In multi-domain multi-technology network slicing (NS), different service providers within the same and/or different administrative domains are involved in meeting a service level agreement (SLA). In fact, the SLA requirements must be dynamically decomposed into portions that each domain can support based on their network condition. This paper presents a framework to design federated multi-domain NS in which providers collaborate to set policies for SLA decomposition, task offloading, routing, and resource sharing across domains. First, a centralized scheme is proposed as a benchmark where a cross-domain orchestrator decomposes the SLA and performs multi-path routing and resource allocation in negotiation with the domain controllers. Then, we adopt decomposition theory to delegate decomposing the SLA to each domain controller and create multi-domain network slices in a fully distributed manner. Through consistency pricing, we enforce fair collaboration between domains to share resources and perform task-split offloading and multi-path multi-domain routing. We evaluate our approach using typical sixth-generation (6G) real-world examples. Our results show that our approach achieves two to four times higher utility compared to schemes without collaboration and/or SLA decomposition.

Ensuring covert and secure communication remains a challenge in the evolving landscape of wireless communications. This paper presents a covert cognitive mobile edge computing network (CCMEC) in which secondary nodes (Alice) aim to transmit securely and offload computing tasks to secondary edge computing nodes (Bob) in the presence of multiple primary wardens (Willie). A two-stage connectivity probing and activation scheme is developed to maximize the data transmitted under covertness and minimize the energy consumption within a latency bound. The proposed scheme combines a distributed probing phase to find secure available connections based on the activity of primary and secondary nodes and a centralized activation phase to jointly optimize power allocation, channel, and Bob selection. The problem is solved by a Restless Multi-Armed Bandit (RMAB) framework with Whittle index optimization. Simulation results show the effectiveness of our approach in achieving covert communication compared to existing solutions.

Heterogeneous Internet of Things (IoT) networks enabled by Unmanned Aerial Vehicles (UAVs) operate in various protocols and spectrum bands (e.g., WiFi, LoRa, Zigbee) to collect and offload data generated from heterogeneous sensors. However, achieving timely and secure communications is challenging due to uncertain data generation, mobility, and location of wardens. This paper presents a collaborative framework that exploits cross-technology communications to achieve covertness constraints. The aim is to minimize the age of covert information (AoCI) and energy consumption by jointly optimizing data scheduling, power allocation, and offloading decisions by collaborating with UAVs. A multi-agent actor-critic algorithm that incorporates federated learning and attention mechanisms (cluster-MAAC-attention) is presented to solve the previous problem. Our simulation results show that our algorithm reduces the worst AoCI by 4 times, and reduces the penalty by 20 times compared to existing schemes.

Dynamic network architectures that utilize communication, computing, and storage resources at the wireless edge are key to delivering emerging services in next-generation networks (e.g., AR/VR, 3D video, intelligent cars, etc). Network slicing can be significantly enhanced by including dynamically available resources throughout the fog/edge/cloud continuum and using mmWave/THz bands. However, network slicing of dynamic multi-tier computing networks remains under-explored. In this paper, we present a self-learning end-to-end network slicing mechanism (SELF-E2E-NS) that facilitates collaboration between the Infrastructure Provider (InP) and tenants to slice their subscribers’ resources (i.e., radio, computing, and storage) as fog resources. To adapt to the uncertain availability of resources at the edge and minimize the risk of non-satisfying service level agreements (SLAs), our slicing mechanism has two operational modes. Operational mode 1 is for joint network slicing (JNS) in which the InP infrastructure is augmented with fog resources and jointly sliced to meet high throughput and delay tolerant requirements. Operational mode 2 is for independent network slicing (INS) in which the InP infrastructure and fog resources are sliced separately to achieve high throughput, low-latency, and high-reliability requirements. Our schemes leverage mmWave/THz, fog/edge/cloud computing, and caching to achieve new service requirements. We design a DQ-E2E-JNS algorithm that uses Deep Dueling network and a MAAC-E2E-INS algorithm based on multi-agent actor-critic, which incorporate service-aware pricing feedback and fog trading matching, respectively. These algorithms find the optimal slice request admission and collaboration policy that maximizes the long-term revenue of the InP and tenants for each mode. The simulation results show that our novel slicing mechanism can serve up to 4 times more requests and effectively exploits different spectrum bands and fog resources to improve revenue and performance.

