arXiv subjects

Ying Cui

Publications and source records attributed to Ying Cui.

At least 145 records · Page 8Linked to original sources

arXiv2017

A complete characterization on the robust isolated calmness of the nuclear norm regularized convex optimization problems

In this paper, we provide a complete characterization on the robust isolated calmness of the Karush-Kuhn-Tucker (KKT) solution mapping for convex constrained optimization problems regularized by the nuclear norm function. This study is motivated by the recent work in [8], where the authors show that under the Robinson constraint qualification at a local optimal solution, the KKT solution mapping for a wide class of conic programming problem is robustly isolated calm if and only if both the second order sufficient condition (SOSC) and the strict Robinson constraint qualification (SRCQ) are satisfied. Based on the variational properties of the nuclear norm function and its conjugate, we establish the equivalence between the primal/dual SOSC and the dual/primal SRCQ. The derived results lead to several equivalent characterizations of the robust isolated calmness of the KKT solution mapping and add insights to the existing literature on the stability of the nuclear norm regularized convex optimization problems.

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arXiv2017

Temporal-Spatial Aggregation for Cache-Enabled Wireless Multicasting Networks with Asynchronous Content Requests

Existing multicasting schemes for massive content delivery do not fully utilize multicasting opportunities in delay tolerant content-oriented applications. In this paper, we propose a novel temporal-spatial aggregation-based multicasting scheme in a large-scale cache-enabled wireless network. The proposed scheme can efficiently exploit multicasting opportunities in asynchronous content requests to improve spectral efficiency. By making use of the delay tolerance of elastic services, the proposed scheme achieves a better energy-throughput-delay tradeoff. Utilizing tools from stochastic geometry, we derive a tractable expression for the successful transmission probability in the general region. Using asymptotic approximations, we derive closed form successful transmission probabilities in the large delay region as well as the large and small user density regions. The asymptotic results reveal that the successful transmission probability increases and the energy consumption decreases at the cost of delay increase in these asymptotic regions. The analysis in this paper provides a new understanding of the energy-throughput-delay tradeoff for massive content delivery in large-scale cache-enabled wireless networks.

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arXiv2017

Partition-based Caching in Large-Scale SIC-Enabled Wireless Networks

Existing designs for content dissemination do not fully explore and exploit potential caching and computation capabilities in advanced wireless networks. In this paper, we propose two partition-based caching designs, i.e., a coded caching design based on Random Linear Network Coding and an uncoded caching design. We consider the analysis and optimization of the two caching designs in a large-scale successive interference cancelation (SIC)-enabled wireless network. First, under each caching design, by utilizing tools from stochastic geometry and adopting appropriate approximations, we derive a tractable expression for the successful transmission probability in the general file size regime. To further obtain design insights, we also derive closed-form expressions for the successful transmission probability in the small and large file size regimes, respectively. Then, under each caching design, we consider the successful transmission probability maximization in the general file size regime, which is an NP-hard problem. By exploring structural properties, we successfully transform the original optimization problem into a Multiple-Choice Knapsack Problem (MCKP), and obtain a near optimal solution with 1/2 approximation guarantee and polynomial complexity. We also obtain closed-form asymptotically optimal solutions. The analysis and optimization results show the advantage of the coded caching design over the uncoded caching design, and reveal the impact of caching and SIC capabilities. Finally, by numerical results, we show that the two proposed caching designs achieve significant performance gains over some baseline caching designs.

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arXiv2017

Random Caching Based Cooperative Transmission in Heterogeneous Wireless Networks

Base station cooperation in heterogeneous wireless networks (HetNets) is a promising approach to improve the network performance, but it also imposes a significant challenge on backhaul. On the other hand, caching at small base stations (SBSs) is considered as an efficient way to reduce backhaul load in HetNets. In this paper, we jointly consider SBS caching and cooperation in a downlink largescale HetNet. We propose two SBS cooperative transmission schemes under random caching at SBSs with the caching distribution as a design parameter. Using tools from stochastic geometry and adopting appropriate integral transformations, we first derive a tractable expression for the successful transmission probability under each scheme. Then, under each scheme, we consider the successful transmission probability maximization by optimizing the caching distribution, which is a challenging optimization problem with a non-convex objective function. By exploring optimality properties and using optimization techniques, under each scheme, we obtain a local optimal solution in the general case and global optimal solutions in some special cases. Compared with some existing caching designs in the literature, e.g., the most popular caching, the i.i.d. caching and the uniform caching, the optimal random caching under each scheme achieves better successful transmission probability performance. The analysis and optimization results provide valuable design insights for practical HetNets.

