Joint Pushing and Caching with a Finite Receiver Buffer: Optimal Policies and Throughput Analysis

Pushing and caching hold the promise of significantly increasing the throughput of content-centric wireless networks. However, the throughput gain of these techniques is limited by the buffer size of the receiver. To overcome this, this paper presents a Joint Pushing and Caching (JPC) method that jointly determines the contents to be pushed to, and to be removed from, the receiver buffer in each timeslot. An offline and two online JPC policies are proposed respectively based on noncausal, statistical, and causal content Request Delay Information (RDI), which predicts a user's request time for certain content. It is shown that the effective throughput of JPC is increased with the receiver buffer size and the pushing channel capacity. Furthermore, the causal feedback of user requests is found to greatly enhance the performance of online JPC without inducing much signalling overhead in practice.Comment: 6 pages, 4 figure

Convergence Analysis of Mixed Timescale Cross-Layer Stochastic Optimization

This paper considers a cross-layer optimization problem driven by multi-timescale stochastic exogenous processes in wireless communication networks. Due to the hierarchical information structure in a wireless network, a mixed timescale stochastic iterative algorithm is proposed to track the time-varying optimal solution of the cross-layer optimization problem, where the variables are partitioned into short-term controls updated in a faster timescale, and long-term controls updated in a slower timescale. We focus on establishing a convergence analysis framework for such multi-timescale algorithms, which is difficult due to the timescale separation of the algorithm and the time-varying nature of the exogenous processes. To cope with this challenge, we model the algorithm dynamics using stochastic differential equations (SDEs) and show that the study of the algorithm convergence is equivalent to the study of the stochastic stability of a virtual stochastic dynamic system (VSDS). Leveraging the techniques of Lyapunov stability, we derive a sufficient condition for the algorithm stability and a tracking error bound in terms of the parameters of the multi-timescale exogenous processes. Based on these results, an adaptive compensation algorithm is proposed to enhance the tracking performance. Finally, we illustrate the framework by an application example in wireless heterogeneous network

Information Asymmetry, Corporate Debt Financing and Optimal Investment Decisions: A Reduced Form Approach

Under the assumption of information asymmetry between market investors and firm managers, a reduced form model of a firm is developed in order to derive optimal investment strategies and capital structures while taking into account the effects of dividend policies and taxes. The motivation of the reduced form approach lies in its empirical implementation tractability. Closed-form solutions for debt issuance prices and debt values from firm managers' perspective are derived. Considering the inconsistency between the two prices incurred from the asymmetric information, a firm's problem of optimal investment risk determination is presented and solved by trading off two opposing effects: asset substitution and default cost. Furthermore, the optimal dividend policy and tax benefits from debt interest payment are also considered, and the application of the model in portfolio management is discussed. Finally, two simple examples are provided. Under these two specific settings, the optimal investment policies are derived explicitly to illustrate the implementation of the model proposed in this paper and demonstrate the general consistency of the results implied by our methodology and the traditional structural framework.Credir Risk, Information Asymmetry, Reduced Form Approach

Credit Risk Modeling and the Term Structure of Credit Spreads

In this paper, by applying the potential approach to characterizing default risk, a class of simple affine and quadratic models is presented to provide a unifying framework of valuing both risk-free and defaultable bonds. It has been shown that the established models can accommodate the existing intensity based credit risk models, while incorporating a security-specific credit information factor to capture the idiosyncratic default risk as well as the one from market-wide influence. The models have been calibrated using the integrated data of both treasury rates and the average bond yields in different rating classes. Filtering technique and the quasi maximum likelihood estimator (QMLE) are applied jointly to the problem of estimating the structural parameters of the affine and quadratic models. The asymptotic properties of the QMLE are analyzed under two criteria: asymptotic optimality under the Kullback-Leibler criterion, and consistency. Relative empirical performance of the two models has been investigated. It turns out that the quadratic model outperforms the affine model in explaining the historical yield behavior of both Treasury and corporate bonds, while producing a larger error in fitting cross-sectional bond spread curves. Moreover, a modified fat-tail affine model is also proposed to improve the cross-sectional term structure fitting abilities of the existing models. Meanwhile, our empirical study provides complete estimates of risk-premia for both market risk and credit default risk including jump event risk.Credit Risk, Credit Spread, Filtering Technique, Affine and Quadratic Models