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The paper considers a cloud-based Content Delivery Network (CDN) with Device-to-Device (D2D) and Unmanned Aerial Vehicle (UAV) enabled caching. The network is managed by the operator that offers content transfer services to the set of Content Providers (CPs). A subscriber of any CP can receive its content either by first, cellular content transfer via a terrestrial Base Station (BS) or a UAV, or by second, D2D content transfer from another subscriber of this CP. Each CP aims to maximize its expected payoff defined as a reciprocal of the weighted sum of the expected content transfer cost and delay for its subscribers. The operator's objective is to maximize its long-term average revenue and stabilize the queuing system which represents the content transfer services. To model the interactions between the operator and the CPs, a novel framework is proposed that combines: first, cooperative game that enables the CPs to form coalitions in which all subscribers can exchange content via D2D links, thereby reducing content transfer costs and delays; second, Lyapunov optimization based dynamic channel and UAVs’ activity allocation policy of the operator. Through analytical and numerical evaluations, it is proven that if the operator and each CP are rational, the network reaches a state where all the CPs are in the stable coalitional structure, whereas, the dynamic policy of the operator is optimal with the trade-off in the total queue backlog. [ABSTRACT FROM AUTHOR]

We present a novel game, called evolutionary Bayesian coalition formation game, to model and analyze the problem of distributed resource sharing in a multi-cell device-to-device (D2D) enabled cellular network where the rationality of the players, i.e., device pairs, is bounded, e.g., due to limited information. Each player can make its decision on the channel to access with and without coordination. In the former case, the player works in D2D mode. In the latter case, the player forms a coalition with some other players and they connect to one base station in cellular mode. In this case, the player realizes its action after observing the actions of other players. Unlike classical coalition formation games where the player decides on its coalition to form by estimating its payoff, in the proposed game, the player forms a coalition and selects an action based on its current population state which is updated using a simple and scalable learning algorithm. We prove that the evolutionary coalition formation process converges to the unique equilibrium that induces a stable coalitional agreement. The proposed process is applied to a long-term evolution-advanced network where it shows a superior performance compared with other baseline resource sharing strategies. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of distributed resource sharing in a device-to-device enabled heterogeneous network, where the various device pairs choose their transmission channels, modes, base stations (BSs), and power levels without any control by the BSs based only on the locally-observable information. This problem is represented as a Bayesian coalition formation game, where the players (device pairs) create coalitions to maximize their long-term rewards with no prior knowledge of the values of potential coalitions and the types of their members. To minimize these uncertainties, a novel Bayesian reinforcement learning (RL) model is derived. In this model, the players update (through repeated coalition formation) their beliefs about the types and coalitional values to reach a stable coalitional agreement. The proposed Bayesian RL-based coalition formation algorithms are implemented in a long-term evolution advanced network and evaluated using simulations. The algorithms show a superior performance when compared with other relevant resource allocation schemes and achieve near-optimal results after a relatively small number of RL iterations. [ABSTRACT FROM PUBLISHER]

We study the problem of joint user association and intercell interference (ICI) mitigation in heterogeneous long-term evolution advanced (LTE-A) networks, presuming the absence of the precise global channel state information (CSI). We confront this problem from a contract-theoretic perspective and model the considered network as a labor market where the LTE evolved NodeBs (eNBs) are regarded as the employers offering a contract to the employees (cellular users). We explore a scenario where the wireless channels are ordered into different types according to their link gains and the cost of power consumption. It is assumed that each user knows the type of its channel but is not aware of the types of the channels belonging to other users. Hence, the channel type is regarded as the private information which is known only by the corresponding user. Under the presence of such asymmetric information, the eNBs formulate the optimal contract given by a set of the contract items (the combinations of user association, allocated spectrum, and power) and pass these items to the users, who then select the best contract items according to their channel types. The performance of a proposed contract-based resource allocation mechanism has been evaluated using the OPNET-based simulations. It has been demonstrated that the efficiency of a contract-based mechanism with asymmetric information is close to the optimal method (where the network resources are allocated based on the precise CSI). It has also been shown that the contract-based method outperforms the other relevant resource allocation techniques in terms of the total network service rate and signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

