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Abstract The accelerated eutrophication of freshwater lakes has become an environmental problem worldwide. Increasing numbers of studies highlight the need to incorporate functional and phylogenetic information of species into bioassessment programms, but it is still poorly understood how eutrophication affects multiple diversity facets of freshwater communities. Here, we assessed the responses of taxonomic, phylogenetic and functional diversity of benthic macroinvertebrates to water eutrophication in 33 lakes in the Yangtze River floodplain in China. Our results showed that macroinvertebrate assemblage structure was significantly different among four lake groups (river-connected, macrophyte-dominated, macrophyte-algae transition, and algae-dominated). Three taxonomic, two phylogenetic and two functional diversity indices were significantly different among the lake groups. Except for the increasing trend of Lambda+, these metrics showed a clear decreasing trend with increasing levels of eutrophication, with highest values detected in river-connected and macrophyte-dominated lakes, followed by macrophyte-algae transition lakes and algal-dominated lakes. Although differing in the number and identity of key environmental and spatial variables among the explanatory models of different diversity indices, environmental factors (eutrophication-related water quality variables) played more important role than spatial factors in structuring all three facets of alpha diversity. The predominant role of environmental filtering can be attributed to the strong eutrophication gradient across the studied lakes. Among the three diversity facets, functional diversity indices performed best in portraying anthropogenic disturbances, with variations in these indices being solely explained by environmental factors. Spatial factors were mostly weak or negligible in accounting for the variation in functional diversity indices, implying that trait-based indices are robust in portraying anthropogenic eutrophication in floodplain lakes. However, variation in some taxonomic and phylogenetic diversity indices were also affected by spatial factors, indicating that conservation practitioners and environmental managers should use these metrics with caution when providing solutions for addressing eutrophication in floodplain lakes.
Abstract Face presentation attacks are the main threats to face recognition systems, and many presentation attack detection (PAD) methods have been proposed in recent years. Although these methods have achieved significant performance in some specific intrusion modes, difficulties still exist in addressing replayed video attacks. That is because the replayed fake faces contain a variety of aliveness signals, such as eye blinking and facial expression changes. Replayed video attacks occur when attackers try to invade biometric systems by presenting face videos in front of the cameras, and these videos are often launched by a liquid-crystal display (LCD) screen. Due to the smearing effects and movements of LCD, videos captured from the real and replayed fake faces present different motion blurs, which are reflected mainly in blur intensity variation and blur width. Based on these descriptions, a motion blur analysis-based method is proposed to deal with the replayed video attack problem. We first present a 1D convolutional neural network (CNN) for motion blur intensity variation description in the time domain, which consists of a serial of 1D convolutional and pooling filters. Then, a local similar pattern (LSP) feature is introduced to extract blur width. Finally, features extracted from 1D CNN and LSP are fused to detect the replayed video attacks. Extensive experiments on two standard face PAD databases, i.e., relay-attack and OULU-NPU, indicate that our proposed method based on the motion blur analysis significantly outperforms the state-of-the-art methods and shows excellent generalization capability.
Abstract Unmanned aerial vehicle (UAV) swarm connected to millimeter wave (mmWave) cellular networks is emerging as a new promising solution to provide ubiquitous high-speed and long distance wireless communication services for supporting various applications. To satisfy different quality of service (QoS) requirements in future large-scale applications of such networks, this article investigates the rate performance, fairness and their tradeoff in the networks with directional antennas in terms of sum-rate maximization, fairness index maximization, max-min fair rate and proportional fairness. We first consider a more realistic mmWave 3D directional antenna array model for UAVs and base station (BS), where the antenna gain depends on the radiation angle of the antenna array. Based on this antenna array model, we formulate the performance, fairness and their tradeoff as four constrained optimization problems, and propose corresponding iterative algorithm to solve these problems by jointly optimizing elevation angle, azimuth angle and height of antenna array at BS in the downlink transmission scenario. Furthermore, we also explore them in uplink transmission scenario, where the interference issue among links is carefully considered. Finally, according to the sum rate, minimum rate and fairness index under each optimization problem, numerical results are provided to illustrate the impacts of network parameters on the performance, fairness and their tradeoff, and also to reveal new findings under both downlink and uplink transmission scenarios, respectively.
