Lingge Jiang

Distributed Wireless Sensor Network Localization Via Sequential Greedy Optimization Algorithm

Node localization is essential to most applications of wireless sensor networks (WSNs). In this paper, we consider both range-based node localization and range-free node localization with uncertainties in range measurements, radio range, and anchor positions. First, a greedy optimization algorithm, named sequential greedy optimization (SGO) algorithm, is presented, which is more suitable for distributed optimization in networks than the classical nonlinear Gauss-Seidel algorithm. Then a unified optimization framework is proposed for both range-based localization and range-free localization, and two convex localization formulations are obtained based on semidefinite programming (SDP) relaxation techniques. By applying the SGO algorithm to the edge-based SDP relaxation formulation, we propose a second-order cone programming (SOCP)-based distributed node localization algorithm. Two distributed refinement algorithms are also proposed by using the SGO algorithm to nonconvex localization formulations. The proposed localization algorithms all can be implemented partially asynchronously in networks. Finally, extensive simulations are conducted to demonstrate the efficiency and accuracy of the proposed distributed localization algorithms.

Chaotic characteristics of a one-dimensional iterative map with infinite collapses

A one-dimensional iterative chaotic map with infinite collapses within symmetrical region [-1, O)/spl cup/(O, +1] is proposed. The stability of fixed points and that around the singular point are analyzed. Higher Lyapunov exponents of proposed map show stronger chaotic characteristics than some iterative and continuous chaotic models usually used. There exist inverse bifurcation phenomena and special main periodic windows at certain positions shown in the bifurcation diagram, which can explain the generation mechanism of chaos. The chaotic model with good properties can be generated if choosing the parameter of the map properly. Stronger inner pseudorandom characteristics can also be observed through /spl chi//sup 2/ test on the supposition of even distribution. This chaotic model may have many advantages in practical use.

Inter-cell interference coordination based on softer frequency reuse in OFDMA cellular systems

In this paper, a novel interference coordination scheme based softer frequency reuse (SerFR) to mitigate inter-cell interference in Orthogonal Frequency Division Multiple Access (OFDMA) cellular systems is presented. In OFDMA systems, inter-cell interference is the most important interference source. Inter-cell interference coordination is considered to improve coverage and increase date rate at cell edge. Soft Frequency Reuse (SFR) interference coordination scheme is proposed in 3GPP Long Term Evolution (LTE). But the scheme cause frequency selective scheduling gain loss, and the peak rate for cell edge users is low because the cell edge users only can use a fraction of the entire frequency band. However, in SerFR scheme, cell edge users can use the entire frequency band, so there are more frequency selective scheduling garn and more peak date rate than SFR scheme.

A Novel Fuzzy Logic Vertical Handoff Algorithm with Aid of Differential Prediction and Pre-Decision Method

In this paper, we propose a novel vertical handoff decision algorithm for overlay wireless networks consisting of cellular and wireless local area networks (WLANs). The target network is selected using a fuzzy logic-based normalized quantitative decision algorithm which, in addition to usual parameters such as the current received signal strength (RSS) and the available bandwidth, also takes a prediction of the RSS into account, resulting in a more accurate handoff. The RSS prediction is obtained using a differential prediction algorithm that has good accuracy. Furthermore, to reduce system load, a pre-decision method is employed before actual handoff decision to filter out users with high mobility or low RSS from using the WLAN. Simulation results show that the proposed algorithm can reduce the call-dropping probability as well as unnecessary handoffs in heterogeneous network environments.

A Novel Spectrum-Sensing Technique in Cognitive Radio Based on Stochastic Resonance

We propose a novel spectrum-sensing method for cognitive radio (CR) based on stochastic resonance (SR). The spectral power of primary users (PUs) can be amplified, and the signal-to-noise ratio (SNR) of a received signal can be increased using SR. This ensures that the detection probability of the proposed approach is higher than that of the traditional energy detector. The detection probability of the proposed method is also theoretically derived under a constant false-alarm rate (CFAR). Performance analyses and computer simulation results show that the effectiveness of the proposed SR-based spectrum-sensing approach, particularly under low SNR circumstances, is better than that of the traditional energy-detection method. The computational complexity of the proposed approach can also be guaranteed to be comparable with the traditional spectrum-sensing methods.

