This paper introduces a novel approach to AI-powered digital-twins-assisted robotic control in automated warehouses, integrating the kinetic models of robots with real-time synchronization of digital-twins. The proposed framework utilizes Ultra-Reliable Low-Latency Communication (URLLC) over 5G networks to enable seamless interaction between the physical robots and AI-driven models in the cyber twin. We formulate an optimization problem aimed at minimizing energy consumption during digital-twins-driven robotic operations, thereby enhancing both operational efficiency and energy efficiency. A Deep Reinforcement Learning (DRL)-based approach is developed for the adaptive learning of the AI models in the cyber twin, facilitating autonomous simulation and real-time decision-making for efficient robotic control. Additionally, we propose a game-theory-based resource allocation strategy to optimize the distribution of computational resources for continuous and adaptive learning within AI models. Numerical results demonstrate that the proposed game-based resource allocation scheme achieves Nash equilibrium, significantly improving performance in terms of energy consumption and resource utilization compared to the state-of-the-art DRL-based resource allocation scheme.

Open Radio Access Network (O-RAN) aims to establish an open and intelligent RAN architecture, enhancing flexibility, scalability, and network optimization. Machine learning (ML) technologies are pivotal in realizing these objectives by facilitating intelligent decision-making, automated optimization, and proactive maintenance. However, effectively selecting and deploying ML models within O-RAN to achieve energy efficiency poses significant challenges. In this paper, we propose a novel orchestration scheme tailored for next-generation systems, building upon and extending the foundational principles of the O-RAN paradigm. Our proposed orchestration policy offers a practical solution for deploying ML applications within the ORAN framework. Through comprehensive evaluation, our scheme demonstrates a remarkable reduction of up to 72.22% in energy consumption compared to the maximum performance baseline, while maintaining an accuracy level of approximately 94.56% relative to the same baseline.

Autonomous robotics play a central role in smart logistics where robots can replace or aid humans in all kinds of tasks, such as items picking, moving, and storing. In this paper, we investigate the problem of task scheduling in automated warehouses with heterogeneous autonomous robotic (HAR) systems. We formulate a long-term non-convex queueing control optimization problem to minimize the queue length of tasks to be processed in the warehouse. Traditional task scheduling solutions based on optimization approaches are inefficient in handling the stochastic nature of the goods/tasks flow and a large number of robots in the system due to their computational cost. We propose a deep reinforcement learning (DRL) based task scheduling algorithm that employs the proximal policy optimization (PPO) method to find an optimal task scheduling policy. Due to the heterogeneity of the system, we propose a proximal weighted federated learning-based algorithm for implementing a decentralized PPO algorithm that improves the performance of the distributed PPO agents that are deployed in the workstations at the geographically distributed warehouses. The simulation results demonstrate the performance improvement of our proposed algorithm compared to the existing methods. Note to Practitioners—Task scheduling for robotic swarms in smart warehouses is substantial for e-commerce. State-of-the-art solutions have focused on efficient task scheduling for homogeneous robotic systems using machine learning techniques implemented in the warehouse management systems (WMS). However, task scheduling for a heterogeneous autonomous robotic (HAR) system has not fully been investigated so far. This article provides a comprehensive task scheduling algorithm for HAR systems that leverages innovative deep reinforcement learning and federated learning techniques. The proposed algorithm can be deployed in the geographically distributed warehouses of an e-commerce company and easily integrated into the WMS to optimally control the operation of the HAR system with stochastic goods/tasks flows in the smart warehousing.

