53279543
Interference Coordination and Resource Allocation Planning With Predicted Average Channel Gains for HetNets
Future average channel gains are recently reported predictable within a minute-level horizon for a mobile user. Predictive resource allocation for non-realtime service with future average channel gains in a time window of one or more minutes long has been demonstrated effective in improving user experience and network throughput as well as reducing energy consumption. Nonetheless, existing studies for predictive resource allocation never consider inter-cell interference (ICI), which severely limits the user experience and network performance in densely-deployed cellular networks. This paper investigates predictive resource allocation in heterogeneous networks, where some base stations generate strong ICIs to some mobile users requesting non-realtime service. Optimizing the resource allocation in a predictive manner for interference networks is challenging, since how to allocate future resources depends on the future signal to interference-plus-noise ratio, which in turn relies on the assigned resources. To deal with this difficulty, we introduce a predictive interference coordination scheme to divide all BSs and users into groups, where the BS-user pairs in each group can communicate simultaneously in a second-level frame. Then, we optimize a common resource allocation plan for all users in each group. The plan essentially determines which BSs in the group should be muted and the users should be associated with which active BS in each frame of the prediction window. By resorting to graph theory, we obtain the optimal solution and derive a low-complexity algorithm. Simulation results show that the proposed scheme outperforms existing relevant methods in terms of user satisfactory rate in heterogeneous networks with heavy traffic load.
8861881
An overview of load balancing in hetnets: old myths and open problems
Matching the demand for resources ("load") with the supply of resources ("capacity") is a basic problem occurring across many fields of engineering, logistics, and economics, and has been considered extensively in both the Internet and wireless networks. The ongoing evolution of cellular communication networks into dense, organic, and irregular heterogeneous networks (HetNets) has elevated load awareness to a central problem, and introduces many new subtleties. This article explains how several long-standing assumptions about cellular networks need to be rethought in the context of a load-balanced HetNet: we highlight these as three deeply entrenched myths that we then dispel. We survey and compare the primary technical approaches to HetNet load balancing: (centralized) optimization, game theory, Markov decision processes, and the newly popular cell range expansion (a.k.a. biasing), and draw design lessons for OFDMA-based cellular systems. We also identify several open areas for future exploration.
8839903
Joint Resource Partitioning and Offloading in Heterogeneous Cellular Networks
In heterogeneous cellular networks (HCNs), it is desirable to offload mobile users to small cells, which are typically significantly less congested than the macrocells. To achieve sufficient load balancing, the offloaded users often have much lower SINR than they would on the macrocell. This SINR degradation can be partially alleviated through interference avoidance, for example time or frequency resource partitioning, whereby the macrocell turns off in some fraction of such resources. Naturally, the optimal offloading strategy is tightly coupled with resource partitioning; the optimal amount of which in turn depends on how many users have been offloaded. In this paper, we propose a general and tractable framework for modeling and analyzing joint resource partitioning and offloading in a two-tier cellular network. With it, we are able to derive the downlink rate distribution over the entire network, and an optimal strategy for joint resource partitioning and offloading. We show that load balancing, by itself, is insufficient, and resource partitioning is required in conjunction with offloading to improve the rate of cell edge users in co-channel heterogeneous networks.
18746839
Tier Association Probability and Spectrum Partitioning for Maximum Rate Coverage in Multi-Tier Heterogeneous Networks
For a wireless multi-tier heterogeneous network with orthogonal spectrum allocation across tiers, we optimize the association probability and the fraction of spectrum allocated to each tier so as to maximize rate coverage. In practice, the association probability can be controlled using a biased received signal power. The optimization problem is non-convex and we are forced to explore locally optimal solutions. We make two contributions in this paper: first, we show that there exists a relation between the first derivatives of the objective function with respect to each of the optimization variables. This can be used to simplify numerical solutions to the optimization problem. Second, we explore the optimality of the intuitive solution that the fraction of spectrum allocated to each tier should be equal to the tier association probability. We show that, in this case, a closed-form solution exists. Importantly, our numerical results show that there is essentially zero performance loss. The results also illustrate the significant gains possible by jointly optimizing the user association and the resource allocation.