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arXiv2017

New Order-Optimal Decentralized Coded Caching Schemes with Good Performance in the Finite File Size Regime

A decentralized coded caching scheme based on independent random content placement has been proposed by Maddah-Ali and Niesen, and has been shown to achieve an order-optimal memory-load tradeoff when the file size goes to infinity. It was then successively shown by Shanmugam et al. that in the practical operating regime where the file size is limited such scheme yields much less attractive load gain. In this paper, we propose a decentralized random coded caching scheme and a partially decentralized sequential coded caching scheme with different coordination requirements in the content placement phase. The proposed content placement and delivery methods aim at ensuring abundant coded-multicasting opportunities in the content delivery phase when the file size is finite. We first analyze the loads of the two proposed schemes and show that the sequential scheme outperforms the random scheme in the finite file size regime. We also show that both our proposed schemes outperform Maddah-Ali--Niesen's and Shanmugam et al.'s decentralized schemes for finite file size, when the number of users is sufficiently large. Then, we show that our schemes achieve the same memory-load tradeoff as Maddah-Ali--Niesen's decentralized scheme when the file size goes to infinity, and hence are also order optimal. Finally, we analyze the load gains of the two proposed schemes and characterize the corresponding required file sizes.

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arXiv2016

Energy-Efficient Resource Allocation for Multi-User Mobile Edge Computing

To increase mobile batteries' lifetime and improve quality of experience for computation-intensive and latency-sensitive applications, mobile edge computing has received significant interest. Designing energy-efficient mobile edge computing systems requires joint optimization of communication and computation resources. In this paper, we consider energy-efficient resource allocation for a multi-user mobile edge computing system. First, we establish on two computation-efficient models with negligible and non-negligible base station (BS) executing durations, respectively. Then, under each model, we formulate the overall weighted sum energy consumption minimization problem by optimally allocating communication and computation resources. The optimization problem for negligible BS executing duration is convex, and we obtain the optimal solution in closed-form to this problem. The optimization problem for non-negligible BS executing duration is NP-hard in general, and we obtain a sub-optimal solution with low-complexity to this problem, by connecting it to a three-stage flow-shop scheduling problem and wisely utilizing Johnson's algorithm. Finally, numerical results show that the proposed solutions outperform some baseline schemes.

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arXiv2016

On the Asymptotic Superlinear Convergence of the Augmented Lagrangian Method for Semidefinite Programming with Multiple Solutions

Solving large scale convex semidefinite programming (SDP) problems has long been a challenging task numerically. Fortunately, several powerful solvers including SDPNAL, SDPNAL+ and QSDPNAL have recently been developed to solve linear and convex quadratic SDP problems to high accuracy successfully. These solvers are based on the augmented Lagrangian method (ALM) applied to the dual problems with the subproblems being solved by semismooth Newton-CG methods. Noticeably, thanks to Rockafellar's general theory on the proximal point algorithms, the primal iteration sequence generated by the ALM enjoys an asymptotic Q-superlinear convergence rate under a second order sufficient condition {for the primal problem}. This second order sufficient condition implies that the primal problem has a unique solution, which can be restrictive in many applications. For gaining more insightful interpretations on the high efficiency of these solvers, in this paper we conduct an asymptotic superlinear convergence analysis of the ALM for convex SDP when the primal problem has multiple solutions (can be unbounded). Under a fairly mild second order growth condition, we prove that the primal iteration sequence generated by the ALM converges asymptotically Q-superlinearly, while the dual feasibility and the dual objective function value converge asymptotically R-superlinearly. Moreover, by studying the metric subregularity of the Karush-Kuhn-Tucker solution mapping, we also provide sufficient conditions to guarantee the asymptotic R-superlinear convergence of the dual iterate.

