Li Lemin

AR modeling-based features extraction of multiple signals for modulation recognition

Modulation recognition of multiple signals is a new topic in the communication signal processing area. A novel feature-based modulation recognition method for multiple signals is proposed. The main idea of this method is that AR modeling is employed to extract instantaneous frequency and bandwidth features of each signal to be recognized from the mixed waveform intercepted by a receiver, and then a MLP neural network classifier is followed to identify the modulation types. To assess the efficiency of these features, computer simulations are also performed.

Traffic-aware VDC embedding in data center: A case study of fattree

Virtualization is a common technology for resource sharing in data center. To make efficient use of data center resources, the key challenge is to map customer demands (modeled as virtual data center, VDC) to the physical data center effectively. In this paper, we focus on this problem. Distinct with previous works, our study of VDC embedding problem is under the assumption that switch resource is the bottleneck of data center networks (DCNs). To this end, we not only propose relative cost to evaluate embedding strategy, decouple embedding problem into VM placement with marginal resource assignment and virtual link mapping with decided source-destination based on the property of fat-tree, but also design the traffic aware embedding algorithm (TAE) and first fit virtual link mapping (FFLM) to map virtual data center requests to a physical data center. Simulation results show that TAE+FFLM could increase acceptance rate and reduce network cost (about 49% in the case) at the same time. The traffic aware embedding algorithm reduces the load of core-link traffic and brings the optimization opportunity for data center network energy conservation.

The fourth-order cumulants based spectral estimation method and its application to direction-finding

Traditional approaches of spatial spectral estimation are usually based on the second-order statistics. The higher-order cumulants and the poly-spectrum contain more information and are capable of reducing the Gaussian noise. In this paper, we present a new spectrum estimation method for direction-finding, the FOMUSIC algorithm, which is based on the eigen-structure analysis of the fourth-order cumulants. The derivation of the algorithm is given in detail and its performance is illustrated by both the computer simulations and the experiments of a direction-finding system. The obtained results demonstrate that the fourth-order cumulants based method outperforms the traditional methods, especially when the noise is an unknown colored one.

Research note: Performance analysis of the constant-bit-rate services in an ATM DQDB MAN

An ATM DQDB MAN can provide integrated services with a pure ATM method which transfers any kind of services in a unified manner. This paper analyzes the performance of the CBR (constant bit rate) services in the ATM DQDB MAN. A four-dimensional Markov chain is used to describe the steady state of a tagged user and the survivor function of the queue length distribution is calculated to design buffer capacity of the tagged user. The analytical method and the results are applied to the prototype we have accomplished. The sojourn time of the CBR services is investigated. The results illustrate that the delay performance of the CBR services is satisfactory in the ATM DQDB MAN if the network is not overloaded seriously. Various priority mechanisms to support integrated services are also compared briefly in the paper.

A NEW DIGITAL MODULATION RECOGNITION METHOD USING FEATURES EXTRACTED FROM GAR MODEL PARAMETERS

Based on the features extracted from generalized autoregressive (GAR) model parameters of the received waveform, and the use of multilayer perceptron(MLP) neural network classifier, a new digital modulation recognition method is proposed in this paper. Because of the better noise suppression ability of the GAR model and the powerful pattern classification capacity of the MLP neural network classifier, the new method can significantly improve the recognition performance in lower SNR with better robustness. To assess the performance of the new method, computer simulations are also performed.

The influence of burstiness and correlation of traffic on an ATM multiplexer

This paper presents a traffic model for an ATM multiplexer providing video, voice, image, and data services. The traffic model classifies the input traffic into two types: real-time and non-real-time. The input process for real-time traffic is a Markov modulated Bernoulli process (MMBP), while that for non-real-time traffic is a Bernoulli process with batches. The z-transform of the cell interarrival time A/sub MMBP/(z) is obtained. The average traffic load /spl rho//sub MMBP/ and the squared coefficient of variation of the time between success cell C/sup 2//sub MMBP/ are then obtained from the respective derivatives of A/sub MMBP/(z). The autocorrelation coefficient of the cell interarrival time with lag 1 /spl psi/, can be also obtained. This paper presents an iterative algorithm that can be used to calculate the MMBP probability distribution of the cell interarrival time. So far few results about discrete-time finite buffer queues with correlated inputs are known. This paper analyzes the performance of an ATM multiplexer considering the correlation and burst in cell arrivals. For the MMBP-stream, the waiting time distribution and the cell loss probability for MMBP+M/sup [X]//D/1/K finite buffer system are all exactly calculated with the use of a recursive algorithm. Calculations show the influence of burstiness and correlation on both the loss probability and waiting time. The accuracy of the analytical results has been largely validated by means of a simulation approach.

Source separation based modulation recognition of cochannel signals

In this paper, a new modulation recognition method of multiple cochannel signals is proposed. The basic idea of this method is that the null-steering beamforming technique is employed to separate each modulated signal from a ULA output data, and then a modulation recognizer, using an instantaneous frequency feature extracted by AR modeling and a RBF neural network classifier, is followed to identify the modulation type individually. Performance of the new method is assessed via computer simulations.

A general class of multidimensional lag windows for higher-order spectral estimations

A new general class of multidimensional lag windows, which is defined as a product function of standard one dimensional windows, is proposed. When the dimension is one, two and three respectively, those multidimensional lag windows can satisfy all constraints of multidimensional lag windows, including all symmetry properties of higher-order statistics, and can be used for conventional higher-order spectral estimations. The most important aspect of this research is to provide a class of suitable three dimensional lag windows for fourth-order spectral estimations.

Analysis of the leaky bucket algorithm for priority services

The leaky bucket algorithm with loss priorities is studied in this paper. The analytical expression of the relation among leaky bucket performance, statistical parameters of input traffic and leaky bucket parameters for various priority services is obtained, and the effect of adjustment factor on leaky bucket performance of higher-priority service and lower-priority service is studied with two priority classes.

Performance analysis of leaky bucket algorithm with bursty traffic input in ATM networks

A bursty traffic model is introduced in this paper to describe the statistical characteristics of packet video. The performance of leady bucket algorithm with bursty traffic input is analyzed. The influences of various parameters on QOS (Quality of Service) are investigated. The analysis shows that although the loss probability decreases through expanding the buffer capacity, the delay and delay jitter increase, whose effect on QOS will not be negligible.