Abstract This article investigates the covertness and secrecy of wireless communications in an untrusted relay-assisted device-to-device (D2D) network consisting of a full-duplex base station (BS), a user equipment (UE), and an untrusted relay \(R\). For the covertness, we attempt to prevent Willie from detecting the very existence of communications via a D2D link from UE to R and cellular link from R to BS, while for the secrecy, we aim to prevent the untrusted relay from eavesdropping the UE message. To explore the fundamental covertness and secrecy in such a network, we first provide theoretical modelings for the average minimum detection error rate of Willie, and the average covert/secrecy rate from UE to BS under the underlay and overlay modes, respectively. Based on these models, th we further explore the optimal power control at UE, R, and BS to achieve the average covert rate maximization (MCR) for UE with the constraints of covertness and security requirements under the underlay mode. We also identify the optimal transmit powers and the optimal spectrum partition factor for MCR under the overlay mode. Finally, the exhaust searching method is adopted to solve the MCR problems, and extensive numerical and simulation results are presented to validate our theoretical analysis and to illustrate the average covert rate and secrecy rate of UE under various scenarios.
Abstract This paper investigates the fundamental rate performances in the highly promising cellular-enabled unmanned aerial vehicle (UAV) swarm networks, which can provide ubiquitous wireless connectivity for supporting various Internet of things (IoT) applications. We first provide the formulations for the sum rate maximization and max-min rate, which are two nonlinear optimization problems subject to the constraints of UAV transmit power and antenna parameters at base station (BS). For the sum rate maximization problem, we propose an iterative algorithm to solve it utilizing the Karush–Kuhn–Tucker (KKT) condition. For the max-min rate problem, we transform it to an equivalent conditional eigenvalue problem based on the nonlinear Perron-Frobenius theory, and thus design an iterative algorithm to obtain the solution of such problem. Finally, numerical results are presented to indicate the effect of some key parameters on the rate performances in such networks.
Abstract This article focuses on the secure routing problem in the decentralized Internet of Things (IoT). We consider a typical decentralized IoT scenario composed of peer legitimate devices, unauthorized devices (eavesdroppers), and selfish helper jamming devices (jammers), and propose a novel incentive jamming-based secure routing scheme. For a pair of source and destination, we first provide theoretical modeling to reveal how the transmission security performance of a given route is related to the jamming power of jammers in the IoT. Then, we design an incentive mechanism with which the source pays some rewards to stimulate the artificial jamming among selfish jammers, and also develop a two-stage Stackelberg game framework to determine the optimal source rewards and jamming power. Finally, with the help of the theoretical modeling as well as the source rewards and jamming power setting results, we formulate a shortest weighted path-finding problem to identify the optimal route for secure data delivery between the source-destination pair, which can be solved by employing the Dijkstra’s or Bellman-Ford algorithm. We prove that the proposed routing scheme is individually rational, stable, distributed, and computationally efficient. Simulation and numerical results are provided to demonstrate the performance of our routing scheme.
Abstract The efficient buffer space management in intermittently connected Internet of Things (IC-IoT) is of great importance for data delivery performance guarantee in such networks. This article considers two typical buffer space management policies for IC-IoT, i.e., buffer-space sharing (BS) and buffer-space allocation (BA). The BS policy allows the buffer space of each device to be fully shared by the exogenous packets and the packets from other devices, while the BA policy divides the buffer space into the source buffer and relay buffer for storing the two kinds of packets separately. With the help of the queueing theory and Markov chain theory, we develop a theoretical framework to capture the sophisticated queueing processes for the buffer space under either BS or BA policy, which enables the limiting distribution of the buffer occupation state to be determined. We then provide theoretical modeling for throughput and expected end-to-end delay to evaluate the fundamental performance of the IC-IoT under the BS and BA policies. Finally, extensive simulation and numerical results are presented to validate theoretical models and to demonstrate the effects of BS and BA policies on the IC-IoT performance.
