Artificial Intelligence Generated Content (AIGC) powered by Generative Diffusion Models (GDMs) has emerged as a transformative paradigm for automated content creation. To satisfy the stringent latency requirements of AIGC services in many edge intelligence scenarios (e.g., smart cities), Mobile Edge Computing (MEC) provides critical computational support by deploying GDMs at edge servers (ES) close to end users. This paper investigates an MEC-enabled AIGC network comprising multiple ES, wireless access points (APs), and mobile users (UEs) with heterogeneous latency and accuracy demands. We formulate aJoint Communication Association and Computation Offloading (JCACO)game, where each UE strategically selects its serving AP, ES, and inference steps to minimize the overall service completion time while meeting accuracy constraints. The problem is challenging due to the network dynamics and the incomplete information. We prove that the JCACO game is apotential gameunder both complete and stochastic information settings, ensuring the existence of Nash Equilibrium (NE) in both cases. To derive the NE efficiently, we develop a distributedMulti-Agent Stochastic Learning(MASL) algorithm that provably converges to the NE with strict performance guarantees. Unlike conventional best-response schemes, MASL requires neither the knowledge of other players’ strategies nor global network information, making it fully distributed and adaptive to dynamic environments. We further provide a strict theoretical convergence analysis for MASL by usingOrdinary Differential Equations(ODEs). Simulation results demonstrate that MASL significantly reduces service completion time compared with benchmark methods while satisfying accuracy constraints, confirming its effectiveness and practicality for real-world MEC-enabled AIGC networks.

Mobile Edge Computing (MEC) is a promising approach for enhancing the quality-of-service (QoS) of AI-enabled applications in the B5G/6G era, by bringing computation capability closer to end-users at the network edge. In this work, we investigate the joint optimization of edge server (ES) deployment, service placement, and computation task offloading under the stochastic information scenario. Traditional approaches often treat these decisions as equal, disregarding the differences in information realization. However, in practice, the ES deployment decision must be made in advance and remain unchanged, prior to the complete realization of information, whereas the decisions regarding service placement and computation task offloading can be made and adjusted in real-time after information is fully realized. To address such temporal coupling between decisions and information realization, we introduce the stochastic programming (SP) framework, which involves a strategic-layer for deciding ES deployment based on (incomplete) stochastic information and a tactical-layer for deciding service placement and task offloading based on complete information realization. The problem is challenging due to the different timescales of two layers' decisions. To overcome this challenge, we propose a multi-timescale SP framework, which includes a large timescale (called period) for strategic-layer decision-making and a small timescale (called slot) for tactical-layer decision making. Moreover, we design a Lyapunov-based algorithm to solve the tactical layer problem at each time slot, and a Markov approximation algorithm to solve the strategic-layer problem in every time period. Simulation results demonstrate that our proposed solution significantly outperforms existing benchmarks that overlook the coupling between decisions and information realization, achieving up to 56% reduction in total system cost.

With rapid advancements in large language models (LLMs), AI-generated content (AIGC) has emerged as a key driver of technological innovation and economic transformation. Personalizing AIGC services to meet individual user demands is essential but challenging for AIGC service providers (ASPs) due to the subjective and complex demands of mobile users (MUs), as well as the computational and communication resource constraints faced by ASPs. To tackle these challenges, we first develop a novel multi-dimensional quality-of-experience (QoE) metric. This metric comprehensively evaluates AIGC services by integrating accuracy, token count, and timeliness. We focus on a mobile edge computing (MEC)-enabled AIGC network, consisting of multiple ASPs deploying differentiated AIGC models on edge servers and multiple MUs with heterogeneous QoE requirements requesting AIGC services from ASPs. To incentivize ASPs to provide personalized AIGC services under MEC resource constraints, we propose a QoE-driven incentive mechanism. We formulate the problem as an equilibrium problem with equilibrium constraints (EPEC), where MUs as leaders determine rewards, while ASPs as followers optimize resource allocation. To solve this, we develop a dual-perturbation reward optimization algorithm, reducing the implementation complexity of adaptive pricing. Experimental results demonstrate that our proposed mechanism achieves a reduction of approximately$64.9\%$in average computational and communication overhead, while the average service cost for MUs and the resource consumption of ASPs decrease by$66.5\%$and$76.8\%$, respectively, compared to state-of-the-art benchmarks.

Cloud and edge computing service providers (SPs) provide heterogeneous computing services to users, which forms the computing market. However, users’ service selections among SPs are unbalanced, resulting in inefficient resource utilization. In this paper, we analyze users’ service selection behaviors and design efficient pricing mechanisms to optimize the social welfare of the computing market. Considering the huge number of users and heterogeneous service providers, users can hardly acquire complete information to make their decisions, and we model users’ interactions as a dynamic service selection evolutionary game. Analyzing the evolutionary stable state (ESS) of the game and designing efficient pricing mechanisms for edge-cloud computing markets are challenging due to the implicit relationship between prices and users’ service selection dynamics, and the heterogeneity of service providers and user populations. We first investigate a single-population scenario where users are homogeneous, for which we prove that the ESS is unique. We design a static pricing mechanism which depends on SPs’ marginal costs and computation capacities, and a dynamic pricing mechanism which also depends on SPs’ real-time congestion tax. We prove that under our pricing mechanisms, the ESS is the socially optimal state and is globally asymptotic stable. We then analyze the general multi-population scenario where users are heterogeneous, for which we prove that the ESS exists but may be not unique. We design a static pricing mechanism (and a dynamic pricing mechanism) which depends on SPs’ marginal costs and the social-optimal average delay costs (and the real-time average delay costs). We prove that under our pricing mechanisms, the socially optimal state is an ESS and is asymptotically stable. Simulation results validate the effectiveness of our designed pricing mechanisms.

Generative Diffusion Model (GDM)-based AI-Generated Content (AIGC) services are rapidly emerging as powerful solutions for creating high-quality, personalized content across various domains, playing an essential role in shaping the future network landscapes. However, the traditional cloud-based implementation of AIGC services faces substantial challenges due to the heterogeneity of GDMs as well as their computation-intensive nature and the low-latency requirement. In this work, we investigate a Mobile Edge Computing (MEC)-empowered heterogeneous AIGC service scenario, where User Equipments (UEs) and Base Stations (BSs) deploy lightweight and heavyweight GDMs, respectively, to deliver different levels of AIGC services at the network edge. Specifically, when initiating an AIGC task, each UE can choose to process the task locally using lightweight GDMs, or offload and process the task on a BS using heavyweight GDMs. In such a scenario, we focus on the joint optimization of offloading, scheduling, and inferencing, aiming to minimize task delay and energy consumption, while maximizing content quality. To address the problem in an online and decentralized manner, we develop a Multi-Agent Proximal Policy Optimization (MAPPO)-based deep reinforcement learning algorithm in a centralized training and decentralized execution framework. Simulation results show that our proposed algorithm outperforms existing intelligent benchmarks, with the performance gains ranging from 5.1% to 13.9%.

Low Earth Orbit (LEO) observation satellite systems play a critical role in a variety of applications, including environmental monitoring, urban planning, and national security. However, transmitting the data collected by numerous observation satellites to Earth remains a significant challenge. One promising approach to improve data transmission efficiency is to utilize LEO communication satellites as data relays. Existing researches in this area primarily focus on the inter-satellite communication scheduling, often neglecting the importance of satellite observation scheduling, which limits the potential performance gain. In this work, we investigate the joint optimization of observation and transmission scheduling in a satellite network with resource-constrained LEO satellites, taking into account the heterogeneity of observation missions and the dynamics of network topology. Specifically, we first introduce a Time-Expanded Graph (TEG) model to effectively represent dynamic network topology and satellite resource constraints. Based on this model, we formulate a network flow problem that incorporates both mission-specific characteristics and satellite energy costs. To reduce the solution complexity in large-scale networks, we propose a novel low-complexity Augmented Lagrangian-based Distributed Parallel Splitting (ALDPS) algorithm. Simulation results show that our proposed algorithm can improve the network utility by 8.3% to 28.1% compared to baseline methods.

Energy efficiency (EE) is vital for 5G networks to manage the increased traffic demand while minimizing operational costs and reducing environmental impact. Optimizing energy usage also supports scalability and helps meet regulatory sustainability goals as network infrastructure expands. In a practical commercial 5G network, a widely-used method to reduce energy consumption is to dynamically shut down underutilization cells and reduce the overlapping cell coverage based on real-time traffic patterns. However, the inherent uncertainty of traffic demands, coupled with the unknown distribution function, significantly complicates the optimization of cell shutdown strategies, rendering both conventional deterministic and stochastic optimization methods ineffective. To tackle these challenges, we propose a distributionally robust optimization framework for EE optimization that does not rely on distributional knowledge. First, we construct a data-driven uncertainty set to model traffic distribution and apply Lagrangian duality to transform the infinite-dimensional optimization problem into a more tractable finite-dimensional one. Then, we employ a Bayesian optimization algorithm to efficiently solve this reformulated problem, which involves a mixed space of high-dimensional combinatorial cell shutdown and continuous Lagrangian multipliers, even with constrained black-box evaluations. Simulations on real-world field data show that our proposed solution outperforms existing benchmarks, achieving energy efficiency improvements ranging from 0.7 % to 11.05 %.

Federated Learning (FL) is an innovative learning paradigm that allows multiple devices to collaboratively train a shared model without uploading the raw data to the cloud, thereby enhancing privacy and security. Leveraging Mobile Edge Computing (MEC), Hierarchical Federated Learning (HFL) can further reduce the communication overhead, thereby increasing the efficiency and scalability of FL systems by enabling model aggregation at the network edge. However, this framework often encounters security challenges, such as single points of failure and the risk of malicious model tampering. To address these challenges, researches have employed blockchain technology to enhance the security of FL systems, but most of these solutions incur significant resource burdens due to the intensive computation demands of blockchain consensus mechanisms, such as Proof-of-Work (PoW). In this work, we aim to explore a lightweight blockchain-enabled federated learning (LBFL) framework that utilizes the Delegated Proof-of-Stake (DPoS) consensus mechanism, which employs a simple voting process to elect a small number of candidate block producers (known as delegates) to aggregate the FL model and produce blocks. This framework significantly reduces the number of consensus nodes, thereby minimizing resource consumption during the consensus process. We study the joint optimization of mobile device association, bandwidth allocation, computing frequency management, and block producer selection, aiming to minimize the overall delay and energy consumption. To address the challenges posed by discrete and continuous decision variables, we decompose the problem into three sequential subproblems and solve them iteratively. Simulation results show that compared with the existing benchmarks, the proposed scheme can reduce overall delay and energy consumption by 15% to 22%.

AI-Generated Content (AIGC) based on Generative Diffusion Models (GDMs) is revolutionizing content creation and promoting substantial advancements in domains like autonomous driving and robotics. Leveraging progress in Mobile Edge Computing (MEC) and model compression techniques, GDMs are increasingly being deployed on Edge Servers (ESs) and User Equipments (UEs), which typically face resource limitations. In such MEC-enabled scenarios, designing an efficient inference scheme for GDMs still remains a significant challenge, due to the resource constraints on ESs and UEs as well as the personalized demands of AIGC users. In this work, we propose a novel hybrid inference scheme, which consists of two stages: public prompt generation and common-to-personalized inference. In the first stage, a Large Language Model (LLM) is adopted to generate public prompts derived from the common features of users' personal prompts. In the second stage, a common inference phase based on public prompts is first executed for all users (to produce common intermediate results), and then a personalized inference phase based on each user's personal prompts is performed for each individual user (to generate final contents). Clearly, by introducing the common inference phase, the total inference steps can be significantly reduced. In such a scheme, we further study a hybrid inference optimization problem to optimize both common and personalized inference steps, aiming to maximize the total Quality of Service (QoS), while minimizing delay and energy consumption. Simulation results show that our proposed scheme significantly outperforms existing benchmarks, with the performance gains ranging from 12.6 % to 102.2 %.

Blockchain is a decentralized and secure digital ledger system that ensures data integrity through immutable records and cryptographic consensus mechanisms. However, in mobile blockchain networks, the computation-intensive proof-of-work (PoW) mining process often imposes a significant burden on mobile users (MUs) who serve as miners, particularly given their limited computing resources. Mobile edge computing (MEC) offers a promising solution to alleviate the burden on MUs, by enabling them to offload their mining tasks to nearby edge servers. While existing studies have explored MEC-assisted blockchain networks in both single-server and multi-server scenarios, they often overlook crucial aspects of blockchain networks, such as the transmission and computation delays inherent in the mining process. In this work, we investigate a more realistic MEC-assisted mobile blockchain network, where mining tasks are explicitly modeled with delay constraints to better capture real-world performance challenges. To analyze the strategic interactions between MUs and edge computing service providers (ECPs), we formulate a two-stage multi-leader and multi-follower Stackelberg game, which consists of an ECP Resource Pricing (ERP) game at Stage I, and an MU Resource Competition (MRC) game at Stage II. Specifically, in the ERP game at Stage I, ECPs, acting as leaders, set the resource prices for MUs; and in the MRC game at Stage II, MUs, acting as followers, determine their computing resource demands based on the prices of ECPs. We first prove the existence of Nash equilibrium (NE) for both games, and then derive the closed-form conditions for the NE of the MRC game at Stage II. Based on the above, we further propose a sub-gradient-based resource pricing algorithm that can converge to the NE of the ERP game at Stage I. Simulation results show that, when compared to the centralized cooperative solution, our proposed non-cooperative game approach can significantly reduce the computational complexity, while incurring only a modest performance degradation, e.g., the social welfare loss ranges from 6.64% to 9.96%.

This study examines a network-assisted full-duplex (NAFD) cell-free massive MIMO (CF-mMIMO) system, in which half-duplex access points (APs) simultaneously serve multiple uplink and downlink user equipment (UEs) using the same frequency resources. NAFD technology facilitates full-duplex transmission over existing half-duplex hardware through dynamic scheduling of AP operating modes, resulting in significant improvements in system spectral efficiency (SE). To ensure fairness among all UEs, we aim to maximize the minimum SE across UEs by jointly optimizing the AP operation modes and the uplink and downlink power control of the UEs, which helps mitigate severe cross-link interference for UEs with the lowest SE. We propose a novel joint optimization scheme based on integer linearization techniques to address this strongly coupled mixed-integer non-convex problem and achieve a near-optimal solution. Simulation results demonstrate that our proposed scheme outperforms the benchmark approach, providing a more equitable quality of service throughout the coverage area.

Beam tracking is a promising technology in mmWave-enabled cellular-connected unmanned aerial vehicle (UAV) communications. However, conventional beam tracking schemes always incur a large training overhead, and it is difficult to determine the time duration of a training cycle due to the high mobility of UAVs, which is essential for improving the effective achievable rate (EAR). To address this issue, we first adopt angular velocity estimation to obtain the beam coherence time, which serves as the time duration of the training cycles. To further reduce the training overhead in each training cycle, we then design an adaptive beam tracking algorithm based on bandit learning, where the actions are taken based on the accuracy of the angular velocity estimation. If the estimation is not accurate, more beams will be swept in the next cycle. In this way, the beam misalignment incurred by estimation inaccuracy will largely alleviate. Thus, the EAR can be effectively improved with smaller training overhead. The simulation results demonstrate the superior performance of the proposed algorithm in terms of the training overhead and the EAR.

In this study, we consider a more general scenario involving multiple simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Our objective is to maximize the weighted sum rate for users by decoupling the original problem into two components: the active beamforming design at the access points (APs) and the passive beamforming design at the STAR-RIS. We employ fractional programming to optimize these components alternately. The rank-one constraint in the passive beamforming design, which is proven to be an NP-hard problem, represents the primary challenge. To address this issue, we introduce a novel low-complexity algorithm based on sequential rank-one constraint relaxation (SROCR). Instead of entirely eliminating the rank-one constraint, our SROCR algorithm utilizes a progressive relaxation approach to gradually ease the constraint and identify a generic rank-one suboptimal feasible solution, ultimately converging to a solution that satisfies the rank-one condition. Numerical results demonstrate that our algorithm achieves comparable performance to existing algorithms while significantly reducing complexity, thus outperforming other baseline algorithms.

In this study, we investigate symbiotic radio-assisted cell-free massive multiple-input multiple-output (SCF-mMIMO) systems in which multiple access points serve primary users and backscatter devices. We propose a semi-blind channel estimation scheme based on the expectation maximization (EM) algorithm to reduce pilot overhead and iteratively achieve performance close to that of maximum likelihood (ML) estimation. Unlike the traditional EM algorithm, we derive a modified EM algorithm by incorporating suitable priors for the channel coefficients to estimate the aggregate channel. Simulation results show that the proposed EM algorithm for the SCF-mMIMO system achieves good performance with fewer pilots, approaching that of the ML estimation. In addition, the modified EM algorithm using channel priors outperforms traditional EM algorithms. These results suggest that semi-blind channel estimation holds considerable promise for SCF-mMIMO systems.

Large-scale wireless networks pose significant challenges in resource scheduling, where the solution space grows exponentially with network size. While network decomposition offers a promising solution by breaking networks into manageable subnetworks, existing approaches, including spectral clustering, fail to effectively capture the complex service relationships between base stations (BSs) and users, particularly in networks with massive user populations. This paper presents BSCCD (Bidirectional Spectral Co-Clustering Based Decomposition), a new decomposition scheme that addresses these challenges through two key innovations: (i) a two-round spectral co-clustering framework that captures bidirectional BS-user relationships, and (ii) a user node merging strategy that handles massive user populations. Extensive experiments on real-world datasets from multiple Chinese cities demonstrate that BSCCD reduces computation latency by up to 61.91 % compared to global optimization, while achieving more than 10 % improvement in solution quality over traditional clustering approaches. The advantage is particularly pronounced in medium-scale networks, where BSCCD outperforms traditional methods by$\mathbf{2 8. 7 6 \%}$. Our results demonstrate BSCCD's practical viability for resource scheduling in contemporary wireless networks, especially in scenarios with complex BS-user interactions and large user populations.