2447303
Rate Maximization Through Structured Spectrum Allocation and User Association in Heterogeneous Cellular Networks
We study joint spectrum allocation and user association in heterogeneous cellular networks with multiple tiers of base stations. A stochastic geometric approach is applied as the basis to derive the average downlink user data rate in a closed-form expression. Then, the expression is employed as the objective function in jointly optimizing spectrum allocation and user association, which is of a non-convex programming in nature. A computationally efficient structured spectrum allocation and user association (SSAUA) approach is proposed, solving the problem optimally and asymptotically optimally in two regions divided by a parameter specific threshold. A surcharge pricing scheme (SPS) is also presented, such that the designed association bias values can be achieved in Nash equilibrium. Simulations and numerical studies are conducted to validate the accuracy and efficiency of the proposed SSAUA approach and SPS.
7104839
Distributed Pricing-Based User Association for Downlink Heterogeneous Cellular Networks
This paper considers optimization of the user and base-station (BS) association in a wireless downlink heterogeneous cellular network under the proportional fairness criterion. We first consider the case where each BS has a single antenna and transmits at fixed power and propose a distributed price update strategy for a pricing-based user association scheme, in which the users are assigned to the BS based on the value of a utility function minus a price. The proposed price update algorithm is based on a coordinate descent method for solving the dual of the network utility maximization problem and it has a rigorous performance guarantee. The main advantage of the proposed algorithm as compared to an existing subgradient method for price update is that the proposed algorithm is independent of parameter choices and can be implemented asynchronously. Further, this paper considers the joint user association and BS power control problem and proposes an iterative dual coordinate descent and the power optimization algorithm that significantly outperforms existing approaches. Finally, this paper considers the joint user association and BS beamforming problem for the case where the BSs are equipped with multiple antennas and spatially multiplex multiple users. We incorporate dual coordinate descent with the weighted minimum mean-squared error (WMMSE) algorithm and show that it achieves nearly the same performance as a computationally more complex benchmark algorithm (which applies the WMMSE algorithm on the entire network for BS association) while avoiding excessive BS handover.
571073
Distributed Linear Precoder Optimization and Base Station Selection for an Uplink Heterogeneous Network
In a heterogeneous wireless cellular network, each user may be covered by multiple access points such as macro/pico/relay/femto base stations (BS). An effective approach to maximize the sum utility (e.g., system throughput) in such a network is to jointly optimize users' linear procoders as well as their BS associations. In this paper, we first show that this joint optimization problem is NP-hard and thus is difficult to solve to global optimality. To find a locally optimal solution, we formulate the problem as a noncooperative game in which the users and the BSs both act as players. We introduce a set of new utility functions for the players and show that every Nash equilibrium (NE) of the resulting game is a stationary solution of the original sum utility maximization problem. Moreover, we develop a best-response type algorithm that allows the players to distributedly reach a NE of the game. Simulation results show that the proposed distributed algorithm can effectively relieve local BS congestion and simultaneously achieve high throughput and load balancing in a heterogeneous network.
6096180
User Association for Load Balancing in Heterogeneous Cellular Networks
For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rate for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor Aj ≥ 1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values Aj are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a maximizing received power association.