Abstract Anthropogenic disturbances have become one of the primary causes of biodiversity decline in freshwater ecosystems. Beyond the well-documented loss of taxon richness in increasingly impacted ecosystems, our knowledge on how different facets of α and β diversity respond to human disturbances is still limited. Here, we examined the responses of taxonomic (TD), functional (FD) and phylogenetic (PD) α and β diversity of macroinvertebrate communities to human impact across 33 floodplain lakes surrounding the Yangtze River. We found that most pairwise correlations between TD and FD/PD were low and non-significant, whereas FD and PD metrics were instead positively and significantly correlated. All facets of α diversity decreased from weakly to strongly impacted lakes owing to the removal of sensitive species harboring unique evolutionary legacies and phenotypes. By contrast, the three facets of β diversity responded inconsistently to anthropogenic disturbance: while FDβ and PDβ showed significant impairment in moderately and strongly impacted lakes as a result of spatial homogenization, TDβ was lowest in weakly impacted lakes. The multiple facets of diversity also responded differently to the underlying environmental gradients, re-emphasizing that taxonomic, functional and phylogenetic diversities provide complementary information on community dynamics. However, the explanatory power of our machine learning and constrained ordination models was relatively low and suggests that unmeasured environmental features and stochastic processes may strongly contribute to macroinvertebrate communities in floodplain lakes suffering from variable levels of anthropogenic degradation. We finally suggested guidelines for effective conservation and restoration targets aimed at achieving healthier aquatic biotas in a context of increasing human impact across the ‘lakescape’ surrounding the Yangtze River, the most important being the control of nutrient inputs and increased spatial spillover effects to promote natural metasystem dynamics.
Abstract Lateral hydrological connectivity (LHC) is a key process in maintaining aquatic biodiversity in river floodplain ecosystems. However, the effects of LHC loss on aquatic biodiversity are rarely studied. Here, we evaluated, for the first time, the responses of multiple facets (i.e., taxonomic, functional and phylogenetic) of alpha and beta diversity of freshwater molluscs to the LHC loss in 23 floodplain lakes in the Yangtze River Basin in China. Our results showed that taxonomic and functional alpha diversities were all significantly higher in connected lakes (CLs) than in disconnected lakes (DLs), whereas phylogenetic alpha diversity (Δ+) was lower in CLs than in DLs. For beta diversity facets, taxonomic (Tβsor) and phylogenetic (Pβsor) dissimilarities were slightly more contributed by the turnover component or equally contributed by the turnover and nestedness-resultant components both in CLs and DLs. Instead, functional beta diversity (Fβsor), generally showing much lower values than Tβsor and Pβsor, was mainly contributed by the nestedness-resultant component (76.6–84.0%), especially in DLs. We found that only functional dissimilarities were significantly higher in DLs than CLs, indicating a high level of functional diversity loss without replacement of species possessing traits sensitive to hydrological disconnection (i.e., large body size, lamellibranch body form, filter feeding, ovoviviparity and burrowing habits). In general, lake area, hydrological connectivity, aquatic vegetation coverage and nutrient levels (TN and TP) played important roles in structuring variation in molluscan alpha and beta diversities, although the three diversity facets responded to different environmental factors. Our results suggest that loss of connectivity to the mainstem river has negative impacts on molluscan assemblages in floodplain lakes. More importantly, as taxonomic, functional and phylogenetic diversities responded somewhat differently to the loss of hydrological connectivity, all of these biodiversity facets should be better incorporated into aquatic biodiversity assessment and conservation programs in large river floodplains.