5G network provides high-rate, ultra-low latency, and high-reliability connections in support of wireless mobile robots with increased agility for factory automation. In this paper, we address the problem of swarm robotics control for mission-critical robotic applications in an automated grid-based warehouse scenario. Our goal is to maximize long-term energy efficiency while meeting the energy consumption constraint of the robots and the ultra-reliable and low latency communication (URLLC) requirements between the central controller and the swarm robotics. The problem of swarm robotics control in the URLLC regime is formulated as a nonconvex optimization problem since the achievable rate and decoding error probability with short block-length are neither convex nor concave in bandwidth and transmit power. We propose a deep reinforcement learning (DRL) based approach that employs the deep deterministic policy gradient (DDPG) method and convolutional neural network (CNN) to achieve a stationary optimal control policy that consists of a number of continuous and discrete actions. Numerical results show that our proposed multi-agent DDPG algorithm outperforms the baselines in terms of decoding error probability and energy efficiency.

Open Radio Access Network (O-RAN) aims to achieve an open and intelligent RAN architecture that provides greater flexibility, scalability, and network optimization. Machine learning (ML) technologies can play a crucial role in achieving these goals by enabling intelligent decision-making, automated optimization, and proactive maintenance. In this paper, we propose an ML pipeline optimization for energy-efficient deployment of ML-based closed-loop controls (CLC) in 5G O-RAN. Specifically, we propose two ML-based CLCs for resource prediction and network slicing in Non-Realtime RIC and Near-Realtime RIC. We also propose an energy-efficient ML pipeline for dynamically deploying these two CLCs in the O-RAN architecture. Our numerical results demonstrate the effectiveness of our proposed ML pipeline deployment compared to fixed centralized and distributed deployment.

In this paper, we consider a UAV-assisted cloud robotic network in which a set of robots is deployed to perform specific missions, e.g., surveillance and rescue, in an area where the communication condition is unfavorable for the robots. Data collected by a robot can be either offloaded to a MEC server or to a remote cloud through the UAVs or to a nearby robot for computation. We formulate this offloading problem as a combinatorial nonconvex problem. A joint scheme for offloading decision-making, robot-UAV association, and computational resource allocation is proposed using KKT conditions, Lagrangian dual decomposition, and the Proximal Policy Optimization method to obtain the solution to the formulated problem. The simulation results show our proposed algorithm achieves a solution close to the optimal solution and outperforms the baselines.

5G intends to use network slicing to support multiple vertical industries such as the power grid. 5G network slicing can provide different levels of physical resources and virtual resources for various applications/services in vertical domains to meet their diversified communication requirements. These heterogeneous Service Level Agreements (SLAs) make the network highly dynamic in nature and challenging to operate and manage efficiently. In this paper, we formulate the SLA-based closed-loop automation network slicing management problem for 5G smart grid services in Open Radio Access Network (O-RAN). The resource scheduling problem is non-convex combinatorial while the resource reservation is a long-term mean-square-error minimization which is difficult to solve. We propose a collaborative game theory and deep learning solution that overcomes the complexity difficulty of the formulated problems. The proposed network slicing mechanism comprises three closed-loop control: closed-loop 1 resource request at the service layer, closed-loop 2 resource scheduling at the radio access layer, and closed-loop 3 resource reservation at the network layer. Simulation results show that the proposed slicing framework is more efficient than the baselines regarding fairness and network throughput.

The fifth-generation (5G) wireless network provides high-rate, ultra-low latency, and high-reliability connections that can meet the Industrial Internet of Things (IIoT) requirements in factory automation, especially for robot motion control. In this paper, we address 5G service provisioning in an automated warehouse scenario, where swarm robotics is controlled by an industrial controller that provides routing and job instructions over the 5G network. Leveraging the coordinated multipoint (CoMP), we formulate a time-varying joint CoMP clustering and 5G ultra-reliable low-latency communication (URLLC) beamforming design problem to control the robots that move around the automated warehouse for goods storage with the planned reference tracks. Traditional iterative optimization approaches are impractical in such a dynamic wireless environment due to high computational time. We propose a game-theoretic CoMP clustering algorithm combined with the Proximal Policy Optimization method to obtain a stationary solution closed to that of the exhaustive search algorithm considered as the global optimal solution.

