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We study the mobility-aware computation offloading design for mobile edge computing (MEC) based vehicular wireless networks which considers random mobility of the vehicle and its potential handover during the offloading process. The computation offloading decision is made by comparing the average total costs due to the local and remote computation task executions at the vehicle and the MEC servers, respectively. Toward this end, we propose an analytical model to determine the average total cost due to remote task execution. We evaluate the performance of the proposed design by comparing its achievable cost with that due to an average mobility-based approximation. Simulation results show a cost reduction of up to 17% compared to the baseline.

In wireless network virtualization, guaranteeing service contracts with different mobile virtual network operators (MVNOs) and optimizing energy efficiency are crucial for the success of the virtualization scheme deployed by an infrastructure provider (InP). In this paper, a novel design framework is proposed for resource allocation in an OFDMA virtualized wireless network (VWN). Treating the virtual resources for a VWN as commodities, the InP wants to maximize its revenue by leasing the infrastructure and resources to the MVNOs while meeting certain contract agreements. Moreover, MVNOs want to serve their users at the best performance and pay the minimum cost to the InP. A Lyapunov based online algorithm is proposed to solve the InP's long-term optimization problem. The short-term optimization problem of the InP is considered as a combinatorial nonconvex problem. A multiple time-scale framework is proposed to solve the optimization problem of the InP, which decomposes the pricing decision, base station assignment, and resource allocation into different time-scale algorithms to achieve the design objectives. First, a distributed matching based algorithm is proposed to solve the base station assignment problem. Second, we propose a successive convex approximation approach to solve the joint subchannel assignment and energy efficiency problem. Finally, we propose a branch and bound based algorithm to optimally solve the price decision problem. Simulation results show the trade-off between energy efficiency, InP's revenue, and the isolation provisioning.

In this paper, we study the trajectory control and sub-channel assignment for unmanned aerial vehicles (UAVs) based wireless networks with wireless backhaul links. This design aims to optimize the max-min rate subject to data transmission demands of ground users (GUs). The underlying problem is a mixed integer nonlinear optimization problem because of the complicated relationship between the UAV-GU channel gains and the UAV’s location in each time slot of the flight period. To tackle this problem, we employ the alternating optimization approach where we iteratively optimize the sub-channel assignment and UAV trajectory control until convergence. Moreover, the difference of convex functions (DC) optimization method and the arithmetic and geometric means (AM-GM) inequality are employed to convexify and solve the non-convex UAV trajectory sub-problem. Via extensive numerical studies, we illustrate the effective UAV’s trajectory considering capacity-limited access and backhaul links and the non-negligible rate gain of the proposed design compared to a baseline employing the circular UAV trajectory around the center of service area and a heuristic algorithm for sub-channel assignment.

In this paper, we study the problem of unmanned aerial vehicles (UAVs) placement and bandwidth allocation for wireless networks with wireless backhaul links. The general model with different possible configurations of line-of-sight (LoS) and non-line-of-sight (NLoS) propagation conditions of wireless links between UAVs and ground users (GUs) is explicitly considered based on which we derive the average rates for wireless access links. The underlying problem is a difficult non-convex optimization problem due to the strong co-channel interference and the complicated relationship between the LoS/NLoS probabilities and UAVs' locations. To solve this challenging problem, we employ the alternative optimization approach where we iteratively optimize the bandwidth allocation and UAV placement until convergence. Moreover, we employ the difference of convex functions (DC) optimization and quadratic transformation approaches to convexify and tackle the non-convex UAV placement sub-problem. Via numerical studies, we show that the proposed scheme achieves a significant throughput gain compared to a baseline in which UAVs are deployed at the centers of hotspot areas.

We study the problem of user clustering and power assignment for a network comprised of cellular users and underlay device-to-device (D2D) users operating under a non-orthogonal multiple access (NOMA) scheme. Our goal is to maximize the sum-rate of the network by jointly optimizing the user clustering and power assignment. Moreover, we also aim to provide interference protection for the cellular users. The formulated optimization problem is a mixed-integer non-convex problem. Thus, the original problem is decomposed into two subproblems. The first subproblem of user clustering is formulated as a matching game with externalities, where this matching game is solved sequentially while the second subproblem pertaining to power assignment is solved using complementary Geometric programming. Finally, an efficient joint iterative algorithm is proposed that can achieve a suboptimal solution for the mix integer non-convex NP-hard problem. Simulation results show that the proposed algorithm can achieve up to 70% and 92% of performance gains in terms of the average sum-rate in comparison with the general NOMA and traditional orthogonal frequency-division multiple access (OFDMA) schemes, respectively. Moreover, our results show that the proposed scheme significantly enhances the network connectivity in terms of the number of admitted users compared with the traditional OFDMA, NOMA, and D2D schemes.

