Shibao Li

A Novel Modification of PSO Algorithm for SML Estimation of DOA

This paper addresses the issue of reducing the computational complexity of Stochastic Maximum Likelihood (SML) estimation of Direction-of-Arrival (DOA). The SML algorithm is well-known for its high accuracy of DOA estimation in sensor array signal processing. However, its computational complexity is very high because the estimation of SML criteria is a multi-dimensional non-linear optimization problem. As a result, it is hard to apply the SML algorithm to real systems. The Particle Swarm Optimization (PSO) algorithm is considered as a rather efficient method for multi-dimensional non-linear optimization problems in DOA estimation. However, the conventional PSO algorithm suffers two defects, namely, too many particles and too many iteration times. Therefore, the computational complexity of SML estimation using conventional PSO algorithm is still a little high. To overcome these two defects and to reduce computational complexity further, this paper proposes a novel modification of the conventional PSO algorithm for SML estimation and we call it Joint-PSO algorithm. The core idea of the modification lies in that it uses the solution of Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) and stochastic Cramer-Rao bound (CRB) to determine a novel initialization space. Since this initialization space is already close to the solution of SML, fewer particles and fewer iteration times are needed. As a result, the computational complexity can be greatly reduced. In simulation, we compare the proposed algorithm with the conventional PSO algorithm, the classic Altering Minimization (AM) algorithm and Genetic algorithm (GA). Simulation results show that our proposed algorithm is one of the most efficient solving algorithms and it shows great potential for the application of SML in real systems.

PSO algorithm for exact Stochastic ML estimation of DOA for incoherent signals

The performance of Stochastic ML (SML) algorithm of Direction-of-Arrival (DOA) is much more superior to many other algorithms in array signal processing. However, the estimation of SML is a non-linear multi-dimensional optimization problem. Therefore its computational complexity is very high. In this paper, firstly we show exact definition of SML estimation of DOA for incoherent signals and brief description of the conventional solving method, Alternating Minimization (AM) algorithm. Then, we propose to use the Particle Swarm Optimization (PSO) algorithm to solve the estimation of SML. Also in this paper, we propose a method to optimize the inertia factor of PSO. Simulation results show that the computational complexity of the proposed PSO algorithm for SML estimation is much lower than that of the conventional AM algorithm.

A low computational complexity SML estimation algorithm of DOA for wireless sensor networks

We address the problem of DOA estimation in positioning of nodes in wireless sensor networks. The Stochastic Maximum Likelihood (SML) algorithm is adopted in this paper. The SML algorithm is well-known for its high resolution of DOA estimation. However, its computational complexity is very high because multidimensional nonlinear optimization problem is usually involved. To reduce the computational complexity of SML estimation, we do the following work. (1) We point out the problems of conventional SML criterion and explain why and how these problems happen. (2) A local AM search method is proposed which could be used to find the local solution near/around the initial value. (3) We propose an algorithm which uses the local AM search method together with the estimation of DML or MUSIC as initial value to find the solution of SML. Simulation results are shown to demonstrate the effectiveness and efficiency of the proposed algorithms. In particular, the algorithm which uses the local AM method and estimation of MUSIC as initial value has much higher resolution and comparable computational complexity to MUSIC.

A Novel Joint Power and Feedback Bit Allocation Interference Alignment Scheme for Wireless Sensor Networks

It is necessary to improve the energy efficiency of batteries in wireless sensor networks (WSNs). The multiple-input multiple-output (MIMO) technique has become an important means to ameliorate WSNs, and interference management is the core of improving energy efficiency. A promising approach is interference alignment (IA), which effectively reduces the interference and improves the throughput of a system in the MIMO interference channels. However, the IA scheme requires perfect channel state information (CSI) at all transceivers in practice, which results in considerable feedback overhead. Thus, limited IA feedback has attracted much attention. In this paper, we analyze the throughput loss of the K-user MIMO interference channels when each transmitter delivers multiple streams in one slot, and derives the upper-bound of the system interference leakage and throughput loss. Then, to reduce the interference leakage and throughput loss for the MIMO interference alignment with limited feedback, a joint power and feedback bit allocation optimization scheme is proposed. The simulation results show that, compared with the conventional schemes, the presented optimal scheme achieves less residual interference and better performance in the system throughput.

A Cooperative Downloading Method for VANET Using Distributed Fountain Code

Cooperative downloading is one of the effective methods to improve the amount of downloaded data in vehicular ad hoc networking (VANET). However, the poor channel quality and short encounter time bring about a high packet loss rate, which decreases transmission efficiency and fails to satisfy the requirement of high quality of service (QoS) for some applications. Digital fountain code (DFC) can be utilized in the field of wireless communication to increase transmission efficiency. For cooperative forwarding, however, processing delay from frequent coding and decoding as well as single feedback mechanism using DFC cannot adapt to the environment of VANET. In this paper, a cooperative downloading method for VANET using concatenated DFC is proposed to solve the problems above. The source vehicle and cooperative vehicles encodes the raw data using hierarchical fountain code before they send to the client directly or indirectly. Although some packets may be lost, the client can recover the raw data, so long as it receives enough encoded packets. The method avoids data retransmission due to packet loss. Furthermore, the concatenated feedback mechanism in the method reduces the transmission delay effectively. Simulation results indicate the benefits of the proposed scheme in terms of increasing amount of downloaded data and data receiving rate.

