Radio map updated method based on subscriber locations in indoor WLAN localization

With the rapid development of wireless local area network （WLAN） technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to overcome signal time-varying. A novel fingerprint positioning algorithm, known as the adaptive radio map with updated method based on hidden Markov model （HMM）, is proposed. It is shown that by using a collection of user traces that can be cheaply obtained, the proposed algorithm can take advantage of these data to update the labeled calibration data to further improve the position estimation accuracy. This algorithm is a combination of machine learning, information gain theory and fingerprinting. By collecting data and testing the algorithm in a realistic indoor WLAN environment, the experiment results indicate that, compared with the widely used K nearest neighbor algorithm, the proposed algorithm can improve the positioning accuracy while greatly reduce the calibration effort.

A Re-Configurable Dual-Mode Delay-Aware Scheduling and Resource Allocation for LTE System

Long Term Evolution( LTE) has been proposed as an advanced wireless radio access technology to provide higher peak data rates and better spectral utilization efficiency,but the classical scheduling and resource allocation algorithms cannot optimally enhance the system performance due to high computational complexity. In this paper,a re-configurable dual mode delay-aware( CDD) scheduling and resource allocation algorithm is proposed to achieve the joint consideration of scheduling pattern,scheduling priority and quantity of scheduled data. In this study,dual-mode scheduling mechanism is associated with three configurable parameters and the CDD algorithm is involved to guarantee queuing delay with low loss of resource utilization and fairness.The computational cost of the scheduling and resource allocation algorithm is significantly reduced by efficiently utilizing Qo S Class Identifier( QCI) and Channel Quality Indicator( CQI) defined by LTE standards. The simulation results based on different application scenarios also represent the computation cost and complexity of scheduling algorithm along with the improved system throughput.

Semi-Supervised Clustering Fingerprint Positioning Algorithm Based on Distance Constraints

With the rapid development of WLAN( Wireless Local Area Network) technology,an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online computation.In this paper,it proposes a novel fingerprint positioning algorithm known as semi-supervised affinity propagation clustering based on distance function constraints. We show that by employing affinity propagation techniques,it is able to use a fractional labeled data to adjust similarity matrix of signal space to cluster reference points with high accuracy. The semi-supervised APC uses a combination of machine learning,clustering analysis and fingerprinting algorithm. By collecting data and testing our algorithm in a realistic indoor WLAN environment,the experimental results indicate that the proposed algorithm can improve positioning accuracy while reduce the online localization computation,as compared with the widely used K nearest neighbor and maximum likelihood estimation algorithms.

Optimal antenna placement in distributed antenna systems

To minimize the outage probability of the cell （OPC） in downlink distributed antenna systems with selection transmission, a complex-encoding genetic algorithm （GA） is proposed to find the optimal locations of the antenna elements （AEs）. First, the outage probability at a fixed location in the cell is investigated. Next, an analytical expression of the OPC is derived, which is a function of the AE locations. Then the OPC is used as the objective function of the antenna placement optimization problem, and the complex- encoding GA is used to find the optimal AE locations in the cell. Numerical results show that the optimal AE locations are symmetric about the cell center, and the outage probability contours are also given with the optimal antenna placement. The algorithm has a good convergence and can also be used to determine the number of AEs which should be installed in order to satisfy the certain OPC value. Lastly, verification of the OPC＇s analytical expression is carried out by Monte Carlo simulations. The OPC with optimal AE locations is about 10% lower than the values with completely random located AEs.

Resource allocation algorithm for multi-user MIMO-OFDM downlink with correlated channels

To minimize transmitting power,an adaptive resource allocation algorithm is proposed for multi-user multiple input multiple output-orthogonal frequency division multiplexing(MIMO-OFDM)downlink with correlated channels,which,based on the user’s grouping according to their spatial correlations,combines the shared manner and the exclusive manner to allocate sub-carriers.Between different groups the shared manner with a null steering method based on group marginal users is applied,whereas within a group the exclusive manner is applied.The simulations show that the power efficiency and spectral efficiency are improved;the base station transmitting antenna number and the computational complexity is decreased.