Low Earth Orbit (LEO) satellite networks are characterized by dynamic network topologies and on-demand service requirements from Internet-of-Things (IoT) applications, which make efficient and intelligent flow scheduling challenging. Conventional schemes rely on static configurations or manual rules, thus making it difficult to capture and respond to diverse service demands. Moreover, they often fail to model task–resource relationships effectively, hindering the generation of real-time, executable scheduling policies. To address these challenges, we propose a knowledge-enhanced, intent-driven flow scheduling (KIFS) framework. Specifically, we design a unified pipeline that first translates user intents into precise Quality of Service (QoS) requirements. It incorporates a network state awareness module to estimate per-link bandwidth and utilization, and constructs a task–resource knowledge graph (KG) to enhance the Deep Q-Network (DQN) agent via state augmentation, action pruning, and reward shaping. Finally, the framework translates the resulting policies into standards-compliant SRv6 configurations for real-time deployment. In simulations, the proposed KIFS framework demonstrates superior performance compared to standard baselines in terms of flow success rate and QoS satisfaction.

To overcome the challenges of dynamic topology and intermittent connectivity in large-scale Low Earth Orbit (LEO) satellite networks, we propose an Intent-Driven routing control framework leveraging Segment Routing over Internet Protocol version 6 (SRv6). The proposed framework enables fine-grained control over routing behaviors while maintaining protocol compatibility through packet-level semantic intent identifiers. The novel contributions include: (1) an Intent-Driven Segment Routing framework supporting diverse routing requirements with packet-level control; (2) an adaptive Polar Region Link Handling Mechanism integrated with dynamic load balancing; and (3) a multi-path-based fault recovery mechanism with rapid convergence characteristics. Experiments on Network Simulator 3 (NS3) platform demonstrate that, compared to traditional routing protocols, the proposed framework reduces end-to-end delay by 64.1% for the Iridium constellation scenario and improves stability by 68.8% in the OneWeb constellation environment.

As the number of satellites and satellite tasks continues to increase, multi-satellite collaboration task planning faces several challenges, including high complexity, high timeliness and limited scalability. This paper presents a method for task planning based on behavior tree, which leverages modularity and hierarchical structure of behavior tree to simplify planning process and enhance timeliness. We also propose an intelligent planning algorithm combining variable neighborhood search (VNS) algorithm with backtracking search algorithm (BSA) to reduce solution space and improve convergence speed. Experimental results show that this method improves satellite task planning timeliness by 16.7%, overcomes the flexibility and timeliness disadvantages of traditional methods in complex environments, and highlights the significant advantages and potential applications of behavior tree in multi-satellite collaboration field.

Intent-Driven Networking (IDN) enables users to express high-level intents in natural language, which are then automatically refined into executable network configurations. Intent refinement plays a critical role in accurately refining human-declarative intents into network-level intents, and further refining into machine-readable policies. To address the limitations of existing intent refinement methods of lacking generalization and weak context awareness, this paper proposes a knowledge-enhanced Large Language Model (LLM) for intent refinement mechanism. The proposed approach achieves accurate and controllable refinement from natural language to structured network intents. Simulation results demonstrate the effectiveness and robustness of the proposed mechanism, particularly in complex and layered intent expression in flying ad hoc networking.

With the advent of the sixth-generation (6G) era, the scale and complexity of communication networks have expanded dramatically, making conventional manual network management methods inefficient and error-prone. Intent-driven network (IDN) enables network operators to express high-level intents using natural language, which are then automatically translated into executable network configurations. However, the unstructured and ambiguous nature of intents poses challenges to achieving accurate intent-to-configuration translation. The emergence of a large language model (LLM) offers a promising solution to this problem. This paper proposes an LLM-empowered framework for generating IDN configurations, which integrates fine-tuning, retrieval-augmented generation, prompt engineering, and knowledge distillation techniques. We validate the effectiveness of the proposed framework through a network slicing use case implemented using open-source tools. Experimental results demonstrate that the proposed framework improves configuration generation time and accuracy by 24% and 25%, respectively, compared to the baseline schemes.

With emerging differentiated network services, intent-driven management and orchestration in the sixth-generation networks face challenges in on-demand and timely network configuration. Conventional intent-driven network approaches rely on structured templates for specific scenarios. This paper develops a large language model (LLM)-enhanced intent-driven management and orchestration framework that automatically refines user intent into abstract network policies. To improve the generality and accuracy of intent refinement, we design a novel intent decomposition mechanism based on a fine-tuned generic LLM, along with intent decomposition prompts. Moreover, we introduce an intent optimization method, leveraging a lightweight proximal policy optimization framework to model distributed energy-saving policies and select the optimal policy. Simulation results show that the proposed framework outperforms baseline schemes by 16% to 31% in terms of intent decomposition accuracy and intent optimization performance.