In this paper, a 2-layer rate-splitting multiple access (RSMA) framework with a balanced clustering design is proposed for cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Aiming at enhancing spectral efficiency (SE) and meanwhile ensuring low-complexity and scalability in large-scale systems, this work addresses different types of multi-user interferences by utilizing a two-stage optimization scheme with a designed spatial reduction matrix that encompasses the clustering design and joint RSMA. First, a balanced clustering design is developed for 2-layer RSMA to manage intra-and inter-cluster interference more efficiently than conventional user-centric clustering, simultaneously considering AP-user connectivity in CF-mMIMO systems and the impact of cluster similarity on RSMA. By employing the spectral clustering method to solve the bipartite graph partitioning problem with the min-max cut objective, the number of clusters is determined adaptively. Based on the above clustering design, a joint optimization of inner and outer RSMA is proposed to mitigate intra-and inter-cluster interferences simultaneously. Simulation results verify the SE enhancement, low-complexity, and scalability of the proposed 2-layer RSMA framework, compared with benchmark frameworks.

Multi-UAV cooperative search (MCS) can significantly enhance the efficiency and effectiveness of search by enabling multiple unmanned aerial vehicles (UAVs) to collaborate in conducting search missions. Thus, it has played a vital role in various applications, such as surveillance, target detection, and information gathering. While existing works in this field mainly focused on a single UAV layer, in this work we consider a multilayered aerial computing network (MACN) scenario, which consists of a low-altitude platform (LAP) layer with multiple high-flexibility and low-capacity UAVs (called LUAVs) and a high-altitude platform (HAP) layer with one low-flexibility and high-capacity UAV (called HUAV). In such a scenario, We focus on the joint optimization of flying trajectories, computation offloading, and resource allocation, aiming at minimizing the uncertainty of search probability map (SPM), and meanwhile maximizing the number of target discovery and coverage rate. The problem is challenging due to the co-existence of discrete and continuous decision variables, as well as the fast and randomly changing of wireless environment. To solve the problem in an online and distributed manner, we propose a multiagent deep reinforcement learning (MADRL) approach based on the parameter sharing and action mask (PSAM), called PSAMMA, where the state-action-reward-state-action (SARSA) method is leveraged to determine the discrete flying and offloading decisions. Experiment results show that 1) the proposed PSAMMA algorithm outperforms existing algorithms in the literature, and can increase the average utility by 9.89%–31.15% and 2) we evaluate the search performance by analyzing the average uncertainty, target rate, and coverage rate under different parameter settings.

Federated learning protects users’ data privacy by sharing users’ local model parameters (instead of raw data) with a server. However, when massive users train a large machine learning model through federated learning, the dynamically varying and often heavy communication overhead can put significant pressure on the network operator. The operator may choose to dynamically change the network prices in response, which will eventually affect the payoffs of the server and users. This paper considers the under-explored yet important issue of the joint design of participation incentives (for encouraging users’ contribution to federated learning) and network pricing (for managing network resources). Due to heterogeneous users’ private information and multi-dimensional decisions, the optimization problems in Stage I of multi-stage games are non-convex. Nevertheless, we are able to analytically derive the corresponding optimal contract and pricing mechanism through proper transformations of constraints, variables, and functions, under three interaction structures of the participants. We show that the coordinated structure is better than the two uncoordinated structures, as it avoids the selfish behaviors of the network operator and the server; the vertically uncoordinated structure is better than the horizontally uncoordinated structure, as it avoids the interests misalignment between the server and the network operator. We also propose multi-period network pricing to reduce the implementation complexity of dynamic pricing. Numerical results based on real-world datasets show that our proposed mechanisms decrease the server's cost by up to 24.87% and increase the network operator's profit by up to 1245.25%, compared with the state-of-the-art benchmarks.

Artificial Intelligence-Generated Content (AIGC) has emerged as a transformative paradigm, enabling the autonomous creation of diverse content. By offloading model inference tasks to the network edge that is closer to mobile users (MUs), Mobile Edge Computing (MEC) has the potential to significantly enhance the performance of AIGC services. In practice, however, it is challenging to optimally manage MEC-empowered AIGC services, due to the lack of well-defined AIGC-specific metrics, as well as the dynamic workload and computation-intensive nature of AIGC services. In this paper, we first define a novel AIGC metric based on extensive real data experiments, and then study thejoint task offloading and resource allocationproblem in a generic MEC-empowered AIGC network, where MUs can offload model inference tasks to local or remote Base Stations (BSs), aiming at maximizing their Quality of Experience (QoE). The problem is challenging due to the fast and randomly changing of environments, as well as the necessity for real-time, asynchronous decision-making. To tackle these challenges, we propose two deep reinforcement learning algorithms based on the Proximal Policy Optimization (PPO) framework:Single-Layer PPO (SL-PPO)andMulti-Layer PPO (ML-PPO), designed for slow-changing and fast-changing environments, respectively. In the SL-PPO algorithm, both task offloading and resource allocation decisions are made simultaneously when tasks arrive. In the ML-PPO algorithm, the task offloading decision is made immediately when tasks arrive, while the resource allocation decision is deferred until tasks are scheduled for processing or transmission in the corresponding queues. Simulation results show that (i) both algorithms outperform existing methods in the literature, and can increase the average utility by up to 47% and 48.8%; (ii) both algorithms can effectively manage the trade-off between latency and energy consumption.

Mobile edge computing (MEC) is a promising technology that enhances the efficiency of mobile blockchain networks, by enabling miners, often acted by mobile users (MUs) with limited computing resources, to offload resource-intensive mining tasks to nearby edge computing servers. Collaborative block mining can further boost mining efficiency by allowing multiple miners to form coalitions, pooling their computing resources and transaction data together to mine new blocks collaboratively. Therefore, an MEC-assisted collaborative blockchain network can leverage the strengths of both technologies, offering improved efficiency, security, and scalability for blockchain systems. While existing research in this area has mainly focused on the singlecoalition collaboration mode, where each miner can only join one coalition, this work explores a more comprehensive multicoalition collaboration mode, which allows each miner to join multiple coalitions. To analyze the behavior of miners and the edge computing service provider (ECP) in this scenario, we propose a novel two-stage Stackelberg game. In Stage I, the ECP, as the leader, determines the prices of computing resources for all MUs. In Stage II, each MU decides the coalitions to join, resulting in an overlapping coalition formation (OCF) game; Subsequently, each coalition decides how many edge computing resources to purchase from the ECP, leading to an edge resource competition (ERC) game. We derive the closed-form Nash equilibrium for the ERC game, based on which we further propose an OCFbased alternating algorithm to achieve a stable coalition structure for the OCF game and develop a near-optimal pricing strategy for the ECP's resource pricing problem. Simulation results show that the proposed multi-coalition collaboration mode can improve the system efficiency by 12.64% ∼ 17.63%, compared to the traditional single-coalition collaboration mode.

Edge-cloud computing networks integrate dispersed computing resources of edges and clouds through networks, which improves resource utilization by flexibly scheduling tasks to suitable computing nodes. The performance of edge-cloud computing networks depends significantly on the amount of computing resources and the task scheduling scheme. In this work, we propose a novel computing resource trading and task scheduling framework for edge-cloud computing networks with arbitrary network topology. Specifically, we consider a third-party platform which incentivizes computing nodes to share computing resources by designing proper resource pricing mechanisms, and charges customers execution fees by scheduling tasks optimally in the edge-cloud computing network. The platform’s resource pricing and task scheduling optimization problem captures the unique features of edge-cloud computing networks including the heterogeneities of computing resources and tasks, as well as the multi-hop offloading in arbitrary topology, which is challenging to solve. We solve the problem for the homogeneous workload scenario and the heterogeneous workload scenario, respectively. For the homogeneous workload scenario, we propose a multi-round proposer-voter algorithm (MPV) that achieves the global optimum in polynomial time for the non-competitive case. For the heterogeneous workload scenario, we first propose a Gibbs sampling based iterative algorithm (GSI), which updates task scheduling strategies iteratively using Gibbs sampling and converges to the global optimum with high probability. We further propose a distributed alternating update algorithm (DAU), which converges to the local optimum in a distributed manner with linear complexity. Numerical results demonstrate the effectiveness of our proposed resource trading and task scheduling schemes.

The mining process in blockchain networks generates substantial computing consumption, which can be very challenging for miners operated by mobile users (MUs) with limited computing resources. Cloud mining service (CMS) offers a viable solution to this challenge by allowing miners to offload computation-intensive mining tasks to cloud mining providers (CMP) with abundant resources. One key problem in such a scenario is to design an effective resource pricing mechanism for the CMP. Existing researches in this field mainly focused on the differentiated pricing mechanisms, where different MUs are required to pay different prices, which are often very complicated. In this work, we explore an efficient resource pricing mechanism, where the CMP sets a uniform price for all MUs but regulates the quality of resources for different MUs. Such a mechanism can capture the key essence of differentiated pricing and greatly reduce complexity. Based on this novel mechanism, we establish a two-stage Stackelberg game between the CMP and MUs, which consists of a resource pricing and quality control problem (for the CMP) as the first stage and a mining competition game (among all MUs) as the second stage. We derive the closed-form Nash equilibrium for the mining competition game in the second stage and propose a successive convex approximation (SCA)-based algorithm that converges to the near-optimal solution in the first stage. Simulation results show that the proposed iterative algorithm can improve system utility by 32.5% compared to other schemes.

This paper investigates the problem of caching placement and wireless backhaul resource allocation for enhanced mobile broadband (eMBB) and ultra-reliable low latency communications (URLLC) coexistence. Different from existing works which focus on the resource allocation for eMBB and URLLC in the access but ignore the backhaul traffic, this paper considers that the backhaul traffic can be alleviated by caching eMBB contents at the edge, such that more transmission resources can be released for scheduling URLLC traffic in wireless backhaul networks. We first propose an upper confidence bound (UCB)-based caching algorithm to reduce eMBB traffic in the backhaul, resulting in more backhaul resources that can be punctured by URLLC traffic. To minimize the URLLC delay while ensuring eMBB throughput, a greedy resource allocation algorithm is then proposed to allow URLLC traffic to puncture resources of eMBB traffic in the backhaul as many as possible. Simulation results demonstrate the superior performance of the proposed algorithms in terms of URLLC delay.

Multi-UAV Cooperative Search (MCS) can significantly enhance the efficiency and effectiveness of search by enabling multiple unmanned aerial vehicles (UAVs) to collaborate in conducting search missions. Thus, it has played a vital role in various applications, such as surveillance, target detection, and information gathering. While existing works in this field mainly focused on a single UAV layer, in this work we consider a Multi-layered Aerial Computing Network (MACN) scenario, which consists of a Low-Altitude Platform (LAP) layer with multiple high-flexibility and low-capacity UAVs (called LUAVs) and a High-Altitude Platform (HAP) layer with one low-flexibility and high-capacity UAV (called HUAV). In such a scenario, We focus on the joint optimization of flying trajectories, computation offloading, and resource allocation, aiming at minimizing the uncertainty of Search Probability Map (SPM). The problem is challenging due to the co-existence of discrete and continuous decision variables, as well as the fast and randomly changing of wireless environment. To solve the problem in an online and distributed manner, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) approach based on Parameter Sharing and Action Mask (PSAM), called PSAMMA, where the State-Action-Reward-State-Action (SARSA) method is leveraged to determine the discrete flying and offloading decisions. Experiment results show that the proposed PSAMMA algorithm outperforms existing methods in terms of the average SPM uncertainty, the target discovery rate, and the coverage rate.

Federated learning (FL) is emerging as a new privacy-preserving learning paradigm that allows multiple devices to collaborate in training a model without sharing their raw data, using a central server for coordination. However, device heterogeneity poses a challenge in FL, as participating devices often have different computing capacities. To address this issue, heterogeneous models need to be designed to accommodate different device computing capacities. The existing approach involves pre-designing multiple heterogeneous models and extracting sub-models from the server model. While such an approach effectively tackles device heterogeneity, it has several drawbacks such as high communication overhead and insufficient personalization. In each training round, the server distributes the entire model parameters to each device, and each device also transmits the entire model parameters to the server for aggregation. In this work, we propose FedPartial, a new framework that overcomes these challenges by introducing a partial model transmission and aggregation mechanism. The FedPartial framework eliminates the need for devices to transmit the entire model parameters in each training round while still benefiting from global model aggregation. Specifically, FedPartial divides the device model into two parts: the shallow part participates in the global aggregation of heterogeneous models, while the deep part remains on the device locally. By keeping the deep part of the model on the device, FedPartial reduces the communication overhead significantly and achieves a certain degree of personalization. Through extensive experiments, we demonstrate that FedPartial outperforms existing state-of-the-art methods, particularly in more complex and statistically heterogeneous scenarios.

Unmanned Aerial Vehicles (UAVs), with their high levels of flexibility and maneuverability, can greatly enhance the capabilities of Mobile Edge Computing (MEC) by acting as edge computing servers. In practice, however, it is often challenging to jointly optimize the flying trajectories of UAVs and the offloading decisions of tasks, due to the fast and randomly changing of physical environments. In this work, we investigate an UAV-enable MEC network with the assistance of Digital Twin (DT), where a DT layer is introduced to simulate the Physical Entity (PE) layer, generate different strategies, and evaluate their performances. Specifically, we formulate a joint flying trajectories, task offloading, and resource allocation problem on the DT layer, aiming at minimizing both task delay and energy consumption, under the maximum tolerated delay and resource constraints. To solve the problem in an online distributed manner and implement the derived strategies on the real PE layer, we propose a hierarchical learning approach, which consists of a Deep Reinforcement Learning (DRL) module and a Constrained Optimization (CO) module. First, the DRL module determines the UAVs' flying trajectories. Then, the CO module determines the MDs' task offloading decisions and the associated resource allocations, given the UAV s' flying decisions. Finally, the outputs of both modules are combined together to train the DRL module by using the Deep Deterministic Policy Gradient (DDPG) method. Experiment results show that our proposed DT-assisted scheme outperforms existing benchmark schemes in terms of both task delay and energy cost.

In this paper, symbiotic radio under simultaneous wireless information and power transfer (SWIPT)-enabled cell-free massive multiple-input multiple-output (CF-mMIMO) is investigated, where multiple access points serve all primary users and backscatter devices (BDs). We derive closed-form expressions for the achievable rates of the primary users and BDs, downlink signal-to-interference-plus-noise-ratio (SINR) and the harvested energy of the primary users. We aim to maximize fairness among BDs through joint optimization of the power splitting factor of SWIPT, uplink and downlink power control and backscatter coefficients of BDs. Being different from traditional low-complexity modified bisection search (MBS) schemes applied in other systems where the non-convex issues of the downlink SINR constraints are not considered, a novel MBS-based resource allocation scheme is proposed for symbiotic radio under SWIPT-enabled CF-mMIMO employing successive convex approximation method to address the non-convex issue and thus enhance the applicability of the MBS scheme. Simulation results show that our proposed scheme can obtain a near-optimal solution with low complexity, which could reduce the processing delay of the central processing unit in CF-mMIMO networks.

While computing task offloading in mobile edge computing (MEC) has been extensively studied, existing research has primarily focused on the mobility-awareness arising from opportunistic contact between edge devices. However, in a crowdsourced MEC system, it is essential to incentivize edge users to relocate for offloading tasks to crowdsourced edge devices. This movement-aware task offloading is particularly important for the crowdsourced system operation and has not yet been thoroughly explored. Moreover, the situation becomes more complex when users are socially connected and D2D-enabled, yet few works have considered these characteristics in combination, particularly from an economic incentive perspective. Therefore, a new incentive framework is needed to analyze the economic issues comprehensively. In this work, we focus on designing a truthful incentive mechanism, where socially-connected D2D users can be incentivized to move around for offloading tasks. To truthfully elicit private information, we model the resource allocation between edge devices and users as a multi-seller multi-buyer double auction mechanism with realistic MEC constraints, such as delay and storage limitation. Theoretically, we show that the proposed mechanism is computationally efficient and achieves desirable economic properties, including truthfulness, individual rationality, and budget balance. Simulations demonstrate that the mechanism achieves good system efficiency, with a performance improvement of 20% compared to state-of-the-art baselines.

Mobile-edge computing (MEC) is a promising computing scheme to support computation-intensive AI applications in vehicular networks, by enabling vehicles to offload computation tasks to edge computing servers deployed on road side units (RSUs) that approximate to them. In this work, we consider an MEC-enabled vehicular edge network (VEN), where each vehicle can offload tasks to edge/cloud computing servers via vehicle-to-infrastructure (V2I) links or to other end-vehicles via vehicle-to-vehicle (V2V) links. In such acloud-edge–endcollaborative offloading scenario, we focus on the joint task offloading, scheduling, and resource allocation problem for vehicles, which is challenging due to the online and asynchronous decision-making requirement for each task. To solve the problem, we propose aMultilayer deep reinforcement learning(DRL)-based approach, where each vehicle constructs and trains three modules to make different layers’ decisions: 1)Offloading Module(first layer), determining whether to offload each task, by using the dueling and double deepQ-network (D3QN) framework; 2)Scheduling Module(second layer), determining where and how to offload each task in the offloading queues, together with the transmission power, by using the parameterized deepQ-network (PDQN) framework; and 3)Computing Module(third layer), determining how much computing resource to be allocated for each task in the computation queues, by using classic optimization techniques. We provide the detailed algorithm design and perform extensive simulations to evaluate its performance. Simulation results show that our proposed algorithm outperforms the existing algorithms in the literature, and can reduce the average cost by 25.86%–72.51% and increase the average satisfaction rate by 3.48%–90.53%.

With the proliferation of edge computing, many business entities deploy their own edge servers to compete for users, which forms multi-server edge computing networks. However, no prior work studies the competition among heterogeneous edge servers and how the competition affects users’ selfish computation offloading behaviors in such a network from an economic perspective. In this paper, we model the interactions between edge servers and users as a two-stage game. In Stage I, edge servers with heterogeneous marginal costs set their service prices to compete for users, and in Stage II, each user selfishly offloads its task to one of the edge servers or the remote cloud. Analyzing the equilibrium of the two-stage game is challenging due to edge servers’ heterogeneity and the congestion effect caused by resource sharing among users. We first investigate the equilibrium when edge servers follow the serve-as-arrive (SAA) model (i.e., serving all offloaded tasks simultaneously), and then extend our analysis to the serve-in-queue (SIQ) model (i.e., serving offloaded tasks one by one following the M/M/1 queue rule). Under the SAA model, we prove that users’ selfish computation offloading game in Stage II is a potential game and admits a unique Nash equilibrium (NE), for which we derive the explicit expressions. Furthermore, for edge servers’ price competition game in Stage I, we characterize the conditions for the uniqueness of the NE and derive its explicit expression. Under the SIQ model, we derive the unique NE of users’ selfish computation offloading game, and show that the NE of edge servers’ price competition game may not always exist. We compare the equilibrium under the two service models and show that at equilibrium, edge servers with low marginal costs can achieve higher profits under the SIQ model when edge servers’ computation capacity is large or the delay incurred on the cloud is moderate; however, edge servers with high marginal costs can obtain higher profits under the SAA model in most cases.

