Union's promotion of local and private 5G networks for InCs. However, we distinguish two forms of innovation practices [144]: *modular innovation* as an improvement at the component level, and *architectural innovation* as fundamental reconfigurations of how networks are designed and operated. With 6G, which is expected to integrate sensing, semantic communications, and fully autonomous networks, raising issues of transparency, interoperability, and ethics in AI-driven infrastructures [173], regulators must both support modular and architectural innovation, by allowing companies to deploy new components, like Advanced Radios (ARs) [22], AI schedulers [204], edge computing modules [100], [221], without regulatory hurdles,

but also several architectures for 6G, like Open RAN (O-RAN) [153], service-based 6G core [51], [53], to keep network robust against threats.

*   **Security, Privacy and AI Regulation:** Security, privacy, and AI regulation is central concerns in 5G networks due to massive data flows, real-time analytics, and decentralized architectures like edge computing. Existing frameworks such as the European Union's General Data Protection Regulation (GDPR) for safeguarding personal data and ensuring privacy in digital communications, and applied broadly across all sectors [127], the U.S. California Consumer Privacy Act (CCPA), which like GDPR grants EU's rights over their personal data, including the ability to access, delete, and opt out of the sale of their information, and the Act on the Protection of Personal Information in Japan [144]. However, 5G introduces complex scenarios involving dynamic data sharing, IoT integration, and AI-driven decision-making that strain current legal protections [121], [186], [190]. AI systems used in 6G for traffic management, anomaly detection, and service personalization raise concerns about algorithmic bias, data misuse, and explainability [43]. Also, in 6G, intelligent automation, distributed ledger technologies, and ubiquitous sensing further obscure control over personal data and accountability [78], [149], [215]. Future regulations must address cross-border data flows, enforceable AI ethics, real-time compliance, and resilience against evolving cyber threats while fostering innovation and trust [176], leading to global interoperability, high credibility with EU's, and infrastructure vulnerabilities reduction.

In the previous sections, we explored the foundational technological aspects of 5G and 6G networks, and make a particular attention on the economic dimensions that influence their development, deployment, and global accessibility. In the next section, we will examine the critical economic questions that must be resolved for 5G and 6G networks to reach their full potential.

## VI. ECONOMIC QUESTIONS NEEDING TO BE ADDRESSED

The evolution from 5G to 6G networks announces large-scale, highly complex deployments that pose pressing economic research questions demanding rigorous investigation.

**Stochastic Modeling of Tokenized Ownership.** In the previous sections, we looked at the basic BMs for network resources. While these models are useful, all network assets come with some level of market risk: prices can go up or down, and returns aren't guaranteed. With blockchain, it's now possible to represent network assets like spectrum slices, physical infrastructures, or service rights as *tokenized network resources* [110], [206]. These resources can be split into smaller units, traded, or leased in digital marketplaces [14], which makes them easier to buy and sell and lowers the barriers for new investors to participate. However, tokenization also exposes investors to volatility and market uncertainty, since token values can rise or fall with changes in demand, regulations, or technology. Modeling these concerns through stochastic processes, such as geometric Brownian motion
[168], [196] can capture continuous price variability driven by market demand, while jump-diffusion models [138], [195] can capture sudden shocks, like new regulation rules, or unexpected large-scale outages. These models help estimating risks and guiding investment strategies.

**Network Slicing and NN.** While network slicing enables differentiated services, it must coexist with NN principles, both paradigms being a priori in contradiction. On the one hand, when slicing allows operators to dedicate resources to specific use cases, NN requires that similar traffic flows be treated equally. A possible reconciliation lies in a class-based slicing model which can be developed using queuing theory [2] and incentive-compatible mechanisms [211] to serve slices: the principle could be to organize applications in classes with similar (pre-established) quality requirements and service those classes in different slices. For example, low-latency slices could serve interactive applications such as cloud gaming or augmented reality, high-throughput slices could accommodate video streaming, and highly reliable but low-bandwidth slices could support IoT monitoring or emergency communications. It would only take into account those requirements for pertaining to a slice, similar flows being treated equally. This should allow to end up with a higher satisfaction from users (in terms of QoE) and providers (in terms of business opportunities), fitting also the objective of fairness of regulators [95]. This way, research can help reconcile user QoS demands with policy constraints and business profitability.

**D2C Models in Terrestrial-Non-Terrestrial Cooperation.** The integration of terrestrial networks (RAN) with non-terrestrial networks (satellites, HAPS), introduces complex challenges in coordination and resource sharing (spectrum and satellites), but also in terms of cooperation between MNOs and NTNPs. To address these issues, cooperative game-theoretic approaches [81] (e.g., Shapley value [29] or Nash bargaining [80] models) and optimization approaches [16], [50] (deterministic, stochastic, multi-objective) can be employed to design fair revenue-sharing mechanisms, efficient cost-allocation strategies among all stakeholders, and effective resources allocation. For example, cooperative models can help decide how MNOs and NTNPs split spectrum access fees in joint deployments, or how satellite backhaul [98], [213] capacity should be adjusted in real time when terrestrial networks become congested. These models can ensure profitability and service continuity across heterogeneous infrastructures, especially in scenarios where terrestrial coverage is insufficient (e.g., rural, maritime, or disaster recovery contexts).