Abstract Revealing how aquatic organisms respond to dam impacts is essential for river biomonitoring and management. Traditional examinations of dam impacts on macroinvertebrate assemblages were frequently conducted within single rivers (i.e., between upstream vs. downstream locations) and based on taxonomic identities but have rarely been expanded to level of entire basins (i.e., between dammed vs. undammed rivers) and from a functional trait perspective. Here, we evaluated the effects of dams on macroinvertebrate assemblages at both the within-river and basin scales using functional traits in two comparable tropical tributaries of the Lancang-Mekong River. At different scales, maximum body size, functional feeding groups (FFG), voltinism and occurrence in drift respond significantly to dam impact. Armoring categories varied significantly between downstream sites and upstream sites, and oviposition behavior, habits and adult life span significantly differed between rivers. The key traits at the within-river scale resembled to those at the between-river scale, suggesting that within-river trait variation could further shape functional trait structure at the basin scale in dammed rivers. Furthermore, water nutrients and habitat quality induced by dams showed the most important role in shaping trait structure, although trait-environment relationships varied between the two different scales. In addition, the trait-environment relationships were stronger in the dry season than in the wet season, suggesting a more important role of environmental filtering processes in the dry season compared with the wet season. This study highlights the utility of the trait-based approach to diagnose the effects of damming and emphasizes the importance of spatial scale to examine dam impacts in riverine systems.
Abstract Objective: Efficient non-contact heart rate (HR) measurement from facial video has received much attention in health monitoring. Past methods relied on prior knowledge and an unproven hypothesis to extract remote photoplethysmography (rPPG) signals, e.g. manually designed regions of interest (ROIs) and the skin reflection model. Approach: This paper presents a short-time end to end HR estimation framework based on facial features and temporal relationships of video frames. In the proposed method, a deep 3D multi-scale network with cross-layer residual structure is designed to construct an autoencoder and extract robust rPPG features. Then, a spatial-temporal fusion mechanism is proposed to help the network focus on features related to rPPG signals. Both shallow and fused 3D spatial-temporal features are distilled to suppress redundant information in the complex environment. Finally, a data augmentation strategy is presented to solve the problem of uneven distribution of HR in existing datasets. Main results: The experimental results on four face-rPPG datasets show that our method overperforms the state-of-the-art methods and requires fewer video frames. Compared with the previous best results, the proposed method improves the root mean square error (RMSE) by 5.9%, 3.4% and 21.4% on the OBF dataset (intra-test), COHFACE dataset (intra-test) and UBFC dataset (cross-test), respectively. Significance: Our method achieves good results on diverse datasets (i.e. highly compressed video, low-resolution and illumination variation), demonstrating that our method can extract stable rPPG signals in short time.
Abstract In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. Based on clustered federated learning, we propose a novel mobility-aware popularity prediction policy, which integrates content popularities in terms of local users and mobile users. For local users, the content popularity is predicted by learning the hidden representations of local users and contents. Initial features of local users and contents are generated by incorporating neighbor information with self information. Then, dual-channel neural network (DCNN) model is introduced to learn the hidden representations by producing deep latent features from initial features. For mobile users, the content popularity is predicted via user preference learning. In order to distinguish regional variations of content popularity, clustered federated learning (CFL) is employed, which enables fog access points (F-APs) with similar regional types to benefit from one another and provides a more specialized DCNN model for each F-AP. Simulation results show that our proposed policy achieves significant performance improvement over the traditional policies.
Abstract In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. In order to obtain accurate prediction with low complexity, we propose a novel context-aware popularity prediction policy based on federated learning. Firstly, user preference learning is applied by considering that users prefer to request the contents they are interested in. Then, users’ context information is utilized to cluster users efficiently by adaptive context space partitioning. After that, we formulate a popularity prediction optimization problem to learn the local model parameters using the stochastic variance reduced gradient (SVRG) algorithm. Finally, federated learning based model integration is proposed to construct the global popularity prediction model based on local models by combining the distributed approximate Newton (DANE) algorithm with SVRG. Our proposed popularity prediction policy not only predicts content popularity accurately, but also significantly reduces computational complexity. Simulation results show that our proposed policy increases the cache hit rate by up to 21.5 % compared to the traditional policies.