5125553
Joint User Association and Resource Allocation in the Downlink of Heterogeneous Networks
In this paper, we consider the intercell interference coordination (ICIC) problem in heterogeneous cellular networks with randomly deployed small-cell base stations (BSs). Current research on ICIC mainly focuses on optimizing the spectrum and power allocations at BSs, whereas the user-BS association is treated as a separate issue. Nevertheless, the user-BS association problem is an important issue in ICIC and should be jointly optimized with resource allocations to achieve global optimality. In this paper, with the objective of maximizing the system sum rate in a distributed manner, we propose a novel belief propagation (BP) algorithm to jointly optimize user association, subchannel assignment, and power allocation. We first develop a factor graph model to decompose the network-wide objective and constraints into multiple local utilities. Then, we transform the maximization of local utilities into the estimations of marginal distributions and propose a distributed BP algorithm to solve the estimations. Simulations show that our distributed BP algorithm dramatically improves the performance compared with the benchmark scheme.
21695113
Performance analysis of IAF relaying mobile D2D cooperative networks
Abstract The outage probability (OP) performance of mobile device-to-device (D2D) cooperative networks with incremental amplify-and-forward (IAF) relaying and transmit antenna selection (TAS) is investigated in this paper. The exact closed-form OP expressions are derived for two TAS schemes. Based on the derived closed-form OP expressions, a power allocation minimization problem is formulated. Then through Monte Carlo simulations under different conditions, we have verified the accuracy of the derived OP expressions. The simulation results showed that the power allocation parameter has an important influence on the OP performance.
1007281
Resource Allocation for D2D Communications Underlay in Rayleigh Fading Channels
Device-to-device (D2D) communication has attracted substantial research attention recently, due to its potential to improve coverage, spectrum efficiency, and energy efficiency within the existing cellular infrastructure. One major challenge for spectrum resource sharing in D2D underlay lies in the mutual interference between cellular user equipments (CUEs) and D2D user equipments (DUEs). Considering this mutual interference constraint, this work investigates the problem of optimal matching of D2D links and CUEs to form spectrum-sharing partners to maximize ergodic sum rates under transmit power and outage constraints. Unlike previous works, full channel-state information (CSI) is not required. To solve the resulting high-complexity problem, candidate DUE sets are first narrowed down according to required outage probability constraints, which are used to construct a simplified bipartite graph. The weight of the bipartite graph is characterized as the maximization of ergodic sum rate of the associated D2D and cellular links under outage constraints for which a low-complexity algorithm is proposed to solve the nonconvex problem. After constructing the bipartite graph, the Hungarian algorithm is used to determine the optimal pairing between D2D links and CUEs. Numerical results demonstrate that the proposed algorithm can improve the outage-constrained spectrum efficiency of D2D networks with practical complexity.
12168849
A Destination and Mobility Path Prediction Scheme for Mobile Networks
Mobile multimedia services are gaining great momentum among subscribers of mobile networks (MNs). An understanding of the network traffic behavior is essential in the evolution of today's MNs and, thus, leads to more efficient planning and management of the network's scarce bandwidth resources. The communication efficiency can be largely improved (i.e, optimizing the allocation of the network's limited resources and sustaining a desirable quality of service) if the network anticipates the needs of its users on the move and, thus, performs reservation of radio resources at cells along the path to the destination. In this vein, we propose a mobility prediction scheme for MNs; more specifically, we first apply probability and Dempster-Shafer processes for predicting the likelihood of the next destination, for an arbitrary user in an MN, based on the user's habits (e.g, frequently visited locations). Then, at each road junction, we apply the second-order Markov chain process for predicting the likelihood of the next road segment transition, given the path from the trip origin to that specific road junction and the direction to the destination. We evaluate our proposed scheme using real-life mobility traces; the simulation results show that the proposed scheme outperforms traditional schemes.
26853250
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks
Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user devices. In this context, this paper provides a comprehensive tutorial that introduces the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications. For this purpose, we present a comprehensive overview on a number of key types of neural networks that include feed-forward, recurrent, spiking, and deep neural networks. For each type of neural network, we present the basic architecture and training procedure, as well as the associated challenges and opportunities. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality and edge caching.For each individual application, we present the main motivation for using ANNs along with the associated challenges while also providing a detailed example for a use case scenario and outlining future works that can be addressed using ANNs. In a nutshell, this article constitutes one of the first holistic tutorials on the development of machine learning techniques tailored to the needs of future wireless networks.