Future mobile networks should provide on-demand services for various industries and applications with the stringent guarantees of Quality of Experience (QoE), which highly challenge the flexibility of network management. However, the diverse requirements of QoE and the management of heterogeneous networks create significant pressure toward communication service providers (CSPs). In the sixth-generation mobile networks, the CSPs should guarantee resilient performance for the communication service consumers with less human involvement. In this work, we turn to Intent-driven network and on-demand slice management, and to decrease the complexity and cost in full life cycle slice management, we first present an intent-driven closedloop (CL) control and management framework that automates the deployment of network slices and manages resources intelligently based on the extended CL architecture. And then, we explore and exploit the deep reinforcement learning algorithm to address the problem of resource allocation, which is formulated as a Markov decision process. Finally, we demonstrate the feasibility of the proposed framework by deploying the open radio access network (RAN) infrastructure in the OpenAirInterface platform and realizing the CL control and management with a near real-time RAN intelligent controller. The emulation results demonstrate the effectiveness of slicing performance, measured in terms of delay and rate.

Collaboration between low Earth orbit (LEO) and geostationary Earth orbit (GEO) satellites in space communication networks has the advantages of wider coverage and higher communication capacity. However, effective resource allocation in the space communication network faces significant challenges due to incomplete information introduced by the highly dynamic communication environment. In this work, we focus onMatchingGame-based resource allocation strategy withIncomplete information in the spaceCommunication network, called MAGIC. Specifically, we formulate the multi-dimensional resource allocation with incomplete information as the revenue maximization problem of access satellite, which is the sum priorities of the successfully accessed users. The revenue maximization problem is a mixed integer nonlinear programming problem, and a three-sided matching game is employed to solve it. Meanwhile, we apply a model-free reinforcement learning framework to pre-train the historical network data to compensate for the shortcomings caused by incomplete information. Furthermore, user-optimal and access satellite-optimal resource allocation algorithms are designed to achieve optimal resource scheduling. Simulation results demonstrate the effectiveness and convergence of proposed algorithms from the single time slot and multiple time slot perspectives of different network parameters.

As Software Defined Networking (SDN) continues to evolve, the demand for enhanced reliability and security within network infrastructures has surged. To address these pressing needs, we put forth a novel, blockchain-based SDN multi-domain network security architecture designed to bolster the safety measures of distributed systems. Furthermore, we’ve developed an innovative primary backup path optimization algorithm, which capitalizes on maximum disjoint in in-band mode to elevate the resilience of communication services. When a network encounters a failure, seamlessly transitioning the control path is crucial for maintaining uninterrupted, dependable operations and fortifying network resilience. In order to validate our approach, we conducted a series of simulation experiments, leveraging Pica8 and ONOS controllers to design the system architecture. Remarkably, the numerical outcomes revealed that our pioneering primary and backup control path algorithms significantly outperform the conventional shortest path algorithm in the face of network failure. By enhancing throughput and reducing packet loss rates, our proposal ultimately augments communication reliability.

Satellite networks are promising paradigms for the sixth-generation (6G) global communications. However, the current satellite network is facing novel technical challenges, such as poor dynamic adjustment capability, diverse user types, and mismatches between service demands and network resources. Therefore, we propose a more general intent detection method under the intent-driven satellite network framework to achieve more intelligent satellite network management. We analyze various characteristics such as user types, user quality of service requirements, and the air interface of different types of satellites. Then we construct an intent classification model and intent extraction model based on transfer learning to provide an intent detection method for on-demand service. Our experimental results demonstrate that the proposed intent detection method exhibits a high degree of flexibility in handling user inputs and achieves high detection accuracy.

This paper presents a comprehensive framework for Intent-Based Networking (IBN). The framework is an open-source project, and its implementation is standards-based. Relevant IBN concepts from the standards organizations, the framework’s architecture, and its implementation on key IBN aspects and features including Intent life-cycle, Intent translation, Intent orchestration, and Intent assurance using closed-loops are discussed. The paper also demonstrates a real intent-based use case realized by the framework in order to show and validate the proof-of-concept. The Future work of this project is also discussed.

To meet novel services and networking requirements towards the next generation applications, intent-driven network is proposed as a promising networking paradigm. It is with capabilities of intent refinement, policy generation, and state awareness. And these distinctive capabilities contribute to its wide applications to the next generation networks. However, current researches lack a generalization model of intent refinement. Additionally, it is difficult to extract available knowledge from huge raw data of the network status, and guarantee the precise generation of network policies. To solve these challenges, we present a knowledge graph-enabled intent-driven network with the digital twin, which is termed as KID in this work. In the KID, knowledge graph is utilized to represent user intents, abstract network status, and express network policies. And the digital twin is applied to validate intents as well as abstract the physical network. The KID enhances the capabilities of intent-driven networks to refine intents, contributing to the continuous assurance of accurate intent fulfillment. Finally, we present a proof of concept implementation of the KID. Simulation results verify the feasibility and effectiveness of the presented KID framework.