Abstract In this paper, we investigate maximum-distance separable (MDS) codes and cluster based coded caching in fog radio access networks (F-RANs). In order to minimize the fronthaul rate, multicast opportunities need to be constructed at the cloud server. Firstly, a redundant MDS codes based coded placement scheme is proposed to provide redundant coded packets and symmetrical cache contents. The redundant coded packets can be used to construct multicast opportunities for requests on the same file. Furthermore, based on the symmetrical cache contents, we propose a cluster cooperation based coded delivery scheme, which can induce considerable multicast opportunities between any two clusters regardless of whether the requests are on the same file or not. Finally, by utilizing the redundant coded packets and the symmetry of cache contents, a joint redundant MDS codes and cluster cooperation based coded caching policy is proposed to minimize the fronthaul rate. Simulation results show that our proposed policy can provide 30% savings of the fronthaul rate compared to the MDS-based uncoded delivery policy.
Abstract In this study, cooperative caching is investigated in fog radio access networks. To maximise the offloaded traffic, a cooperative caching optimisation problem is formulated. By analysing the relationship between clustering and cooperation and utilising the solutions of the knapsack problems, the above challenging optimisation problem is transformed into a clustering subproblem and a content placement subproblem. To further reduce complexity, the authors propose an effective graph-based approach to solve the two subproblems. In the graph-based clustering approach, a node graph and a weighted graph are constructed. By setting the weights of the vertices of the weighted graph to be the incremental offloaded traffics of their corresponding complete subgraphs, the objective cluster sets can be readily obtained by using an effective greedy algorithm to search for the max-weight independent subset. In the graph-based content placement approach, a redundancy graph is constructed by removing the edges in the complete subgraphs of the node graph corresponding to the obtained cluster sets. Furthermore, they enhance the caching decisions to ensure each duplicate file is cached only once. Compared with traditional approximate solutions, their proposed graph-based approach has lower complexity. Simulation results show remarkable improvements in terms of offloaded traffic by using the proposed approach.
Abstract In this paper, energy-efficient power control for small cells underlaying a macrocellular network is investigated. We formulate the power control problem in self-organizing small-cell networks as a noncooperative game and propose a distributed energy-efficient power control scheme, which allows the small base stations (SBSs) to take individual decisions for attaining the Nash equilibrium (NE) with minimum information exchange. In particular, in the noncooperative power control game, a nonconvex optimization problem is formulated for each SBS to maximize their energy efficiency (EE). By exploiting the properties of parameter-free fractional programming and the concept of perspective function, the nonconvex optimization problem for each SBS is transformed into an equivalent constrained convex optimization problem. Then, the constrained convex optimization problem is converted into an unconstrained convex optimization problem by exploiting the mixed penalty function method. The inequality constraints are eliminated by introducing the logarithmic barrier functions, and the equality constraint is eliminated by introducing the quadratic penalty function. We also theoretically show the existence and the uniqueness of the NE in the noncooperative power control game. Simulation results show remarkable improvements in terms of EE by using the proposed scheme.
Abstract In this paper, the edge caching problem in fog radio access networks (F-RAN) is investigated. By maximizing the cache hit rate, we formulate the edge caching optimization problem to find the optimal edge caching policy. Considering that users prefer to request the contents they are interested in, we propose to implement online content popularity prediction by leveraging the content features and user preferences, and offline user preference learning by using the "Follow The (Proximally) Regularized Leader" (FTRL-Proximal) algorithm and the "Online Gradient Descent" (OGD) method. Our proposed edge caching policy not only can promptly predict the future content popularity in an online fashion with low computational complexity, but also can track the popularity changes in time without delay. Simulation results show that the cache hit rate of our proposed policy approaches the optimal performance and is superior to those of the traditional policies.
Abstract In this paper, the edge caching problem in fog radio access networks (F-RAN) is investigated. Considering that fog access points (F-APs) can offer cooperation gain by jointly transmitting the same file or content diversity gain by concurrently transmitting the coded subfiles, we propose to use both joint transmission (JT) strategy and parallel transmission (PT) strategy to serve users. Using stochastic geometry, we first derive the successful transmission probability (STP) with different transmission strategies, and further derive the fractional offloaded traffic (FOT). Finally, the optimal caching design is obtained by maximizing the STP and FOT. Simulation results show that our proposed caching design achieves a significant performance gain in comparison with the baselines.