8352668
Learning radio maps for UAV-aided wireless networks: A segmented regression approach
This paper targets the promising area of unmanned aerial vehicle (UAV)-assisted wireless networking, by which communication-enabled robots operate as flying wireless relays to help fill coverage or capacity gaps in the networks. In order to feed the UAV's autonomous path planning and positioning algorithm, a radio map is exploited, which must be, in practice, reconstructed from UAV-based measurements from a limited subset of locations. Unlike existing methods that ignore the segmented propagation structure of the radio map, this paper proposes a machine learning approach to reconstruct a finely structured map by exploiting both segmentation and signal strength models. A data clustering and parameter estimation problem is formulated using a maximum likelihood approach, and solved by an iterative clustering and regression algorithm. Numerical results demonstrate significant performance advantage in radio map reconstruction as compared to the baseline.
67874278
Trajectory Prediction with Recurrent Neural Networks for Predictive Resource Allocation
Trajectory prediction of mobile users plays a key role in making the plan for predictive radio resource allocation, i.e., determining which base stations alongside the trajectory of a user serve the user with how much resources. Predictive resource allocation in existing literature requires the prediction with second-level resolution and minute-level horizon. However, the trajectories predicted with existing methods are either too coarse-grained or with too short-horizon. In this paper, we strive to filling this gap by developing a recurrent neural network based trajectory prediction method. With proper network architecture and output structure, the proposed method can provide high-accuracy prediction with horizon of one minute. We investigate the performance of large-scale channel prediction with a perfect radio map. We also provide the statistics of the prediction errors for trajectory and large-scale channel gains, which is useful for the robust optimization of predictive resource allocation.
12776837
Achieving high throughput with predictive resource allocation
Big data analytics makes predicting human behavior possible, but it is unclear how to exploit the predictable information for improving performance of wireless networks. In this paper, we investigate the potential of predictive resource allocation in supporting high throughput by exploiting excess resources. To this end, we assume that the requests and trajectories of mobile users and the average resource usage status of base stations can be predicted within a window. To fully use resources within the prediction window and reserve resources for the unpredictable traffic arrived after the window, we optimize a resource allocation plan to minimize the maximal transmission completion time. To assist the base stations for user scheduling, we introduce a method to make a transmission plan. These two plans determine where, when and what to transmit to the users with how much resources. Simulation results show that the predictive resource allocation can provide substantial gain over non-predictive strategy in terms of both network throughput and user experience.
13336598
Toward green media delivery: location-aware opportunities and approaches
Mobile media has undoubtedly become the predominant source of traffic in wireless networks. The result is not only congestion and poor quality of experience, but also an unprecedented energy drain at both the network and user devices. In order to sustain this continued growth, novel disruptive paradigms of media delivery are urgently needed. We envision that two key contemporary advancements can be leveraged to develop greener media delivery platforms: The proliferation of navigation hardware and software in mobile devices has created an era of location awareness, where both the current and future user locations can be predicted; and the rise of context-aware network architectures and self-organizing functionalities is enabling context signaling and in-network adaptation. With these developments in mind, this article investigates the opportunities of exploiting location awareness to enable green end-to-end media delivery. In particular, we discuss and propose approaches for location-based adaptive video quality planning, in-network caching, content prefetching, and long-term radio resource management. To provide insights on the energy savings, we then present a cross-layer framework that jointly optimizes resource allocation and multi-user video quality using location predictions. Finally, we highlight some of the future research directions for location-aware media delivery in the conclusion.
