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.

Both Multiple Input and Multiple Output (MIMO) and Smart Antenna (SA) have been widely accepted as promising schemes to improve the spectrum efficiency and coverage of mobile communication systems. This paper addresses the issue of Direction of Arrival (DOA) estimation in Virtual MIMO (VMIMO) systems which adopt SA simultaneously. First of all, we propose a VMIMO scheme for DOA estimation in which a set of User Equipments (UEs) are grouped together to simultaneously communicate with the Base Station (BS) on a given Resource Block (RB). In this scheme, the BS has multiple antennas and can estimate DOA of each UE in the group simultaneously. However, in practical environment because of reflection and refraction of signals, the received signals may be coherent. It is desirable that the DOA estimation in VMIMO could provide high resolution when the number of independent signals is unknown, which can not be achieved by existing algorithms. In order to solve this problem, we propose an Automatic Weighted Subspace Fitting (AWSF) algorithm that can detect the number of independent signals automatically and show accurate DOA estimation. Then with consideration of computational cost and sampling cost while using the AWSF algorithm, we propose a utility factor to determine the optimal number of UEs in VMIMO scheme, i.e., the order of VMIMO. Finally, the performance of the proposed AWSF algorithm and the efficiency of the utility factor criterion are demonstrated by simulations.

This paper proposes a low-complexity estimation algorithm for weighted subspace fitting (WSF) based on the Genetic Algorithm (GA) in the problem of narrow-band direction-of-arrival (DOA) finding. Among various solving techniques for DOA, WSF is one of the highest estimation accuracy algorithms. However, its criteria is a multimodal nonlinear multivariate optimization problem. As a result, the computational complexity of WSF is very high, which prevents its application to real systems. The Genetic Algorithm (GA) is considered as an effective algorithm for finding the global solution of WSF. However, conventional GA usually needs a big population size to cover the whole searching space and a large number of generations for convergence, which means that the computational complexity is still high. To reduce the computational complexity of WSF, this paper proposes an improved Genetic algorithm. Firstly a hypothesis technique is used for a rough DOA estimation for WSF. Then, a dynamic initialization space is formed around this value with an empirical function. Within this space, a smaller population size and smaller amount of generations are required. Consequently, the computational complexity is reduced. Simulation results show the efficiency of the proposed algorithm in comparison to many existing algorithms.

Different loads in the network require distinct QoS standard, while present routing strategies for complex networks ignored this fact. To solve this problem, we designed a routing strategy RS-MP with multiple priorities by which packets are classified into privileged-packets and common-packets. In RS-MP, privileged-packets route by the Shortest Path Algorithm, and do not need to queue up. Common-packets' routes are determined by a new factor BJ _max of the network. The BJ _max stands for the largest betweenness centrality. By minimizing BJ _max, the throughout capacity of the network can be maximized. The simulation results show that RS-MP can guarantee privileged-packets with the shortest path length and smallest delay, and maximized throughout capacity for common packets in the no-congestion state.