Xiangyuan Bu

Constrained Total Least-Squares Location Algorithm Using Time-Difference-of-Arrival Measurements

Determining the location of a source from its emissions has gained considerable interest over the past few years. In this paper, by utilizing the time difference of arrival (TDOA) of a signal received at spatially separated sensors, a novel algorithm for source location is proposed. The algorithm is based on the constrained total least-squares (CTLS) technique, and an iterative technique based on Newtons method is utilized to give a numerical solution. By using a perturbation analysis, the bias and covariance of the proposed CTLS algorithm are also derived. Simulation results show that the proposed CTLS algorithm gives sufficient accuracy with lower computational cost, and more importantly, it is more robust to large measurement noise than the compared algorithms.

Energy-Efficient Resource Allocation for Device-to-Device Communications Overlaying LTE Networks

In this paper, we investigate the energy-efficient resource allocation problem for the device-to- device (D2D) communications overlaying LTE networks, where the D2D user equipment (UE) shares the spectrum with the cellular UE in an orthogonal way such that the interference between them is completely eliminated. We consider both the non- orthogonal and orthogonal resource allocation strategies for D2D communications, where the resources allocated to different D2D pairs are non-orthogonal and orthogonal respectively. In the non-orthogonal strategy, the interference exists among different D2D pairs, and the resource allocation only concerns the transmit power control for each D2D pair; whereas in the orthogonal strategy, there is no interference, and the resource allocation concerns both the resource block (RB) allocation and the transmit power control. In the two strategies, the related resource allocation problems are firstly formulated as a fractional programming (FP) problem and a mixed-integer nonlinear fractional programming (MINLFP) problem respectively, both of which are then transformed into equivalent optimization problems in parametric subtractive form by exploiting the property of FP. As the transformed equivalent problems are non-concave, we develop the sub-optimal energy-efficient resource allocation schemes by solving them based on Dinkelbach and Powell-Hestenes-Rockafellar augmented Lagrangian methods. Simulation results demonstrate the effectiveness of the proposed schemes and show that the non-orthogonal strategy outperforms the orthogonal one in terms of the energy efficiency.

A TOA-Based Location Algorithm for NLOS Environments Using Quadratic Programming

Location of a source is of considerable interest in wireless sensor networks. A novel algorithm for source location by utilizing the time-of-arrival (TOA) measurements of a signal received at spatially separated sensors under non-line-of-sight (NLOS) environments is proposed. The algorithm is based on quadratic programming, which is a special type of mathematical optimization problem. Comparisons of performance with other algorithms are made, and Monte Carlo simulations are performed. Simulation results show that the proposed algorithm gives better results.

Adaptive bayesian beamforming with sidelobe constraint

The conventional Bayesian beamformer suffers substantial performance degradation, when the true direction-of-arrival is deterministic and is not included in the priori. In this letter, we propose a method with sidelobe constraint to improve the robustness of the Bayesian beamforming method. Support vector machine is used to obtain the weights. Numerical results show that the proposed beamformer can improve the Bayesian beamforming performance, and can output a relatively higher signal-to-noise-plus-interference ratio even when the desired direction-of-arrival is not included in the Bayesian priori region.

Radar waveform design and multi-target detection in vehicular applications

In traffic environment, conventional FMCW radar with triangular transmit waveform may bring out many false targets in multi-target situations and result in a high false alarm rate. An improved FMCW waveform and multi-target detection algorithm for vehicular applications is presented. The designed waveform in each small cycle is composed of two-segment: LFM section and constant frequency section. They have the same duration, yet in two adjacent small cycles the two LFM slopes are opposite sign and different size. Then the two adjacent LFM bandwidths are unequal. Within a determinate frequency range, the constant frequencies are modulated by a unique PN code sequence for different automotive radar in a big period. Corresponding to the improved waveform, which combines the advantages of both FSK and FMCW formats, a judgment algorithm is used in the continuous small cycle to further eliminate the false targets. The combination of unambiguous ranges and relative velocities can confirm and cancel most false targets in two adjacent small cycles.