Blind channel estimation in MIMO-OFDM systems

MIMO-OFDM is one of the promising scheme for achieving high data rate and large system capacity over wireless networks. In this paper, we investigate the blind channel estimation in MIMO-OFDM systems based on the noise subspace method. We exploit the cyclostationarity induced by OFDM with cyclic prefix. The sufficient identifiability conditions were established, and the noise subspace algorithm was developed for MIMO-OFDM channel estimation. The algorithm only requires knowledge of the upper bound on the channel length, it does not impose restrictions on channel zeros and it exhibits low sensitivity to stationary noise. Finally, we demonstrate validity of the algorithm presented in this paper by Monte Carlo simulations.

Performance Analysis of Fixed Gain MIMO Relay Systems in the Presence of Co-Channel Interference

The performance of a dual-hop fixed gain amplify-and-forward multiple-input multiple-output relay network is analyzed. Both the relay station and the mobile station are subjected to multiple co-channel interferers. An exact closed-form expression is derived for the outage probability which is validated by the Monte Carlo simulations. Furthermore, to render insights into the effect of multi-antenna and interference on the network performance, the asymptotic behavior of the outage probability is also investigated. Our results show that the diversity order is determined by the minimum of the antenna numbers at the base station and the mobile station and the interference degrade the performance by reducing the coding gain.

A Novel Price-Based Power Control Algorithm in Cognitive Radio Networks

This letter investigates price-based power control problem in the spectrum sharing cognitive radio networks (CRNs). The base station (BS) of primary users (PUs) can admit secondary users (SUs) to access if their interference power is under the interference power constraint (IPC). In order to access the spectrum, the SUs need to pay for their interference power. The BS first decides the price for each SUs to maximize its revenue. Then, each SU controls its transmit power to maximize its revenue based on non-cooperative game. We model the interaction between the BS and the SUs as a Stackelberg game. Using backward induction, the revenue function of the BS is expressed as a function of the transmit power of the SUs. After we depict the property of optimal transmit power of the SUs, we propose a novel price-based power control algorithm to maximize the revenue of the BS. Simulation results show the proposed algorithm improves the revenue of both the BS and SUs compared with the proportionate pricing algorithm.

Low Complexity MMSE Vector Precoding Using Lattice Reduction for MIMO Systems

In this paper, a lattice-reduction-aided (LRA) minimum mean square error (MMSE) vector precoding (VP) is proposed for multiple input multiple output (MIMO) systems. Three schemes are provided for the perturbation vector design by Babais approximation procedures based on lattice reduction method to reduce the complexity. Performance and complexity analysis are provided. Simulation results show that the proposed schemes significantly outperform the conventional MMSE Tomlinson-Harashima precoding (THP) and the zero forcing (ZF) VP. Compared with the MMSE VP via closest-point search, our LRA approach provides a simple alternative with little performance loss.

Finite Queuing Model Analysis for Energy and QoS Tradeoff in Contention-Based Wireless Sensor Networks

In contention-based sensor networks, nodes compete to access a shared channel for data transmission and collision is a common challenge. For power conservation, periodical active/sleep dynamics is adopted in the design of medium access protocol (MAC). At the same time, the quality of service (QoS) requirements (e.g. packet delay, packet loss rate and throughput) need to be satisfied. We develop a finite queuing model for the sensor node and derive the network performance in contention-based sensor networks. Furthermore, the impact of active/sleep dynamics and node buffer size on the tradeoff between power efficiency and QoS requirements is studied based on the model. Simulation results match well with our analysis results and validate the accuracy of our model and approach.