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arXiv2016

Scaled VIP Algorithms for Joint Dynamic Forwarding and Caching in Named Data Networks

Emerging Information-Centric Networking (ICN) architectures seek to optimally utilize both bandwidth and storage for efficient content distribution over the network. The Virtual Interest Packet (VIP) framework has been proposed to enable joint design of forwarding and caching within the Named Data Networking (NDN) architecture. The virtual plane of the VIP framework captures the measured demand for content objects, but does not reflect interest collapse and suppression in the NDN network. We aim to further improve the performance of the existing VIP algorithms by using a modified virtual plane where VIP counts are appropriately scaled to reflect interest suppression effects. We characterize the stability region of the modified virtual plane with VIP scaling, develop a new distributed forwarding and caching algorithm operating on the scaled VIPs, and demonstrate the throughput optimality of the scaled VIP algorithm in the virtual plane. Numerical experiments demonstrate significantly enhanced performance relative to the existing VIP algorithm, as well as a number of other baseline algorithms.

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arXiv2016

Enhanced VIP Algorithms for Forwarding, Caching, and Congestion Control in Named Data Networks

Emerging Information-Centric Networking (ICN) architectures seek to optimally utilize both bandwidth and storage for efficient content distribution over the network. The Virtual Interest Packet (VIP) framework has been proposed to enable joint design of forwarding, caching, and congestion control strategies within the Named Data Networking (NDN) architecture. While the existing VIP algorithms exhibit good performance, they are primarily focused on maximizing network throughput and utility, and do not explicitly consider user delay. In this paper, we develop a new class of enhanced algorithms for joint dynamic forwarding, caching and congestion control within the VIP framework. These enhanced VIP algorithms adaptively stabilize the network and maximize network utility, while improving the delay performance by intelligently making use of VIP information beyond one hop. Generalizing Lyapunov drift techniques, we prove the throughput optimality and characterize the utility-delay tradeoff of the enhanced VIP algorithms. Numerical experiments demonstrate the superior performance of the resulting enhanced algorithms for handling Interest Packets and Data Packets within the actual plane, in terms of low network delay and high network utility.

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arXiv2016

User-Centric Interference Nulling in Downlink Multi-Antenna Heterogeneous Networks

In heterogeneous networks (HetNets), strong interference due to spectrum reuse affects each user's signal-to-interference ratio (SIR), and hence is one limiting factor of network performance. In this paper, we propose a user-centric interference nulling (IN) scheme in a downlink large-scale HetNet to improve coverage/outage probability by improving each user's SIR. This IN scheme utilizes at most maximum IN degree of freedom (DoF) at each macro-BS to avoid interference to uniformly selected macro (pico) users with signal-to-individual-interference ratio (SIIR) below a macro (pico) IN threshold, where the maximum IN DoF and the two IN thresholds are three design parameters. Using tools from stochastic geometry, we first obtain a tractable expression of the coverage (equivalently outage) probability. Then, we analyze the asymptotic coverage/outage probability in the low and high SIR threshold regimes. The analytical results indicate that the maximum IN DoF can affect the order gain of the outage probability in the low SIR threshold regime, but cannot affect the order gain of the coverage probability in the high SIR threshold regime. Moreover, we characterize the optimal maximum IN DoF which optimizes the asymptotic coverage/outage probability. The optimization results reveal that the IN scheme can linearly improve the outage probability in the low SIR threshold regime, but cannot improve the coverage probability in the high SIR threshold regime. Finally, numerical results show that the proposed scheme can achieve good gains in coverage/outage probability over a maximum ratio beamforming scheme and a user-centric almost blank subframes (ABS) scheme.

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arXiv2016

A Linear Network Code Construction for General Integer Connections Based on the Constraint Satisfaction Problem

The problem of finding network codes for general connections is inherently difficult in capacity constrained networks. Resource minimization for general connections with network coding is further complicated. Existing methods for identifying solutions mainly rely on highly restricted classes of network codes, and are almost all centralized. In this paper, we introduce linear network mixing coefficients for code constructions of general connections that generalize random linear network coding (RLNC) for multicast connections. For such code constructions, we pose the problem of cost minimization for the subgraph involved in the coding solution and relate this minimization to a path-based Constraint Satisfaction Problem (CSP) and an edge-based CSP. While CSPs are NP-complete in general, we present a path-based probabilistic distributed algorithm and an edge-based probabilistic distributed algorithm with almost sure convergence in finite time by applying Communication Free Learning (CFL). Our approach allows fairly general coding across flows, guarantees no greater cost than routing, and shows a possible distributed implementation. Numerical results illustrate the performance improvement of our approach over existing methods.

