Halil Yigit

A novel localization technique for wireless sensor networks using adaptive antenna arrays

In this work, we introduce a novel location estimation technique which uses adaptive antenna arrays (AAA) at the central node in wireless sensor networks (WSN). This localization technique can be used at the setup phase in the routing protocol. This technique is based on scanning the desired region in azimuth and radial directions by changing parameters of downlink beam. This sweeping process can activate the nodes in the desired region which is specified by beamwidth and beamdirection of the transmit beam and also by minimum and maximum thresholds (Rmin and Rmax) for the received signal strength indicator (RSSI). Active nodes in the desired region transmit their IDs and RSSI levels via multi hop communication to the central node. Unlike GPS-based or beacon based localization techniques, the proposed technique does not require any modification in the sensor nodes. The accuracy of location estimation depends on beam direction, beamwidth and transmit power for downlink beams. The results show that by carefully adjusting these parameters, desired performance can be achieved.

A weighting approach for KNN classifier

In this paper, a weighting approach for k nearest neighbors (kNN) algorithm is proposed. The motivation of the proposed approach is to find the optimal weights via Artificial Bee Colony (ABC) algorithm. To test the validity of the hybrid algorithm called ABC based distance-weighted kNN, dW-ABC kNN, four UCI data sets (Iris, Haberman, Breast Cancer, and Zoo) are used. The results reveal that dW-ABC kNN algorithm improves the correct classification performance in Iris, Haberman, and Breast Cancer data set. The performance degradation occurs when it is applied on Zoo data set. It can be concluded that ABC algorithm is applicable to kNN algorithm.

A Smart Antenna Module Using OMNeT++ for Wireless Sensor Network Simulation

We introduce a smart antenna (SA) module to be used in OMNeT++ for wireless sensor networks (WSN). This module is a collection of tools in OMNeT++ for adding smart antenna capability to a central node in WSNs. It is based on sectoral sweeper (SS) scheme which was shown to provide efficient task management and easy localization scheme. The number of sensing nodes, energy consumption of nodes, and data traffic carried in the network are reduced with the SS. OMNeT++ is an open source object-oriented modular discrete event network simulator consisting of hierarchically nested modules. With the SA module, desired task region can be specified by a task beam with changing beam width and beam direction parameters in the OMNeT++ configuration file. In addition, each node in the network model that uses SA module has capability of Mobility framework which is intended to support wireless and mobile simulations within OMNeT++. The simulation results provide performance evaluation using the developed module in OMNeT++ for WSNs.

Design and Hardware Implementation of a Novel Smart Antenna Algorithm Using TI DSPs

This paper presents design and implementation of a novel smart antenna algorithm referred as space code correlator (SCC). The algorithm is based on storing predetermined array response vectors, and performing code correlation with the desired users code and then spatial correlation of despread signal with stored array response vectors. The design methodology aims to find a beamforming weight vector that provides satisfactory SINR performance with fixed execution time so that convergence problem is avoided. SCC algorithm is implemented on the different Texas Instruments (TI) TMS320C67x floating-point digital signal processors (DSP) taking into account multipath propagation conditions. The implementation steps and results pertaining weight vector estimation time and SINR performance are presented. Results show that with careful selection of DOA search window and multipath SNR level, the SCC algorithm provides implementation time less than 10 ms frame interval of cdma2000 system

ABC-based distance-weighted kNN algorithm

This paper suggests a weighting proposal for k nearest neighbours (kNN) classifier, which uses the Artificial Bee Colony (ABC) algorithm. The proposed approach is named as ABC-based distance-weighted kNN (dW-ABC kNN). The main idea of the hybrid algorithm is to assign random weights over sorted distances of kNN and to find the optimal weights, which achieve better classification performance. ABC algorithm carries out the weight determination operation. To evaluate the results, the data-sets from UCI machine learning repository are used. The classification performance of the dW-ABC kNN is compared with distance-weighted kNN (dW-kNN) and equally weighted kNN (eW-kNN) algorithms. The numerical simulations show that dW-ABC kNN always outperforms eW-kNN approach. It improves the correct classification performance of dW kNN in most data-sets, especially at small values of k. Experimental results confirm the merit of dW-ABC kNN approach.