Mobile edge caching is promising to improve content delivery and alleviate backbone burden by caching contents at the network edges. The commercial deployment relies on a comprehensive understanding of the economic interactions involved. This paper studies the edge caching enabled Internet ecosystem including a Content Provider (CP), a Global ISP (G-ISP) providing backbone services, a Local ISP (L-ISP) providing access services, and End-Users (EUs). The CP serves EUs via Internet servers or L-ISP's edge cache. We formulate their multi-tiered interactions as a three-stage dynamic game. In Stage I, CP determines edge cache storage to purchase from L-ISP and cache access fee to charge EUs. In Stage II, G-ISP and L-ISP determine backbone and access prices. In Stage III, EUs decide whether to choose edge cache services, considering cache hit probability, cache access fee, and backbone access prices. We analyze the subgame perfect equilibrium by elaborately designing five cases of EUs' choices, five regions of ISPs' pricing, and three patterns of CP's caching, undercooperativeandcompetitiveISP pricing scenarios. Our analysis demonstrates that adopting edge caching leads to win-win outcomes for all parties involved. Furthermore, we find that competitive pricing is more advantageous for CP's profit when cache costs are low, while cooperative pricing is more beneficial when cache costs are high.

Federated Learning (FL) is a distributed machine learning approach that enables multiple individual devices (clients) to collaboratively train a global machine learning model without directly sharing their raw data with each other. By keeping the raw data on local devices, FL can effectively preserve data privacy and security. However, the performance of FL system is highly dependent on the amount and quality of client data, as well as the relevance of data from different clients. In this article, we investigate the client and data selection problem in FL system from both system and individual perspectives, while considering the impacts of data quality and data relevance. Specifically, from the system perspective, we establish a centralized optimization problem that aims to optimize social welfare in a centralized manner. That is, a central controller decides on the amount of each client's data to be utilized for the FL system, aiming at maximizing the overall social welfare. From the individual perspective, we formulate a two-stage Stackelberg game for incentivizing clients to contribute their data and resources to the FL system in a decentralized manner. In the first stage, the FL server acts as the game leader and specifies a reward mechanism. In the second stage, each client acts as a game follower and competes for the reward by deciding on the amount of data to contribute to the FL system, aiming at maximizing its individual payoff. We analyze the centralized optimization problem and the Stackelberg game systematically for both low and high data relevance scenarios. In particular, we derive the closed-form optimal solution and game equilibrium for the low data relevance scenario, and propose iterative algorithms that effectively converge to a suboptimal solution and subgame equilibrium for the high data relevance scenario. Simulation results verify that data relevance has a significant negative impact on the system performance. That is, the social welfare achieved through centralized optimization and distributed Stackelberg game approaches in the high data relevance scenario is only 19.7% and 24.0%, respectively, of those achieved in the low data relevance scenario.

Mobile edge computing (MEC) is a promising technology for improving the efficiency and security of mobile blockchain networks, by allowing miners with limited computing resources to offload the computation-intensive mining tasks to edge computing servers that are proximate to them. Collaborative block mining can further improve the mining efficiency and increase the miner profit, by enabling multiple miners to pool their computation resources and transaction data together to mine new blocks collaboratively. Thus, an MEC-assisted collaborative blockchain network can leverage the advantages of both technologies, offering superior efficiency, security, and scalability for blockchains. While existing research in this area mainly focused on the single-coalition collaboration mode where each miner can only join one collaborative coalition, this work explores a more comprehensive multi-coalition collaboration mode, which allows each miner to join multiple collaborative coalitions. To analyze the miner behavior in such a scenario, we formulate a novel two-layer sequential game, consisting of a coalition formation game as the first-layer and an edge resource competition game (among the formed coalitions) as the second layer. Specifically, in the first layer, each miner acts as a game player and selects multiple coalitions to join, leading to an overlapping coalition formation (OCF) game among miners. In the second layer, each established coalition acts as a game player and decides the amount of edge computing resource to invest, leading to an edge resource competition (ERC) game among coalitions. We derive the closed-form Nash equilibrium for the ERC game, and propose an iterative algorithm that converges to a stable coalition structure for the OCF game. Simulation results show that the proposed multi-coalition collaboration mode can improve the system efficiency by 34.1% ~ 54.3%, compared to the single-coalition collaboration mode.

Mobile Edge Computing (MEC) is emerging as a promising computing scheme to support AI-enabled applications in vehicular networks, via offloading some tasks to edge servers deployed on Road Side Units (RSUs) that approximates to vehicles. In this work, we consider a general vehicular edge network (VEN), where each vehicle can offload tasks to edge servers or cloud server via a vehicle-to-infrastructure (V2I) transmission link, or to other vehicles via a vehicle-to-vehicle (V2V) transmission link. To characterize different task flows in different transmission links or computing servers, we introduce a V2V transmission queue, a V2V transmission queue, and a local computation queue for each vehicle, and an edge computation queue for each edge server. In such a queue-based VEN, we focus on the joint task offloading and scheduling problem for vehicles, which consists of (i) offloading problem, i.e., whether to offload tasks, and (ii) scheduling problem, i.e., where and how to offload tasks. The problem is challenging due to the online and asynchronous offloading and scheduling decisions for each task. We propose a Multi-layer Deep Reinforcement Learning (DRL) approach, where each vehicle trains three neural networks (called agents) to make different layers' decisions: (i) offloading agent, determining whether to offload each task when tasks arrive, and (ii) V2I and V2V scheduling agents, determining where and how to offloading each task in V2I and V2V transmission queues, respectively. We provide the detailed algorithm design of each agent by using the Double Deep Q-Network (DDQN) approach. Simulation results show that our proposed multi-layer DRL approach outperforms the existing baseline approaches in terms of both the cost performance and the convergence speed.

Federated learning protects users’ data privacy though sharing users’ local model parameters (instead of raw data) with a server. However, when massive users train a large machine learning model through federated learning, the dynamically varying and often heavy communication overhead can put significant pressure on the network operator. The operator may choose to dynamically change the network prices in response, which will eventually affect the payoffs of the server and users. This paper considers the under-explored yet important issue of the joint design of participation incentives (for encouraging users’ contribution to federated learning) and network pricing (for managing network resources). Due to heterogeneous users’ private information and multi-dimensional decisions, the optimization problems in Stage I of multi-stage games are non-convex. Nevertheless, we are able to analytically derive the corresponding optimal contract and pricing mechanism through proper transformations of constraints, variables, and functions, under both vertical and horizontal interaction structures of the participants. We show that the vertical structure is better than the horizontal one, as it avoids the interests misalignment between the server and the network operator. Numerical results based on real-world datasets show that our proposed mechanisms decrease server’s cost by up to 24.87% comparing with the state-of-the-art benchmarks.

With the highly increasing demands of vehicular applications, the cloud intelligence is pushed towards the edge by placing services next to vehicle users. Due to the limited resources of edge nodes and the high mobility of vehicle users, it is challenging to place vehicular services effectively at the edge to serve vehicle users with high quality of experience (QoE). In this paper, we investigate a vehicular service placement problem with unknown demands. Different from previous works, we consider that vehicle users have various service demands, which are related to their contexts. To enable on-demand service placement, we propose a context-aware vehicular service placement algorithm based on the estimated service demands. For better demand estimation, a fine-grained partition method is developed to divide the context space. The simulation results show that the proposed algorithm has superior performance in terms of cumulative system rental utility.

Mobile Edge Computing (MEC) is a promising approach for enhancing the quality-of-service (QoS) of AI-enabled applications in the B5G/6G era, via providing computation services at the network edge that approximate end-users. In this work, we focus on the joint optimization of edge server (ES) deployment, service placement, and computation task offloading under stochastic information scenario. In traditional solutions, these decisions are often treated equally without considering differences in the information realization. In practice, however, the ES deployment decision needs to be made in advance before the complete information is realized, while the service placement and computation task offloading decisions can be made after the complete information is realized. To capture the time coupling between different decisions and information realizations, we formulate a two-layer stochastic programming (SP) problem, which consists of a strategic-layer decision for ES deployment, and a tactical-layer decision for service placement and computation task offloading. The strategic-layer decision will be made based on the stochastic information (i.e., before the complete information is realized), while the tactical-layer decision will be made based on every information realization. The problem is very challenging due to the large number of information realizations and the corresponding tactical-layer decisions. To solve the problem effectively, we propose a Sample Average Approximate (SAA) method to approximate the optimal solution, which involves generating a large number of randomly sampled information scenarios and using their averages to estimate the expected value of the objective function. Numerical simulations show that our proposed SP approach outperforms the traditional solutions that do not consider the coupling between decisions and information realizations. Moreover, compared with the ideal benchmark solution that assumes complete information, our proposed SP approach only results in a small performance degradation of 1.03%$\sim$6.26%.

An Internet of Things (IoT) system can include several different types of service providers, who sell IoT service, network service, and computation service to customers, either jointly or separately. A deep understanding of complicated coupling among these providers in terms of pricing and service decisions is critical to the success of IoT networks. This paper studies the impact of the provider interaction structures on the overall IoT system with heterogeneous customers. Specifically, we first study a generic IoT scenario with three interaction structures: coordinated, vertically-uncoordinated, and horizontally-uncoordinated structures. Despite the challenging non-convex optimization problems involved in modeling and analyzing these structures, we successfully obtain the closed-form optimal pricing strategies of providers in each interaction structure. We further extend the analysis to a specific IoT scenario with local computation capability (e.g., Internet of Vehicles (IoV)). We prove that the coordinated structure is better than two uncoordinated structures for both providers and customers, as it avoids selfish price markup behaviors in uncoordinated structures. Between the two uncoordinated structures, when customers' demand variance is large and utility-cost ratio is medium, vertically-uncoordinated structure is better than horizontal one for both providers and customers, due to the complementary providers' competition in horizontally-uncoordinated structure. Counter-intuitively, we identify that providers' optimal prices do not change with their costs at the critical point of customers' full participation in the vertically-uncoordinated structure.

Unsupervised dimension reduction and clustering are frequently used as two separate steps to conduct clustering tasks in subspace. However, the two-step clustering methods may not necessarily reflect the cluster structure in the subspace. In addition, the existing subspace clustering methods do not consider the relationship between the low-dimensional representation and local structure in the input space. To address the above issues, we propose a robust discriminant subspace (RDS) clustering model with adaptive local structure embedding. Specifically, unlike the existing methods which incorporate dimension reduction and clustering via regularizer, thereby introducing extra parameters, RDS first integrates them into a unified matrix factorization (MF) model through theoretical proof. Furthermore, a similarity graph is constructed to learn the local structure. A constraint is imposed on the graph to guarantee that it has the same connected components with low-dimensional representation. In this spirit, the similarity graph serves as a tradeoff that adaptively balances the learning process between the low-dimensional space and the original space. Finally, RDS adopts the$\ell _{2,1}$-norm to measure the residual error, which enhances the robustness to noise. Using the property of the$\ell _{2,1}$-norm, RDS can be optimized efficiently without introducing more penalty terms. Experimental results on real-world benchmark datasets show that RDS can provide more interpretable clustering results and also outperform other state-of-the-art alternatives.

Federated Learning (FL) is a novel distributed learning framework to train the global model locally without collecting the raw data of clients. However, the performance of FL is greatly restricted by the limited network communication capacity between the cloud and clients. MEC-assisted Hierarchical Federated Learning (HFL) can effectively relieve the network pressure in FL, by transmitting and aggregating model parameters at the network edge based on the idea of Mobile Edge Computing (MEC). The existing researches on HFL often adopt the traditional multiple access techniques (e.g., OFDMA) for the model transmission between clients and edge servers, which may be inefficient. In this work, we consider a novel Over-the-Air Computing (AirComp) based HFL framework, where clients send model parameters to edge servers simultaneously, and edge servers can directly complete the aggregation of model in the air by exploiting the superposition property of wireless channels. In such a scenario, we study the joint client association, transmission, and computation optimization problem, aiming at minimizing the overall energy consumption and latency. The problem is challenging due to the multi-level coupling between edge servers and clients. We decouple it into a client association subproblem and a resource optimization subproblem. We first show that the second subproblem is convex and can be easily solved by a coordinate descent algorithm. We then show that the first subproblem is a combinational optimization, and propose a near-optimal solution where each client is associated with the nearest edge server. Simulation results show that the AirComp-based HFL scheme outperforms the existing OFDMA-based schemes in terms of both energy consumption and latency.

Mobile edge computing (MEC) is a promising approach to reduce the network traffic load and alleviate the back-haul congestion by pushing computation down to the network edge (e.g., base stations) that are close to the origin of data. However, when many mobile devices (MDs) offload tasks to a base station (BS) in a dynamic and stochastic environment (e.g., with time-varying wireless channels and uncertain task models), it is often challenging for MDs to make offloading decisions in decentralized manner. In this work, we consider a collaborative MEC scenario, where an MD can offload its task to the associated BS or to other BSs through the associated BS. In such a scenario, we study the joint computation offloading and resource allocation problem, aiming at minimizing the expected long-term delay, taking the energy consumption constraint into consideration. The problem is challenging due to time-varying system and distributed decisions. To solve the problem in an online and decentralized manner, we propose a deep reinforcement learning (DRL) based distributed online algorithm. By incorporating the double deep Q network and dueling deep Q network technique, the proposed algorithm can improve the performance of the whole system significantly. Simulation results show that the proposed DRL-based algorithm outperforms baseline methods and can reduce the average delay of tasks by 76.4%-91.2%.

An Internet of Things (IoT) system can include several different types of service providers, who sell IoT service, network service, and computation service to customers, either jointly or separately. The complicated coupling among these providers in terms of pricing and service decisions is an under-explored research area, the understanding of which is critical to the success of IoT networks. This paper studies the impact of the provider interaction structures on the overall IoT system with massive heterogeneous customers. Specifically, we consider three interaction structures: coordinated, vertically-uncoordinated, and horizontally-uncoordinated structures. Despite the challenging non-convex optimization problems involved in modeling and analyzing these structures, we successfully obtain the closed-form optimal pricing strategies of providers in each interaction structure. We prove that the coordinated structure is better than two uncoordinated structures for both providers and customers, as it avoids selfish price markup behaviors in uncoordinated structures. When customers’ demand variance is large and utility-cost ratio is medium, vertically-uncoordinated structure is better than horizontal one for both providers and customers, due to the complementary providers’ competition in horizontally-uncoordinated structure. Counter-intuitively, we identify that providers’ optimal prices do not change with their costs at the critical point of customers’ full participation in the vertically-uncoordinated structure.

Multi-arm bandit (MAB) is a classic online learning framework that studies the sequential decision-making in an uncertain environment. The MAB framework, however, overlooks the scenario where the decision-maker cannot take actions (e.g., pulling arms) directly. It is a practically important scenario in many applications such as spectrum sharing, crowdsensing, and edge computing. In these applications, the decision-maker would incentivize other selfish agents to carry out desired actions (i.e., pulling arms on the decision-maker’s behalf). This paper establishes the incentivized online learning (IOL) framework for this scenario. The key challenge to design the IOL framework lies in the tight coupling of the unknown environment learning and asymmetric information revelation. To address this, we construct a special Lagrangian function based on which we propose a socially-optimal mechanism for the IOL framework. Our mechanism satisfies various desirable properties such as agent fairness, incentive compatibility, and voluntary participation. It achieves the same asymptotic performance as the state-of-art benchmark that requires extra information. Our analysis also unveils the power of crowd in the IOL framework: a larger agent crowd enables our mechanism to approach more closely the theoretical upper bound of social performance. Numerical results demonstrate the advantages of our mechanism in large-scale edge computing.

In this article, we investigate the long-term energy consumption and transmission delay (EC-TD) tradeoff in a wireless-powered body area network that consists of a multiantenna hybrid access point and a number of single-antenna sensor nodes (SNs). The beamforming technique and the simultaneous wireless information and power transfer (SWIPT) technique are adopted. Each SN is equipped with a battery and data buffer for storing harvested energy and sensory data. The long-term energy consumption minimization problem is addressed subject to the constraint of transmission delay. Meanwhile, the residual energy constraints of SNs are considered, which enable the setting up of the available energy of the SNs according to requirements. By employing the Lyapunov optimization theory, the original stochastic optimization problem is transformed into an equivalent instantaneous nonconvex problem in which the long-term EC-TD tradeoff can be adjusted using a system control parameter$V$. A joint power and time allocation scheme is then proposed to solve this instantaneous problem. Moreover, based on the derived upper bounds of the long-term energy consumption and data buffer length, we reveal that the proposed resource allocation scheme achieves an EC-TD tradeoff as$[\mathcal {O}(1/V),\mathcal {O}(V)]$. Since the value of$V$can be adjusted to achieve different energy consumption and transmission delay, the flexibility and applicability of the proposed scheme are enhanced. The simulation results validate the theoretical analysis and verify the effectiveness of the proposed scheme.

Mobile crowdsensing (MCS) booms the implementation of the Internet of Things (IoT) in different areas due to flexibility and low deployment cost. However, collecting sufficient high quality sensing data is crucial for the success of various applications. Incentive mechanism design plays a critical role in the successful implementation of mobile MCS systems. Most of existing work consider that the platform exactly knows the trajectory of mobile users. However, in most cases, it is difficult to obtain the accurate information of the location of mobile users due to either privacy issue or the lack of information. In this article, we consider nondeterministic mobility of mobile users, where only the probability distribution of users’ mobility is available. We design an effective mechanism to achieve the quality data collection with the objective of maximizing the expected social welfare. Simulation results show that the proposed mechanism achieves her expected social welfare compared with four existing schemes, while satisfying truthfulness, individual rationality, and computational efficiency.

Mobile network operators (MNOs) provide wireless data services based on a tariff data plan with a month data cap. Traditionally, the data cap is only valid for domestic data consumption and users have to pay extra roaming fees for overseas data consumption. A recently emerged location-flexible service allows users to access the domestic data cap in overseas locations (by configuring location-flexibility with a daily fee). This paper studies the economic impact of the location-flexibility on the overseas market. The overseas market tracks the travelers on a monthly basis, hence it is month-variant. Each user in overseas market decides his joint flexibility configuration and data consumption (J-FCDC) every day, which corresponds to an on-line payoff maximization problem. We first analyze the off-line version of J-FCDC problem (which is NP-hard), and then we design an on-line strategy with a provable performance guarantee. Moreover, we propose a pricing policy for the location-flexible service without the need of knowing the market statistic information. We find that the location-flexibility induces users to consume more data in low-valuation days, and the MNO benefits from stimulating users’ data consumption through an appropriate pricing. Numerical results show that the location-flexibility improves the MNO’s revenue and the users’ payoffs.