Intent-driven network (IDN, or intent-based network, IBN) is a novel networking paradigm, which can enable user intents to drive network management autonomously and improve the network’s operational efficiency. Although artificial intelligence (AI) has been found for several applications to the IDN, there lacks a systematic discussion and research on this topic. In this work, we present a survey of the application of AI at each layer of IDN. Then, a general IDN management architecture, State-Action-Intent (SAI), is proposed. The presented SAI is a new IDN implement framework to automate the operational intents in a closed loop to overcome the challenges of complex network services. To verify the availability and effectiveness of SAI, a proof-of-concept demonstration is provided, and the obtained performance is discussed.

Satellite networks can help extend wider communication coverage and provide more types of services; and introducing service function chain (SFC) to satellite networks can enhance their flexibility and scalability. However, this highly challenges the complexity and efficiency of network service management. In this work, we first present an intent-driven satellite network service management architecture. It provides a user-oriented programmable and customizable service provisioning mechanism, which can improve the flexibility and efficiency in service delivery and provisioning. Furthermore, we elaborate an intent-driven SFC deployment scheme, which is termed as ISFC. The presented ISFC is with the intent parsing, network function virtualization infrastructure point of presence selecting, and the optimal service function path generation. Finally, we provide the ISFC deployment algorithm. And the simulation results show that the presented ISFC scheme can well satisfy user’s requirements with much lower delay.

Flying Ad hoc Network (FANET) is with limited node energy, time-varying dynamic topology, and unstable wire-less links, thus leading to a challenging network management. The network management of the FANET faces frequent network reconfiguration and strict monitoring, which makes the traditional human-involved network management methods unsuitable. Therefore, an autonomous network management method is urgently needed. This paper proposes the intent-driven network management system for FANET, termed as IMF for short. Aiming at the various management intents of FANET, the proposed IMF system explores and exploits intent-driven networking capabilities to improve the network management automation. Through intent translation, policy management, policy verification, and state awareness, the network management intent can be converted into machine-actionable policies at the underlying Unmanned Aerial Vehicles (UAVs) node, which reduces the complexity of network management in a timely and robust way. At last, we present a use case of Quality of Service (QoS)-aware routing management in the proposed IMF system framework. The refined routing intents and the node state information are stored and transmitted in the re-designed time slot reservation request (TREQ) and topology control (TC) packets. A proof of concept of the implementation platform is built to verify the effective control and management of the presented intent-driven QoS-aware routing management.

Mobility Load Balancing (MLB) is an important use case of the self-organized networks (SON), which can transfer the load from heavy-loaded cells to light-loaded cells through the handovers of users and achieve a balanced load distribution. However, there exist several limitations in current MLB meth-ods. On the one hand, traditional MLB methods focus more on the offloading of heavy-loaded cells but ignore the service and experience of transferred users. On the other hand, the adjustment of mobility parameter may cause a large number of handover, many of which are unnecessary in fact. In this paper, we propose an intent-driven MLB (IDMLB) method to optimize the handover of users. Taking the network intent and the user intent into consideration, we design a more fine-grained handover scheme and avoid the deterioration of user experience after the handover. Finally, we simulate the IDMLB in LTE scenario and the simulation results show that the proposed mechanism can effectively reduce the number of handover.

Network attacks are becoming more intense and characterized by complexity and persistence. Mechanisms that ensure network resilience to faults and threats should be well provided. Different approaches have been proposed to network resilience; however, most of them rely on static policies, which is unsuitable for current complex network environments and real-time requirements. To address these issues, we present a Belief-Desire-Intention (BDI) based multi-agent resilience network controller coupled with blockchain. We first clarify the theory and platform of the BDI, then discuss how the BDI evaluates the network resilience. In addition, we present the architecture, workflow, and applications of the resilience network controller. Simulation results show that the resilience network controller can effectively detect and mitigate distributed denial of service attacks.

In the face of the user-centric access network architecture adopted by the fifth-generation (5G) mobile communication network terminals, the communication capability of terminals faces significant challenges. In this case, the combination of 5G and artificial intelligence (AI) has become a significant trend to meet the various communication needs of terminal devices. Toward the problem that the data analysis and decision making for cell load estimation are primarily accomplished on the access side of the network, terminals can only passively access the network, but cannot predict the load estimation on the access side of the network in advance and cannot make network selection decisions in real time. In this article, we propose a cell load estimation algorithm based on a generative adversarial network (GAN) for 5G mobile communication networks, which considers estimating the cell load according to the wireless information measured by the terminals. The algorithm effectively estimates the cell load at the terminal side, which reflects the intelligence of the terminals and solves the problems of low data transmission rate, high signaling cost, and time delay in the existing techniques for load estimation schemes at the network side, assisting users in making a real-time decision. The performance is evaluated through the system-level simulation, and the results indicate that the proposed model improves load estimation accuracy, simultaneously improving the network throughput and reducing the packet queuing delay, suitable for different heterogeneous network scenarios.