Abstract In this paper, power control in the uplink for two-tier small-cell networks is investigated. We formulate the power control problem as a Stackelberg game, where the macrocell user equipment (MUE) acts as the leader and the small-cell user equipment (SUE) acts as the follower. To reduce the cross-tier and cotier interferences and the power consumption of both the MUE and SUE, we propose optimizing not only the transmit rate but also the transmit power. The corresponding optimization problems are solved through a two-layer iteration. In the inner iteration, the SUE items (SUEs) compete with each other, and their optimal transmit powers are obtained through iterative computations. In the outer iteration, the optimal transmit power of the MUE is obtained in a closed form based on the transmit powers of the SUEs through proper mathematical manipulations. We prove the convergence of the proposed power control scheme, and we also theoretically show the existence and uniqueness of the Stackelberg equilibrium (SE) in the formulated Stackelberg game. The simulation results show that the proposed power control scheme provides considerable improvements, particularly for the MUE.
Abstract In this paper, cooperative edge caching problem is investigated in fog radio access networks (F-RANs). By considering the non-deterministic polynomial hard (NP-hard) property of this problem, a federated deep reinforcement learning (FDRL) framework is put forth to learn the content caching strategy. Then, in order to overcome the dimensionality curse of reinforcement learning and improve the overall caching performance, we propose a dueling deep Q-network based cooperative edge caching method to find the optimal caching policy in a distributed manner. Furthermore, horizontal federated learning (HFL) is applied to address issues of over-consumption of resources during distributed training and data transmission process. Compared with three classical content caching methods and two reinforcement learning algorithms, simulation results show the superiority of our proposed method in reducing the content request delay and improving the cache hit rate.
Abstract In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. In order to obtain accurate prediction with low complexity, we propose a novel context-aware popularity prediction policy based on federated learning (FL). Firstly, user preference learning is applied by considering that users prefer to request the contents they are interested in. Then, users’ context information is utilized to cluster users efficiently by adaptive context space partitioning. After that, we formulate a popularity prediction optimization problem to learn the local model parameters by using the stochastic variance reduced gradient (SVRG) algorithm. Finally, FL based model integration is proposed to learn the global popularity prediction model based on local models using the distributed approximate Newton (DANE) algorithm with SVRG. Our proposed popularity prediction policy not only can predict content popularity accurately, but also can significantly reduce computational complexity. Moreover, we theoretically analyze the convergence bound of our proposed FL based model integration algorithm. Simulation results show that our proposed policy increases the cache hit rate by up to 21.5 % compared to existing policies.
Abstract The fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs). Due to the limited resource of F-APs, it is important to design an efficient task offloading scheme. In this paper, by considering time-varying network environment, a dynamic computation offloading and resource allocation problem in F-RANs is formulated to minimize the task execution delay and energy consumption of MDs. To solve the problem, a federated deep reinforcement learning (DRL) based algorithm is proposed, where the deep deterministic policy gradient (DDPG) algorithm performs computation offloading and resource allocation in each F-AP. Federated learning is exploited to train the DDPG agents in order to decrease the computing complexity of training process and protect the user privacy. Simulation results show that the proposed federated DDPG algorithm can achieve lower task execution delay and energy consumption of MDs more quickly compared with the other existing strategies.
Abstract In this paper, the edge caching optimization problem in fog radio access networks (F-RANs) is investigated. Taking into account time-variant user requests and ultra-dense deployment of fog access points (F-APs), we propose a distributed edge caching scheme to jointly minimize the request service delay and fronthaul traffic load. Considering the interactive relationship among F-APs, we model the optimization problem as a stochastic differential game (SDG) which captures the dynamics of F-AP states. To address both the intractability problem of the SDG and the caching capacity constraint, we propose to solve the optimization problem in a distributive manner. Firstly, a mean field game (MFG) is converted from the original SDG by exploiting the ultra-dense property of F-RANs, and the states of all F-APs are characterized by a mean field distribution. Then, an iterative algorithm is developed that enables each F-AP to obtain the mean field equilibrium and caching control without extra information exchange with other F-APs. Secondly, a fractional knapsack problem is formulated based on the mean field equilibrium, and a greedy algorithm is developed that enables each F-AP to obtain the final caching policy subject to the caching capacity constraint. Simulation results show that the proposed scheme outperforms the baselines.