In mobile Internet ecosystem, mobile users (MUs) purchase wireless data services from Internet service provider (ISP) to access to Internet and acquire the interested content services (e.g., online game) from Content Provider (CP). The popularity of intelligent functions (e.g., AI and 3D modeling) increases the computation-intensity of the content services, leading to a growing computation pressure for the MUs’ resource-limited devices. To this end,edge computing serviceis emerging as a promising approach to alleviate the MUs’ computation pressure while keeping their quality-of-service, via offloading some computation tasks of MUs to edge (computing) servers deployed at the local network edge. Thus, edge service provider (ESP), who deploys the edge servers and offers the edge computing service, becomes an upcoming new stakeholder in the ecosystem. In this work, we study the economic interactions of MUs, ISP, CP, and ESP in the new ecosystem with edge computing service, where MUs can acquire the computation-intensive content services (offered by CP) and offload some computation tasks, together with the necessary raw input data, to edge servers (deployed by ESP) through ISP. We first study the MU's Joint Content Acquisition and Task Offloading (J-CATO) problem, which aims to maximize his long-term payoff. We derive theoff-linesolution with crucial insights, based on which we design anonlinestrategy with provable performance. Then, we study the ESP's edge service monetization problem. We propose a pricing policy that can achieve aconstant fractionof the ex post optimal revenue with an extraconstant lossfor the ESP. Numerical results show that the edge computing service can stimulate the MUs’ content acquisition and improve the payoffs of MUs, ISP, and CP.

Mobile Edge Caching is a promising technique to enhance the content delivery quality and reduce the backhaul link congestion, by storing popular contents at the network edge or mobile devices (e.g. base stations and smartphones) that are proximate to content requesters. In this work, we study a novel mobile edge caching framework, which enables mobile devices to cache and share popular contents with each other via device-to-device (D2D) links. We are interested in the incentive-related problem of mobile device users: whether and which users are willing to cache and share what contents, taking the user mobility and cost/reward into consideration. The problem is challenging in a large-scale network. We introduce the evolutionary game theory, an effective tool for analyzing large-scale dynamic systems, to analyze the mobile users' content caching and sharing strategies. Specifically, we first derive the users' best caching and sharing strategies, and then analyze how these best strategies change dynamically over time. Based on the above, we further characterize the system equilibrium systematically. Simulation results show that the proposed scheme outperforms the existing schemes in terms of the total transmission cost and the cellular load. In particular, in our simulations, the total transmission cost can be reduced by 42.5%~55.2% and the cellular load can be reduced by 21.5%~56.4%.

Mobile Edge Computing (MEC) is a promising solution to reduce the task execution delay by placing the computation resource at the network edge close to the end-users, and has received an extensive attention in the 5G era. In this work, we study a multi-layer task offloading framework for MEC, where each task generated by a mobile device can be offloaded to other mobile devices via D2D links, or edge servers with cellular links, or remote cloud server via Internet. We consider a generic task model, where each task can be divided into a set of dependent subtasks and each subtask can be offloaded to different locations. In such multi-layer offloading framework with dependence-aware tasks, we are interested in the optimal subtask offloading problem for mobile devices, that is, how to optimally offload the subtasks of all devices. To study this, we formulate an Energy Consumption Minimization problem for mobile devices, which decides when and where each subtask will be scheduled, aiming at minimizing the total energy consumption of mobile devices. The problem is challenging due to the non-convex constraints. We propose some mathematical operations to relax the nonlinear constraints into linear constraints, and hence transform the original non-convex problem into a linear programming, which can be solved efficiently. Simulation results show that our proposed solution outperforms the existing solutions in terms of energy consumption and task success rate. For example, it can reduce the mobile devices’ energy consumption by up to 40%.

Many online platforms are providing valuable real-time contents (e.g., traffic) by continuously acquiring the status of different Points of Interest (PoIs). In status acquisition, it is challenging to determine how frequently a PoI should upload its status to a platform, since they are self-interested with private and possibly time-varying preferences. This paper considers a general multi-period status acquisition system, aiming to maximize the aggregate social welfare and ensure the platform freshness. The freshness is measured by a metric termed age of information. For this goal, we devise a long-term decomposition (LtD) mechanism to resolve the time-varying information asymmetry. The key idea is to construct a virtual social welfare that only depends on the current private information, and then decompose the per-period operation into multiple distributed bidding problems for the PoIs and platforms. The LtD mechanism enables the platforms to achieve a tunable trade-off between payoff maximization and freshness conditions. Moreover, the LtD mechanism retains the same social performance compared to the benchmark with symmetric information and asymptotically ensures the platform freshness conditions. Numerical results based on real-world data show that when the platforms pay more attention to payoff maximization, each PoI still obtains a non-negative payoff in the long-term.

As edge computing provides crucial support for delay-sensitive and computation-intensive applications, many business entities deploy their own edge servers to compete for users, which forms multi-server edge computing networks. However, no prior work studies the competition among heterogeneous edge servers and how the competition affects users’ selfish computation offloading behaviors in such a network from an economic perspective. In this paper, we model the interactions between edge servers and users as a two-stage game. In Stage I, edge servers with heterogeneous marginal costs set their service prices to compete for users, and in Stage II, each user selfishly offloads its task to one of the edge servers or the remote cloud. Analyzing the equilibrium of the two-stage game is challenging due to edge servers’ heterogeneity and the congestion effect caused by resource sharing among users. We first prove that in Stage II, users’ selfish computation offloading game is a potential game and admits a unique Nash equilibrium (NE), for which we derive the explicit expression. We then analyze edge servers’ price competition game in Stage I and characterize the conditions for the uniqueness of the NE. We show that at equilibrium, users only choose low-priced edge servers, and hence edge servers with low marginal costs can win the price competition, which reflects the improvement of economic efficiency in competitive markets. Moreover, it is surprising that the equilibrium prices do not monotonically increase with the task execution delay. This is because a long execution delay gives a chance to edge servers with high marginal costs to win the competition, which results in more fierce competition among edge servers.

In this article, we focus on a wireless-powered body area network in which the simultaneous wireless information and power transfer (SWIPT) technique is adopted. We consider two scenarios based on whether sensor nodes (SNs) are equipped with battery. For the first time, energy consumption minimization with throughput heterogeneity (ECM-TH) problem is addressed for both scenarios. For the battery-free scenario, a low-complexity time allocation scheme is proposed. This scheme solves the ECM-TH problem based on a hybrid method of gradient descent and bisection search algorithms. Consequently, compared with the interior-point method, our scheme has a lower computational complexity for the same energy consumption performance of the network. For the battery-assisted scenario, the nonconvex ECM-TH problem is first transformed into a convex optimization problem by introducing auxiliary variables. Then, a joint time and power allocation scheme based on the Lagrange dual subgradient method is proposed to solve it. Compared with the battery-free scenario, energy consumption and outage probability are both decreased in the battery-assisted scenario. Moreover, we address a special case wherein the feasible set of the above-mentioned ECM-TH problems may be empty owing to poor channel conditions or high throughput requirements of SNs.

To alleviate the ever-increasing data demands, edge caching plays a crucial role in improving the performance of system, especially in data-intensive applications. Previous works mainly focus the caching policy over reliable channels. For unreliable channel scenarios, the system performance is jointly affected by the user preference and the channel reliability, whereas both the user preference and the reliability are unknown commonly. A high retrieval cost may be incurred on unreliable channels even when the requested content is in the nearby cache. To solve the issues mentioned above, we jointly optimize the service scheduling policy and the content caching policy in this paper. We propose a maximal reward priority (MRP) policy to serve user requests, and a collaborative multi-agent actor critic (CMA-AC) policy to update the local cache. Simulation results show that the proposed MRP policy outperforms the shortest distance priority (SDP) policy [4]. And the proposed CMA-AC policy obtains a better performance compared with a distributed multi-agent deep Q-network (DMA-DQN) policy, especially when the number of contents and the capacity of local cache are large. Furthermore, the proposed CMA-AC policy is robust.

Mobile edge caching is a promising approach for enhancing content delivery efficiency and alleviating backbone network burden, via caching popular contents at network edge devices (e.g., base stations or WiFi access points). The successful commercial deployment relies on a comprehensive understanding of the economic interactions among different stakeholders involved. In this paper, we study an edge caching system consisting of a Content Provider (CP), an Internet Service Provider (ISP) who provides the backbone network service, a wireless Access Provider (AP) who provides the wireless access service, and a set of mobile End-Users (EUs), where the CP provides contents for EUs either via the remote server (on the Internet) or via the edge cache (purchased from the AP). We formulate their interactions as a three-stage Stackelberg game. In Stage I, the CP decides the edge cache space to purchase from the AP and cache access fee to charge EUs. In Stage II, the ISP and AP determine the backbone and wireless access service prices, respectively. In Stage III, EUs decide whether to subscribe to the CP' s edge cache service, taking the cache hit probability, cache access fee, backbone and wireless access prices into consideration. We analyze the subgame perfect equilibrium of the dynamic game systematically under two different network pricing scenarios: cooperative pricing and competitive pricing, depending on whether ISP and AP cooperate or compete with each other to make their pricing decisions. Our analysis and simulation results show that all profits of the CP, ISP, AP, and utilities of EUs can be increased by adopting edge cache, compared with the case without edge cache.

Mobile Edge Computing (MEC) is a promising solution to tackle the upcoming computing tsunami in 5G era, by effectively utilizing the idle resource at the mobile edge. In this work, we study such an MEC scenario, where mobile devices at edge share their heterogeneous resources with each other, hence forming a multi-dimensional resource crowdsourcing (sharing) framework. We are interested in the problem of how to optimally offload tasks to mobile devices under this framework, aiming at minimizing the total energy cost and maximizing the overall task completion. To study the problem, we first propose a general task model, where each task is divided into multiple sequential subtasks according to their functionalities as well as resource requirements. Then, based on the task model, we propose a Joint Energy Consumption and Task Failure Probability Minimization Problem, which decides when and where each subtask will be offloaded to. The problem is challenging to solve, mainly due to the inherent constraints between the scheduling of different subtasks. Therefore, we propose several linearization methods to relax the constraints, and convert the original problem into an integer linear programming (ILP), which can be solved by many classic methods effectively. We further perform simulations, which show that our proposed solution outperforms the existing solutions (with indivisible tasks or without resource sharing) in terms of both the total cost and the task failure probability. Precisely, our proposed solution can reduce the total cost by 25%~85% and the task failure probability by 10%~35%.

Mobile edge computing (MEC) has shown its potential in serving computation intensive tasks via offloading. However, the heterogeneity of MEC systems and the dynamic nature of wireless environment pose a great challenge to the design of offloading policies. In this paper, we investigate this computation offloading problem, where the heterogeneities of computational resource, channel state, task type and input data size are considered. We first propose a greedy algorithm, in which each arrival task is greedily offloaded to the edge server with minimal utility, based on a global information of network states. While this greedy algorithm performs well in terms of system utility, the overhead incurred to collect the global information is large, especially in dense MEC scenarios and time-varying channel scenarios. Inspired by this observation, we then propose a model-free offloading algorithm based on reinforcement learning, which does not rely on such kind of information and can make offloading decisions based on learning experience. By so doing, the communication overhead can be largely reduced. Extensive simulations show that the two proposed algorithms have similar performance in terms of system utility and can decrease the system utility by up to 50% compared with two widely used algorithms. The robustness of the two proposed algorithms is further verified.

Mobile Network Operators (MNOs) provide wireless data services based on a tariff data plan with a month data cap. Traditionally, the data cap is only valid for domestic data consumption and users have to pay extra roaming fees for overseas data consumption. A recent location-flexible service allows the user to access the domestic data cap in overseas locations (by configuring location-flexibility with a daily fee). This paper studies the economic effect of the location-flexibility on the overseas market. The overseas market comprises users who travel overseas within the month, thus is monthly variant. Each user decides his joint flexibility configuration and data consumption (J-FCDC) every day. The user's J-FCDC problem is an on-line payoff maximization. We analyze its off-line problem (which is NP-hard) and design an on-line strategy with provable performance. Moreover, we propose a pricing policy for the location-flexible service without relying on the market statistic information. We find that the location-flexibility induces users to consume more data in low-valuation days, and the MNO benefits from stimulating users' data consumption through an appropriate pricing. Numerical results based on empirical data show that the location-flexibility improves the MNO's revenue by 18% and the users' payoffs by 12% on average.

Cooperative caching between multiple small base stations (SBSs) plays a critical role for easing the traffic congestion of the backhaul link. Previous works assume that cooperative caching policies can achieve good performance when each user in the region is mainly served by its nearest SBS, which may not be the case in small cell networks with heterogeneous channel qualities. In this paper, we study cooperative caching problem in a small cell network with heterogeneous channel qualities when content popularity profile is unknown. These two features impose new challenges in optimizing content placement in multiple SBSs. To address this problem, we first propose a bayes-based learning algorithm that learn the popularity profile by sampling from a Beta distribution at each time period. Based on the estimated popularity profile, we then optimize the content placement at each time period by caching contents with higher popularity/size ratio in SBSs with better channel qualities. Numerical results show that the proposed algorithms outperforms three baselines in terms of average transmission delay and cache hit rate.

In this paper, we consider the nondeterministic mobility of mobile users, where the platform only has the probability distribution about users' mobility. We design an effective mechanism to achieve high quality data collection with the objective of maximizing the expected social welfare. Simulation results show the better performance of the proposed mechanism compared with four counterparts. In addition, the proposed mechanism also satisfies truthfulness and individual rationality.

Crowdsourced mobile edge caching and sharing (Crowd-MECS) is emerging as a promising content delivery paradigm by employing a large crowd of existing edge devices (EDs) to cache and share popular contents. The successful technology adoption of Crowd-MECS relies on a comprehensive understanding of the complicated economic interactions and strategic decision making of different stakeholders in the ecosystem. In this article, we focus on studying the economic and strategic interactions between one content provider (CP) and a large crowd of EDs, where the CP designs the incentive scheme for EDs to cache and share contents, and EDs decide whether to cache and share contents for the CP. We formulate their interactions as a two-stage Stackelberg game. In Stage I, the CP decides the ratio of revenue (as incentives) shared with EDs who choose to cache and share contents, aiming at maximizing its own profit. In Stage II, EDs choose to beagentswho cache and share contents, and meanwhile gain a certain revenue from the CP, orrequesterswho do not cache but request contents in the on-demand fashion. We first analyze the EDs’ best responses and prove the existence and uniqueness of the equilibrium in Stage II by using the nonatomic game theory. Then, we identify the piecewise structure and the unimodal feature of the CP’s profit function, based on which we design a tailored low-complexity 1-D search algorithm to achieve the optimal revenue sharing ratio for the CP in Stage I. The simulation results show that both the CP’s profit and the EDs’ total welfare can be improved significantly (e.g., by 120% and 50%, respectively,) by using the proposed Crowd-MECS system, comparing with the non-MEC system where the CP serves all EDs directly.

The growing demand for mobile data and the evolution of next-generation networks, particularly fifth-generation (5G) wireless networks, has called for new approaches to pricing and managing the limited capacity of existing network resources and infrastructures. In particular, emerging mobile applications like autonomous vehicles, augmented/virtual reality, and more broadly the Internet-of-Things will have heterogeneous demand patterns and service requirements, raising questions on how they should pay for their data usage and how next-generation networks can meet their demands with limited resources. Several recent policy changes and regulatory initiatives have been proposed to address the shift in demands due to next-generation networks and technologies. These include the FCC’s “5G Fast Plan,” which outlines strategies for modifying spectrum policies, infrastructure policies, and existing regulations, in light of emerging 5G technologies. This plan has included the rollback of net neutrality rules in June 2018, allowing broadband providers to offer a wider variety of service options.

The growing competition drives the mobile network operators (MNOs) to explore adding time flexibility to the traditional data plan, which consists of a monthly subscription fee, a data cap, and a per-unit fee for exceeding the data cap. The rollover data plan, which allows the unused data of the previous month to be used in the current month, provides the subscribers with the time flexibility. In this paper, we formulate two MNOs' market competition as a three-stage game, where the MNOs decide their data mechanisms (traditional or rollover) in Stage I and the pricing strategies in Stage II, and then users make their subscription decisions in Stage III. Different from the monopoly market where an MNO always prefers the rollover mechanism over the traditional plan in terms of profit, MNOs may adopt different data mechanisms at an equilibrium. Specifically, the high-QoS MNO would gradually abandon the rollover mechanism as its QoS advantage diminishes. Meanwhile, the low-QoS MNO would progressively upgrade to the rollover mechanism. The numerical results show that the market competition significantly limits MNOs' profits, but both MNOs obtain higher profits with the possible choice of the rollover data plan.

An effective way for a Mobile network operator (MNO) to improve its revenue is price discrimination, i.e., providing different combinations of data caps and subscription fees. Rollover data plan (allowing the unused data in the current month to be used in the next month) is an innovative data mechanism with time flexibility. In this paper, we study the MNO's optimal multi-cap data plans with time flexibility in a realistic asymmetric information scenario. Specifically, users are associated with multi-dimensional private information, and the MNO designs a contract (with different data caps and subscription fees) to induce users to truthfully reveal their private information. This problem is quite challenging due to the multi-dimensional private information. We address the challenge in two aspects. First, we find that a feasible contract (satisfying incentive compatibility and individual rationality) should allocate the data caps according to users' willingness-to-pay (captured by the slopes of users' indifference curves). Second, for the non-convex data cap allocation problem, we propose a Dynamic Quota Allocation Algorithm, which has a low complexity and guarantees the global optimality. Numerical results show that the time-flexible data mechanisms increase both the MNO's profit (25 percent on average) and users' payoffs (8.2 percent on average) under price discrimination.