An Improved Eigenvalue-Based Algorithm for Cooperative Spectrum Sensing

Spectrum sensing is one of the most challenging issues in cognitive spectrum sensing. In this paper, an improved eigenvalue based algorithm is proposed using Random Matrix Theory. This improved method overcomes the effect of noise uncertainty exiting in other method, for the good nature of Random Matrix Theory. In addition, it has better detecting probability in both AWGN and fading channels. Also in this paper, the thresholds of the algorithm are deduced and calculated. Performance of the proposed algorithm is simulated and analyzed. Compared with MME and LSC method, the results show that it has better performance in detecting probability and the sensing time.

Spectrum Sensing for OFDM Systems Based on Cyclostationary Statistical Test

As the first step of cognitive radio, spectrum sensing is one of the most important technologies which find the available spectrum for the cognitive users. At present, the promising OFDM techniques are widely used in many wireless communication systems. As a result, sensing the existence of OFDM signals becomes vital for CR system. For this, a cyclostationary statistical test based on spectrum sensing algorithm (CST method) is proposed in this paper. The algorithm takes advantage of the asymptotic properties of the cyclic-covariance to infer the asymptotic distribution. The thresholds of the algorithm are deduced and a simplified method is also derived. Performance of the proposed algorithm is simulated and analyzed. Simulations are carried out in AWGN channel to verify the validation of the proposed method. It is shown that our method can satisfy the detection and requirement with a low false alarm probability, and the simplified method also has good performances.

Nonbinary LDPC Coded FH Systems over Partial-Band Jamming Channels

The frequency hopping (FH) communication systems are efficient systems for anti-jamming. When on the general partial-band jamming condition, the channel can be considered as burst jamming channel. In traditional FH systems, the Reed-Solomon (RS) codes are commonly used to correct the burst errors. A better form of coding may be nonbinary low-density parity-check (LDPC) codes, which have been shown to outperform binary LDPC codes and RS codes on additive white Gaussian noise (AWGN) channels and magnetic recording channels. In this paper, we report on our investigation of nonbinary LDPC coded FH systems on partial-band jamming channels. We show that nonbinary LDPC coded FH systems outperform RS coded FH systems in both low speed FH systems and high FH systems.

Energy-Efficient Power Control for Device-to-Device Communications with Max-Min Fairness

In this paper, we investigate the energy-efficient power control for device-to-device (D2D) communications underlaying cellular networks with max-min fairness, where uplink resource blocks allocated to one cellular user equipment are reused by multiple D2D pairs to improve the frequency reuse factor, and the minimum individual energy efficiency (EE) is maximized. This is a generalized fractional programming (GFP) problem, and is hard to tackle due to its non-concave nature, which means the complexity of global optimal solution is unaffordable. In order to give sub-optimal solution with reasonable complexity, we first transform the GFP problem into equivalent optimization problem in a parametric subtractive form, and then add constraints on the co-channel interferences to convert the non-concave GFP problem into concave one. The sub-optimal solution, which can be obtained through solving the deduced concave problem based on sophisticated convex optimization methods, gives a tight lower bound on the optimal EE. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

A Novel Channel Estimation Scheme for Wideband OFDM Aeronautical Communication

The aeronautical wireless channel is a time-frequency double-selective channel, which will destroy the orthogonality of the OFDM sub-carriers remarkably, inducing a severe inter-carrier interference (ICI). The aeronautic channel is also a sparse channel. So we could utilize the property of sparseness to design an appropriate channel estimation method. In this paper, a novel channel estimation scheme is proposed for OFDM systems applied in wideband aeronautical communication. The scheme mainly consists of three parts, including the blocks of phase lock loop, channel parameters estimator, and iterative cancellation. We investigated the scheme in en-route scenarios. Simulation results were carried out to evaluate the BER performances of OFDM systems with the proposed estimation scheme.