The number of users engaged in social media through social networks continues to grow as people become more passionate on current social issues and events. People using social networks tend to have different opinions or positions regarding these issues and events. However, social network users share similar characteristics such as political orientation, age, and gender. Since the users of social networks can be grouped according to their similarities, then we would like to observe how these users affect the belief and opinion of other users in the same or different groups. Inspired by this phenomenon, we propose a multi-population mean field game approach to capture the belief and opinion evolution of a social network with several populations. Through the proposed model, we can gain information on the behavior of social network users belonging to different groups. Moreover, we can utilize the proposed model to predict how social network users affect the belief and opinion of each other. The multi-population social network mean field game problem is solved analytically using an adjoint method. Then, simulations are provided to show the belief and opinion evolution of users in a multi-population social network.

Multi-access edge computing (MEC) has been proposed to reduce latency inherent in traditional cloud computing. One of the services offered in an MEC network (MECN) is computation offloading in which computing nodes, with limited capabilities and performance, can offload computation-intensive tasks to other computing nodes in the network. Recently, mean-field-type game (MFTG) has been applied in engineering applications in which the number of decision makers is finite and where a decision maker can be distinguishable from other decision makers and have a non-negligible effect on the total utility of the network. Since MECNs are implemented through finite number of computing nodes and the computing capability of a computing node can affect the state (i.e., the number of computation tasks) of the network, we propose non-cooperative and cooperative MFTG approaches to formulate computation offloading problems. In these scenarios, the goal of each computing node is to offload a portion of the aggregate computation tasks from the network that minimizes a specific cost. Then, we utilize a direct approach to calculate the optimal solution of these MFTG problems that minimizes the corresponding cost. Finally, we conclude the paper with simulations to show the significance of the approach.

Mobile edge computing has been proposed to reduce latency inherent in traditional cloud computing. One of the services offered in a mobile edge computing network is computation offloading in which computing nodes with limited capabilities and performance can offload a computation-intensive task to other computing nodes in the network. Recently, mean-field-type game (MFTG) has been applied in engineering applications in which the number of decision makers is finite and where a decision maker can be distinguishable and have a non-negligible effect on the total utility of the network. Since mobile edge computing networks have a finite number of computing nodes where the computing capability of a computing node can affect the state (i.e., the amount of computation task) of the network, we propose a MFTG approach to formulate and solve a computation offloading problem. In this scenario, the goal of each computing node is to compute the portion of the aggregate computation task it can offload from the network that minimizes its cost. Then, we utilize a direct approach to solve for the optimal portion of the aggregate computation task that minimizes the cost incurred by a computing node. Finally, we conclude the paper with simulations to show the significance of the approach.

With the drastic increase of space information network (SIN) traffic and the diversity of network traffic types, the optimal allocation of the scarce network resources is of great significance for optimizing the SIN system capability. In this paper, we propose a multi-resource management method for multi-tier SIN using the cooperative Nash bargaining solution. Since the original problem is a non-convex problem, we firstly make logarithmic transition, and then find a tightest lower bound function to convert the initial problem into a convex one. In order to carry out the optimal bandwidth and power allocation in SIN, we construct a joint bandwidth and power allocation (JBPA) algorithm. Simulation results show the performance improvement of the JBPA scheme and the convergence of JBPA algorithm.

The mobile telecom carriers have deployed massive infrastructures that support or carry the data signal transmission. Most of them are passive devices lacking the ability to actively monitor and automatically report information, which are called “dumb devices.” At present, the dumb device management has problems, such as information incomplete or inaccurate, and lack of dynamic update mechanism. For catering to the construction of smart cities, we design an information management system considering the full lifecycle management for dumb devices to realize real-time or periodical context awareness and information transmission based on narrow band Internet of Things (NB-IoT). Compared to the existing radio frequency identification (RFID)-based solutions, which require RFID readers and electronic tags and have a limited sensing distance, the NB-IoT-based solution for dumb device management has advantages in transmission distance and communication stability. The NB-IoT terminal is attached to the dumb device, and a global positioning system module is installed on it to obtain the positioning information. The NB-IoT terminal is controlled by an real-time clock (RTC) alarm to periodically enter the low power mode and then wake up to automatically collect the location and battery information and upload it to the server. The application objects of this information management system can be extended to dumb devices in other industries.