Mobile crowdsourced edge caching is emerging as a promising caching paradigm by crowdsourcing the storage resources of massive edge devices (EDs) for content caching. The successful technology adoption and commercial deployment rely on a comprehensive understanding of the economic interactions among different network entities involved in such a system. In this paper, we focus on the economic interactions between one content provider (CP) and a large number of EDs, where the CP shares a certain revenue with EDs as the incentive of caching contents, and EDs decide whether to cache contents and share the cached contents with others. We formulate their interactions as a two-stage Stackelberg game. In Stage I, the CP decides the ratio of revenue shared with EDs, aiming at maximizing its own profit. In Stage II, each ED chooses to be an agent who caches contents and shares the cached contents with other EDs, or a requester who does not cache but requests contents from agents. e first analyze the existence and uniqueness of the Stage II subgame equilibrium by using the evolutionary game theory. Then, we identify the piece-wise structure of the CP's profit function, and derive the optimal revenue sharing ratio for the CP in Stage I. Simulation results show that a higher revenue sharing ratio for EDs or a larger serving capacity of EDs can drive more EDs to choose to be agents and meanwhile achieve a higher total welfare for EDs at the equilibrium. Moreover, a larger content price of the CP will lead to a larger welfare loss for EDs.

Mobile Network Operators (MNOs) are providing more flexible wireless data services to attract subscribers and increase revenues. For example, the data trading market enables user-flexibility by allowing users to sell leftover data to or buy extra data from each other. The rollover mechanism enables time-flexibility by allowing a user to utilize his own leftover data from the previous month in the current month. In this paper, we investigate the economic viability of offering the data trading market together with the rollover mechanism, to gain a deeper understanding of the interrelationship between the user-flexibility and the time-flexibility. We formulate the interactions between the MNO and mobile users as a multi-slot dynamic game. Specifically, in each time slot (e.g., every day), the MNO first determines the selling and buying prices with the goal of revenue maximization, then each user decides his trading action (by solving a dynamic programming problem) to maximize his long-term payoff. Due to the availability of monthly data rollover, a user's daily trading decision corresponds to a dynamic programming problem with two time scales (i.e., day-to-day and month-to-month). Our analysis reveals an optimal trading policy with a target interval structure, specified by a buy-up-to threshold and a sell-down-to threshold in each time slot. Moreover, we show that the rollover mechanism makes users sell less and buy more data given the same trading prices, hence it increases the total demand while decreasing the total supply in the data trading market. Finally, numerical results based on real-world data unveil that the time-flexible rollover mechanism plays a positive role in the user-flexible data trading market, increasing the MNO's revenue by 25% and all users' payoff by 17% on average.

In this paper, we exploit the radio diversity gain in a multi-user hybrid radio network wirelessly powered by a power beacon station (PBS). Each user has a dual-mode radio that can switch between the passive and active modes, according to the channel and energy conditions. This provides extra degree of freedom to improve the overall network performance. As such, we propose a throughput maximization problem by jointly optimizing the PBS' energy beamforming and the radios' transmission scheduling strategies in two modes. We show that the throughput maximization is easily tractable by solving a semi-definite program. However, it becomes non-convex and intractable when we allow radios' cooperation in data transmissions. To this end, we propose a set of heuristic algorithms with different complexities for cooperative relay transmissions, which are shown to significantly improve the sum throughput compared to the non-cooperative case. The simulation results show that a simple adaptive scheme can achieve the maximum throughput according to the PBS' power supply.

We investigate an amplify-and-forward (AF) full-duplex (FD) relay system, where the FD incurred self-interference (SI), through partial cancellation at relay, is treated as a useful source at destination to enhance degree of freedom in signal detection, while reducing the signal processing cost of SI cancellation. An independent component analysis (ICA) based equalization structure is employed at destination to separate and detect the desired signal from the residual SI in a semi-blind way. The mode of SI cancellation at relay is chosen adaptively based on the threshold of signal-to-interference ratio (SIR) at relay. The proposed FD relay system not only features reduced signal processing cost of SI cancellation, but also achieves much higher energy efficiency (EE) than conventional FD relay systems where SI is canceled as much as possible. Also, the proposed system enables full resource utilization via consecutive data transmission at all time and the same frequency, leading to much higher throughput and EE than the conventional time-splitting and power-splitting based SI recycling approaches that occupy partial resources. Last but not least, the proposed system demonstrates a bit error rate (BER) performance that is robust against a wide range of SI and close to the ideal case with perfect channel state information (CSI) and perfect SI cancellation, while requiring no training sequence for estimation of any channel involved.

Filtered-orthogonal frequency division multiplexing (F-OFDM) is a promising candidate waveform for the fifth generation (5G) wireless communications because of its high flexibility and low out-of-band emission (OOBE). However, it suffers from dramatic peak-to-average-power ratio (PAPR), which is higher than that of OFDM and results in the power amplifier (PA) not working in the high-efficiency region. We propose a hybrid PAPR reduction scheme including precoding, time-domain selected mapping (TSLM) and companding techniques, for F-OFDM systems with independent component analysis (ICA) based blind channel equalization, which can achieve significant PAPR reduction over the previous work. Also, this is the first work to reduce PAPR while enabling the PA to work with the highest possible efficiency. The reciprocal of the hybrid PAPR reduction is embedded in the ambiguity elimination process of ICA, and therefore does not require any dedicated side information from the transmitter or any exclusive signal processing at the receiver, leading to a much higher spectral efficiency (SE) and lower computational complexity than the previous work. The bit error rate (BER) performance of the system with the proposed hybrid PAPR reduction scheme is shown to be close to the ideal case with perfect channel state information (CSI), while no side information and training sequence are required for PAPR reduction and channel estimation, thanks to the effectiveness of the ICA based blind channel equalization.

In this work, we exploit the signal and energy cooperation in wireless powered multi-user networks. In particular, multiple relays are employed to assist data transmissions from a multi-antenna hybrid access point (HAP) to a distant receiver. The HAP also transfers wireless power to the relays in either a power-splitting (PS) or time-switching (TS) protocol. With dense user deployment, the direct links from the HAP to the receivers are short and can contribute considerably to the overall throughput. To account for the direct links, we propose a throughput maximization problem by jointly optimizing the HAP's beamforming strategy to control the information and power transfer to the relays as well as individual relays' energy harvesting and collaborative beamforming strategies. The main challenge lies in that the direct links require the beamforming design to balance the performances of relay and direct transmissions. Though the throughput maximization problem is non-convex and the globally solution may not be available, we obtain two feasible lower performance bounds corresponding to the PS and TS protocols. Our simulation results also verify that the new design with direct links achieves significant performance improvement compared with the conventional scheme that ignores the direct links.

In this paper, we consider a wireless powered multi-relay network in which a multi-antenna hybrid access point underlaying a cellular system transmits information to distant receivers. Multiple relays capable of energy harvesting are deployed in the network to assist the information transmission. The hybrid access point can wirelessly supply energy to the relays, achieving multi-user gains from signal and energy cooperation. We propose a joint optimization for signal beamforming of the hybrid access point as well as wireless energy harvesting and collaborative beamforming strategies of the relays. The objective is to maximize the network throughput subject to probabilistic interference constraints at the cellular user equipment. We formulate the throughput maximization with both the time-switching and power-splitting schemes, which impose very different couplings between the operating parameters for wireless power and information transfer. Although the optimization problems are inherently non-convex, they share similar structural properties that can be leveraged for an efficient algorithm design. In particular, by exploiting monotonicity in the throughput, we maximize it iteratively via customized polyblock approximation with reduced complexity. The numerical results show that the proposed algorithms can achieve close to optimal performance in terms of the energy efficiency and throughput.

Adaptive bitrate streaming enables video users toadapttheir playing bitrates to the real-time network conditions, hence achieving the desirable quality-of-experience (QoE). In a multi-user wireless scenario, however, existing single-user based bitrate adaptation methods may fail to provide the desirable QoE, due to lack of consideration of multi-user interactions (such as the multi-user interferences and network congestion). In this work, we propose a novel user cooperation framework based onuser-provided networkingfor multi-user mobile video streaming over wireless cellular networks. The framework enables nearby mobile video users to crowdsource their cellular links and resources for cooperative video streaming. We first analyze the social welfare performance bound of the proposed cooperative streaming system by introducing a virtual time-slotted system. Then, we design a low complexity Lyapunov-based online algorithm, which can be implemented in an online and distributed manner without the complete future and global network information. Numerical results show that the proposed online algorithm achieves an average 97 percent of the theoretical maximum social welfare. We further conduct experiments with real data traces, to compare our proposed online algorithm with the existing online algorithms in the literature. Experiment results show that our algorithm outperforms the existing algorithms in terms of both the achievable bitrate (with an average gain of 20$\sim$30 percent) and social welfare (with an average gain of 10$\sim$50 percent).

Recently, the mobile network operators (MNOs) are exploring more time flexibility with the rollover data plan, which allows the unused data from the previous month to be used in the current month. Motivated by this industry trend, we propose a general framework for designing and optimizing the mobile data plan with time flexibility. Such a framework includes the traditional data plan, two existing rollover data plans, and a new credit data plan as special cases. Under this framework, we formulate a monopoly MNO's optimal data plan design as a three-stage Stackelberg game: In Stage I, the MNO decides the data mechanism. In Stage II, the MNO further decides the corresponding data cap, subscription fee, and the per-unit fee. Finally, in Stage III, users make subscription decisions based on their own characteristics. Through backward induction, we analytically characterize the MNO's profit-maximizing data plan and the corresponding users' subscriptions. Furthermore, we conduct a market survey to estimate the distribution of users' two-dimensional characteristics, and evaluate the performance of different data mechanisms using the real data. We find that a more time-flexible data mechanism increases MNO's profit and users' payoffs, hence improves the social welfare.

In conventional mobile data plans, the data is associated with a fixed period (e.g., one month) and the unused data will be cleared at the end of each period. To take advantage of consumers' heterogeneous demands across different periods and meanwhile to provide more time flexibility, some mobile data service providers (SP) have offered data plans with different lengths of period. In this paper, we consider the data plan design problem for a single SP, who provides data plans with different lengths of period for consumers with different characteristics of data demands. We propose a contract-theoretic approach, wherein the SP offers a period-price data plan contract which consists of a set of period and price combinations, indicating the prices for data with different periods. We study the optimal data plan contract designs under two different models: discrete and continuous consumer-type models, depending on whether the consumer type is discrete or continuous. In the former model, each type of consumers are assigned with a specific period-price combination. In the latter model, the consumers are first categorized into a finite number of groups, and each group of consumers (possibly with different types) are assigned with a specific period-price combination. We systematically analyze the incentive compatibility (IC) constraint and individual rationality (IR) constraint, which ensure each consumer to choose the data plan with the period-price combination intended for his type. We further derive the optimal contract that maximizes the SP's expected profit, meanwhile satisfying the IC and IR constraints of consumers. Our numerical results show that our proposed optimal contract can increase the SP's profit over 35% comparing with the conventional monthly-period data plan.

Mobile collaborative Internet access (MCA) enables mobile users to share their Internet through flexible tethering arrangements. This can potentially make better use of network resources. However, from a mobile network operator's (MNO's) viewpoint, it can either reduce revenue or increase congestion, and thus has been blocked by some MNOs in practice. We propose a hybrid pricing framework for MNOs who charge users separately for access and tethering. This scheme serves to coordinate the tethering decisions of mobile users with MNO network management objectives. We analyze the MNOs' equilibrium pricing strategies in both cooperative and competitive scenarios. In the cooperative scenario, at the equilibrium, each user's cost is independent of any chosen tethering links. We then characterize the optimal hybrid pricing strategies of MNOs in this scenario. For the competitive scenario, we formulate the MNOs' competitive interactions as a pricing game, and we show that MNO competition leads to equalized prices for users if an equilibrium exists but does not guarantee its existence. Both insights motivate a quantity competition game, which is shown to guarantee equilibrium. Simulation results show that in scenarios of interest the proposed hybrid pricing schemes can double both MNOs' profit and users' payoff and such improvements increase with the degree of network heterogeneity.

Ride-sharing systems enable individual car owners with idle time to provide commercial taxi-like services via an online platform. By crowdsourcing a large population of individual car owners, it can provide more flexible services with a lower serving cost, comparing with the traditional taxi system. Due to the autonomous nature of car owners (drivers), a decentralized driver dispatching algorithm that can achieve a stable (self-motivated) and optimal passenger-driver matching is highly desired for a ride-sharing system. In this paper, we will study such a driver dispatching algorithm systematically. We first show that the optimal passenger-driver matching achieved by the centralized driver dispatching algorithm is often not stable, in the sense that some drivers and passengers may break with their matched partners and form new matching pairs. To this end, we introduce a virtual order fee on each passenger (which the platform will charge the drives who want to serve the passenger) to motivate the behaviors of drivers. Specifically, we propose a novel auction-based decentralized driver dispatching algorithm, where each driver proposes the most profitable passenger that he wants to serve, considering the potential profit that he can achieve and the order fee that he needs to pay from/to serving each passenger. The virtual order fee on a passenger will be gradually increased when multiple drivers want to serve the passenger, until there exists only one driver who is willing to serve. We analytically show that such a decentralized driver dispatching algorithm will converge to an equilibrium (stable) outcome, which achieves the optimal passenger-driver matching (i.e., that maximizes the social income of the whole system). Simulation results further show how the converging speed and the achieved social income change with the system parameters such as the step size of order fee increasement. Moreover, it is easy to implement the proposed distributed algorithm in a practical system.

Mobile network operators (MNOs) have been offering mobile data plans with different data caps and subscription fees as an effective way of achieving price discrimination and improving revenue. Recently, some MNOs are investigating innovative data plans with time flexibility based on the multi-cap scheme. The rollover data plan and the credit data plan are such innovative data plans with time flexibility. In this paper, we study how the MNO optimizes its multi-cap data plan with time flexibility in the realistic asymmetric information scenario, where each user is associated with multidimensional private information, i.e., the data valuation and the network substitutability. Specifically, we consider a multi-dimensional contract-theoretic approach, and analyze the optimal data caps and the subscription fees design systematically. We find that each user's willingness-to-pay for a particular data cap can be captured by the slope of his indifference curve on the contract plane, and the feasible contract (satisfying the incentive compatibility and individual rationality conditions) will allocate larger data caps for users with higher willingness-to-pay. Furthermore, we conduct a market survey to estimate the statistical distribution of users' private information, and examine the performance of our proposed multi-dimensional contract design using the empirical data. Numerical results further reveal that the optimal contract may provide price discounts (i.e., negative subscription fees) to attract low valuation users to select a small-cap (possibly zero-cap) contract item. A data mechanism with better time flexibility brings users higher payoffs and the MNO more profit, hence increases the social welfare.

Cellular data sponsoring (CDS) is a traditional data sponsor scheme widely used in cellular video delivery networks, where content providers (CPs) bear the cellular data downloading cost for mobile video users (MUs), so as to attract more MUs and achieve higher revenue (e.g., via more attached advertisements). Edge caching sponsoring (ECS) is a novel data sponsor scheme recently introduced in the emerging 5G network, where CPs cache popular video contents on the edge network in advance and deliver them to local MUs directly. Thus, it can not only achieve the benefits of CDS (i.e., attracting more MUs and achieving higher revenue), but also reduce the congestion of backhaul network. In this work, we will perform a competitive analysis of CDS and ECS for mobile video streaming. Specifically, we consider a mobile video delivery network with two CPs who adopt CDS and ECS, respectively. MUs can choose one or neither of these two sponsor schemes (from the corresponding CPs) for his video content requests. We formulate the interaction of CPs and MUs as a two-stage Stackelberg game, where CPs act as leaders determining the efforts of their adopted sponsor schemes in the first stage, and MUs act as followers choosing the best sponsor schemes for their content requests in the second stage. We analyze the sub-game perfect equilibrium systematically for both cooperative and competitive scenarios (depending on whether two CPs cooperate or compete with each other). Numerical results show that in the competitive scenario, the joint sponsor of ECS and CDS can increase the total MU payoff by 36% ~ 140%, comparing with that with only one sponsor scheme. Moreover, the CPs can benefit more from ECS than from CDS when the revenue is higher.

In this paper, we address the optimal autonomous vehicle (AV) coordination problem at road intersections, which is of great importance in modern intelligent transportation systems (ITS). We first formulate it as a general collision-free traffic scheduling framework. Aiming at the dynamic characteristics of vehicles' arrival, a corresponding dynamic coordination strategy is thus proposed to achieve better quality of service (QoS), i.e., the traffic throughput and delay. The road stability is also guaranteed using the Rate Stability Theorem and Lyapunov Theorem. Provided numerical results validate our analysis and show the performance improvements achieved by the proposed framework.

The integration of wireless power transfer (WPT) with the backscatter communications provides a promising way to sustain batteryless wireless networks. In this paper, we consider a backscatter communication network, in which the passive radio uses the harvested energy from a power beacon station (PBS) to supply its data transmissions, while some other radios can help as the wireless relays. To improve the throughput performance of a distant transceiver pair, we propose a two-hop backscatter relay model and formulate a throughput maximization problem to jointly optimize WPT and the relay strategies. Noting that the proposed problem is non-convex, an iterative algorithm with reduced complexity is proposed to decompose the original problem into a power allocation subproblem in the outer loop and an optimization of the relay strategy in the inner loop. Numerical results reveal that the power allocation converges to the optimum and the relay strategy significantly improves the throughput when the radios' power demand is low.

An operator-assisted crowdsourced WiFi community network can provide high-speed wireless data services in an inexpensive way, by encouraging a set of individual users to form a community and share their private home WiFi access points (APs) with others. Such a novel paradigm has shown great promise in achieving the ubiquitous and full coverage networks. In this paper, we perform a systemic analysis for such a community network, where users are heterogeneous in terms of both the network evaluation and the home location popularity. We formulate the interactions between the network operator and users as a non-cooperative game, and focus on the operator's pricing scheme design and the users' behavior analysis. Specifically, we propose a hybrid pricing scheme combining both the fixed price (e.g., the monthly fee) and the usage-based price (proportional to the WiFi connection time) for AP sharing among users. After analyzing users' best response towards the given pricing scheme, we characterize the dynamic changes of the membership distribution over time and indicate the market equilibrium. Simulation results show that under the different pricing schemes and different roaming qualities, the equilibrium social welfare can be increased to 137% to 147%, comparing with the tradition non-crowdsourced system.

