Chengnian Long

The Yellow active queue management algorithm

A new rate-based active queue management (AQM) algorithm, called Yellow, is proposed in the paper. Yellow uses the load factor (link utilization) as a main merit to manage congestion. To improve congestion control performance, a queue control function (QCF) is introduced as a secondary merit. The sufficient condition for globally asymptotic stability is presented based on Lyapunov theory. Furthermore, the principle for parameter settings is given based on the bounded stable conditions. We then illustrate that Yellow outperforms the recently proposed AQM algorithms in terms of link utilization, packet loss and robust performance through extensive simulations.

The end-to-end rate control in multiple-hop wireless networks: Cross-layer formulation and optimal allocation

In this paper, we study the theoretical problem of the end-to-end rate assignment for multi-hop wireless networks. Specifically, we consider the problem of joint congestion control, random access and power control design with multi-hop transmissions and interference-limited link rates. In order to address both the end-to-end throughput maximization and energy efficiency, we formulate this problem into a cross-layer design problem under a realistic interference-based communication model, which captures the attainable link capacity in practice. There are primarily three challenges in this design: 1) how to formulate the cross-layer design; 2) how to solve the non- convex and non-separable problem efficiently; more importantly 3) under a reasonably complexity, how to design a distributed algorithm that can realize this formulation while maintaining the architectural modularity among different layers. First, we propose a novel method that can convert a non- convex and non-separable programming into an equivalent convex programming problem. The problem is solved by a dual decomposition technique. We show that the resulting algorithm can be practically realized. We then design a distributed algorithm that jointly considers random access and power control to adapt for the transport layer congestion status. Simulation results confirm that the proposed algorithm can achieve close to the global optimum within reasonable convergence times.

Local stability of REM algorithm with time-varying delays

We investigate the local stability in equilibrium for an Internet congestion control algorithm proposed by Low (see IEEE/ACM Transactions on Networking, vol.7, p.861-875,1999). The network consists of multisource and one-bottleneck link with heterogenous time-varying propagation delays. Linear matrix inequality (LMI) stability criteria is presented for discrete congestion control algorithm of TCP/REM dual model, which can be efficiently and easily solved by the LMI toolbox provided by Matlab software. An important feature is to acquire the maximum network delays to guarantee the stability of congestion control algorithm, i.e., the scale stability domain of REM algorithm.

Channel-Aware Access for Cognitive Radio Networks

This paper addresses the problem of designing channel-aware access control (CAAC) algorithms for cognitive radio networks. To protect the primary transmission, the access probabilities of secondary users are adjusted based on the channel-state information and the measured interference temperature. It is shown that the proposed CAAC algorithm always converges to a fixed point. Furthermore, the CAAC algorithm can be interpreted as a noncooperative game. Then, the game model is extended to include general access utilities, unsuccessful transmission discount, and interference constraints. Sufficient conditions are established to guarantee the uniqueness of the Nash equilibrium (NE) of the proposed general game model, and a distributed iteration algorithm is proposed to find the unique NE. The convergence properties of this algorithm in continuous-time iteration, as well as in the discrete-time version, are proved under some sufficient conditions. Simulation results demonstrate the convergence and effectiveness of the distributed channel-access algorithms.

Robust stabilization of uncertain dynamic time-delay systems with unknown bounds of uncertainties

The problem of robust stabilization for a class of uncertain dynamic systems with state and multiple delayed state perturbations is considered. It is assumed that each perturbation of state and time-delayed state is bounded by a linear function with unknown gains. For such uncertain systems, two classes of continuous state feedback controllers are proposed in this paper. Based on Lyapunov stability theory and Lyapunov-Krasovskii function, a robust adaptive feedback controller is proposed. By employing Razumikhin lemma, we also develop a novel nonlinear feedback controller. Both the controllers are completely independent of the time delays and can render the closed-loop systems globally stable in the sense of uniform ultimate boundedness. Finally, a numerical example is given to demonstrate the validity of the results.

Nonconvex Optimization for Power Control in Wireless CDMA Networks

In this paper, we propose an efficient power control algorithm for the downlink wireless CDMA systems. The goal of our paper is to achieve the optimum and fair resource utilization by maximizing a weighted sum utility with the power constraint. In fact, the objective function in the power optimization problem is always nonconcave, which makes the problem difficult to solve. We make progress in solving this type of optimization problem using PSO (particle swarm optimization). PSO is a new evolution algorithm based on the movement and intelligence of swarms looking for the most fertile feeding location, which can solve discontinuous, nonconvex and nonlinear problems efficiently. It’s proved that the proposed algorithm converges to the global optimal solutions in this paper. Numerical examples show that our algorithm can guarantee the fast convergence and fairness within a few iterations. It also demonstrates that our algorithm can efficiently solve the nonconvex optimization problems when we study the different utility functions in more realistic settings.

P-FAD: Real-time face detection scheme on embedded smart cameras

Face detection is known to be a resource-hungry process in computer vision. Most of existing detection algorithms, such as Viola-Jones detector, are too time-consuming to be implemented on resource-limited embedded smart cameras. Observing that the computation increases proportionally to its pixel manipulation, and inspired from the cascade structure which imposes more complex processing on the promising regions, hierarchical approach may be a feasible solution. The challenge issue in the design is how to construct a multi-layer architecture, in which the complex processing can be split from the pixel manipulation and guarantee detection accuracy simultaneously. To remedy this problem, we propose a novel Pyramid-like FAce Detection (P-FAD) scheme that results in a significant reduction of computation during face detection process. P-FAD has five-layer architecture, in which the operating units decrease dramatically from top to down while imposing complex computations in the last layer. We have implemented our scheme both on a notebook and our embedded smart camera platform. Our experimental results demonstrate that the P-FAD schemes resource-aware properties while still hold the acceptable detection accuracy.

Joint random access and power control game in ad hoc networks with noncooperative users

We consider a distributed joint random access and power control scheme for interference management in wireless ad hoc networks. To derive decentralized solutions that do not require any cooperation among the users, we formulate this problem as noncooperative joint random access and power control game, in which each user minimizes its average transmission cost with a given rate constraint. Using supermodular game theory, the existence and uniqueness of Nash equilibrium are established. Furthermore, we present an asynchronous distributed algorithm to compute the solution of the game based on myopic best response updates, which converges to Nash equilibrium globally. Finally, a link admission algorithm is carried out to guarantee the reliability of the active users. Performance evaluations via simulations show that the game-theoretical based cross-layer design achieves high performance in terms of energy consumption and network stability.

Hybrid Clustering and Routing Strategy with Low Overhead for Wireless Sensor Networks

Dynamic Clustering is an efficient topology management approach for sensor networks. Observing the fact that the clustering has tight relations with inter-cluster routing problem, we propose a hybrid clustering and routing protocol (HCR) that considers both the cluster head selection and routing discovery problems. Random backoff and gradient routing strategies are used to achieve our design goals with low overhead. Considering the limited transmission range bounded by the hardware, the clustered network generated by HCR is ensured to be connected. Simulation results demonstrate that our proposed strategies ensure the connectivity of the network and improve the energy efficiency for data transmission.

SAVQ: stabilized adaptive virtual queue management algorithm

We observe that the desired utilization parameter /spl gamma/ in AVQ algorithm has an influence on the dynamics of queue and link utilization. It is difficult to achieve a fast system response and high link utilization simultaneously using a constant value /spl gamma/. An adaptive setting method for /spl gamma/ is proposed according to the instantaneous queue size and the given reference queue value. This new algorithm, called stabilized AVQ (SAVQ), stabilizes the dynamics of queue maintaining a high link utilization.