In this paper, an energy-efficient resource allocation problem is investigated for the wireless power transfer (WPT)-enabled OFDMA multicell networks. In the considered system, multiple base stations (BSs) with a large number of antennas are responsible to provide WPT in the downlink, and the users can recycle and utilize the received energy for uplink data transmission. The role of BS is to execute WPT; thus, there are no data transmissions in the downlink. A time-division protocol is considered to divide the time of downlink WPT and uplink wireless information transfer into separate time slots. With the objective to improve the energy efficiency, we propose the time, subcarrier, and power allocation schemes and antenna selection algorithms. As the perfect channel state information (CSI) is hard to obtain in the practical systems, we also take the case where only estimated CSI is available into consideration when executing resources allocation decisions and analyze the corresponding performance. Due to the non-convexity of the formulated optimization problem, we first apply the nonlinear programming scheme to convert it to a convex optimization problem. Then, an efficient alternating direction method of multipliers-based distributed resource allocation algorithm is applied to address the transformed problem. Performance evaluations are conducted to demonstrate the advantages of the proposed schemes.

Intelligent terminals support a large number of multimedia such as picture, audio, video, and so on. The coexistence of various multimedia makes it necessary to provide service for different requests. In this paper, we consider interference-aware coordinated multi-point (CoMP) to mitigate inter-cell interference and improve total throughput in the fifth-generation (5G) mobile networks. To select the scheduled edge users, cluster the cooperative base stations (BSs), and determine the transmitting power, a novel dynamic bargaining approach is proposed. Based on affinity propagation, we first select the users to be scheduled and the cooperative BSs serving them. Then, based on the Nash bargaining solution (NBS), we develop a power control scheme considering the transmission delay, which guarantees a generalized proportional fairness among users. Simulation results demonstrate the superiority of the user-centric scheduling and power control methods in 5G CoMP systems.

Interference-aware coordinated multi-point can mitigate inter-cell interference and improve total throughput. However, it is crucial to select the scheduled users, cluster the cooperative base stations, and determine the transmit power of each base station over all physical resource blocks. Select the scheduled users and the cooperative BSs which served the users respectively based on affinity propagation at first. Then, we develop a power allocation scheme which considers the fairness among users. The scheme is a generalized proportional fairness based on Nash bargaining solutions. Simulation results demonstrate the superiority of the user-centric approach of scheduling and power control in CoMP.

Extensive deployment of heterogeneous small cells in cellular networks results in ultra-dense small-cell networks (USNs). The USNs have been established as one of the vital networking architectures in the 5G to expand system capacity and augment network coverage. However, intensive deployment of cells results in a complex interference problem. In this paper, we propose a distributed multi-domain interference management scheme among cooperative small cells. The proposed scheme mitigates the interference while optimizing the overall network utility. In addition, we jointly investigate OFDMA scheduling, TDMA scheduling, interference alignment (IA), and power control. We model small cells' coordination behavior as an overlapping coalition formation game. In this game, each base station can make an autonomous decision and participate in more than one coalition to perform IA and suppress intra-coalition interference. To achieve this goal, we propose a distributed joint interference management (JIM) algorithm. The proposed algorithm allows each small-cell base station to self-organize and interact into a stable overlapping coalition structure and reduce interference gradually from multi-domain, thus achieving an optimal tradeoff between costs and benefits. Compared with existing approaches, the proposed JIM algorithm provides appreciable performance improvement in terms of total throughput, which is demonstrated by simulation results.

Fog computing, as a promising technique, is with huge advantages in dealing with large amounts of data and information with low latency and high security. We introduce a promising multiple access technique entitled nonorthogonal multiple access to provide communication service between the fog layer and the Internet of Things (IoT) device layer in fog computing, and propose a dynamic cooperative framework containing two stages. At the first stage, dynamic IoT device clustering is solved to reduce the system complexity and the delay for the IoT devices with better channel conditions. At the second stage, power allocation based energy management is solved using Nash bargaining solution in each cluster to ensure fairness among IoT devices. Simulation results reveal that our proposed scheme can simultaneously achieve higher spectrum efficiency and ensure fairness among IoT devices compared to other schemes.

Ultra-dense deployment of small cells can enhance capacity, extend coverage, and improve the spectrum and energy efficiency. However, intra-tier interference among different spectrum-sharing small cells is critical and needs to be well coordinated from a resource utilization perspective, such as the distributed power control. Especially in the ultra-dense small cell networks, the effects of intra-tier interference cannot be ignored, although the perceived interference from one small cell is infinitesimal for a specific small cell when designing a power control policy. It is difficult to make the model, analysis, and design of the distributed power control due to a huge number of small cells. In this paper, we formulate the power control of a generic small cell as a mean field game (MFG) with an interference dominator, where we derived the coupled Hamilton-Jacobi-Bellman (HJB) and the Fokker-Planck-Kolmogorov (FPK) equations. The presented MFG framework of power control can characterize the complex interference interaction and strategically rational decision-making of the generic and dominating players. Simulation results show the dominating effects of the interference dominator on the power control.