Wireless network virtualization is identified as one of the key enabling technologies to bring fifth-generation networks into fruition. In this letter, we model the service selection and resource purchasing problem as a two-stage combinatorial optimization problem. To solve this problem, we propose a hierarchical matching game-based scheme, which satisfies the efficient resource allocation and strict isolation requirements. Simulation results show that our proposal outperforms the fixed sharing approach by 32% and achieves up to 97% of performance obtained by the optimal approach (general sharing scheme) in terms of average sum rate.

Internet traffic volume is continuing to increase rapidly. Named data networking (NDN) has been introduced to support this Internet traffic growth through caching contents close to consumers. While caching in NDN is beneficial to both Internet service providers (ISPs) and content providers (CPs), ISPs serve cached contents independently without any coordination with CPs. By authorizing the ISPs to cache and distribute the contents accessible on payments, it becomes impractical for CPs to control content access and payments. In this paper, we address these challenges by proposing a joint incentive mechanism and a price-based cache replacement (PBCR) policy for paid content in NDN that improves the ISP's and CPs' profits. We use an auction theory, where the ISP earns profits from caching by alleviating traffic load on transit links and participating in contents selling. Therefore, before the ISP starts selling cached contents, it needs to cache them first. Furthermore, the ISP cache capacity is limited; therefore, we propose PBCR, where the PBCR triggers the content that needs to be replaced when the cache storage is full based on both content price and link cost. The simulation results show that our proposal increases the profits of all the network players involved in paid content caching and improves cache hit ratio. Published version

Device to device (D2D) communication is considered as an effective technology for enhancing the spectral efficiency and network throughput of existing cellular networks. However, enabling it in an underlay fashion poses a significant challenge pertaining to interference management. In this paper, mode selection and resource allocation for an underlay D2D network is studied while simultaneously providing interference management. The problem is formulated as a combinatorial optimization problem whose objective is to maximize the utility of all D2D pairs. To solve this problem, a learning framework is proposed based on a problem-specific Markov chain. From the local balance equation of the designed Markov chain, the transition probabilities are derived for distributed implementation. Then, a novel two phase algorithm is developed to perform mode selection and resource allocation in the respective phases. This algorithm is then shown to converge to a near optimal solution. Moreover, to reduce the computation in the learning framework, two resource allocation algorithms based on matching theory are proposed to output a specific and deterministic solution. The first algorithm employs the one-to-one matching game approach whereas in the second algorithm, the one-to many matching game with externalities and dynamic quota is employed. Simulation results show that the proposed framework converges to a near optimal solution under all scenarios with probability one. Moreover, our results show that the proposed matching game with externalities achieves a performance gain of up to 35 percent in terms of the average utility compared to a classical matching scheme with no externalities.

In this paper, a novel framework is proposed to jointly optimize user association and resource allocation in the uplink cognitive femtocell network (CFN). In the considered CFN, femtocell base stations (FBSs) are deployed to serve a set of femtocell user equipments (FUEs) by reusing subchannels used in a macrocell base station (MBS). The problem of joint user association, subchannel assignment, and power allocation is formulated as an optimization problem, in which the goal is to maximize the overall uplink throughput while guaranteeing FBSs overloading avoidance, data rate requirements of the served FUEs, and MBS protection. To solve this problem, a distributed framework based on the matching game is proposed to model and analyze the interactions between the FUEs and FBSs. Using this framework, distributed algorithms are developed to enable the CFN to make decisions about user association, subchannel allocation, and transmit power. The algorithms are then shown to converge to a stable matching and exhibit a low computational complexity. Simulation results show that the proposed approach yields a performance improvement in terms of the overall network throughput and outage probability, with a small number of iterations to converge.