Heterogeneous Internet of Things (IoT) networks, which operate using various protocols and spectrum bands like WiFi, Bluetooth, Zigbee, and LoRa, bring many opportunities to collaborate and achieve timely data collection. However, several challenges must be addressed due to heterogeneous data patterns, coverage, spectrum bands, and mobility. This paper introduces a cross-technology IoT network architecture design that facilitates collaboration between service providers (SPs) to share their spectrum bands and offload computing tasks from heterogeneous IoT devices using multi-protocol mobile gateways (M-MGs). The objective is to minimize the age of information (AoI) and energy consumption by jointly optimizing collaboration between M-MGs and SPs for bandwidth allocation, relaying, and cross-technology data scheduling. A pricing mechanism is presented to incentivize different levels of collaboration and matching between M-MGs and SPs. Given the uncertainty due to mobility and task requests, we design a cross-technology federated matching algorithm (CT-Fed-Match) based on a multi-agent actor-critic approach in which M-MGs and SPs learn their strategies in a distributed manner. Furthermore, we incorporate federated learning to enhance the convergence of the learning process. The numerical results demonstrate that our CT-Fed-Match-RC algorithm with cross-technology and relaying collaboration reduces the AoI by 30 times and collects 8 times more packets than existing approaches.

In this paper, we present a multi-domain network slicing scheme for satellite-terrestrial edge computing networks (STECNs) that admits different slice configurations. Each slice is configured to include terrestrial-air, terrestrial-satellite, terrestrial-air-satellite, or terrestrial-air-satellite-gateway domain topologies. However, the multi-domain nature of STECNs makes slicing especially challenging since the cross-domain orchestrator has no knowledge of the resource availability in different domains. Our goal is to design an algorithm that builds a belief in resource availability to jointly optimize the slice configuration, service level agreement (SLA) decomposition, routing, and resource allocation. We model the slice/resource availability as a Markov process to track the probability of achieving the SLA per configuration. To solve the multi-domain slicing problem, the cross-domain orchestrator interacts with the configuration coordinator to define an index-based slice configuration policy based on restless multi-armed bandits (RMABs), which is aware of the network traffic. The configuration coordinator decomposes the SLA and each domain controller solves the optimum routing and resource allocation. Our slicing scheme is evaluated using five typical application scenarios for STECNs. Simulation results show that our scheme achieves six times higher reward than agnostic schemes and efficiently performs multi-domain slicing with low complexity.

Satellite-Airborne-Terrestrial Networks (SATNs) are expected to provide communication and edge-computing services for a plethora of IoT applications. However, preserving the freshness of information is challenging since it requires timely data collection, bandwidth, and offloading decisions across different administrative domains. In this paper, we aim to optimize the age of information (AoI) and energy consumption tradeoff when serving multiple traffic classes in SATNs. A cross-domain federated computation offloading algorithm (Fed-SATEC-Off) is presented in which different service providers (SPs) collaborate to allocate the bandwidth while unmanned aerial vehicles (UAVs) and satellites make decisions to collect, relay, and offload the computing tasks. Given the requirements of each traffic class, the optimum collaborative strategies between SPs, UAVs, and satellites are obtained together with the computation offloading topology. Our algorithm is based on multi-agent actor-critic and incorporates federated learning to improve the convergence of the learning process. The numerical results show that Fed-SATEC-Off reduces the AoI by factor 4 and achieves faster convergence than existing approaches.

Satellite–terrestrial edge computing networks (STECNs) emerged as a global solution to support multiple Internet of Things (IoT) applications in 6G networks. The enabling technologies to slice STECNs, such as software-defined networking (SDN), satellite edge computing (EC), and network function virtualization (NFV) are key to realizing this vision. In this article, we survey and analyze network slicing (NS) solutions for STECNs. We discuss slice management and orchestration for different STECNs integration architectures, satellite EC, mmWave/THz, and artificial intelligence solutions to make NS adaptive. In addition, we identify challenges and open issues to slice STECNs. In particular, resilient NS is crucial for essential and critical services. Network failures are unavoidable in large networks and can cause significant disruptions in NS, compromising many services. To this end, we present a resilient NS design to cope with failures and guarantee service continuity which is agnostic to the integration architecture and inherently multidomain. Further, we present strategies to achieve resilient networking and slicing in STECNs, including planning and provisioning of redundant network resources, design rules for service level agreement decomposition, and cross-domain solutions to detect and mitigate failures. Finally, promising future research directions are highlighted. This article provides valuable guidelines for slicing STECNs and will benefit key sectors, such as smart healthcare, e-commerce, Industrial IoT, education, and among others.

Enabling timely data collection in heterogeneous IoT networks under different protocols and spectrum bands (e.g., WiFi, Bluetooth, Zigbee, LoR$a$) is crucial to implementing large-scale IoT systems. This paper presents a federated matching framework for heterogeneous IoT networks in which an intermediate layer of multi-protocol mobile gateways (M-MGs) is deployed by different service providers (SPs) to collect and relay data from IoT objects and perform computing tasks. The aim is to develop collaborative strategies between M-MGs and SPs to minimize the average weighted sum of the age-of-information and energy consumption. A novel collaborative framework based on a 2-level multi-protocol multi-agent actor-critic (MP-MAAC) is presented, where M-MGs and SPs can learn the interactive strategies through their own observations. The M-MGs strategies include the selection of IoT objects for data collection, execution, and offloading t o S Ps' a ccess points while SPs decide on the spectrum allocation. Moreover, we incorporate federated matching (Fed-Match) into the multi-agent collaborative framework to improve the convergence of the learning process. The numerical results show that our Fed-Match algorithm reduces the Aol by factor 4, collects twice more packets than existing approaches and establishes design principles for the stability of the training process.