Ultra-dense networks can further improve the spectrum efficiency (SE) and the energy efficiency (EE). However, the interference avoidance and the green design are becoming more complex due to the intrinsic densification and scalability. It is known that the much denser small cells are deployed, the more cooperation opportunities exist among them. In this paper, we characterize the cooperative behaviors in the Nash bargaining cooperative game-theoretic framework, where we maximize the EE performance with a certain sacrifice of SE performance. We first analyze the relationship between the EE and the SE, based on which we formulate the Nash-product EE maximization problem. We achieve the closed-form sub-optimal SE equilibria to maximize the EE performance with and without the minimum SE constraints. We finally propose a CE2MG algorithm, and numerical results verify the improved EE and fairness of the presented CE2MG algorithm compared with the non-cooperative scheme.

Device-to-device (D2D) communications can enhance spectrum and energy efficiency due to direct proximity communication and frequency reuse. However, such performance enhancement is limited by mutual interference and energy availability, especially when the deployment of D2D links is ultra-dense. In this paper, we present a distributed power control method for ultra-dense D2D communications underlying cellular communications. In this power control method, in addition to the remaining battery energy of the D2D transmitter, we consider the effects of both the interference caused by the generic D2D transmitter to others and the interference from all others caused to the generic D2D receiver. We formulate a mean-field game (MFG) theoretic framework with the interference mean-field approximation. We design the cost function combining both the performance of the D2D communication and cost for transmit power at the D2D transmitter. Within the MFG framework, we derive the related Hamilton-Jacobi-Bellman and Fokker-Planck-Kolmogorov equations. Then, a novel energy and interference aware power control policy is proposed, which is based on the Lax-Friedrichs scheme and the Lagrange relaxation. The numerical results are presented to demonstrate the spectrum and energy efficiency performances of our proposed approach.

Device-to-device (D2D) communication underlaying cellular networks improves spectral efficiency but causes interference to cellular users (CUs). Such interference can be utilized to help CUs prevent wiretapping. This paper aims to achieve the twofold goal of security provisioning for CUs and spectral efficiency enhancement for D2D links by optimizing the resource sharing of CUs and D2D links. We first provide the necessary and sufficient conditions for the accessibility of a CU channel by a D2D link. Then, we derive the jointly optimal resource sharing strategy, including the closed-form power control and the optimal channel pairing of CUs and D2D links. Numerical results show that the proposed strategy can improve both the CUs' security and D2D spectral efficiency.

Extensive deployment of small cells in heterogenous cellular networks introduces both challenges and opportunities. Challenges come with the reuse of the limited frequency resource for improving spectral efficiency, which always introduces serious mutual inter- and intracell interference between or among small cells and macrocells. The opportunities refer to more potential chances of inter- and intratier cooperations among small cells and macrocells. Energy efficiency will be a critical performance requirement for future green communications, especially when small cells are densely deployed to enhance the quality of user's experience. We exploit the potential cooperation diversities to combat the interference and energy management challenges. To capture the complicated interference interaction and also the possible coordination behavior among small cells and macrocells, this paper proposes a novel bargaining cooperative game (BCG) framework for energy efficient and interference-aware power coordination in a dense small cell network. In particular, a new adjustable utility function is employed in the BCG framework to jointly address both the spectral efficiency and energy efficiency issues. Using the BCG framework, we then derive the closed-form power coordination solutions and further propose a joint interference-aware power coordination scheme (Joint) with the considerations of both interference mitigation and energy saving. Moreover, a simplified algorithm (Simplified) is presented to combat the heavy signaling overhead, which is one of the significant challenges in the scenario of extensive deployment of small cells. Finally, numerical results are provided to illustrate the effectiveness of the proposed Joint and Simplified schemes.

Abstract Heterogeneous cellular networks (HetNets) have emerged as a new promising paradigm to further enhance capacity, where multiple types of low power smallcells are overlaid in a high power macrocell. They provide more opportunities to explore the potential cognition and cooperation diversities to improve the spectral efficiency. On the other hand, energy efficiency is a critical performance metric, which deserves more attention from academia, industry, and standardization, in particular, in scenarios where smallcells are densely deployed. In this paper, a systematic architecture is presented to efficiently utilize network resources and thus improve the overall energy efficiency. The architecture is referred to as OCRT because it combines a multi‐tier energy efficiency considerations of operators, core networks, radio access networks and terminals (e.g., OCRT). Furthermore, a corresponding triply‐cycle‐based functional structure is proposed for the OCRT to make various interactions between the corresponding functional entities clear. An implementation scheme of OCRT based on cognitive information interaction cycle and an energy efficiency‐aware protocol is presented. Finally, a use case of the presented OCRT green design is provided for energy efficiency optimization in a cognition‐and‐cooperation‐characterized HetNet. Copyright © 2015 John Wiley & Sons, Ltd.