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arXiv2016

Distributed Stochastic Optimization for Weakly Coupled Systems with Applications to Wireless Communications

In this paper, a framework is proposed to simplify solving the infinite horizon average cost problem for the weakly coupled multi-dimensional systems. Specifically, to address the computational complexity issue, we first introduce a virtual continuous time system (VCTS) and obtain the associated fluid value function. The relationship between the VCTS and the original discrete time system is further established. To facilitate the low complexity distributed implementation and address the coupling challenge, we model the weakly coupled system as a perturbation of a decoupled base system and study the decoupled base system. The fluid value function of the VCTS is approximated by the sum of the per-flow fluid value functions and the approximation error is established using perturbation analysis. Finally, we obtain a low complexity distributed solution based on the per-flow fluid value function approximation. We apply the framework to solve a delay-optimal control problem for the K-pair interference networks and obtain a distributed power control algorithm. The proposed algorithm is compared with various baseline schemes through simulations and it is shown that significant delay performance gain can be achieved.

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arXiv2016

Stochastic Content-Centric Multicast Scheduling for Cache-Enabled Heterogeneous Cellular Networks

Caching at small base stations (SBSs) has demonstrated significant benefits in alleviating the backhaul requirement in heterogeneous cellular networks (HetNets). While many existing works focus on what contents to cache at each SBS, an equally important problem is what contents to deliver so as to satisfy dynamic user demands given the cache status. In this paper, we study optimal content delivery in cache-enabled HetNets by taking into account the inherent multicast capability of wireless medium. We consider stochastic content multicast scheduling to jointly minimize the average network delay and power costs under a multiple access constraint. We establish a content-centric request queue model and formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP). By using \emph{relative value iteration} and special properties of the request queue dynamics, we characterize some properties of the value function of the MDP. Based on these properties, we show that the optimal multicast scheduling policy is of threshold type. Then, we propose a structure-aware optimal algorithm to obtain the optimal policy. We also propose a low-complexity suboptimal policy, which possesses similar structural properties to the optimal policy, and develop a low-complexity algorithm to obtain this policy.

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arXiv2016

Optimal Caching and User Association in Cache-enabled Heterogeneous Wireless Networks

Heterogenous wireless networks (Hetnets) provide a powerful approach to meet the massive growth in traffic demands, but also impose a significant challenge on backhaul. Caching at small base stations (BSs) and wireless small cell backhaul have been proposed as attractive solutions to address this new challenge. In this paper, we consider the optimal caching and user association to minimize the total time to satisfy the average demands in cached-enabled Hetnets with wireless backhaul. We formulate this problem as a mixed discrete-continuous optimization for given bandwidth and cache resources. First, we characterize the structure of the optimal solution. Specifically, we show that the optimal caching is to store the most popular files at each pico BS, and the optimal user association has a threshold form. We also obtain the closed-form optimal solution in the homogenous scenario of pico cells. Then, we analyze the impact of bandwidth and cache resources on the minimum total time to satisfy the average demands. Finally, using numerical simulations, we verify the analytical results.

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arXiv2016

Analysis and Optimization of Caching and Multicasting in Large-Scale Cache-Enabled Heterogeneous Wireless Networks