In this paper, we investigate the problem of task scheduling in automated warehouses with hetero-geneous autonomous robotic systems. We formulate the task scheduling for a heterogeneous autonomous robots (HAR) system in each warehouse as a queueing control optimization problem in which we aim to minimize the queue length of tasks that are waiting to be processed. We propose a deep reinforcement learning (DRL) based approach that employs the proximal policy optimization (PPO) to achieve an optimal task scheduling policy. We then propose a federated learning based algorithm to improve the performance of the PPO agents. The simulation results fully demonstrate the performance improvement of our proposed algorithm in terms of average queue length compared to the distributed learning algorithm.

The fifth-generation (5G) wireless net-work provides high-rate, ultra-low latency, and high-reliability connections that can meet the industrial IoT requirements in factory automation especially for swarm robotics communication. In this paper, we address 5G service provisioning in an automated warehouse scenario where swarm robotics is controlled by an industrial controller that provides routing and job instructions over the 5G network. Leveraging the co-ordinated multipoint (CoMP), we formulate a joint CoMP clustering and 5G ultra-reliable low-latency communication (URLLC) beamforming design problem to control the robots that move around the automated warehouse for goods storage with the planed reference tracks. Traditional iterative optimization approaches are impractical in such dynamic wireless environments due to high computational time. We propose a game-theoretic CoMP clustering algorithm combined with the Proximal Policy Optimization method to obtain a stationary solution closed to that of the exhaustive search algorithm considered as the global optimal solution.

Future generation vehicles equipped with modern technologies will impose unprecedented computational demand due to the wide adoption of compute-intensive services with stringent latency requirements. The computational capacity of the next generation vehicular networks can be enhanced by incorporating vehicular edge or fog computing paradigm. However, the growing popularity and massive adoption of novel services make the edge resources insufficient. A possible solution to overcome this challenge is to employ the onboard computation resources of close vicinity vehicles that are not resource-constrained along with the edge computing resources for enabling tasks offloading service. In this paper, we investigate the problem of task offloading in a practical vehicular environment considering the mobility of the electric vehicles (EVs). We propose a novel offloading paradigm that enables EVs to offload their resource hungry computational tasks to either a roadside unit (RSU) or the nearby mobile EVs, which have no resource restrictions. Hence, we formulate a non-linear problem (NLP) to minimize the energy consumption subject to the network resources. Then, in order to solve the problem and tackle the issue of high mobility of the EVs, we propose a deep reinforcement learning (DRL) based solution to enable task offloading in EVs by finding the best power level for communication, an optimal assisting EV for EV pairing, and the optimal amount of the computation resources required to execute the task. The proposed solution minimizes the overall energy for the system which is pinnacle for EVs while meeting the requirements posed by the offloaded task. Finally, through simulation results, we demonstrate the performance of the proposed approach, which outperforms the baselines in terms of energy per task consumption.

Multi-access edge computing (MEC) is the key enabling technology that supports compute-intensive applications in 5G networks. By deploying powerful servers at the edge of wireless networks, MEC can extend the computational capacity of the mobile devices by migrating compute-intensive tasks to the MEC servers. In this paper, we consider a multi-user MEC wireless network in which multiple mobile devices can associate and perform computation offloading via wireless channels to MEC servers attached to the base stations (BSs). The decision whether the computation task is executed locally at the user device or to be offloaded for MEC server execution should be adaptive to the time-varying network dynamics. Taking into account the dynamic of the environment, we propose a deep reinforcement learning (DRL) based approach to solve the formulated nonconvex problem of minimizing computation cost in terms of total delay. However, real-world networks tend to have a large number of users and MEC servers involving large numbers of different actions (continuous and discrete), where evaluating the combination of every possible action becomes impractical. Therefore, conventional DRL methods may be difficult or even impossible to directly apply to the proposed model. Based on the recursive decomposition of the action space available to each state, we propose a DRL-based algorithm for joint server selection, cooperative offloading, and handover in a multi-access edge wireless network. Numerical results show that the proposed DRL based algorithm significantly outperforms the traditional Q-learning method and local computation in terms of task success rate and total delay.