Today, many mobile network operators (MNOs) provide data services through a three-part tariff data plan, which involves a fixed subscription fee, a data cap, and a per-unit fee for the data over-usage exceeding the data cap. To increase their market competitiveness, MNOs have been trying to provide more time flexibility in the data plans. One of such innovations is the rollover data plan, which allows a subscriber to use the unused data of the previous month in the current month. Depending on the consumption priority of the rollover data, different rollover data plans can have different levels of time flexibility. The interactions among multiple MNOs offering rollover data plans, however, are quite complicated and sometimes counter-intuitive. To examine this issue, in this paper we build a simple market model of two MNOs competing to serve the same pool of heterogeneous users. We formulate the market competition as a two-stage game: in Stage I, the MNOs simultaneously decide their pricing strategies of their chosen data mechanisms; In Stage II, users make their subscription decisions among the two MNOs. We characterize the sub-game perfect equilibrium (SPE) of the two-stage game through backward induction. Comparing with a monopoly market where a better time flexibility always improves the MNO's profit, our analysis reveals a rather complicated story in the duopoly market: (i) with a mild competition, the stronger MNO will increase both MNOs' profits by adopting a data plan with a better time flexibility, while the weaker MNO will decrease both MNOs' profits by adopting a data plan with a better time flexibility; (ii) with a fierce competition, any MNO will increase its profit and decrease the competitor's profit by adopting a data plan with a better time flexibility.

Mobile crowdsensing (MCS) is becoming more and more popular with the increasing demand for various sensory data in many wireless applications. In the traditional server-client MCS system, a central server is often required to handle massive sensory data (e.g., collecting data from users who sense and dispatching data to users who request), hence it may incur severe congestion and high operational cost. In this work, we introduce a peer-to-peer (P2P) based MCS system, where the sensory data is stored in user devices locally and shared among users in an P2P manner. Hence, it can effectively alleviate the burden on the server, by leveraging the communication, computation, and cache resources of massive user devices. We focus on the economic incentive issue arising in the sharing of data among users in such a system, that is, how to incentivize users to share their sensed data with others. To achieve this, we propose a data market, together with a hybrid pricing mechanism, for users to sell their sensed data to others. We first study how would users choose the best way of obtaining desired data (i.e., sensing by themselves or purchasing from others). Then we analyze the user behavior dynamics as well as the data market evolution, by using the evolutionary game theory. We further characterize the users' equilibrium behaviors as well as the market equilibrium, and analyze the stability of the obtained equilibrium.

Tactile Internet often requires: 1) the ultra-reliable and ultra-responsive network connection and 2) the proactive and intelligent actuation at edge devices. A promising approach to address these requirements is to enable mobile edge devices to share their communication, computation, and caching (3C) resources via device-to-device connections. In this paper, we propose a general 3C resource sharing framework, which includes many existing 1C/2C sharing models in the literature as special cases. Comparing with the 1C/2C models, the proposed 3C framework can further improve the resource utilization efficiency by offering more flexibilities in terms of the device cooperation and resource scheduling. As a typical example, we focus on the energy utilization under the proposed 3C framework. Specifically, we formulate an energy consumption minimization problem, which is an integer non-convex optimization problem. To solve the problem, we first transform it into an equivalent integer linear programming problem that is much easier to solve. Then, we propose a heuristic algorithm based on linear programming, which can further reduce the computation time and produce an empirically close-to-optimal solution. Moreover, we evaluate the energy reduction due to the 3C sharing both analytically and numerically. Numerical results show that, comparing with the existing 1C/2C approaches, the proposed 3C sharing framework can reduce the total energy consumption by 83.8% when the D2D energy is negligible. The energy reduction is still 27.5% when the D2D transmission energy per unit time is twice as large as the cellular transmission energy per unit time.

Mobile crowdsensing (MCS) is a novel and appealing sensing paradigm that leverages the diverse embedded sensors of massive mobile devices to collect different kinds of data. One of the key challenges in MCS is to efficiently schedule mobile device users to perform different sensing tasks. Prior effort to this problem mainly focused on the interaction between the task-layer and the user-layer, without considering the similar data requirements of tasks and the heterogeneous sensing capabilities of users. In this work, we introduce a new data-layer between tasks and users, and propose a three-layer data-centric MCS framework, which enables different tasks to reveal their common data requirements and hence reuse the common data items. We focus on studying the joint task selection and user scheduling problem under this new framework, aiming at maximizing the social welfare. Specifically, we first analyze theoretical performance gain due to data reuse in the ideal scenario with complete information. We then consider the practical scenario with private information of both tasks and users, and propose a two-sided randomized auction mechanism, which is computationally efficient, individually rational, incentive compatible (truthful) in expectation, and close-to-optimal. We further show that the proposed randomized auction may not be budget balanced, and hence introduce a reserve price into the auction to achieve the desired budget balance at the cost of certain welfare loss. Simulation results show that with data reuse, the social welfare achieved in the proposed randomized auction can be increased from 270 up to 4,500 percent, comparing with those without data reuse.

Mobile crowdsensing (MCS) is a new paradigm of sensing by taking advantage of the rich embedded sensors of mobile user devices. However, the traditional server-client MCS architecture often suffers from the high operational cost on the centralized server (e.g., for storing and processing massive data), hence the poor scalability. Peer-to-peer (P2P) data sharing can effectively reduce the server's cost by leveraging the user devices' computation and storage resources. In this work, we propose a novel P2P-based MCS architecture, where the sensing data is saved and processed in user devices locally and shared among users in a P2P manner. To provide necessary incentives for users in such a system, we propose a quality-aware data sharing market, where the users who sense data can sell data to others who request data but not want to sense the data by themselves. We analyze the user behavior dynamics from the game-theoretic perspective, and characterize the existence and uniqueness of the game equilibrium. We further propose best response iterative algorithms to reach the equilibrium with provable convergence. Our simulations show that the P2P data sharing can greatly improve the social welfare, especially in the model with a high transmission cost and a low trading price.

The explosive growth of mobile video traffic introduces new challenges for the network infrastructure. Edge caching, as one of the key technologies in 5G wireless networks, has shown great potential to improve the quality of mobile video services by reducing the transmission overhead over backhaul links. With edge caching, content providers (CPs) need to decide not only the traditional data sponsoring strategy on cellular networks (where CPs cover part or all of the mobile users' cellular data cost), but also a novel cache sponsoring strategy on the edge caching networks (where CPs place part of contents on edge networks in advance). In this paper, we study the joint optimization of both sponsors on cellular and edge caching networks for a single CP, aiming at maximizing the CP's revenue. Specifically, we formulate the joint optimization problem as a two-stage sequential decision problem. In stage I, the CP determines the edge caching policy (for a relatively long time period). In stage II, the CP decides the real-time data sponsoring strategy for each content request within the period. We analyze this two-stage decision problem systematically. First, we propose an online sponsoring strategy in stage II based on Lyapunov optimization framework. Then, we propose an edge caching strategy in stage I via predicting the number of aggregate user requests. Simulations on real data traces show that such a joint optimization policy can increase the CP's revenue by 124%-154%, comparing with the traditional data sponsoring policy (i.e., without edge caching). Moreover, the proposed online strategy can achieve 90% of the maximum revenue in the offline benchmark.

It is well known that a game equilibrium can be far from efficient or fair, due to the misalignment between individual and social objectives. The focus of this paper is to design a new mechanism framework that induces an efficient and fair equilibrium in a general class of games. To achieve this goal, we propose a taxation framework, which first imposes a tax on each player based on the perceived payoff (income), and then redistributes the collected tax to other players properly. By turning the tax rate, this framework spans the continuum space between strategic interactions (of selfish players) and altruistic interactions (of unselfish players), hence provides rich modelling possibilities. The key challenge in the design of this framework is the proper taxing rule (i.e., the tax exemption and tax rate) that induces the desired equilibrium in a wide range of games. First, we propose a flat tax rate (i.e., a single tax rate for all players), which is necessary and sufficient for achieving an efficient equilibrium in any static strategic game with common knowledge. Then, we provide several tax exemption rules that achieve some typical fairness criterions (such as the Max-min fairness) at the equilibrium. We further illustrate the implementation of the proposed taxation framework in the game of Prisoners' Dilemma.

Participatory sensing (PS) is a novel and promising sensing network paradigm for achieving a flexible and scalable sensing coverage with a low deploying cost, by encouraging mobile users to participate and contribute their smartphones as sensors. In this work, we consider a general PS system model with location-dependent and time- sensitive tasks, which generalizes the existing models in the literature. We focus on the task scheduling in the user-centric PS system, where each participating user will make his individual task scheduling decision (including both the task selection and the task execution order) distributively. Specifically, we formulate the interaction of users as a strategic game called Task Scheduling Game (TSG) and perform a comprehensive game-theoretic analysis. First, we prove that the proposed TSG game is a potential game, which guarantees the existence of Nash equilibrium (NE). Then, we analyze the efficiency loss and the fairness index at the NE. Our analysis shows the efficiency at NE may increase or decrease with the number of users, depending on the level of competition. This implies that it is not always better to employ more users in the user-centric PS system, which is important for the system designer to determine the optimal number of users to be employed in a practical system.

Data sponsoring is a widely-used incentive method in today's cellular networks, where video content providers (CPs) cover part or all of the cellular data cost for mobile users so as to attract more video users and increase data traffic. In the forthcoming 5G cellular networks, edge caching is emerging as a promising technique to deliver videos with lower cost and higher quality. The key idea is to cache video contents on edge networks (e.g., femtocells and WiFi access points) in advance and deliver the cached contents to local video users directly (without involving cellular data cost for users). In this work, we aim to study how the edge caching will affect the CP's data sponsoring strategy as well as the users' behaviors and the data market. Specifically, we consider a single CP who offers both the edge caching service and the data sponsoring service to a set of heterogeneous mobile video users (with different mobility and video request patterns). We formulate the interactions of the CP and the users as a two-stage Stackelberg game, where the CP (leader) determines the budgets (efforts) for both services in Stage I, and the users (followers) decide whether and which service(s) they would like to subscribe to. We analyze the sub-game perfect equilibrium (SPE) of the proposed game systematically. Our analysis and experimental results show that by introducing the edge caching, the CP can increase his revenue by 105%.

Today's mobile devices are capable of tackling various complicated tasks that may require a large amount of communication, computation, and caching (3C) resources. Due to users' heterogeneous resources and service requirements, it is challenging for each user to always accomplish his task satisfactorily. To alleviate this issue, mobile users can exploit the heterogeneity and crowdsource their resources to enhance the task execution performance. In this paper, we propose a general 3C framework that enables mobile users to share all three types of resources through device- to-device connections. Such a framework generalizes many existing 1C/2C resource sharing models (that only shares one or two types of resources among users). To quantify the benefit of the proposed framework, we focus on an energy minimization problem, and show that the 3C framework always achieves a smaller total energy consumption, comparing with other 1C/2C models. Furthermore, we show that the energy reduction is maximized, when user connection probability and content caching ratio are neither too large nor too small. Our numerical results show that, when ignoring device-to-device transmission energy, the general 3C framework can reduce the total energy consumption by 82.98%, comparing with the 1C/2C models.

Mobile Crowd Sensing (MCS) is a new paradigm of sensing, which can achieve a flexible and scalable sensing coverage with a low deployment cost, by employing mobile users/devices to perform sensing tasks. In this work, we propose a novel MCS framework with data reuse, where multiple tasks with common data requirement can share (reuse) the common data with each other through an MCS platform. We study the optimal assignment of mobile users and tasks (with data reuse) systematically, under both information symmetry and asymmetry, depending on whether the user cost and the task valuation are public information. In the former case, we formulate the assignment problem as a generalized Knapsack problem and solve the problem by using classic algorithms. In the latter case, we propose a truthful and optimal double auction mechanism, built upon the above Knapsack assignment problem, to elicit the private information of both users and tasks and meanwhile achieve the same optimal assignment as under information symmetry. Simulation results show that by allowing data reuse among tasks, the social welfare can be increased up to 100~380%, comparing with those without data reuse.

Crowdsourced WiFi community network enables individual users to form a community and share their private home WiFi access points (APs) with each other, hence can provide a much higher WiFi coverage for each user. In this work, we consider a novel crowdsourced WiFi network model, which allows users to join the community in different roles: (a) Contributor, who contributes to the community by sharing his home AP with others, (b) Beneficiary, who benefits from the community by accessing the APs of others, and (c) Hybrid Contributor and Beneficiary, who both contributes his home AP and benefits from other APs. Such a model extends and generalizes the existing models by allowing users to make separate decisions on contributing and benefiting. We study the users' best choices as well as their behavior dynamics systematically by using the evolutionary game theory. We characterize the equilibriums of the proposed evolutionary game, and further analyze the stability of each equilibrium. Simulations show that under the desired stable equilibrium, the social welfare gain can be up to 110% to 170%, comparing with that in the traditional non-crowdsourced WiFi network. Our equilibrium analysis also provides insights into the way of finding the desired stable equilibrium.

Crowdsourced mobile video streaming enables nearby mobile video users to aggregate their network resources to improve the video streaming performance. However, users are often selfish and may not be willing to cooperate without proper incentives. Designing an incentive mechanism for such a scenario is challenging due to the users' asynchronous downloading behaviors as well as their private valuations for multi-bitrate encoded videos. In this work, we propose a multi-object multi-dimensional auction-based incentive framework, through which users can download multiple video segments with different bitrates for multiple nearby users (and themselves). Based on this incentive framework, we propose a Vickrey-score auction, which is the first multi-object multi-dimensional auction that achieves both truthfulness and efficiency. Simulations with real traces show that crowdsourced mobile streaming outperforms noncooperative streaming by 48.6% (on average) in terms of social welfare. We further implement our proposed auction mechanism in a demostration system, and show that the crowdsourced framework together with the auction mechanism can substantially increase mobile user's welfare and video service stability.

Mobile Crowdsourced Access (MCA) enables mobile users (MUs) to share their Internet connections by serving as tethers to other MUs, hence can improve the quality of service of MUs as well as the overall utilization of network resources. However, MCA can also reduce the revenue-generating mobile traffic and increase the network congestion for mobile network operators (MNOs), and thus has been blocked by some MNOs in practice. In this work, we reconcile the conflicting objectives of MNOs and MUs by introducing a pricing framework for MCA, where the direct traffic and tethering traffic are charged independently according to a data price and a tethering price, respectively. We derive the optimal data and tethering prices systematically for MUs with the α-fair utility in two scenarios with cooperative and competitive MNOs, respectively. We show that the optimal tethering prices are zero and the optimal usage-based data prices are identical for all MUs, in both the cooperative and competitive scenarios. Such optimal pricing schemes will lead to mutually beneficial results for MNOs and MUs. Our simulation results show that the proposed pricing scheme approximately triples both the MNOs' profit and the MUs' payoff when the MNOs cooperate, comparing to the case where MCA is blocked. Moreover, competition among MNOs will decrease MNOs' profit and further increase the MUs' payoff.

In this paper, we address the interference problem caused by the cross-polarization components in a massive dualpolarized MIMO (DP-MIMO) system over Ricean-K fading Channel. To effectively suppress the interference, a novel precoding design based on oblique projection is proposed. Furthermore, compared with an Nt × Nr uni-polarized MIMO (UP- MIMO), Nt×Nr DP-MIMO can maintain the same diversity order while achieve twice the multiplexing gain of UP-MIMO in symbol error rate (SER) performance by using the proposed precoding design. The expression of the moment generation function (MGF) of signal noise ratio (SNR) for the proposed scheme is derived, and an analytical expression of SER with M-ary phase-shift keying (M-PSK) modulation is obtained. The effectiveness of the proposed scheme is demonstrated through extensive numerical results.

Rollover data plans are attractive to mobile users by allowing them to keep their unused data for future use, and hence has been widely implemented by Mobile Network Operators (MNOs) around the world. In this work, we formulate a three-stage Stackelberg game to analyze the interactions between an MNO and its subscribed users under both traditional and rollover data plans. Specifically, in Stage I, the MNO decides which data plan(s) to implement; In Stage II, the MNO decides the price(s) of the data plan(s) to maximize its expected revenue; In Stage III, users make their individual subscription decisions to maximize their expected payoffs. Our analysis shows that in general, high evaluation users are more likely to choose the rollover data plan than medium evaluation users. More precisely, as the network substitutability increases, high evaluation users tend to choose the rollover data plan, while medium evaluation users tend to choose the traditional data plan. We further prove that the MNO can achieve the maximum revenue by only providing the rollover data plan (without bundling with the traditional data plan). Numerical results show that the rollover data plan can increase not only the MNO's revenue but also the users' payoffs (and hence the social welfare) comparing with the traditional data plan. We also compare two rollover data plans that differ in whether the rollover data is consumed prior to monthly data cap, and show that allowing the rollover data to be consumed before the monthly data cap is more beneficial to both users and the MNO.

Dynamic spectrum access (DSA) can effectively improve the spectrum efficiency and alleviate the spectrum scarcity, by allowing unlicensed secondary users (SUs) to access the licensed spectrum of primary users (PUs) opportunistically. Cooperative spectrum sharing is a new promising paradigm to provide necessary incentives for both PUs and SUs in dynamic spectrum access. The key idea is that SUs relay the traffic of PUs in exchange for the access time on the PUs' licensed spectrum. In this paper, we formulate the cooperative spectrum sharing between multiple PUs and multiple SUs as a two-sided market, and study the market equilibrium under both complete and incomplete information. First, we characterize the sufficient and necessary conditions for the market equilibrium. We analytically show that there may exist multiple market equilibria, among which there is always a unique Pareto-optimal equilibrium for PUs (called PU-Optimal-EQ), in which everyPU achieves a utility no worse than in any other equilibrium. Then, we show that under complete information, the unique Pareto-optimal equilibrium PU-Optimal-EQ can always be achieved despite the competition among PUs; whereas, under incomplete information, the PU-Optimal-EQ may not be achieved due to the mis-representations of SUs (in reporting their private information). Regarding this, we further study the worse-case equilibrium for PUs, and characterize a Robustequilibrium for PUs (called PU-Robust-EQ), which provides every PU a guaranteed utility under all possible mis-representation behaviors of SUs. Numerical results show that in a typical network where the number of PUs and SUs are different, the performance gap between PU-Optimal-EQ and PU-Robust-EQ is quite small (e.g., less than 10 percent in the simulations).