20339556
Robust Content Delivery and Uncertainty Tracking in Predictive Wireless Networks
Predictive resource allocations (PRAs) have recently gained attention in wireless network literature due to their significant energy-savings and quality of service (QoS) gains. This enhanced performance was primarily demonstrated while assuming the perfect prediction of both mobility traces and anticipated channel rates. While the results are very promising, several technical challenges need to be overcome before PRAs can be practically adopted. Techniques that model the prediction uncertainty and provide probabilistic quality of service (QoS) guarantees are among such challenges. This differs from the traditional robust optimization of wireless resources, as PRAs use a time horizon with predicted demands and anticipated data rates. In this paper, we tackle this problem and present an energy-efficient stochastic PRAs framework that is robust to prediction uncertainty under generic error probability density functions. The framework is applied for video delivery, where the desired video demands are modeled as probabilistic chance constraints over the prediction time horizon, and a deterministic closed form is then derived based on the Bernstein approximation (BA). In addition to handling prediction uncertainty, mechanisms that track the variance of the channel in real-time are practically needed. Towards this end, we demonstrate how a particle filter (PF) can be adopted to effectively achieve this functionality. A low complexity guided heuristic algorithm is also integrated with the BA-based allocations, and particle filter (PF), to provide a real-time solution. Extensive numerical simulations using a standard compliant long term evolution system are then presented to examine the developed solutions under various operating conditions. Results indicate the ability of our framework to significantly reduce base station energy consumption while satisfying users’ QoS under practical prediction uncertainty.
8550010
Optimizing stored video delivery for mobile networks: The value of knowing the future
This paper considers the design of cross-layer opportunistic transport for stored video over wireless networks with a slow varying (average) capacity. We focus on two key ideas: (1) scheduling data transmissions when capacity is high; and (2), exploiting knowledge of future capacity variations. The latter is possible when users' mobility is known or predictable, e.g., users riding on public transportation or using navigation systems. We consider the design of cross-layer transmission schedules which minimize system utilization (and thus possibly transmit/receive energy) while avoiding, if at all possible, rebuffering/delays, in several scenarios. For the single-user anticipative case where all future capacity variations are known beforehand; we establish the optimal transmission schedule is a Generalized Piecewise Constant Thresholding (GPCT) scheme. For the single-user partially anticipative case where only a finite window of future capacity variations is known, we propose an online Greedy Fixed Horizon Control (GFHC). An upper bound on the competitive ratio of GFHC and GPCT is established showing how performance loss depends on the window size, receiver playback buffer, and capacity variability. Finally we consider the multiuser case where we can exploit both future temporal and multiuser diversity. Our simulations and evaluation based on a measured wireless capacity trace exhibit robust potential gains for our proposed transmission schemes.
13196808
Energy-Saving Predictive Resource Planning and Allocation
Predictive resource allocation is an emerging approach to improve the performance of mobile systems as human behavior is reported predictable by leveraging big data analytics. Yet what information can be predicted by big data, what information need to be predicted for wireless access optimization, how to translate the information, and how to exploit the synthetic knowledge for allocating radio resources are not well understood and largely explored. In this paper, we are concerned with the latter two issues. In particular, we devise an energy-saving resource planning and allocation policy for multiple base stations (BSs) to serve mobile users with non-real-time (NRT) traffic by exploiting the user, network, and application levels of context information, where RT traffic may occupy partial resources of each BS. Inspired by the solution from an energy minimization problem with future instantaneous information, a low complexity multi-timescale predictive policy is proposed. Upon the arrival of each NRT user request, the resource planning is made with the user and network level context information, defined as the average channel gains of the NRT users and the statistics of residual bandwidth after serving RT traffic, with which the scheduling, power allocation, and BS sleeping can be accomplished after instantaneous channel information and residual network resource are available at each BS in each time slot. Simulation results show that the proposed policy can dramatically reduce the energy consumed by the BSs for serving the NRT traffic.
