Downlink CoMP Resource Allocation Based on Limited Backhaul Capacity

Coordinated Multi-Point(CoMP) transmission is put forward in the Long Term Evolution-Advanced(LTE-A) system to improve both average and cell-edge throughput. In this paper, downlink CoMP(DL-CoMP) resource allocation scheme based on limited backhaul capacity is designed to take a tradeoff between system throughput and fairness. Resource allocation of proportional fairness based on querying table is proposed. It updates RB allocation matrix when center cell has completed resource allocation and delivers the matrix to adjacent cells for their own RB allocation. Furthermore, Water-Filling algorithm based on adaptive water level(AWF) is used for power allocation to boost system fairness. In this paper, performance of downlink CoMP based on limited backhaul capacity and single-point transmission is contrasted, and results indicate that CoMP dramatically enhances system throughput and spectral efficiency. Moreover, AWF power allocation scheme obtains higher system fairness than conventional Water-Filling(WF) algorithm, although it gets slightly lower system throughput. Finally, this paper discussed that the system performance is partially affected by the percentage of CoMP resource.

Multi-Source Adaptive Selection and Fusion for Pedestrian Dead Reckoning

Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter(KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements.The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation.The proposed algorithm exhibits good robustness, adaptability,and value on applications.

A Fast Algorithm for Training Large Scale Support Vector Machines

The manuscript presents an augmented Lagrangian—fast projected gradient method (ALFPGM) with an improved scheme of working set selection, pWSS, a decomposition based algorithm for training support vector classification machines (SVM). The manuscript describes the ALFPGM algorithm, provides numerical results for training SVM on large data sets, and compares the training times of ALFPGM and Sequential Minimal Minimization algorithms (SMO) from Scikit-learn library. The numerical results demonstrate that ALFPGM with the improved working selection scheme is capable of training SVM with tens of thousands of training examples in a fraction of the training time of some widely adopted SVM tools.