Heterogeneous networks (HetNets), where multiple low power small cell eNodeBs (SeNBs) are overlaid on the coverage of a high power macrocell eNodeB (MeNB), serve as promising paradigm to enhance spectral efficiency of future cellular wireless networks. To capture the complicated interference interaction and also the coordination behavior among MeNB and SeNBs, this paper proposes a bargaining cooperative game (BCG) framework for interference-aware power coordination in a HetNet. In particular, a new adjustable utility function is employed in the BCG framework to jointly address the spectral and energy efficiencies as well as to achieve the optimal tradeoff between them. We then derive the closed-form power coordination solutions and further propose an interference-aware power coordination scheme with the considerations of both interference mitigation and energy saving. Finally, the numerical results are provided to illustrate the convergence property and efficiency of the proposed power coordination scheme.

In this paper, we investigate the interference mitigation in femtocell networks, where femtocell access points (FAPs) are allowed to cooperate as different cooperative groups to allocate resources. We model the femtocell cooperation characteristics as a coalition formation game in partition form with non-transferable utility. Furthermore, a distributed coalition formation algorithm is proposed to enable each FAP to decide to depart from or join in a coalition independently, moreover, we devise a low complex iterative algorithm to optimize the allocation of each coalition's multi-dimensional resources for maximizing its FAPs' payoffs. By applying our proposed coalition formation scheme, a Nash stable FAP partition is formed and FAPs in each coalition can effectively exploit the cooperative gain to mitigate the interference and maximize the sum rate. Numerical results are provided to corroborate our proposed studies.

Spectrum leasing is a novel promising technique to improve the spectrum efficiency (SE) through the spectrum owner leasing its free or under-utilization spectrum to unlicensed users for superfluous spectrum revenue, while the unlicensed users should pay for spectrum renting. Recently, spectrum leasing is used between femtocell service provider (FSP) and the coexisting macrocell service provider (MSP) in heterogeneous networks (HetNets). However, the energy efficiency (EE), a critical performance metric in HetNets, especially when multiple FSPs are densely overlaid on the coverage of MSP, has been largely neglected in available SE-oriented spectrum leasing. In this paper, a cooperative spectrum leasing framework is proposed to both mitigate interference and save energy, where a Stackelberg coordination game is formulated to analyze a joint spectrum leasing, pricing and interference coordination process between FSPs and MSP with the novel energy-aware utility functions for the players of FSPs and MSP. We rigorously derive the optimal closed-form solutions of spectrum leasing, pricing, and power coordination solutions for the FSP and the MSP. We propose a multi-stage distributed algorithm to approach these solutions. Finally, simulation results are provided to clarify effects of multiple parameters in the gaming process and verify the final improved performance.

Mobility load balancing (MLB) is an important use case of Self-Optimizing Networks (SONs). To combat the commonly encountered issues in conventional MLBs, such as the blind offloading without equilibrium and optimality guarantees, we propose an Enhanced MLB (ELB) scheme to conquer these problems with the double threshold design including the common trigger threshold and the fairness-aware ending one. First, we introduce the rationale behind our idea with simple analysis, then the newly presented the fairness-aware ending threshold is given by modeling the fairness metric during the optimal target cell selection process. Based on these analysis, we propose the ELB scheme with a double-threshold design. Simulation results show that the presented ELB scheme can well improve the system performance and the user experience quality.

The deployment of smallcell eNodeBs (SeNBs), overlaid on existing macrocell eNodeB (MeNB) is widely accepted as a key solution for improving spectral efficiency (SE). However, both SeNB and MeNB may suffer significant performance degradation due to inter/intra-tier interference. Meanwhile, the energy efficiency (EE) is another promising requirement especially when SeNB/MeNB are densely deployed. In this paper, a utility function with an α-adjustable parameter is proposed to achieve an optimal tradeoff between EE and SE. Then, an energy-aware capacity offload between the MeNB and multiple SeNBs is formulated as a Nash bargaining game, which is significantly simplified during the following analysis. To attain a win-win optimality for both the relatively involved SeNBs and MeNB, an energy-aware trigger of source-MeNB, interference-related right selected target-SeNB, and mutual interference compensation are all provided in this paper, which help to attain more dimensions of diversities and gains. Finally, simulation results show the improved performance of our proposed scheme.