Heterogeneous wireless networks (HetNets) provide a powerful approach to meet the dramatic mobile traffic growth, but also impose a significant challenge on backhaul. Caching and multicasting at macro and pico base stations (BSs) are two promising methods to support massive content delivery and reduce backhaul load in HetNets. In this paper, we jointly consider caching and multicasting in a large-scale cache-enabled HetNet with backhaul constraints. We propose a hybrid caching design consisting of identical caching in the macro-tier and random caching in the pico-tier, and a corresponding multicasting design. By carefully handling different types of interferers and adopting appropriate approximations, we derive tractable expressions for the successful transmission probability in the general region as well as the high signal-to-noise ratio (SNR) and user density region, utilizing tools from stochastic geometry. Then, we consider the successful transmission probability maximization by optimizing the design parameters, which is a very challenging mixed discrete-continuous optimization problem due to the sophisticated structure of the successful transmission probability. By using optimization techniques and exploring the structural properties, we obtain a near optimal solution with superior performance and manageable complexity. This solution achieves better performance in the general region than any asymptotically optimal solution, under a mild condition. The analysis and optimization results provide valuable design insights for practical cache-enabled HetNets.

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arXiv2016

Forwarding, Caching and Congestion Control in Named Data Networks

Emerging information-centric networking architectures seek to optimally utilize both bandwidth and storage for efficient content distribution. This highlights the need for joint design of traffic engineering and caching strategies, in order to optimize network performance in view of both current traffic loads and future traffic demands. We present a systematic framework for joint dynamic interest request forwarding and dynamic cache placement and eviction, within the context of the Named Data Networking (NDN) architecture. The framework employs a virtual control plane which operates on the user demand rate for data objects in the network, and an actual plane which handles Interest Packets and Data Packets. We develop distributed algorithms within the virtual plane to achieve network load balancing through dynamic forwarding and caching, thereby maximizing the user demand rate that the NDN network can satisfy. Next, we show that congestion control can be optimally combined with forwarding and caching within this framework to maximize user utilities subject to network stability. Numerical experiments within a number of network settings demonstrate the superior performance of the resulting algorithms for the actual plane in terms of high user utilities, low user delay, and high rate of cache hits.

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arXiv2016

Optimal Dynamic Multicast Scheduling for Cache-Enabled Content-Centric Wireless Networks

Caching and multicasting at base stations are two promising approaches to support massive content delivery over wireless networks. However, existing scheduling designs do not make full use of the advantages of the two approaches. In this paper, we consider the optimal dynamic multicast scheduling to jointly minimize the average delay, power, and fetching costs for cache-enabled content-centric wireless networks. We formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP). It is well-known to be a difficult problem due to the curse of dimensionality, and there generally only exist numerical solutions. By using relative value iteration algorithm and the special structures of the request queue dynamics, we analyze the properties of the value function and the state-action cost function of the MDP for both the uniform and nonuniform channel cases. Based on these properties, we show that the optimal policy, which is adaptive to the request queue state, has a switch structure in the uniform case and a partial switch structure in the nonuniform case. Moreover, in the uniform case with two contents, we show that the switch curve is monotonically non-decreasing. Then, by exploiting these structural properties of the optimal policy, we propose two low-complexity optimal algorithms. Motivated by the switch structures of the optimal policy, to further reduce the complexity, we also propose a low-complexity suboptimal policy, which possesses similar structural properties to the optimal policy, and develop a low-complexity algorithm to compute this policy.

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arXiv2016

Analysis and Optimization of Caching and Multicasting in Large-Scale Cache-Enabled Wireless Networks

Caching and multicasting at base stations are two promising approaches to support massive content delivery over wireless networks. However, existing analysis and designs do not fully explore and exploit the potential advantages of the two approaches. In this paper, we consider the analysis and optimization of caching and multicasting in a large-scale cache-enabled wireless network. We propose a random caching and multicasting scheme with a design parameter. By carefully handling different types of interferers and adopting appropriate approximations, we derive a tractable expression for the successful transmission probability in the general region, utilizing tools from stochastic geometry. We also obtain a closed-form expression for the successful transmission probability in the high signal-to-noise ratio (SNR) and user density region. Then, we consider the successful transmission probability maximization, which is a very complex non-convex problem in general. Using optimization techniques, we develop an iterative numerical algorithm to obtain a local optimal caching and multicasting design in the general region. To reduce complexity and maintain superior performance, we also derive an asymptotically optimal caching and multicasting design in the asymptotic region, based on a two-step optimization framework. Finally, numerical simulations show that the asymptotically optimal design achieves a significant gain in successful transmission probability over some baseline schemes in the general region.

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