Crowdsourced wireless community networks can effectively alleviate the limited coverage issue of Wi-Fi access points (APs), by encouraging individuals (users) to share their private residential Wi-Fi APs with others. In this paper, we provide a comprehensive economic analysis for such a crowdsourced network, with the particular focus on the users' behavior analysis and the community network operator's pricing design. Specifically, we formulate the interactions between the network operator and users as a two-layer Stackelberg model, where the operator determining the pricing scheme in Layer I, and then users determining their Wi-Fi sharing schemes in Layer II. First, we analyze the user behavior in Layer II via a two-stage membership selection and network access game, for both small-scale networks and large-scale networks. Then, we design a partial price differentiation scheme for the operator in Layer I, which generalizes both the complete price differentiation scheme and the single pricing scheme (i.e., no price differentiation). We show that the proposed partial pricing scheme can achieve a good tradeoff between the revenue and the implementation complexity. Numerical results demonstrate that when using the partial pricing scheme with only two prices, we can increase the operator's revenue up to 124.44 percent comparing with the single pricing scheme, and can achieve an average of 80 percent of the maximum operator revenue under the complete price differentiation scheme.

The surging global mobile data traffic challenges the economic viability of cellular networks and calls for innovative solutions to reduce the network congestion and improve user experience. In this context, user-provided networks (UPNs), where mobile users share their Internet access by exploiting their diverse network resources and needs, turn out to be very promising. Heterogeneous users with advanced handheld devices can form connections in a distributed fashion and unleash dormant network resources at the network edge. However, the success of such services heavily depends on users' willingness to contribute their resources, such as network access and device battery energy. In this paper, we introduce a general framework for UPN services and design a bargaining-based distributed incentive mechanism to ensure users' participation. The proposed mechanism determines the resources that each user should contribute in order to maximize the aggregate data rate in UPN, and fairly allocate the benefit among the users. The numerical results verify that the service can always improve users' performance, and such improvement increases with the diversity of the users' resources. Quantitatively, it can reach an average 30% increase of the total served traffic for a typical scenario even with only six mobile users.

The proliferation of public Wi-Fi hotspots has brought new business potentials for Wi-Fi networks, which carry a significant amount of global mobile data traffic today. In this paper, we propose a novelWi-Fi monetizationmodel for venue owners (VOs) deploying public Wi-Fi hotspots, where the VOs can generate revenue by providing two different Wi-Fi access schemes for mobile users (MUs): 1) thepremium access, in which MUs directly pay VOs for their Wi-Fi usage, and 2) theadvertising sponsored access, in which MUs watch advertisements in exchange of the free usage of Wi-Fi. VOs sell their ad spaces to advertisers (ADs) via an ad platform, and share the ADs’ payments with the ad platform. We formulate the economic interactions among the ad platform, VOs, MUs, and ADs as a three-stage Stackelberg game. In Stage I, the ad platform announces its advertising revenue sharing policy. In Stage II, VOs determine the Wi-Fi prices (for MUs) and advertising prices (for ADs). In Stage III, MUs make access choices and ADs purchase advertising spaces. We analyze the sub-game perfect equilibrium (SPE) of the proposed game systematically, and our analysis shows the following useful observations. First, the ad platform’s advertising revenue sharing policy in Stage I will affect only the VOs’ Wi-Fi prices but not the VOs’ advertising prices in Stage II. Second, both the VOs’ Wi-Fi prices and advertising prices are non-decreasing in the advertising concentration level and non-increasing in the MU visiting frequency. Numerical results further show that the VOs are capable of generating large revenues through mainly providing one type of Wi-Fi access (the premium access or advertising sponsored access), depending on their advertising concentration levels and MU visiting frequencies.

Mobile crowdsensing emerges as a promising sensing paradigm through leveraging the diverse embedded sensors in massive mobile devices. A key objective in mobile crowdsensing is to efficiently schedule mobile device users to perform multiple sensing tasks. Prior work mainly focused on the interactions between the task layer and the user layer, without considering the similarity of tasks' data requirements and the heterogeneity of users'sensing capabilities. In this work, we propose a three-layer data-centric crowdsensing model by introducing a new data layer between tasks and users, which allows us to effectively leverage both the task similarity and the user heterogeneity. We formulate a joint task selection and user scheduling problem on top of the data layer, aiming at maximizing the social welfare. This problem is difficult to solve due to the combinatorial nature as well as the two-sided private information of tasks and users. To address both issues, we propose a two- sided randomized auction mechanism, which is computationally efficient, individually rational, and incentive compatible in expectation. Simulations show that (i) the proposed randomized auction can achieve 90% of the maximum social welfare (benchmark), and (ii) the social welfare gain due to data reuse increases with the task similarity and reaches up to 1300% in our simulations.

In this work, we study the joint optimization of edge caching and data sponsoring for a video content provider (CP), aiming at reducing the content delivery cost and increasing the CP's revenue. Specifically, we formulate the joint optimization problem as a two-stage decision problem for the CP. In Stage I, the CP determines the edge caching policy (for a relatively long time period). In Stage II, the CP decides the real-time data sponsoring strategy for each content request within the period. We first propose a Lyapunov- based online sponsoring strategy in Stage II, which reaches 90% of the offline maximum performance (benchmark). We then solve the edge caching problem in Stage I based on the online sponsoring strategy proposed in Stage II, and show that the optimal caching policy depends on the aggregate user request for each content in each location. Simulations show that such a joint optimization can increase the CP's revenue by 30%↑100%, comparing with the purely data sponsoring (i.e., without edge caching).

Accurate mobility prediction is becoming increasingly important in human behavior research, mainly due to many location-based applications such as mobile social networks and mobile advertisements. In this work, we propose a new crowd-sourced human mobility prediction model for public regions. We first analyze human trajectories collected through a cluster of densely deployed Wi-Fi access points (AP) in a shopping mall, and then characterize the close relationship between the human mobility patterns and the spatio-temporal contexts. Based on the distinct features of human trajectories in different types of public regions, we further propose a Markov-based crowdsourced mobility prediction method utilizing spatio-temporal contexts. We evaluate the performance of the proposed method using real traces, and show that our method is 28% more accurate in predicting human location transitions and incurs 14% smaller error in stay time prediction than the baseline methods.

There has been a proliferation of public Wi-Fi hotspots that serve a significant amount of global mobile traffic today. In this paper, we propose a general Wi-Fi monetization model for public Wi-Fi hotspots deployed by venue owners (VOs), where VOs generate revenue from providing both the premium Wi-Fi access and the advertising sponsored Wi-Fi access to mobile users (MUs). With the premium access, MUs directly pay VOs for their Wi-Fi usage; while with the advertising sponsored access, MUs watch advertisements for the free usage of Wi-Fi. VOs sell their ad spaces to advertisers (ADs) via an ad platform, and share a proportion of the revenue with the ad platform. We formulate the economic interactions among the ad platform, VOs, MUs, and ADs as a three-stage Stackelberg game. By analyzing the equilibrium, we show that the ad platform's advertising revenue sharing policy affects a VO's Wi-Fi price but not the VO's advertising price. Moreover, we prove that a single term called equilibrium indicator determines whether a VO will fully rely on the premium access, or fully rely on the advertising sponsored access, or obtain revenue from both types of access. Numerical results show that the VO obtains a large revenue under a large advertising concentration level and a medium MU visiting frequency.

Crowdsourced wireless community networks enable individual users to share their private Wi-Fi access points (APs) with each other, hence can achieve a large Wi-Fi coverage with a low deployment cost. This paper presents the first Wi-Fi sharing mechanism design for the community network operator under incomplete information, where the quality of each user-provided Wi-Fi access is his private information. Specifically, we propose a contract-based incentive mechanism, where the operator offers a set of contract items to users, each consisting of a Wi-Fi access price (that a user can charge others who access his AP) and a subscription fee (that a user needs to pay the operator). Different from prior contract mechanisms for wireless networks, here each user's best contract choice depends not only on his private information, but also on other users' choices. This greatly complicates the contract design, as the operator needs to analyze the equilibrium choices of all users, rather than the best choice of each single user. We derive the feasible contract that guarantees the user participation and truthful information disclosure under the equilibrium. Our analysis shows that a higher type user (who provides a higher quality access) is more likely to choose a higher price and subscription fee. Simulation results further show that when increasing the ratio of higher type users in the system, the operator can gain more profit, while counter-intuitively, offering lower prices and subscription fees for all users.

Adaptive bitrate video streaming is a widely-used technology for mobile video streaming over HTTP. In this work, we study a crowdsourced video streaming framework, which enables nearby mobile users to crowdsource their radio resources for cooperatively adaptive bitrate video streaming. We propose a multi-dimensional auction based incentive mechanism to promote the user cooperation, supporting the asynchronous downloading and the bitrate adapting of video users. In this mechanism, each user initiates an auction whenever he is ready to download a new data segment in an asynchronous fashion, and all nearby users compete for the downloading opportunity by submitting a multidimensional bid consisting of the intended segment bitrate and the associated value. Design of such a multi-dimensional auction is very challenging, as we need to guarantee the user's truthful reporting on the information on multiple dependent dimensions. We first propose a truthful second-score (multi-dimensional) auction framework, within which we further derive the efficient mechanism that maximizes the social welfare (of each segment downloading) and the sub-optimal mechanism that approximately maximizes the auctioneer payoff. Experiment results show that our proposed crowdsourced streaming can achieve 60% ˜ 76% of the maximum social welfare even when 80 percentage of users lose their direct network connections.

A database-assisted TV white space network can achieve the goal of green cognitive communication by effectively reducing the energy consumption in cognitive communications. The success of such a novel network relies on a proper business model that provides substantial incentives for all parties involved. In this paper, we propose an integrated spectrum and information market for a database-assisted TV white space network, where a geolocation database acts as an online platform providing services to both a spectrum market and an information market. We model the interactions among the database operator, the spectrum licensee, and the unlicensed users as a three-stage sequential decision process. Specifically, Stage I characterizes the negotiation between the database and the spectrum licensee, in terms of the commission for the licensee to use the spectrum market platform, Stage II models the pricing decisions of the database and the spectrum licensee, and Stage III characterizes the subscription behaviors of the unlicensed users. Analyzing such a three-stage model is very challenging due to the co-existence of positive and negative network externalities in the information market. We explicitly characterize the impact of network externalities on the equilibrium behaviors of all parties involved. We also analytically show that the spectrum licensee can never get a market share larger than half in the integrated market. Our numerical results further show that the proposed integrated market can outperform the pure information market in terms of network profit up to 87%.

Mobile crowdsensing is a new sensing paradigm relying on computation and storage capabilities of mobile devices. However, traditional server-client mobile crowdsensing models suffer from a high operational cost on the server, and hence a poor scalability. Peer-to- peer (P2P) mobile crowdsensing models can effectively reduce the server's operational cost, by leveraging the mobile devices' under-utilized computation and storage resources. In a P2P mobile crowdsensing model, the sensing data is saved and processed in mobile users' devices in a distributed fashion, and is shared among mobile users directly in a P2P manner. In this work, we focus on the incentive issue in such a P2P mobile crowdsensing model. Specifically, we propose a data market and a generic pricing scheme for the data sharing among data sensors and requesters. We analyze the user interactions in such a data market from a game theoretic perspective, and prove the existence and uniqueness of the market equilibrium. We further propose a generalized best response dynamics to reach the market equilibrium. Our theoretic analysis and numerical results indicate that the equilibrium social welfare decreases with the data transfer cost and data prices, while the ratio of the equilibrium social welfare to the maximum social welfare benchmark increases with the data transfer cost and data prices.

A properly designed incentive mechanism is important for cooperative relay networks, as it will encourage relays assisting sources' data transmissions. However, previous related studies didn't give enough consideration to the topology effect, i.e., how the network topology can substantially influence the relay selection and profit distribution in cooperations. In this paper, we quantify the topology effect in multi-source-multi-relay networks analytically, by using a multi-node Nash bargaining framework based on the network exchange theory. The proposed multinode Nash bargaining outcome guarantees not only the individual satisfaction for each node, but also the social optimality for the entire network (of all nodes). Then, we propose a distributed incentive mechanism, named as natural algorithm, which enables each node to take advantage of the network topology to reach a multi-node Nash bargaining outcome through proper source/relay selection and payment bargaining. Simulation results illustrate the profit distribution among relays and sources under different network topologies.

Providing an adequate long-term user participation incentive is important for a participatory sensing system to maintain enough number of active users (sensors), so as to collect a sufficient number of data samples and support a desired level of service quality. In this work, we consider the sensor selection problem in a general time-dependent and location-aware participatory sensing system, taking the long-term user participation incentive into explicit consideration. We study the problem systematically under different information scenarios, regarding both future information and current information (realization). In particular, we propose a Lyapunov-based VCG auction policy for the on-line sensor selection, which converges asymptotically to the optimal off-line benchmark performance, even with no future information and under asymmetry of current information. Extensive numerical results show that our proposed policy outperforms the state-of-art policies in the literature, in terms of both user participation (e.g., reducing the user dropping probability by 25% ~ 90%) and social performance (e.g., increasing the social welfare by 15% ~ 80%).

We propose a hybrid spectrum and information market for database-assisted TV white space networks, where a geo-location white space database serves as the platform for both the spectrum market and the information market. We study the interactions among the database operator, the spectrum licensee, and unlicensed users systematically, using a three-layer hierarchical model. In Layer I, the licensee negotiates with the database regarding the commission fee of using the spectrum market platform. In Layer II, the database and the licensee compete for selling information or channels to unlicensed users. In Layer III, unlicensed users determine whether to buy the exclusive usage right of licensed channels from the licensee, or to buy the information regarding unlicensed channels from the database. Analyzing such a three-layer model is challenging, due to the coexistence of both positive and negative network externalities in the information market. We characterize the market equilibrium systematically, and analyze how the network externalities affect the equilibrium behaviours of all parties involved. Our numerical results show that the proposed hybrid market can improve the network profit more than 80%, compared with a pure information market. Meanwhile, the achieved network profit is very close to the coordinated benchmark (e.g., the gap is less than 4%).

A crowdsourced wireless community network can effectively alleviate the limited coverage issue of Wi-Fi access points (APs), by encouraging individuals (users) to share their private residential Wi-Fi APs with each other. This paper presents the first study on the users' joint membership selection and network access problem in such a network. Specifically, we formulate the problem as a two-stage dynamic game: Stage I corresponds to a membership selection game, in which each user chooses his membership type; Stage II corresponds to a set of network access games, in each of which each user decides his WiFi connection time on the AP at his current location. We analyze the Subgame Perfect Equilibrium (SPE) of the two-stage game, and analyze whether and how best response dynamics can reach the equilibrium. We further numerically explore how the equilibrium changes with the users' mobility patterns and network access evaluations. We show that a user with a more popular home location, a smaller travel time, or a smaller network access evaluation is more likely to choose the Bill membership type. We further demonstrate how the network operator can optimize its pricing and incentive mechanism based on the equilibrium analysis.

In this paper, we investigate an oligopoly-competitive TV white space (TVWS) market, where multiple secondary network operators compete to serve a common pool of secondary end users by using TVWS purchased from a white space database. We first study the competitive interactions among secondary operators. Specifically, we formulate the interactions as a noncooperative price-inventory competition game, where operators determine the spectrum inventory (purchased from the database) and the service price (charged to end users) simultaneously. We prove the existence and uniqueness of the Nash equilibrium using the supermodular game theory. Then, we study the impact of the database manager's wholesale pricing strategy on the market equilibrium. Specifically, we analytically show how the wholesale prices affect the operators' equilibrium inventory and pricing decisions. Based on this analysis, we further propose two different spectrum wholesale pricing strategies that maximize the database manager's profit and the total network profit, respectively. Our simulations evaluate the performance difference between these two wholesale pricing strategies.

Geo-location database-assisted TV white space network reduces the need for energy-intensive processes (such as spectrum sensing), and hence can achieve green cognitive communication effectively. The success of such a network relies on a proper business model that provides incentives for all parties involved. In this paper, we propose a Model of INformation markEt for GeO-Location Database (MINE GOLD), which enables databases to sell spectrum information to unlicensed white space devices (WSDs) for profit. Specifically, we focus on an oligopoly information market with multiple databases, and study the interactions among databases and WSDs using a two-stage hierarchical model. In Stage I, databases compete to sell information to WSDs by optimizing their information prices. In Stage II, each WSD decides whether and from which database to purchase the information, to maximize his benefit of using the TV white space. We first characterize how the WSDs' purchasing behaviors dynamically evolve, and what is the equilibrium point under fixed information prices from the databases. We then analyze how the system parameters and the databases' pricing decisions affect the market equilibrium, and what is the equilibrium of the database price competition. Our numerical results show that, perhaps counter-intuitively, the databases' aggregate revenue is not monotonic with the number of databases. Moreover, numerical results show that a large degree of positive network externality would improve the databases' revenues and the system performance.

The unprecedented growth of mobile data traffic challenges the performance and economic viability of today's cellular networks and calls for novel network architectures and communication solutions. Mobile data offloading through third-party Wi-Fi or femtocell access points (APs) can significantly alleviate the cellular congestion and enhance user quality of service (QoS), without requiring costly and time-consuming infrastructure investments. This solution has substantial benefits both for the mobile network operators (MNOs) and the mobile users, but comes with unique technical and economic challenges that must be jointly addressed. In this paper, we consider a market where MNOs lease APs that are already deployed by residential users for the offloading purpose. We assume that each MNO can employ multiple APs, and each AP can concurrently serve traffic from multiple MNOs. We design an iterative double-auction mechanism that ensures the efficient operation of the market by maximizing the differences between the MNOs' offloading benefits and APs' offloading costs. The proposed scheme takes into account the particular characteristics of the wireless network, such as the coupling of MNOs' offloading decisions and APs' capacity constraints. Additionally, it does not require full information about the MNOs and APs and creates nonnegative revenue for the market broker.

User-provided connectivity (UPC) is a promising paradigm to achieve a low-cost ubiquitous connectivity. In this paper, we study a network-assisted UPC service model, where a mobile virtual network operator (MVNO) enables its subscribers to operate as mobile WiFi hotspots (hosts) and provide Internet connectivity for others. A unique aspect of this service model is that the MVNO offers some free data quota to hosts as reimbursements (incentives) for connectivity sharing. This reimbursing scheme, together with a usage-based pricing, constitute a revolutionary hybrid data pricing-reimbursing scheme, which has not been considered before. We analyze the different impacts of data price and reimbursement on the host's connectivity sharing decision systematically. Based on this analysis, we further derive the optimal hybrid pricing-reimbursing policy that maximizes the MVNO's revenue. Our numerical result indicates that by using the proposed hybrid pricing policy, the MVNO can increase its revenue by 20% to 135% under an elastic client demand, and by 20% to 550% under an inelastic client demand, comparing to those achieved under a pricing-only policy.