Unmanned aerial vehicle (UAV)-assisted communications have several promising advantages, such as the ability to facilitate on-demand deployment, high flexibility in network reconfiguration, and high chance of having line-of-sight (LoS) communication links. In this paper, we aim to optimize the UAV control for maximizing the UAV's energy efficiency, in which both aerodynamic energy and communication energy are considered while ensuring the communication requirements for each ground terminal (GT) and backhaul link between the UAV and the terrestrial base station (BS). The mobility of the UAV and GTs lead to time-varying channel conditions that make the environment dynamic. We formulate a nonconvex optimization for controlling the UAV considering the practical angle-dependent Rician fading channels between the UAV and GTs, and between the UAV and the terrestrial BS. Traditional optimization approaches are not able to handle the dynamic environment and high complexity of the problem in real-time. We propose to use the Trust Region Policy Optimization (TRPO) method that can improve the performance of the UAV compared to the Deep Deterministic Policy Gradient (DDPG) method in such a dynamic environment as in this paper.

In this paper, we investigate the problem of robot swarm control in 5G mission-critical robotic applications, i.e., in an automated grid-based warehouse scenario. Such application requires both the kinematic energy consumption of the robots and the ultra-reliable and low latency communication (URLLC) between the central controller and the robot swarm to be jointly optimized in real-time. The problem is formulated as a nonconvex optimization problem since the achievable rate and decoding error probability with short block-length are neither convex nor concave in bandwidth and transmit power. We propose a deep reinforcement learning (DRL) based approach that employs the deep deterministic policy gradient (DDPG) method and convolutional neural network (CNN) to achieve a stationary optimal control policy that consists of a number of continuous and discrete actions. Numerical results show that our proposed multi-agent DDPG algorithm achieves a performance close to the optimal baseline and outperforms the single-agent DDPG in terms of decoding error probability and energy efficiency.

In this paper, we propose a deep reinforcement learning (DRL) based approach to solving the problem of joint server selection, task offloading and handover in a multi-access edge computing (MEC) wireless network. The 5G networks tend to have a large number of users and MEC servers involving large numbers of different states and actions (both continuous and discrete), in which evaluating every possible combination becomes very challenging for traditional DRL methods. In addition, user mobility in 5G requires multiple handover decisions to be made in real-time, adding a new level of complexity to this already hard problem. Based on the recursive decomposition of the action space available to each state, we propose a deep Q-network (DQN) based online algorithm for this high-complexity problem. Numerical results show the proposed algorithm significantly outperforms the traditional Q-learning method and local computation in terms of task success rate and total delay.

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.

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 the resourceful cloud, MEC server has limited resources. When each MEC server operates independently, it cannot handle all computational and big data demands stemming from users devices. Consequently, the MEC server cannot provide significant gains in overhead reduction of data exchange between users devices and remote cloud. Therefore, joint Computing, Caching, Communication, and Control (4C) at the edge with MEC server collaboration is needed. 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 jointly optimize a linear combination of the bandwidth consumption and network latency. However, the formulated problem is shown to be non-convex. As a result, a proximal upper bound problem of the original formulated problem is proposed. To solve the proximal upper bound problem, the block successive upper bound minimization method is applied. Simulation results show that the proposed approach satisfies computation deadlines and minimizes bandwidth consumption and network latency.

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.

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 OFDMAvirtualized 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 shortterm 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.

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.

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.

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.