Adaptive limited feedback for interference alignment in MIMO interference channels

It is very important that the radar sensor network has autonomous capabilities such as self-managing, etc. Quite often, MIMO interference channels are applied to radar sensor networks, and for self-managing purpose, interference management in MIMO interference channels is critical. Interference alignment (IA) has the potential to dramatically improve system throughput by effectively mitigating interference in multi-user networks at high signal-to-noise (SNR). However, the implementation of IA predominantly relays on perfect and global channel state information (CSI) at all transceivers. A large amount of CSI has to be fed back to all transmitters, resulting in a proliferation of feedback bits. Thus, IA with limited feedback has been introduced to reduce the sum feedback overhead. In this paper, by exploiting the advantage of heterogeneous path loss, we first investigate the throughput of IA with limited feedback in interference channels while each user transmits multi-streams simultaneously, then we get the upper bound of sum rate in terms of the transmit power and feedback bits. Moreover, we propose a dynamic feedback scheme via bit allocation to reduce the throughput loss due to limited feedback. Simulation results demonstrate that the dynamic feedback scheme achieves better performance in terms of sum rate.

A JPSO algorithm for SML estimation of DOA

The estimation of DOA is an important problem in sensor array signal processing and its industrial applications. Among all the solving techniques for DOA, the Stochastic Maximum Likelihood (SML) algorithm is well-known for its high accuracy of DOA estimation. However, its computational complexity is very high because a multi-dimensional nonlinear optimization problem is involved. The Particle Swarm Optimization (PSO) algorithm is considered as a rather efficient way for multi-dimensional non-linear optimization problems in DOA estimation. However Conventional PSO algorithm usually needs a large number of particles and the iteration number is also a litter high when all the particles converge. As a result, the computational complexity is still a litter high. This paper proposes a low complexity Joint-PSO (JPSO) algorithm for SML estimation. It uses the solution of Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) and stochastic Cramer-Rao bound (CRB) to determine a novel initialization space. In this case, smaller number of particles and less iteration number are required. Therefore, the computational complexity can be greatly reduced. Simulation results are also shown to demonstrate the validity of proposed JPSO algorithm.

An adaptive resource allocation algorithm based on space-frequency-time domain grouping for MIMO-OFDM system

In this paper, we propose an adaptive resource allocation algorithm based on space-frequency-time domain grouping (SFTDG) for multiple-input multiple-output orthogonal-frequency-division-multiplexing (MIMO-OFDM) system. The MIMO-OFDM channel can be decomposed into a group of parallel subchannels by singular value decomposition (SVD). Firstly, the subchannels of the first frame are grouped in space domain. Then, the space-frequency domain subchannel groups (SFD-SCGs) with the same size are obtained by a simplified frequency domain grouping algorithm. The optimal algorithm is used to allocate bits and power for these SFD-SCGs, and the allocation results are saved. Finally, the subchannels with the same serial number are divided into a time-domain subchannel group (SCG-TD). For non-first frames, the bit and power allocation on the sorted sub-channels are as the same as the saved allocation results on the subchannels with the same serial number. Simulation results show that the proposed algorithm has little performance loss compared with the optimal algorithm, but the complexity of the proposed algorithm is significantly reduced.

A Kalman gain modify algorithm based on BP neural network

In practical application of modified gain extended Kalman filter (MGEKF) algorithm, generally used erroneous measured values instead of the real values, so the modified results also contain errors. To solve this problem, this paper proposes an improved MGEKF based on back propagation neural network (BPNN), termed BPNN-MGEKF algorithm. At BPNN training time, it uses measured values as the input, and modified results by true values as the output. So the trained network includes correction of errors, even if input measurement values can be obtained more accurate results. This paper applies the BPNN-MGEKF to single moving station bearing-only position experiment, experimental results showed that: compare to other algorithms, BPNN-MGEKF has a faster convergence speed and higher accuracy.

An improved algorithm for symbol segmentation of mathematical formula images

Due to the complex species, the changing dimensions and 2D nesting structure of mathematical formula symbols, the accuracy of symbol segmentation still cannot meet the actual needs. Projection is only suitable for simple mathematical formula without subscript and hierarchy. This paper presents a kind of improved algorithm based on the connected domain for symbol segmentation of mathematical formula images. At first, the image of mathematical formula is pre-processed, including gray scale processing, adaptive median filter, image thinning and other operations. Then the segmentation of mathematical formula image is studied and a segmentation method based on improved connected domain presented. This method makes full use of the inherent connected features of the symbols to obtain connected domain, then combine them according to the structural characteristics of symbols, finally achieves a precise character segmentation effect on segmenting the mathematical formula symbols with complex 2D nesting structure.