Crowd-sourced mobile Internet access services enable mobile users to connect with each other and share their Internet connections. This is a promising solution for addressing users' increasing needs for ubiquitous connectivity and alleviating network congestion. The success of such services heavily depends on users' willingness to contribute their resources. In this paper, we consider a general model for such services, and design a distributed incentive mechanism for encouraging users' participation. This bargaining based scheme ensures that the contribution of user resources, in terms of Internet access bandwidths and battery energy, and the allocation of service capacity, measured in the delivered mobile data, are Pareto efficient and proportionally fair. The numerical results verify that the service always improves users' performance and that these benefits depend on the diversity of the users' resources.

In this paper, we propose a novel information market for TV white space networks, where the spectrum database operator sells the information regarding TV white space to secondary users. Different from the traditional spectrum market, the information market processes the unique property of positive externality, as more users purchasing the information service will increase the value of the service to each buyer. We systematically characterize the market equilibrium and the database operator's optimal information pricing strategy. Specifically, we first study how the market share dynamically evolves over time and eventually converge to a market equilibrium. We show that the market equilibrium increases with the initial market share, and there exist several tipping points of the initial market share, around which a slight change will lead to a significant change on the emerging market equilibrium. Based on the market equilibrium analysis, we further study the impact of the database operator's information pricing strategy on the market equilibrium, and derive the optimal information price that maximizes the database operator's revenue. Theoretical analysis and numerical result indicate that this is a promising business model for creating incentives for the database operator in TV white space networks.

The unprecedented growth of mobile data traffic challenges the performance and economic viability of today's cellular networks and calls for novel network architectures and communication solutions. Data offloading through third-party WiFi or femtocell access points (APs) can effectively alleviate the cellular network congestion in low operational and capital expenditure. This solution requires the cooperation and agreement of mobile cellular network operators (MNOs) and AP owners (APOs). In this paper, we model and analyze the interaction among one MNO and multiple APOs (for the amount of MNO's offloading data and the respective APOs' compensations) by using thew Nash bargaining theory. Specifically, we introduce a one-to-many bargaining game among the MNO and APOs and analyze the bargaining solution (game equilibrium) systematically under two different bargaining protocols: 1) sequential bargaining, where the MNO bargains with APOs sequentially, with one APO at a time, in a given order; and 2) concurrent bargaining, where the MNO bargains with all APOs concurrently. We quantify the benefits for APOs when bargaining sequentially and earlier with the MNO, and the losses for APOs when bargaining concurrently with the MNO. We further study the group bargaining scenario where multiple APOs form a group bargaining with the MNO jointly and quantify the benefits for APOs when forming such a group. Interestingly, our analysis indicates that grouping of APOs not only benefits the APOs in the group but may also benefit some APOs not in the group. Our results shed light on the economic aspects and the possible outcomes of the MNO/APOs interactions and can be used as a roadmap for designing policies for this promising data offloading solution.

Providing economic incentives to all parties involved is essential for the success of dynamic spectrum access. Cooperative spectrum sharing is one effective way to achieve this, where secondary users (SUs) relay traffics for primary users (PUs) in exchange for dedicated spectrum access time for SUs' own communications. In this paper, we study the cooperative spectrum sharing under incomplete information, where SUs' wireless characteristics are private information and not known by a PU. We model the PU-SU interaction as a labor market using contract theory. In contract theory, the employer generally does not completely know employees' private information before the employment and needs to offers employees a contract under incomplete information. In our problem, the PU and SUs are, respectively, the employer and employees, and the contract consists of a set of items representing combinations of spectrum accessing time (i.e., reward) and relaying power (i.e., contribution). We study the optimal contract design for both weakly and strongly incomplete information scenarios. In the weakly incomplete information scenario, we show that the PU will optimally hire the most efficient SUs and the PU achieves the same maximum utility as in the complete information benchmark. In the strongly incomplete information scenario, however, the PU may conservatively hire less efficient SUs as well. We further propose a decompose-and-compare (DC) approximate algorithm that achieves a close-to-optimal contract. We further show that the PU's average utility loss due to the suboptimal DC algorithm and the strongly incomplete information are relatively small (less than 2 and 1.3 percent, respectively, in our numerical results with two SU types).

The successful deployment of a TV white space network requires the coordination and cooperation of all involved parties (including licensees, databases, secondary operators, and end-users), which form the White Space Ecosystem. In this paper, we study the white space ecosystem from the perspective of secondary network operators. Specifically, we consider a competitive white space network, where multiple secondary operators compete for the same pool of end-users. Each operator serves the attracted end-users by using either the dedicated spectrum (pre-ordered in advance) or the shared spectrum (requested in real-time). The key problem for each operator is to (i) determine the order quantity of dedicated spectrum, considering the uncertainty of end-user demand, and (ii) decide the price to the end-users, considering the competition of other operators. We formulate the interaction of operators as a non-cooperative Price-Quantity competition game (PQ-game), and study the existence and uniqueness of the Nash equilibrium (NE) systematically. We further characterize the impacts of the operator competition on the social welfare and the operators' own profits. Our results show that such impacts depend largely on the operators' cost of purchasing spectrum from the database or licensee: when the cost is low, the operator competition will decrease the social welfare and the operators' profits; when the cost is high, however, the competition will increase the social welfare and the operators' profits.

Mobile data offloading is a promising approach to alleviate network congestion and enhance quality of service (QoS) in mobile cellular networks. In this paper, we investigate the economics of mobile data offloading through third-party WiFi or femtocell access points (APs). Specifically, we consider a market-based data offloading solution, where macrocellular base stations (BSs) pay APs for offloading traffic. The key questions arising in such a marketplace are following: (i) how much traffic should each AP offload for each BS? and (ii) what is the corresponding payment of each BS to each AP? We answer these questions by using the non-cooperative game theory. In particular, we define a multi-leader multi-follower data offloading game (DOFF), where BSs (leaders) propose market prices, and accordingly APs (followers) determine the traffic volumes they are willing to offload. We characterize the subgame perfect equilibrium (SPE) of this game, and further compare the SPE with two other classic market outcomes: (i) the market balance (MB) in a perfect competition market (i.e., without price participation), and (ii) the monopoly outcome (MO) in a monopoly market (i.e., without price competition). Our results analytically show that (i) the price participation (of BSs) will drive market prices down, compared to those under the MB outcome, and (ii) the price competition (among BSs) will drive market prices up, compared to those under the MO outcome.

Providing proper economic incentives to all parties involved is essential for the success of dynamic spectrum access. Market-driven secondary spectrum trading is an effective way to achieve this goal, where primary spectrum owners (POs) temporarily lease their licensed spectrum bands to unlicensed secondary users (SUs). In this paper, we consider the short-term secondary spectrum trading between one PO (seller) and multiple SUs (buyers) in a hybrid spectrum market with both guaranteed contracts (futures market) and spot transactions (spot market). In particular, we focus on the PO's expected profit maximization under stochastic network information. The optimal solution consists of (i) a policy that maximizes the ex-ante expected profit based on the stochastic distribution of network information, and (ii) a selling mechanism that determines the real-time allocation and charging based on the realized network information and the derived policy. We study the optimal solution systematically under both information symmetry and asymmetry, depending on whether the PO can observe the SUs' realized private information. Under information symmetry, we show that the optimal solution can be achieved by a perfect price discrimination mechanism, which maximizes both the PO's expected profit (optimality) and the social welfare (efficiency). Under information asymmetry, we propose an integrated contract and auction design-ContrAuction-to elicit SUs' private information effectively. We derive analytically the optimal ContrAuction mechanisms that maximize the PO's expected profit with and without the constraint of efficiency, and characterize systematically the tradeoff between the PO's profit and the social welfare.

Geo-location database driven white space network is a very promising approach for improving secondary spectrum utilization. In this paper, we consider the business modeling for geo-location database driven white space network. In our proposed model, the database acts as a spectrum broker buying (reserving) bandwidth from spectrum licensees in advance, and then resells the reserved bandwidth to unlicensed white space devices (WSDs) in real-time. We study the optimal bandwidth reservation for the database with WSDs' demand uncertainty under both information symmetry and asymmetry. Under information symmetry, the database and the WSD experience the same degree of uncertainty about the market demand. We derive the optimal bandwidth reservations in a centralized/integrated manner (as a benchmark). Under information asymmetry, the WSD has more information (i.e., with less uncertainty) about demand (due to the proximity to end-users). We propose a contract-based bandwidth reservation mechanism, which ensures WSDs share their local information with the database credibly. We further characterize the optimal bandwidth reservation contract systematically. Simulations show that under information asymmetry, the optimal bandwidth reservation contract improves both the database's profit and the social welfare significantly (larger than 30% in our simulations) without sacrificing the WSDs' benefits, comparing to those mechanisms without information sharing.

Cell selection and resource allocation (CS-RA) are processes of determining cell and radio resource which provide service to mobile station (MS). Optimizing these processes is an important step towards maximizing the utilization of current and future networks. In this paper, we investigate the problem of CS-RA in heterogeneous wireless networks. Specifically, we propose a distributed cell selection and resource allocation mechanism, in which the CS-RA processes are performed by MSs independently. We formulate the problem as a two-tier game named as inter-cell game and intra-cell game, respectively. In the first tier, i.e. the inter-cell game, MSs select the best cell according to an optimal cell selection strategy derived from the expected payoff. In the second tier, i.e., the intra-cell game, MSs choose the proper radio resource in the serving cell to achieve maximum payoff. We analyze the existence of Nash equilibria of both games, the structure of which suggests the interesting property that we can achieve automatic load balance through the two-tier games. Furthermore, we propose distributed algorithms named as CS-Algorithm and RA-Algorithm to enable the independent MSs converge to Nash equilibria. Simulation results show that the proposed algorithms converge effectively to Nash equilibria and that the proposed CS-RA mechanism achieves better performance in terms of throughput and payoff compared to conventional mechanisms.

Cognitive radio is a promising paradigm to achieve efficient utilization of spectrum resource by allowing the unlicensed users (i.e., secondary users, SUs) to access the licensed spectrum. Market-driven spectrum trading is an efficient way to achieve dynamic spectrum accessing/sharing. In this paper, we consider the problem of spectrum trading with single primary spectrum owner (or primary user, PO) selling his idle spectrum to multiple SUs. We model the trading process as a monopoly market, in which the PO acts as monopolist who sets the qualities and prices for the spectrum he sells, and the SUs act as consumers who choose the spectrum with appropriate quality and price for purchasing. We design a monopolist-dominated quality-price contract, which is offered by the PO and contains a set of quality-price combinations each intended for a consumer type. A contract is feasible if it is incentive compatible (IC) and individually rational (IR) for each SU to purchase the spectrum with the quality-price intended for his type. We propose the necessary and sufficient conditions for the contract to be feasible. We further derive the optimal contract, which is feasible and maximizes the utility of the PO, for both discrete-consumer-type model and continuous-consumer-type model. Moreover, we analyze the social surplus, i.e., the aggregate utility of both PO and SUs, and we find that, depending on the distribution of consumer types, the social surplus under the optimal contract may be less than or close to the maximum social surplus.

In this paper, we propose an agent-based spectrum trading model, where an agent can play a third-party role in the spectrum trading process. Providing service to Secondary Users (SUs) with spectrum bought from Primary Users (PUs), the agent can make profits during the process by providing service to secondary users. During each trading period, the agent has to decide how much spectrum it should lease from PUs and what price it should charge SUs. Therefore, the most significant challenge to implement this spectrum trading model is finding the most profitable strategy for agent(s). We address this challenge under two scenarios in which: 1) a single agent and 2) multiple agents. Instead of quantifying SUs' spectrum demand by a deterministic function of price, we take the randomness of secondary users' demand or demand uncertainty into consideration. To the best of our knowledge, this is the first solution to agent-based spectrum trading considering demand uncertainty.

Cognitive radio (CR) is a promising paradigm to achieve efficient utilization of the limited spectrum resource by allowing the unlicensed users to access the licensed spectrum, and dynamic spectrum access (DSA) is one of the fundamental functions of CR networks. Market-driven spectrum auction has been recognized as an effective way to achieve DSA. In spectrum auction, the primary spectrum owners (POs) act as auctioneers who are willing to sell idle spectrum bands for additional revenue, and the secondary users (SUs) act as bidders who are willing to buy spectrum bands from POs for their services. However, conventional spectrum auction designs are restricted within the scenario of single auctioneer. In this paper, we study the spectrum auction with multiple auctioneers and multiple bidders, which is more realistic for practical CR networks. We propose MAP, a Multiauctioneer Progressive auction mechanism, in which each auctioneer systematically raises the trading price and each bidder subsequently chooses one auctioneer for bidding. The equilibrium is defined as the state that no auctioneer and bidder would like to change his decision. We show analytically that MAP converges to the equilibrium with maximum spectrum utilization of the whole system. We further analyze the incentive for POs and SUs joining the auction and accepting the auction result. Simulation results show that MAP well converges to the equilibrium, and the spectrum utilization is arbitrary closed to the global optimal solution according to the length of step.

In this paper, we propose an agent-based spectrum trading model, where agent plays a third-party role in the trading process. Providing service to secondary users (SUs) with spectrum bought from primary users (PUs), agent makes profit in the spectrum trading process. We address the challenge of finding the most profitable strategy of agent(s) when spectrum demand is uncertain. We first address this challenge for the secondary network where single agent operates, and extend it to a multiple-agent system. To our best knowledge, this is the first solution to agent-based spectrum trading considering demand uncertainty.

We consider the problem of cooperative spectrum sharing among a primary user (PU) and multiple secondary users (SUs), where the PU selects a proper set of secondary users to serve as the cooperative relays for its transmission. In return, the PU leases portion of channel access time to the selected SUs for their own transmission. The PU decides the portion of channel access time it will leave for the selected SUs (i.e., the cooperative relays), and the cooperative relays decide their respective power level used to help PU's transmission in order to achieve proportional access time to the channel. We assume that the PU and SUs are rational and selfish, i.e., they only aim at maximizing their own utility. As SU's utility is in term of their own transmission rate and the power cost for PU's transmission, so they will choose a proper power level to meet the tradeoff between transmission rate and power cost. PU will choose a proper portion of channel access time for the cooperative relays to attract them to employ higher power level. We formulate the problem as a non-cooperative game between PU and SUs, and prove that the proposed game converges to a unique Stackelberg equilibrium. By employing an iterative updating algorithm, we can achieve the unique equilibrium point.

In this paper, we consider a scenario where multiple secondary users search for idle frequency bands in a spectrum consisting of multiple channels. The state of each channel is modeled as a discrete-time Markov process. In each time slot, one secondary user(SU) chooses one channel to sense and decides whether to use the channel based on the sensing result. When there is only one SU, former work has proved that a myopic policy maximizing the current reward is optimal when the channel state transitions are positively correlated over time. In this paper, we extend the myopic policy to multiple-SU scenario. We propose and analyze two approaches that make use of the myopic policy more efficiently when multiple SUs exist.

Spectrum under-utilization is one of the bottlenecks of the development of wireless communication, and dynamic spectrum access (DSA) is envisioned as a novel mechanism to solve the problem of spectrum scarcity. Spectrum auction has been recognized as an effective way to achieve DSA, wherein the primary spectrum owner (PO) acts as an auctioneer who has free channels and is willing to sell them for additional revenue, and the secondary user (SU) acts as a bidder who is willing to buy a channel from POs for its service. In this paper, we adopt a progressive spectrum auction named MAP, which has been proved optimal and incentive compatible in DSA networks with distributed POs and SUs. However, in MAP, the profit of POs is not maximized under the equilibrium point due to the scarcity of SUs' private information known by POs. We propose an information sharing mechanism, in which the POs exchange their local information with each other. We show analytically that, allowing information sharing, each PO is able to learn the private information of SUs and increase its profit accordingly. Long term profit acts as the incentive for information sharing that all the POs automatically reveal the true information when they are aware of this. It is notable that information sharing doesn't affect social optimality. Simulation shows the increase of POs' profits in the sense of long term interests.

Channel allocation was extensively investigated in the framework of cellular networks, but it was rarely studied in the wireless ad hoc networks, especially in the multihop networks. In this paper, we study the competitive multiradio multichannel allocation problem in multihop wireless networks in detail. We first analyze that the static noncooperative game and Nash equilibrium (NE) channel allocation scheme are not suitable for the multihop wireless networks. Thus, we model the channel allocation problem as a hybrid game involving both cooperative game and noncooperative game. Within a communication session, it is cooperative; and among sessions, it is noncooperative. We propose the min-max coalition-proof Nash equilibrium (MMCPNE) channel allocation scheme in the game, which aims to maximize the achieved data rates of communication sessions. We analyze the existence of MMCPNE and prove the necessary conditions for MMCPNE. Furthermore, we propose several algorithms that enable the selfish players to converge to MMCPNE. Simulation results show that MMCPNE outperforms NE and coalition-proof Nash equilibrium (CPNE) schemes in terms of the achieved data rates of multihop sessions and the throughput of whole networks due to cooperation gain.

Channel allocation was extensively researched in the framework of cellular networks, but it was rarely studied in the ad-hoc wireless networks, especially in the multi-hop ad-hoc networks. In this paper, we study the problem of competitive multi-radio multi-channel allocation in multi-hop wireless networks in detail. We model the channel allocation problem as a static cooperative game, and then derive a min-max coalition-proof Nash equilibrium (MMCPNE) in this game. We study the existence of MMCPNE in the static game and prove the necessary and sufficient conditions for MMCPNE. Finally, we propose a two-step distributed algorithm that enable the selfish players to converge to MMCPNE.

Channel allocation was extensively investigated in the framework of cellular networks, but it was rarely studied in the wireless ad-hoc networks, especially in the multi-hop ad-hoc networks. In this paper, we study the competitive multi-radio channel allocation problem in multi-hop wireless networks in detail. We model the channel allocation problem as a static cooperative game, in which some players collaborate to achieve high date rate. We propose the min-max coalition-proof Nash equilibrium (MMCPNE) channel allocation scheme in the game, which is aiming to max the achieved date rates of communication links. We study the existence of MMCPNE and prove the necessary conditions for MMCPNE. Furthermore, we propose several algorithms that enable the selfish players to converge to MMCPNE. Simulation results show that MMCPNE outperforms CPNE and NE schemes in terms of achieved data rates of the multi-hop links due to cooperation gain.