We propose a dynamic resource allocation algorithm for device-to-device (D2D) communication underlying a Long Term Evolution Advanced (LTE-A) network with reinforcement learning (RL) applied for unlicensed channel allocation. In a considered system, the inband and outband resources are assigned by the LTE evolved NodeB (eNB) to different device pairs to maximize the network utility subject to the target signal-to-interference-and-noise ratio (SINR) constraints. Because of the absence of an established control link between the unlicensed and cellular radio interfaces, the eNB cannot acquire any information about the quality and availability of unlicensed channels. As a result, a considered problem becomes a stochastic optimization problem that can be dealt with by deploying a learning theory (to estimate the random unlicensed channel environment). Consequently, we formulate the outband D2D access as a dynamic single-player game in which the player (eNB) estimates its possible strategy and expected utility for all of its actions based only on its own local observations using a joint utility and strategy estimation based reinforcement learning (JUSTE-RL)with regret algorithm. Aproposed approach for resource allocation demonstrates near-optimal performance after a small number of RL iterations and surpasses the other comparable methods in terms of energy efficiency and throughput maximization. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of resource allocation for device-to-device (D2D) communication in a Third-Generation Partnership Project (3GPP) Long-Term Evolution Advanced (LTE-A) network. The users in the network can operate either in a traditional cellular mode, communicating with each other via the evolved NodeB (eNB), or in a D2D mode, communicating with each other without traversing the eNB. In the considered model, the D2D users and cellular users share the same radio resources. Particularly, each resource block (RB) within the available bandwidth can be occupied by one cellular and several D2D users. Hence, the problem of interference management is crucial for effective performance of such a network. The twofold aim of the proposed algorithm is to 1) mitigate the interference between cellular and D2D users and 2) improve the overall user-perceived quality of service (QoS). To control the interference, for each user, we define a certain target interference level and constrain the interference from the other users to stay below this level. The corresponding optimization problem maximizes the QoS of the users by minimizing the size of the buffers of user equipments (UEs). The performance of the algorithm has been evaluated by using the OPNET-based simulations. The algorithm shows improved performance in terms of mean packet end-to-end delay and loss for UEs when compared to other relevant schemes. [ABSTRACT FROM PUBLISHER]

We study the problem of autonomous operation of the device-to-device (D2D) pairs in a heterogeneous cellular network with multiple base stations (BSs). The spectrum bands of the BSs (that may overlap with each other) comprise the sets of orthogonal wireless channels. We consider the following spectrum usage scenarios: 1) the D2D pairs transmit over the dedicated frequency bands and 2) the D2D pairs operate on the shared cellular/D2D channels. The goal of each device pair is to jointly select the wireless channel and power level to maximize its reward, defined as the difference between the achieved throughput and the cost of power consumption, constrained by its minimum tolerable signal-to-interference-plus-noise ratio requirements. We formulate this problem as a stochastic non-cooperative game with multiple players (D2D pairs) where each player becomes a learning agent whose task is to learn its best strategy (based on the locally observed information) and develop a fully autonomous multi-agent Q-learning algorithm converging to a mixed-strategy Nash equilibrium. The proposed learning method is implemented in a long term evolution-advanced network and evaluated via the OPNET-based simulations. The algorithm shows relatively fast convergence and near-optimal performance after a small number of iterations. [ABSTRACT FROM PUBLISHER]

We investigate the problem of resource allocation in a cognitive long-termevolution (LTE) network, where the available bandwidth resources are shared among the primary (licensed) users (PUs) and secondary (unlicensed) users (SUs). Under such spectrum sharing conditions, the transmission of the SUs should have minimal impact on quality of service (QoS) and operating conditions of the PUs. To achieve this goal, we propose to assign the network resources based on the buffer sizes of the PUs and SUs in the uplink (UL) and downlink (DL) directions. To ensure that the QoS requirements of the PUs are satisfied, we enforce some upper bound on the size of their buffers considering two network usage scenarios. In the first scenario, PUs pay full price for accessing the spectrum and get full QoS protection; the SUs access the network for free and are served on a best-effort basis. In the second scenario, PUs pay less in exchange for sharing the bandwidth and get the reduced QoS guarantees; SUs pay some price for their access without any QoS guarantees. Performance of the algorithms proposed in the paper is evaluated using simulations in OPNET environment. The algorithms show superior performance when compared with other relevant techniques. [ABSTRACT FROM AUTHOR]

This paper explores the problem of joint mode selection, spectrum management, power control, and interference mitigation for device-to-device (D2D) communication underlaying a Long Term Evolution-Advanced (LTE-A) network. We consider a dynamic mode selection scenario, in which the modes (D2D or cellular) of the devices depend on optimal allocations. To improve the quality of service (QoS) for the users, the optimization objective in a corresponding problem is formulated in terms of buffer size of user equipments (UEs), which is estimated based on buffer status information collected by the UEs. The realizations of a resource allocation approach presented in the paper include its real-time and non-real-time implementations, as well as two modifications applicable to a standard LTE-Direct (LTE-D) network. Performance of the proposed algorithms has been evaluated using the OPNET-based simulations. All algorithms show improved performance in terms of mean packet end-to-end delay when compared to most relevant schemes proposed earlier. [ABSTRACT FROM AUTHOR]