Abstract In this paper, the edge caching problem in fog radio access network (F-RAN) is investigated. By maximizing the overall cache hit rate, the edge caching optimization problem is formulated to find the optimal policy. Content popularity in terms of time and space is considered from the perspective of regional users. We propose an online content popularity prediction algorithm by leveraging the content features and user preferences, and an offline user preference learning algorithm by using the online gradient descent (OGD) method and the follow the (proximally) regularized leader (FTRL-Proximal) method. Our proposed edge caching policy not only can promptly predict the future content popularity in an online fashion with low complexity, but also can track the content popularity with spatial and temporal popularity dynamic in time without delay. Furthermore, we design two learning-based edge caching architectures. Moreover, we theoretically derive the upper bound of the popularity prediction error, the lower bound of the cache hit rate, and the regret bound of the overall cache hit rate of our proposed edge caching policy. Simulation results show that the overall cache hit rate of our proposed policy is superior to those of the traditional policies and asymptotically approaches the optimal performance.
Abstract In this paper, an edge caching resource allocation problem in fog radio access networks (F-RANs) is investigated. To motivate content providers (CPs) to participate in this resource allocation procedure, we introduce an incentive mechanism. By treating fog access points (F-APs) as a specific type of edge caching resource, the cloud server sets non-uniform prices of F-APs and leases them to the CPs, while the CPs cache the most popular contents in the storage of F-APs and get rewarded by the raised content hit rate. We formulate the interaction between the cloud server and the CPs as a Stackelberg game and solve the corresponding optimization problems to achieve Nash equilibrium (NE). In particular, by exploiting the multiplier penalty function method, we transform the constrained optimization problem for the cloud server into an equivalent non-constrained optimization problem. Then, we propose an edge caching resource pricing algorithm to solve the non-constrained optimization problem by applying the simplex search method. We also theoretically prove the existence and uniqueness of the NE. Simulation results show the rapid convergence of the proposed algorithm and the superiority performance in improving content hit rate.
Abstract In this paper, we investigate maximum-distance separable (MDS) codes and weighted graph based coded caching in fog radio access networks (F-RANs). In the placement phase, the redundant MDS based coded placement scheme is used to provide redundant coded packets and homogeneous cached contents. The redundant coded packets can be used to construct multicast opportunities for similar requests. In the delivery phase, the weighted graph based coded delivery scheme is conducted based on homogeneous cached contents, which can induce considerable multicast opportunities. By integrating the above two schemes, a joint MDS codes and weighted graph based coded caching policy is proposed to minimize the fronthaul load. Finally, we theoretically analyze the performance of the proposed policy by deriving the lower and upper bounds of the fronthaul load. Simulation results show that our proposed policy can provide 44% savings in the fronthaul load compared to the MDS-based uncoded delivery policy.
Abstract In this paper, we investigate maximum distance separable (MDS) codes based group coded caching in fog radio access networks (F-RANs). The goal is to minimize the average fronthaul rate under nonuniform file popularity. Firstly, an MDS codes and file grouping based coded placement scheme is proposed to provide coded packets and allocate more cache to the most popular files simultaneously. Next, a fog access point (F-AP) grouping based coded delivery scheme is proposed to meet the requests for files from different groups. Furthermore, a closed-form expression of the average fronthaul rate is derived. Finally, the parameters related to the proposed coded caching scheme are optimized to fully utilize the gains brought by MDS codes and file grouping. Simulation results show that our proposed scheme obtains significant performance improvement over several existing caching schemes in terms of fronthaul rate reduction.