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For vehicle-to-network communications, handover (HO) management enables vehicles to maintain the connection with the network while transiting through coverage areas of different base stations (BSs). However, the high mobility of vehicles means shorter connection periods with each BS that leads to frequent HOs, hence raises the necessity for optimal HO decision making for high quality infotainment services. Machine learning is capable of capturing underlying pattern via data driven methods to find optimal solutions to complex problems, and much learning-based HO optimization research has been conducted focusing on specific network setups. However, attention still needs to be paid to the actual deployment aspect and standardized datasets or simulation environments for evaluation. This paper proposes a deep reinforcement learning-based HO algorithm using the input parameters that are configurable in the existing measurement report of cellular networks. The performance of the proposed algorithm is evaluated using the well-known network simulator ns-3 with its official LTE module. A realistic network setup in the city center of Glasgow (U.K.) is configured with vehicle trajectories generated by the routes mobility model using Google Maps Directions API. Evaluation results reveal that the proposed algorithm significantly outperforms the A3 RSRP baseline with an average of 25.72% packet loss reduction per HO, suggesting significant improvement in quality of service of phone call and video streaming, etc. The proposed algorithm also has a small implementation cost compared to some state-of-the-art and should be deployed by a software update to a local BS controller. [ABSTRACT FROM AUTHOR]

Modern vehicular applications are resource-intensive and have different Quality of Service (QoS) requirements. In this direction, Vehicular Cloud Computing (VCC) has emerged as a new paradigm to host such applications in the cloud. In this paper, we extend the VCC paradigm to include federation, wherein the data centers from multiple cloud providers are pooled in to support applications that can not be supported in a single provider. We address the problem of dynamic resource allocation in a federated vehicular cloud. We propose a dynamic resource pricing model for sharing resources between the cloud providers, based on which we formulate resource allocation as an optimization problem. The objective is to minimize the cost of the vehicular service provider while meeting the delay bounds of different vehicular applications, for which a cost and delay-aware, polynomial-time algorithm is proposed. We evaluate the proposed algorithm under different scenarios to show that exploiting federation brings great benefits for the service providers and end-users alike. The proposed algorithm is seen to meet the delay requirements with a significant cost reduction to the provider up to 50% compared to baseline methods. We also show that the proposed algorithm can serve more than 90% of requests with lower cost and fewer VM migrations. It reduces the number of VM migrations by 72%. [ABSTRACT FROM AUTHOR]

In order to overcome the inherent latency in multi-user orthogonal multiple access (OMA) unmanned aerial vehicle (UAV) networks. In this paper, we investigate a UAV-enabled uplink non-orthogonal multiple access (NOMA) network, where a UAV is deployed to collect the messages transmitted by the ground users. To maximize the users’ sum-rate, we formulate a optimization problem, in which the UAV deployment position and the power control are jointly optimized subject to the transmission power constraints and the quality of service (QoS) constraints. However, this problem is a mixed integer non-convex optimization problem and thus it is difficult to solve directly. Therefore, we first transform the non-convex optimization problem into a convex one based on the first-order approximation technique and the penalty function method. Then, a successive convex approximate (SCA) technique based iterative algorithm as well as its initialization scheme are developed to find a suboptimal solution to the original problem. Afterwards, a low-complexity approximate algorithm is proposed to reduce the high computational complexity of the iterative algorithm. Numerical results show that our proposed iterative algorithm and low-complexity approximate algorithm can achieve a similar performance, and they can efficiently improve the sum-rate compared with OMA scheme and conventional NOMA scheme. [ABSTRACT FROM AUTHOR]