**BMs for 6G Private Networks.** Private 6G networks are expected to play a central role in vertical industries like healthcare and manufacturing, logistics and smart campuses, enabling highly customized services and strict performance guarantees. Research can assess viable BMs by studying revenue streams (service subscriptions, data-driven services, or premium low-latency applications), cost structures (spectrum licensing fees, infrastructure deployment, maintenance, and energy consumption), and sustainability trade-offs (carbon footprint of dense deployments, renewable energy sources use). For instance, in manufacturing, private 6G networks could support digital twin operations [194] and collaborative

robotics [143], while in logistics, they could enable real-time fleet tracking [59] and dynamic route optimization [159]. The goal is to ensure tailored strategies that maximize revenues, allow regulatory and environmental alignment, but also that 6G private networks are economically viable.

**Analyze the impact of new actors.** We have highlighted new actors in the various network-based markets. A challenge is to study and model the relation with established actors. A simple but typical example is the impact of tower companies: by providing passive infrastructures (towers, sites, energy supply), they reduce entry barriers for new service providers and can shift the balance of power away from MNOs. The presence of these new players can influence the output of the economic game, in particular when resource sharing (e.g., co-location models, spectrum pooling [92], [208], or neutral host models [23], [130]) is allowed. This raises important research questions: Should it be enforced by regulation, and if so, up to which level? How can mechanisms be designed to fairly allocate resources, prevent anti-competitive behavior, and at the same time encourage innovation? To understand the impact of these new players, we need models that show how cooperation and competition interact, while also considering economic efficiency and policy goals.

**Strategies to optimize network management in terms of energy and encourage good practices.** With the increasing awareness of the need to reduce carbon emission, there is a strong interest in modeling and analyzing novel techniques that adapt the energy consumption of next generation networks to the increasing share of non-controllable energy (such as wind and solar) in electricity production. It can be done thanks to adaptation by QoS modulation [140] (e.g., adjusting service levels during periods of energy scarcity), by dynamic price incentives [15] (encouraging operators to align their consumption with green energy availability) and/or the introduction of regulation procedures in all markets described in the previous section. For example, MNOs could schedule non-critical tasks [201] (such as software updates or data backups) during periods of abundant renewable generation, or temporarily reduce bandwidth for delay-tolerant applications [154] during supply shortages. At a broader level, market mechanisms could reward actors that demonstrate energy-efficient practices [172], [193] or penalize excessive carbon use. Such approaches not only contribute to climate objectives but also create opportunities for cost savings and new BMs centered around sustainability.

**Customized Security in Network Slicing.** SecaaS can be built from network slicing by letting MNOs work hand in hand with TSPs. Instead of applying the same protection everywhere, policies can define the right level of security for each slice. For instance by differentiating the service of (stronger) safeguards for healthcare data or financial services from (lighter) protection for entertainment services. Economic models [175] can make sure both MNOs and TSPs benefit when higher levels of security are offered. For example, premium slices with advanced security could be priced higher, with a share of the additional revenue going to the TSPs and the rest to the MNOs. These models make sure everyone benefits: MNOs can offer trusted, differentiated services [42], [147], and TSPs get rewarded for delivering stronger
protections. In this way, security is no longer just about ticking a compliance box, but becoming a real value-added service [118], [160] that builds user trust, creates new business opportunities, and makes next-generation networks more resilient and profitable.

**Platform-centric BMs for 6G.** In the 6G era, BMs centered around platforms are likely to become much more important, as networks move beyond simply providing connectivity to orchestrating entire ecosystems. Rather than just delivering access, platforms can create multi-sided markets [76], [178] where MNOs, NAPs, CPs, NTNPs, InCs, CCPs, INEPs and EUs all interact. The platform acts as a hub, offering not just connectivity, but also services like data analytics, edge computing, and AI-driven orchestration. When studying platform-centric BMs [72], it is important to understand how value is created and shared. At the same time, questions of governance, fairness, and regulation come into play: who controls the platform, how are profits divided, and how can monopolistic behavior [214] be avoided? These platforms benefit from network effects: the more participants join, the more valuable the ecosystem becomes, but they also need careful design to maintain interoperability, trust, and long-term sustainability.

## VII. CONCLUSION

This paper has analyzed the evolving economic dynamics of 5G and 6G networks, focusing on the service classes, enabling technologies, and the shifting roles of key actors across technical and market layers. We showed that 6G is not merely a technological enhancement but introduces a structural transformation in how digital infrastructures are monetized, accessed, and governed. Unlike in 5G, where MNOs remained central, 6G brings increased participation of NTNPs, NAPs, and TSPs, creating a more disaggregated and multi-tenant economy. This shift challenges existing pricing, investment, and coordination models. New economic paradigms emerge through decentralized service provisioning, programmable infrastructure, and intelligent agents mediating operational and economic decisions in real-time. These developments raise critical questions regarding pricing of programmable resources, platform competition, governance of AI-enabled decision-making, and secure, transparent inter-operator coordination. Regulatory frameworks must evolve to address not only traditional issues like fair competition, but also novel challenges such as AI accountability, spectrum/compute resource sharing, and market concentration in orchestration platforms. Our analysis underlines the need for future research in several directions: (i) designing distributed, incentive-compatible mechanisms for resource allocation; (ii) understanding the economic implications of AI-agent delegation in network operations; (iii) modeling decentralized economic coordination through tokenization of network resources; and (iv) examining how sustainability goals reshape cost structures and pricing strategies in 6G infrastructure. Addressing these challenges will be essential to realizing 6G's potential as a trustworthy, inclusive, and economically viable digital ecosystem.

## ACKNOWLEDGMENT

The authors acknowledge the support from France 2030 program, operated by the Agence Nationale de la Recherche (ANR), under the grant ANR-22-PEFT-0002.

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