Strategic bargaining cooperative games have found extensive applications to resource management in wireless networks. In this survey, basics of a strategic bargaining game and solution concepts are firstly presented. Geometrical interpretations are introduced to better understand real meanings of them. Then, the authors survey the applications of various strategic bargaining games for the emerging wireless networks, where the authors concentrate on several interesting problems based on their previous systematic studies: (i) distributed resource management design for cognitive radio networks based on geometrical interpretation of the cooperative solution; (ii) asymmetric bargaining modelling for green communications; (iii) a unified utility tradeoff design between spectral and energy efficiency in heterogeneous cellular networks; (iv) the cooperative rate splitting game for Long Term Evolution‐coordinated multi‐point system; and (v) a general bargaining formulation with different tradeoffs between efficiency and fairness. In addition, the authors survey the applications of strategic bargaining games to cooperation incentive mechanism, bargaining game on capacity region of interference channel and multiuser and multimedia applications. Finally, challenges and potential research direction are summarised in this work.

We investigate the interactive behavior and strategic decision-making between multiple secondary users (SUs) and primary users (PUs), both of which are end-to-end performance aware in cognitive networks. A Stackelberg game is utilized to formulate the spectrum utilization maximization problem after the complex interference situation is analyzed. Especially, an interference power cap (IPC) function predefined by PUs as a pricing function is introduced into the utility function design of SUs to guarantee QoS of PUs, as well as to decouple constraints. Further, an asymmetric information situation can be formed by considering PUs as leaders who employ the optimal water-filling algorithm, and the closed-form power strategy of SUs can be derived. And accordingly, SUs as followers can observe the available information to do more foresighted decision by learning. What is more, we prove the optimality and existence of the deceived solutions. Numerical results demonstrate that the proposed distributed algorithm provides more spectrum revenue and better QoS guarantees to PUs with limited iterations.

From an energy-saving perspective, the authors investigate the downlink power control issue of the two-tier heterogeneous networks (HetNets) using a cognitive radio train of thought. The authors consider the HetNets scenario of one macro-cell evolved-NodeB (eNB) and multiple femto-cell Home evolved NodeBs (HeNBs) cooperatively coexisting to provide better services. A specific HeNB allows macro-mobile station (macro-MS) previously associated with eNB to access to it for better signal-to-interference plus noise ratio (SINR) guarantee. As a reward, the macro-MS pays a certain of revenue to HeNB as the incentive mechanism for this HeNB's downlink extra power consumption, which is manifested in the design of the price function. The throughput bound of Macro-MSs in HeNB cell is given. Then, the authors select the SINR as the performance measure and formulate the power control of selected HeNBs as multi-constrained optimisation problem. Meanwhile, the authors derive the sub-optimal and closed-form power control of individual HeNB, based on which the authors design the distributed algorithm with the aid of eNB and HeNBs cooperatively to implement the pricing information exchange. Simulation results show the improved performance of the convergence, the energy-efficiency measured by ‘energy-per-bit’ and the throughput of the ‘Proposed-Cognitive-x’ power control algorithm.

Efficiency and fairness are the two critical and conflicting criterion indicators to measure radio resource management performance including the power and transmit control in the cognitive context. The best tradeoff is achieved using the dynamic game-theoretical approach in this paper, from the individual rationality and the social fairness perspective. Therefore, a two-step resource management scheme is investigated in the cognitive radio networks context, where the initial allocation focus on the personal efficiency and the second adjustment focus on the social fairness and the overall performance. Both the proposed algorithms during the two-steps scheme are distributed, with the help of the iterative water-filling algorithm and the sub-gradient method. Simulation numerical results tell the performance of the proposed algorithm.

We capture both the economical and technical aspects of the dynamic spectrum sharing to improve the underutilization of the scarce but valuable radio spectrum and the service providers' revenue. Especially, we investigate the optimal mapping between the service requester pool (SRP) and the service provider pool (SPP) in the coexistence context of multiple heterogenous cognitive radios. Both the optimal pricing schemes of the SPP for the specific shared spectrum and optimal transmission strategies of the SRP for rational spectrum requirement are designed. The issue of the coexistence spectrum sharing is formulated as the secondary network utility maximization (SNUM) problem. Using Lagrange duality decomposition technique and the Stackelberg game modeling approach, we obtain the distributed algorithms with low implementation complexity and limited iterations. The proposed PBDSS will strategically interact with each other until converge to dominating solution termed as Stackelberg equilibrium solution (SES), and the numerical results reflect the improvement of the overall performance and the spectrum utilization of our proposed spectrum sharing approach.

A parameter adaptively adjustable utility function based on the asymmetric sigmoid function is investigated for the non-cooperative power control game (NPCG) model in cognitive radio networks (CRN). Each secondary user (SU) can adaptively adjust the parameter to track along with the wireless interference environment for the optimal power strategy. From the fairness of view, a pricing function related to the channel gain of each SU is designed, which can improve the Pareto optimality of the Nash equilibrium solution (NES). A parallel utility function choosing approach for each SU is proposed according to channel state information (CSI) and the utility obtained at this time. The simulation results show that the proposed power control scheme achieves a better performance compared with the fixed utility function method, and the pricing function actually improves the optimality of the NES.