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 heterogeneous networks (HetNets), low power smallcells, i.e., Wifi, can be offered an economic incentive in order to offload traffic from high-power macrocell, which is usually overloaded. This becomes important in order to maintain efficient operation of the network and generate benefit of tradeoff between macrocell and smallcells. The benefit to smallcells comes from the economic incentive offered by macrocell and the benefit to macrocell is achieved by reducing the load and saving spectrum. However, two important challenges are faced in this cooperation: 1) How much economic incentive can be offered by macrocell, and 2) How much offloading traffic volumes can be admitted by the smallcells. In this paper, we propose a novel game based approach for data offloading scheme to determine the amount of economic incentive a macrocell should offer to smallcells and to determine how much traffic each smallcell should admit from the macrocell. In our proposal, a two-stage non-cooperative Stackelberg game theory is applied to optimize the strategies of both macrocell and smallcells in order to maximize their utilities.

The installation of low power small cells under macro cells using the same spectrum is a promising approach to enhance the spectral efficiency and data-rate for the end users. These installations are becoming very dense in order to support the users' requirements (especially 5G networks) which make resource allocation using the same spectrum a very challenging problem. In this study, we address the downlink resource allocation problem for underlay small cell tier. We formulate the optimization problem for resource (channel) allocation in small cells while keeping the total interference to macro tier under an acceptable level. The objective of resource allocation is to maximize the throughput of small cells under the cross tier interference constraint. We employ matching theory to find a stable match for the resource allocation problem. We simulate our proposition to validate the stability of the network and the convergence of the resource allocation algorithm in terms of rate in a dense heterogeneous network. The matching results in an optimal solution which outperforms the existing sub-optimal resource allocation solutions.

In this paper, we study the relay-aided cooperative modeling that supports the load-sharing in uplink two-tier cellular networks. In our model, users in heavily loaded macrocell are shifted to lightly loaded smallcells with the assistance of relay users to mitigate Signal to Interference plus Noise Ratio (SINR) degradation problem in conventional direct handover. In order to promote relaying data of users which are selfish and rational, a trading exchange model based on Stackelberg game is proposed to optimize strategies of users. Relay users have pricing-based strategies on theirs power unit while shifted heavily loaded macrocell users have strategies to buy power levels of relay users. Optimal strategies are investigated using the backward induction analysis. Specifically, problems of NP-hard combinatorial optimization in relay user selections in the game are solved with a distributed algorithm based on matching theory. We intensively evaluate our proposed model by simulating it in Matlab which shows the efficiency of our proposal.

Heterogeneous cellular networks offload the mobile data traffic to small cell base stations to reduce the workload on the macro base stations. Our objective is to maximize the sum rate of the down-links for the whole network under outage QoS constraint. To achieve the objective, we have to jointly solve the user association problem and resource allocation problem. We formulate the two problems into a joint optimization problem and convert it into an equivalent game theoretic formulation. We employ payoff based log linear learning and propose an algorithm that converges to one of the existing Nash equilibrium. We then provide extensive simulation results to verify the performance of our proposed algorithm.

In two-tier LTE heterogeneous networks (HetNets), picocells can be offered radio resource in order to mitigate interference to picocell users in downlink transmission from high-power macrocell base station (MBS). This becomes important in order to maintain efficient operation of the network and generate benefit tradeoff between macrocell and picocells. In this paper, we propose a game based approach for joint resource partitioning and data offloading scheme to determine the amount of radio resource a MBS should offer to picocells and to determine how much traffic each picocell access point (AP) should admit from MBS. In our proposal, a two-stage Stackelberg game theory is applied to optimize the strategies of both MBS and APs in order to maximize both of their utilities and this scheme is implemented using the notion of Almost Blank Subframes (ABS) proposed in the LTE standard.

In this paper, we develop an opportunistic scheduling policy for allocating spectrum in cognitive radio networks. We maximize the throughput utility of secondary users subject to maximum collision constraints with the primary users. Particularly, we consider a cognitive radio network with a subset of the secondary users desire to use the licensed channels of primary system in a stochastic environment. Based on Lyapunov technique, we formulate the above problem as a Lyapunov optimization problem on stability region of virtual and actual queues. Then, we propose an online flow control, scheduling and spectrum allocation algorithm that meets the desired objectives and provides explicit performance guarantees.