In this letter, a game-theoretic framework is proposed for coordinating resource partitioning and data offloading in LTE-based heterogeneous networks (HetNets). The goal of this framework is to determine the amount of radio resources a macrocell should offer to neighboring small cells (SCs) and the amount of traffic each SC should admit from the macrocell. A two-stage Stackelberg game is applied to optimize the strategies of both the macrocell (the leader) and SCs (the followers). The macrocell’s strategy is shown to be a mixed-boolean nonlinear program, which is NP-hard. To solve this problem efficiently, a branch and bound based method is proposed to obtain the global optimal. We also show that this two-stage game has a unique Stackelberg equilibrium. Numerical results show that the proposed framework outperforms the traditional design by 50% in term of offloaded data. Additionally, reduction of 14% was observed in term of cost paid by MBS.

We study the problem of joint user clustering and resource allocation for wireless network virtualization (WNV) us­ing non-orthogonal multiple access (NOMA). We aim to maximize the weighted total sum-rate while taking into account the isolation constraint of the mobile virtual network operators (MVNOs). To solve the non-convex formulated problem, we decouple it into three subproblems, i.e., user clustering, resource block (RB) allocation and power assignment. We apply the framework of matching game with externalities to solve the user clustering problem while the solutions for RB and power allocation are derived by using the Lagrange dual approach and complementary Geometric programming, respectively. An alternative maximiza­tion algorithm is provided to achieve a suboptimal solution for the original problem. We propose to classify user equipments (UEs) into three classes, i.e., strong, normal and weak UEs and compare our proposed scheme with general NOMA scheme with two UEs per cluster. Simulation results revel a performance gain of 2.5% in terms of throughput. Moreover, the proposed scheme outperforms the traditional OFDMA scheme in terms of throughput and energy efficiency by up to 40% and 58%, respectively.

The concept of multi-access edge computing (MEC) has been recently introduced to supplement cloud computing by deploying MEC servers to the network edge so as to reduce the network delay and alleviate the load on cloud data centers. However, compared to a resourceful cloud, an MEC server has limited resources. When each MEC server operates independently, it cannot handle all of the computational and big data demands stemming from the users devices. Consequently, the MEC server cannot provide significant gains in overhead reduction due to data exchange between users devices and remote cloud. Therefore, joint computing, caching, communication, and control (4C) at the edge with MEC server collaboration is strongly needed for big data applications. In order to address these challenges, in this paper, the problem of joint 4C in big data MEC is formulated as an optimization problem whose goal is to maximize the bandwidth saving while minimizing delay, subject to the local computation capability of user devices, computation deadline, and MEC resource constraints. However, the formulated problem is shown to be non-convex. To make this problem convex, a proximal upper bound problem of the original formulated problem that guarantees descent to the original problem is proposed. To solve the proximal upper bound problem, a block successive upper bound minimization (BSUM) method is applied. Simulation results show that the proposed approach increases bandwidth-saving and minimizes delay while satisfying the computation deadlines.

Caching is the key feature of Content Centric Networking (CCN) that allows the Internet Service Provider (ISP) to reduce network traffic crossing its network, and save bandwidth usage cost. On the other hand, it is also on benefit of the Content Providers (CPs) to cache the contents within the ISP network near the consumers. However, caching paid contents (the contents that only paying consumers can access), which are the main source of income for CP, in the ISP network complicates the CP's task of controlling content access and payment. Thus, ISP manages content placement inside its cache-enabled routers and serves content based on user demands, without any coordination with CP. There is no profit sharing mechanism between both ISP and CPs. Therefore, a payment mechanism between ISP and CPs that considers paid content caching and distribution inside the ISP network is needed. To address this challenge, we propose a new incentive mechanism for paid content caching that satisfies both ISP and CPs through the use of reverse auction. The ISP monetizes its cache storage through caching contents from multiple CPs and selling them to its customers. The reverse auction helps the ISP to get prices from multiple CPs, and to select the price that minimize its total payment. The simulation results show that our proposal satisfies all network players involved in in- network caching through increasing their utilities.

The successful virtualization of wireless access networks is strongly affected by the way in which radio resources are managed. The infrastructure provider (InP) is required to deploy efficient and flexible resource allocation techniques to dynamically allocate the resources for the users associated with different mobile virtual network operators (MVNOs). Service contracts with different MVNOs and fairness among their users are crucial to the success of the virtualization scheme deployed by the InP. In this paper, a game-theoretic framework is proposed for resource allocation in OFDMA virtualized wireless network. The framework considers a market model consisting an InP and multiple MVNOs. Regarding the virtual resource for a virtualized wireless network as commodities, the InP wants to maximize its revenue by leasing the infrastructure to the MVNOs while meeting certain contract agreements. Moreover, MVNOs want to serve their users at the best performance and want to pay the minimum to InP. A two-stage Stackelberg game is applied to optimize the strategies of both the InP (the leader) and MVNOs (the followers). We show that this two-stage game has a unique Stackelberg equilibrium.