The application of network slicing to mobile edge computing (MEC) systems has aroused great interests from both academia and industry. However, the optimization of network slicing and MEC in most existing research works only focuses on resource slicing, energy scheduling, and power allocation from the perspective of mobile devices, without considering the operator's revenue. In this paper, we propose a novel framework for network slicing in MEC systems, including slice request admission and a revenue model, to investigate the operator's revenue escalation problem while considering traffic variations. The revenue model is mainly composed of the longer-term revenue and short-term revenue. Particularly, we jointly optimize slice request admission in the long-term and resource allocation in the short-term to maximize the operator's average revenue. By employing the Lyapunov optimization technique, we develop an algorithm without requiring any prior-knowledge of traffic distributions, referred to as the DNSRA, to solve the problem. To reduce the computational complexity of directly solving the DNSRA, we decouple the optimization variables for efficient algorithm design. By this, the strategies for user association and CPU-cycle frequency are obtained in closed forms, respectively. Power allocation and subcarriers assignment are obtained by employing the successive convex approximation approach. Meanwhile, we develop a heuristic algorithm to obtain the dynamic slice request admission decision. Simulation results show that the proposed DNSRA can strike a flexible balance between the average revenue and the average delay, and can significantly increase the operator's revenue against existing schemes. [ABSTRACT FROM AUTHOR]

Traffic offloading via device-to-device communications is expected to play a major role to meet the exponential data traffic growth in wireless networks. In this paper, we focus on the problem of user capacity maximization in ultra-dense heterogeneous networks with device-to-device cooperation, where a large number of users in a given geographical area request common data content, such as video on demand streaming, with strict quality of service guarantees. We aim at finding the best strategy for delivering the content either over long range connectivity from the access points or short range connectivity from peer mobile terminals while meeting a target rate per served user. We formulate the multiuser resource management problem as an optimization problem including traffic offloading and channel allocation considering a very high user density reaching up to 1.75 users per square meter. We extend the system model to include mobile terminals acting as content owners by having the content cached locally, in order to further enhance the network capacity and coverage. We then propose an iterative resource management solution, which first solves the optimization subproblem of traffic offloading with orthogonal channels, and then optimally allocates channels to the transmitters with minimized interference due to channel reuse. We also propose efficient suboptimal hierarchical tree-based algorithms that operate in real time with dynamic and fast solutions for ultra-dense networks. For performance evaluation, we consider a realistic scenario consisting of a stadium topology with thousands of mobile terminals active simultaneously. We generate results as a function of a wide range of system parameters, and demonstrate that the proposed algorithms achieve near-optimal performance with notably low time complexity. [ABSTRACT FROM AUTHOR]

The rapidly growing mobile traffic calls for higher quality of service (QoS) and lower on-grid energy cost. Integrating energy harvesting to small-cell networks (SCNs) with caching has been regarded as a promising solution. However, considering the limited resources of the small-cell base stations (SBSs), how to cache contents and serve the mobile users (MUs) is a crucial problem. In this paper, we jointly design a green content caching and mobile user–base station (MU-BS) association mechanism in the SCNs, where the number of MUs’ content requests served by the SBSs is maximized. This mechanism maximizes the number of requests handled by the SBSs under the constraints of the available energy and backhaul capacity of the SBSs, while concurrently ensures the QoS of the MUs. First, the joint optimization problem is formulated, which is NP-hard. To decrease the complexity of the problem, we divide it into two subproblems: MU-BS association and content caching. But when the number of SBSs is large, these two subproblems cannot be efficiently solved by the conventional centralized approaches. Thus, we construct a two-dimensional-action weakly acyclic game to optimize these two subproblems distributedly and iteratively, in which the existence of the two-dimensional-action Nash equilibrium has been proved. Finally, we propose a two-dimensional iteration algorithm to solve the problem. The simulation results show that the proposed algorithm can achieve better performance when compared with the other existing algorithms. [ABSTRACT FROM AUTHOR]

Rising user expectations are changing the way mobile operators manage their networks. In this paper, a self-tuning algorithm for adjusting parameters in a multiservice packet scheduler of a Long-Term Evolution base station is proposed to optimize the overall system Quality of Experience (QoE) based on network performance statistics. For this purpose, the algorithm iteratively changes service priority parameters to reprioritize services so as to make the most of available resources. The proposed algorithm ensures that the best overall system QoE is always reached by analyzing optimality conditions, unlike previous works, which only guarantee a minimum user satisfaction level or aim to balance QoE among services. Method assessment is carried out with a dynamic system-level simulator in a realistic service scenario. Simulation results show that the overall network QoE can be improved up to $35\%$ by tuning service priority parameters. [ABSTRACT FROM PUBLISHER]