222122
Distributed Energy Efficient Fair User Association in Massive MIMO Enabled HetNets
Massive multiple-input and multiple-output (MIMO) and heterogeneous networks (HetNets) have been recognized as key enabling technologies for future fifth generation (5G) mobile networks. However, the circuit power consumption of massive MIMO scales with the tremendous number of antennas. As a result, the problem of energy efficient user association in massive MIMO enabled HetNets is of vital importance. We investigate the energy efficient user association problem in massive MIMO enabled HetNets, and formulate the network logarithmic utility maximization problem. Based on the Lagrangian dual analysis, a low complexity distributed user association algorithm is developed for energy efficient fair user association while considering quality of service (QoS) provision for users. Simulation results demonstrate the effectiveness of the proposed algorithm in improving the energy efficiency and user fairness, compared to other user association algorithms.
1305570
Optimizing User Association and Spectrum Allocation in HetNets: A Utility Perspective
The joint user association and spectrum allocation problem is studied for multi-tier heterogeneous networks (HetNets) in both downlink and uplink in the interference-limited regime. Users are associated with base-stations (BSs) based on the biased downlink received power. Spectrum is either shared or orthogonally partitioned among the tiers. This paper models the placement of BSs in different tiers as spatial point processes and adopts stochastic geometry to derive the theoretical mean proportionally fair utility of the network based on the coverage rate. By formulating and solving the network utility maximization problem, the optimal user association bias factors and spectrum partition ratios are analytically obtained for the multi-tier network. The resulting analysis reveals that the downlink and uplink user associations do not have to be symmetric. For uplink under spectrum sharing, if all tiers have the same target signal-to-interference ratio (SIR), distance-based user association is shown to be optimal under a variety of path loss and power control settings. For both downlink and uplink, under orthogonal spectrum partition, it is shown that the optimal proportion of spectrum allocated to each tier should match the proportion of users associated with that tier. Simulations validate the analytical results. Under typical system parameters, simulation results suggest that spectrum partition performs better for downlink in terms of utility, while spectrum sharing performs better for uplink with power control.
122765886
A Simple and Faster Branch-and-Bound Algorithm for Finding a Maximum Clique with Computational Experiments
SUMMARY Many problems can be formulated as maximum clique problems. Hence, it is highly important to develop algorithms that can find a maximum clique very fast in practice. We propose new approximate coloring and other related techniques which markedly improve the run time of the branch-and-bound algorithm MCR (J. Global Optim., 37, pp.95–111, 2007), previously shown to be the fastest maximum-clique-finding algorithm for a large number of graphs. The algorithm obtained by introducing these new techniques in MCR is named MCS. It is shown that MCS is successful in reducing the search space quite efficiently with low overhead. Extensive computational experiments confirm the superiority of MCS over MCR and other existing algorithms. It is faster than the other algorithms by orders of magnitude for several graphs. In particular, it is faster than MCR for difficult graphs of very high density and for very large and sparse graphs, even though MCS is not designed for any particular type of graph. MCS can be faster than MCR by a factor of more than 100,000 for some extremely dense random graphs. This paper demonstrates in detail the effectiveness of each new techniques in MCS, as well as the overall contribution.
16712297
Multiuser MIMO in Distributed Antenna Systems With Out-of-Cell Interference
Distributed antenna systems (DAS) augment the base station's transmit capability by adding multiple remote radio units, connected to the base station via a high bandwidth and low latency link. With DAS, the base station operates as if it had multiple antennas, but the antennas happen to be in different geographic locations. DAS have been shown to enhance coverage and capacity in cellular systems, in a variety of different configurations. This paper proposes, analyzes, and compares several downlink multiuser multiple input multiple output (MIMO) DAS strategies in terms of per-user throughput and area spectral efficiency. Zero-forcing transmit beamforming is used for transmission, the remote radio units may have one or more antennas, and the subscriber has a single receive antenna. Techniques considered include beamforming across all remote radio units (full transmission), using the same beamforming vector for each remote radio unit (simplified transmission), and selecting a subset of remote radio units. To facilitate rapid simulation and design space exploration, approximations of the ergodic rate are proposed for each technique assuming path-loss, small-scale Rayleigh fading, and out-of-cell interference. Simulations accounting for multiple interfering cells are used to compare the different transmission techniques. Full transmission is found to have the best performance even accounting for out-of-cell interference, though gains diminish for higher numbers of active users. Simplified transmission improves over no DAS but performance degrades with more active remote radio units.
