18807692
Iterative Joint Channel Estimation and Multi-User Detection for Multiple-Antenna Aided OFDM Systems
Multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems have recently attracted substantial research interest. However, compared to single-input-single-output (SISO) systems, channel estimation in the MIMO scenario becomes more challenging, owing to the increased number of independent transmitter-receiver links to be estimated. In the context of the Bell layered space-time architecture (BLAST) or space division multiple access (SDMA) multi-user MIMO OFDM systems, none of the known channel estimation techniques allows the number of users to be higher than the number of receiver antennas, which is often referred to as a "rank-deficient" scenario, owing to the constraint imposed by the rank of the MIMO channel matrix. Against this background, in this paper we propose a new genetic algorithm (GA) assisted iterative joint channel estimation and multi-user detection (GA-JCEMUD) approach for multi-user MIMO SDMA-OFDM systems, which provides an effective solution to the multi-user MIMO channel estimation problem in the above-mentioned rank-deficient scenario. Furthermore, the GAs invoked in the data detection literature can only provide a hard-decision output for the forward error correction (FEC) or channel decoder, which inevitably limits the system's achievable performance. By contrast, our proposed GA is capable of providing "soft" outputs and hence it becomes capable of achieving an improved performance with the aid of FEC decoders. A range of simulation results are provided to demonstrate the superiority of the proposed scheme.
6000360
Iterative Joint Channel Estimation and Symbol Detection for Multi-User MIMO OFDM
Multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems have recently attracted substantial research interest. However, compared to single-input-single-output (SISO) systems, channel estimation in the MIMO scenario becomes more challenging, owing to the increased number of independent transmitter-receiver links to be estimated. In the context of the Bell layered space-time architecture (BLAST) or space division multiple access (SDMA) multi-user MIMO OFDM literature, no channel estimation technique allows the number of users to be higher than the number of receiver antennas, which is often referred to as an "overloaded" scenario. In this contribution we propose a new genetic algorithm (GA) assisted iterative joint channel estimation and multi-user detection approach for MIMO SDMA-OFDM systems, which exhibits a robust performance in the above-mentioned overloaded scenario. Furthermore, GA-aided multi-user detection (MUD) techniques found in the literature can only provide a hard-decision output, while the proposed GA is capable of providing "soft" outputs, hence achieving an improved performance with the aid of channel decoders. Finally, a range of simulation results are provided to demonstrate the superiority of the proposed scheme
24444493
Uplink Multi-User MIMO OFDM Enhancement Using Genetically Improved Turbo Receiver
Despite the assistance of well-recognized hot technologies such as Multiple-Input Multiple-Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM), cell-edge mobile users still suffer significant performance degradation in uplink, thus restricting the system coverage. In the context of the European B3G/4G WINNER system, we propose a hybrid solution which takes the advantages of both Genetic Algorithm (GA) and turbo processing, resulting in an enhanced MIMO OFDM receiver. Simulation results show that the proposed scheme is capable of achieving a good link performance, whilst improving the robustness of the multi-links and thus the cell coverage.
992437
Iterative MIMO Channel Estimation for Next Generation Wireless Systems
The combination of multiple-input multiple-output (MIMO) technology and orthogonal frequency division multiplexing (OFDM) has been considered as an attractive solution for the next generation wireless communication networks. In comparison with single-input single-output (SISO) systems, channel estimation in the MIMO scenario becomes more challenging, owing to the increased number of independent transmitter-receiver links to be estimated. In this paper, we introduce an enhanced iterative channel estimation method, which is capable of providing genetically optimized channel information for uplink multi-user MIMO OFDM receivers, approaching the optimal benchmark arrangement utilizing perfect channel knowledge. The effectiveness of the proposed scheme is demonstrated by a range of numerical results under realistic scenarios.
16557639
Soft-Information Assisted Near-Optimum Nonlinear Detection for BLAST-type Space Division Multiplexing OFDM Systems
In this contribution, a nonlinear hybrid detection scheme based on a novel soft-information assisted genetic algorithm (GA) is proposed for a turbo convolutional (TC) coded space division multiplexing (SDM) aided orthogonal frequency division multiplexing (OFDM) system. Our numerical results show that the performance of the currently known GA-assisted system can be improved by about 2 dB with the aid of the GA's population-based soft solution, approaching the optimum performance of the soft-information assisted maximum likelihood (ML) detection, while exhibiting a lower complexity, especially in high-throughput scenarios. Furthermore, the proposed scheme is capable of achieving a good performance even in the so-called overloaded systems, where the number of transmit antennas is higher than the number of receiver antennas
22084654
An LMI-Based Approach to the Global Stability of Bidirectional Associative Memory Neural Networks with Variable Delay
Based on the linear matrix inequality (LMI), new sufficient conditions on the global exponential stability and asymptotic stability of bidirectional associative memory neural networks with variable delay are presented, and exponential converging velocity index is estimated. Furthermore, the results in this paper are less conservative than the ones reported so far in the literature. One example is given to illustrate the feasibility of our main results.
30215856
Towards Workflow Oriented Virtual Enterprise Based on Policy Driven and Multi-agent Service Composition
This paper presents a model of workflow-oriented virtual enterprise based on policy driven and multi-agent service composition. The cooperation of the members in virtual enterprise will be implemented through the dynamic composition and execution of the services, which is workflow-oriented. Each service has its own policies for selection of the suitable services to achieve the cooperation and composition, which are described in WSPL and also registered into UDDI center as same as service description. In the policies, it defines the conditions for composition and cooperation, such as the accepted range of QoS with corresponding price charge, etc. The whole composition process will be executed through the interactions among multi agents, which include business analysis agent, service mapping agent and service allocation agent. Through such policy driven and multi-agent service composition, it can address win-win in the dynamically establishment of the workflow-oriented virtual enterprise.
22663495
Improved Results on Solving Quadratic Programming Problems with Delayed Neural Network
In this paper, in terms of a linear matrix inequality (LMI), using a delayed Lagrangian network to solve quadratic programming problems, sufficient conditions on delay-dependent and delay-independent are given to guarantee the globally exponential stability of the delayed neural network at the optimal solution. In addition, exponential convergence rate is estimated by the equation in the paper. Furthermore, the results in this paper improved the ones reported in the existing literatures and the proposed sufficient condition can be checked easily by solving LMI. Two simulation examples are provided to show the effectiveness of the approach and applicability of the proposed criteria.
37277758
Boundedness and global exponential stability for generalized Cohen-Grossberg neural networks with variable delay
Abstract A generalized Halanay inequality is established, and the boundedness of generalized Cohen–Grossberg neural networks is investigated. By applying the generalized Halanay inequality and Lyapunov functional method, new sufficient conditions are obtained ensuring the global exponential stability of the solution of the generalized Cohen–Grossberg neural networks with variable delay. Three examples are also given for illustration.
7128649
Load balancing in distributed simulations on the grid
Multiprocessors, networks of workstations and cluster computers have well served the computational needs of the simulation community by supporting the distributed simulations of larger models. However, the application of simulation to more complex problems calls for ever more computational power. The emergence of the grid provides an unrivalled opportunity to address this problem and thus facilitate advances in the modelling and the analysis of large-scale systems by harnessing the power of many computers. The aim of this paper is to present a framework for the execution of optimistic distributed discrete event simulations on the grid. A generic architecture is described and a load-balancing scheme is introduced in the context of a grid-aware TWarp simulation kernel.