Next-generation networks will provide intelligent infrastructure and management using machine learning. In real-world applications, demand for resources and performance within a service class may vary over time. Infrastructure providers choose which requests to accept with the goal of long-term profit maximization – a process known as slice admission control. In this paper, we envision a dynamic system with varying service requests attributes in urgency, duration, and amount of resources (i.e., computing, network, and storage). Further, we develop a dynamic pricing model that is responsive to demand and supply resulting in demand and supply reserve shaping. Then, we propose a solution to the slice admission control problem by using a reinforcement learning approach with Deep-Q Networks, where the state of the system is modeled using an array of parameters, similar to the input matrix in computer vision. Results show that our computer vision-inspired approach is capable of learning how the better policy to navigate this complex environment by selecting service requests that maximize the provider’s long-term profit.

To accommodate increasingly diverse traffic demands in future 6G wireless networks, intelligent and dynamic network slicing schemes are needed to exploit available edge/fog resources (i.e., radio, computing, storage resources). In this paper, we present a new dynamic edge/fog network slicing scheme (EFNS) in which tenants can temporarily lease back to the Infrastructure Provider (InP) unused resources to serve demands exceeding its current resources in stock. To efficiently use the resources, tenants can also lease their subscribers' terminals when idle to act as fog nodes augmenting the infrastructure and service capability of the InP. Our goal is to find an optimal slice request admission policy, which includes slices with augmented resources, to maximize the long-term revenue of the InP. A semi-Markov decision process is used to model the arrival of slice requests by taking into account the dynamics of users' demands and availability of resources. To find the optimal policy under uncertain resource demands, a Q-learning (Q-EFNS) algorithm is developed. Additionally, to improve the convergence time and reduce the computational complexity of Q-learning in large-scale scenarios, a Deep reinforcement learning (DQ-EFNS) algorithm and an enhancement based on a Deep Dueling (Dueling DQ-EFNS) algorithm are presented. Finally, simulation results show improvements in the range between 20% to 60% by our algorithms compared to conventional fixed network slicing when only 8% of the subscribers act as fog nodes. Besides, by using Dueling DQ-EFNS, the optimal slice request admission policy is obtained in just a few iterations.

Cloud radio access network (C‐RAN) has been introduced as a promising network paradigm for improving the spectral and energy efficiency of next‐generation mobile systems. In C‐RAN, the computation resources of the centralised baseband units (BBUs) can be virtualised and dynamically shared among cells for energy‐efficient BBU pool utilisation. In this study, a BBU virtualisation scheme is proposed to minimise the total power consumption in the BBU pool subject to constraints on users’ quality of service in terms of real‐time requirements, individual fronthaul capacity and BBU capacity. As the BBU processing time and transmission delay for each user data can be compromised to meet the user's real‐time requirements while minimising the BBU power consumption, a priori user association phase is proposed and formulated as an optimisation problem to maximise the users’ transmission rate, and hence minimising their transmission delay. Then, the BBU processing allocation phase is formulated as a bin‐packing problem to minimise the overall power consumption in the BBU pool. Since this problem is combinatorial, a heuristic algorithm is proposed based on best‐fit‐decreasing algorithm to solve it. Extensive simulations show that the proposed scheme outperforms the comparable ones in terms of power consumption with reduction up to 33%.

Device‐to‐device (D2D) communication underlaying cellular networks can improve the spectrum efficiency as a result of sharing the radio resources allocated to cellular user equipments (CUEs). However, the severe interference between D2D and CUEs communications may lead to performance degradation of cellular system if not coordinated properly. In this study, a joint channel assignment and power allocation algorithm is proposed, which addresses the intra‐cell and inter‐cell interference management problems for D2D communication underlaying cellular network. The case of multiple D2D‐user equipments (DUEs) sharing the same channel while each DUE can reuse multiple channels is considered. The proposed algorithm is designed with two complementary steps in such a way that its computational complexity can be adapted according to the network condition. The preliminary set of CUEs candidate channels that can be reused by each DUE is adaptively decided in the first step. In the second step, the optimal power allocation for each DUE is determined using Lagrangian dual decomposition to maximise the network sum‐rate. Simulation results show that the proposed algorithm outperforms the current comparable algorithms especially in terms of achievable throughput. Moreover, the effect of various system parameters on the performance of the proposed technique is also investigated.