Wireless Power Transmission (WPT) is a technique to charge electrical devices (EDs) remotely. In WPT, power source (Smart Wireless Charger) transmits power wirelessly, using air as the medium, to EDs. In this paper, we present an Auction mechanism to obtain the energy trading between Smart Wireless Chargers (SWCs) and EDs. In our proposed Double-Auction based charging trade mechanism, our priorities are to increase utility of the SWCs as well as increase EDs utilities. In our proposal, first we analyze the system architecture of the WPT environment and then form an optimization problem for the auction system. We then, introduce two algorithms to solve the combinatorial optimization problem such that the wireless charging system is stable and have high efficiency. We have numerically analyzed our system using python, the results show that the proposed mechanism achieve higher total utility for the whole system with satisfying budget balancing, individual rationality and truthfulness.

Cognitive femotcell networks can opportunistically access the licensed spectrum to enhance spectrum utilization. However, interference management plays a crucial role to effectively utilize the spectrum. In this paper, we consider the joint resource allocation and power control problem for an uplink transmission for a network consisting of a licensed macrocell and multiple cognitive femtocells. Furthermore, our problem imposes crucial constraints of both cross-tier interference for macrocell base station and quality of service for femtocell user. The joint problem is shown to be mix-integer nonlinear nonconvex optimization problem, which is NP-hard. To solve this problem efficiently, we employ a scheme consisting of two distributed algorithms. Numerical results show that the proposed scheme converges to the optimal power and resource allocation with a fast convergence speed. Additionally, our scheme guarantees the interference threshold at MBS and outage QoS for all cognitive femtocell users.

The proliferation of novel network access devices and demand for high quality of service by the end users are proving to be insufficient and are straining the existing wireless cellular network capacity. An economic and promising alternate to enhance the spectral efficiency and network throughput is device to device (D2D) communication. However, enabling D2D communication poses significant challenges pertaining to the interference management. In this paper, we address the resource allocation problem for underlay D2D pairs. First, we formulate the resource allocation optimization problem with an objective to maximizes the throughput of all D2D pairs by imposing interference constraints for protecting the cellular users. Second, to solve the underlying mixed-integer non linear resource allocation problem, we propose a stable, self-organizing and distributed solution using matching theory. Finally, we simulate our proposition to validate the convergence, cellular user protection, and network throughput gains achieved by the proposal. Simulation results reveal that D2D pairs can achieve significant throughput gains (i.e., up to 45 – 91%) while protecting the cellular users compared to the scenario in which no D2D pairs exist.

In this paper, we consider distributed power control schemes for downlink co-tier interference coordination in a two-tier heterogeneous network consisting of multiple femtocells under the coverage of a macrocell. We formulate a non cooperative game in order to solve this problem. First we propose an algorithm in which all femto access point simultaneously update their transmit power to maximize their individual payoff function according to a dynamic best response function. Numerical results show that in this non-cooperative game, all femtocells' transmit power converges quickly to Nash equilibrium points. Second, we develop a novel dynamic pricing power control scheme by using the update mechanism named gradient play to update the transmission powers of all femtocells. Numerical results show that the second proposal can attain lower transmission power but higher throughput in comparison with our first proposal, and both proposals outperform the conventional power control schemes.

The dense and pervasive deployment of wireless small cells can boost the performance of existing macrocellular networks; however, it poses significant challenges pertaining to the cross-tier interference management. In this letter, the downlink resource allocation problem for an underlay small cell network is studied. In this network, the protection of the macrocell tier is achieved by imposing cross-tier interference constraints in the resource allocation problem. To solve the underlying mixed-integer resource allocation problem, we propose two different algorithms. The first algorithm is developed by applying the duality-based optimization approach for the relaxed problem, which enables distributed implementation. The second distributed algorithm, which enables coordination is devised based on matching theory. Simulation results show that the proposed duality-based algorithm outperforms the greedy approach by 4% in terms of sum-rate whereas the matching-based algorithm with tier-coordination yields performance gains up to 17% compared with the duality-based approach.