For massive MIMO public channel with any sector size in either microwave or millimeter wave (mmwave) band, this paper studies the beamforming design to minimize the transmit power while guaranteeing the quality of service for randomly deployed users. First the ideal beampattern is derived via Parseval Identity, based on which a beamforming design problem is formulated to minimize the gap with the idea beampattern. The problem is transformable to a multiconvex one and an iterative optimization algorithm is used to obtain the full-digital beamformer. In addition, with the help of same beampattern theorem, the power amplifier efficiency of the beamformer is improved with unchanged beampattern. Finally, the practical hybrid implementation is obtained that achieves the full-digital beamformer solution. Simulations verify the advantages of the proposed scheme over existing ones. [ABSTRACT FROM PUBLISHER]

A software-defined wireless local area network (SDWLAN) has gained significant interest recently from both the academic and industrial communities and initiated a paradigmatic reconsideration on the stereotyped management and control of current WLAN due to its flexibility and programmability. However, with the proliferation of mobile devices, efficient client association with quality of service (QoS) guarantees in the high-density SDWLAN is a very challenging issue. In this paper, we study the client association problem in the SDWLAN with new features, including centralized association, global network state awareness, seamless handoff, and flow-level association. We formulate the client association in a high-density scenario as an optimization problem aiming to minimize the interpacket delay of individual flows, based on an unsaturated and heterogeneous Markovian analytical model. Furthermore, we interpret the optimization problem as an NP-hard supermodular set function minimization problem, which is solved by two low-complexity heuristic methods, namely the greedy algorithm and the bounded local search algorithm. Through simulations, we demonstrate the effectiveness of our proposed client association solution. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of power control for sum-rate maximization on multiple interfering links [transmitter–receiver pairs (Tx–RX)] under a sum power constraint. We consider a single-frequency network, where all pairs are operating in the same frequency band, thereby creating interference with each other. We study the power-allocation problem for sum-rate maximization with and without quality-of-service (QoS) requirements on individual links. When the objective is only sum-rate maximization without QoS guarantees, we develop an analytic solution to decide optimal power allocation for a two-TX–RX-pair problem. We also develop a low-complexity iterative algorithm for a three-TX–RX-pair problem. For a generic N>\3 TX–RX pair problem, we develop two low-complexity suboptimal power-allocation algorithms. The first algorithm is based on the idea of making clusters of two or three TX–RX pairs and then leveraging the power-allocation results obtained for the two- and three-TX–RX-pair problems. The second algorithm is developed by using a high signal-to-interference-plus-noise ratio (SINR) approximation, and this algorithm can be also implemented in a distributed manner by individual TXs. We then consider the same problem but with additional QoS guarantees for individual links. We again develop an analytic solution for the two-TX–RX-pair problem and a distributed algorithm for N>\2 TX–RX pairs. [ABSTRACT FROM PUBLISHER]

Spectral efficiency has improved significantly with the deployment of beyond third generation (3G) cellular air interfaces. However, the scarcity of unallocated radio spectrum bands, coupled with the need to provide ubiquitous wireless data services with different Quality of Service (QoS) requirements to a large number of users, has continued to drive extensive research efforts in radio resource management (RRM). In order to adapt to the changing wireless channel conditions and meet the varying and diverse QoS requirements, much of the published work in RRM has focused on exploiting multi-user and multi-channel diversities and more recently on exploiting multi-application diversity to take advantage of the mechanisms and optimization features introduced in the air interfaces. In this paper, we propose a bitQoS-aware resource allocation framework to increase the flexibility and granularity of the resource allocation algorithms by adaptively matching the QoS requirements of the user application bits to the characteristics of the Orthogonal Frequency Division Multiplexing (OFDM) subcarriers in a mixed-traffic environment. We show through an adaptive joint subcarrier, power and bit allocation algorithm, that with the finesse control of bitQoS-aware scheduling, it is possible to simultaneously achieve both an increase in user throughput and a reduction in user packet drop probability by accepting a within scheduling delay threshold increase in user latency. The performance gains obtainable are quantified in terms of system throughput, user throughput, user latency, user jitter and user packet drop probability for systems under different traffic loads. [ABSTRACT FROM PUBLISHER]