16988525
Understanding traffic dynamics in cellular data networks
We conduct the first detailed measurement analysis of network resource usage and subscriber behavior using a large-scale data set collected inside a nationwide 3G cellular data network. The data set tracks close to a million subscribers over thousands of base stations. We analyze individual subscriber behaviors and observe a significant variation in network usage among subscribers. We characterize subscriber mobility and temporal activity patterns and identify their relation to traffic volume. We then investigate how efficiently radio resources are used by different subscribers as well as by different applications. We also analyze the network traffic from the point of view of the base stations and find significant temporal and spatial variations in different parts of the network, while the aggregated behavior appears predictable. Broadly, our observations deliver important insights into network-wide resource usage. We describe implications in pricing, protocol design and resource and spectrum management.
13673880
Unmanned Aerial Vehicle With Underlaid Device-to-Device Communications: Performance and Tradeoffs
In this paper, the deployment of an unmanned aerial vehicle (UAV) as a flying base station used to provide the fly wireless communications to a given geographical area is analyzed. In particular, the coexistence between the UAV, that is transmitting data in the downlink, and an underlaid device-to-device (D2D) communication network is considered. For this model, a tractable analytical framework for the coverage and rate analysis is derived. Two scenarios are considered: a static UAV and a mobile UAV. In the first scenario, the average coverage probability and the system sum-rate for the users in the area are derived as a function of the UAV altitude and the number of D2D users. In the second scenario, using the disk covering problem, the minimum number of stop points that the UAV needs to visit in order to completely cover the area is computed. Furthermore, considering multiple retransmissions for the UAV and D2D users, the overall outage probability of the D2D users is derived. Simulation and analytical results show that, depending on the density of D2D users, the optimal values for the UAV altitude, which lead to the maximum system sum-rate and coverage probability, exist. Moreover, our results also show that, by enabling the UAV to intelligently move over the target area, the total required transmit power of UAV while covering the entire area, can be minimized. Finally, in order to provide full coverage for the area of interest, the tradeoff between the coverage and delay, in terms of the number of stop points, is discussed.
1730595
Caching in the Sky: Proactive Deployment of Cache-Enabled Unmanned Aerial Vehicles for Optimized Quality-of-Experience
In this paper, the problem of proactive deployment of cache-enabled unmanned aerial vehicles (UAVs) for optimizing the quality-of-experience (QoE) of wireless devices in a cloud radio access network is studied. In the considered model, the network can leverage human-centric information, such as users’ visited locations, requested contents, gender, job, and device type to predict the content request distribution, and mobility pattern of each user. Then, given these behavior predictions, the proposed approach seeks to find the user-UAV associations, the optimal UAVs’ locations, and the contents to cache at UAVs. This problem is formulated as an optimization problem whose goal is to maximize the users’ QoE while minimizing the transmit power used by the UAVs. To solve this problem, a novel algorithm based on the machine learning framework of conceptor-based echo state networks (ESNs) is proposed. Using ESNs, the network can effectively predict each user’s content request distribution and its mobility pattern when limited information on the states of users and the network is available. Based on the predictions of the users’ content request distribution and their mobility patterns, we derive the optimal locations of UAVs as well as the content to cache at UAVs. Simulation results using real pedestrian mobility patterns from BUPT and actual content transmission data from Youku show that the proposed algorithm can yield 33.3% and 59.6% gains, respectively, in terms of the average transmit power and the percentage of the users with satisfied QoE compared with a benchmark algorithm without caching and a benchmark solution without UAVs.