Adaptation using neural network in frequency selective MIMO-OFDM systems

In this paper, we proposed a neural network (NN) framework as a machine learning technique for link adaptation based on adaptive modulation and coding in 802.11n MIMO-OFDM wireless system to predict the best modulation and coding scheme (MCS) index under packet error rate (PER) constraints. Our approach is compared with the k-nearest neighbour (k-NN) algorithm in frequency selective wireless channels. Simulation results validate the implementation of proposed neural network framework in frequency selective channels, and show that the neural network technique outperforms k-NN algorithm especially in terms of PER when low MCS index selection which provide higher communication reliability is exploited.

Capacity Improvement for TDD-MIMO Systems via AR Modeling Based Linear Prediction

The quality of channel state information (CSI) affects the performance of multiple input multiple output (MIMO) systems which employ multi-elements antenna arrays at both the transmitter and the receiver. In a time division duplex (TDD) systems, the CSI for downlink can be obtained from uplink channel using reciprocity principal. However, the performance of a MIMO system can be degraded due to channel impairments especially in fast fading scenarios when the CSI obtained from uplink is used for downlink transmission. In this paper, we study performance of autoregressive (AR) modeling based MIMO channel prediction under varying channel propagation conditions (mobile speed, multipath number and angle spread) and prediction filter order. Our simulation results show that using the predicted CSI for downlink provides capacity improvement compared to conventional method.

A new wireless sensor networks localization scheme with antenna arrays

In wireless sensor networks (WSN) location awareness is required to use the communication advantages of adaptive antenna arrays (AAA) and use energy efficiency location based routing protocols. In this work, we introduce a simple and practical location estimation technique which uses AAAs at the central node in WSNs. This localization technique can be used at the setup phase in the location based routing protocol. This technique is based on scanning the desired region in azimuth and radial directions by changing parameters of downlink beam. This sweeping process can activate the nodes in the desired region which is specified by antenna beam width and beam direction of the localization beam and also by minimum and maximum thresholds (Rmin and Rmax) for the received signal strength indicator (RSSI). Active nodes in the desired region transmit their IDs and RSSI levels via multi hop communication to the central node. Unlike GPS-based or beacon based localization techniques, the proposed technique does not require any modification in the sensor nodes. We demonstrate different scenarios for performance analysis of this localization technique. The results show that by carefully adjusting these parameters, desired performance can be achieved. The accuracy of location estimation depends on beam direction, beam width and transmitting power for downlink beams.

Analytical Derivation of 2 2 MIMO Channel Capacity in Terms of Multipath Angle Spread and Signal Strength

Abstract The capacity of multiple-input multiple-output (MIMO) wireless communication channels is affected by the multipath angle spread and relative multiple signal strength (RMSS) at both sides of the transmitter and the receiver. In this paper, we study analytically how these two factors emerge in the MIMO capacity equation when the channel state information (CSI) is unknown at the transmitter and perfectly known at the receiver. Mathematical expression for the channel capacity is carried out for 2×2 MIMO system and two propagation paths between the base station (BS) and the mobile terminal (MS) are considered. The proposed analytical model is verified through numerical results, which show that channel capacity increases with increasing angle spread. Also, as the relative strength of multipaths becomes larger, the better channel capacity is obtained.

Spectrum sensing performance with adaptive threshold in MIMO cognitive radio

Cognitive radio (CR) is the key technology that provides to use frequency spectrum effectively. Spectrum sensing, a critical function of CR, detects spectrum holes. Energy based detection (ED) is the most preferred spectrum sensing technique by researchers because it only requires the energy of received signals. The sensing performance of the ED is mainly based on the wireless communication channel. The channel environment is continuously varying in time. It is therefore difficult to detect primary users signal with a fixed threshold when the energy of received signal is affected by disturbances such as fading, shadowing, noise and interference. In this study, ET sensing performance based on adaptive threshold value in cognitive radio with MIMO technology has been investigated in terms of the number of antennas at the receiver and the transmitter side, the number of multipath, the speed of mobile receiver and modulation type parameters. The simulation results show that the adaptive threshold value provides better detection performance than the fixed threshold value especially at low SNRs.