Tiong Sieh Kiong

Null steering of adaptive beamforming using linear constraint minimum variance assisted by particle swarm optimization, dynamic mutated artificial immune system, and gravitational search algorithm.

Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program.

A routing protocol based on trusted and shortest path selection for mobile ad hoc network

A mobile ad-hoc network (MANET) is a peer-to-peer wireless network where nodes can communicate with each other without the use of infrastructure such as access points or base stations. Nodes can join and leave the network at anytime and are free to move randomly and organize themselves arbitrarily. Due to this nature of MANET, it is possible that there could be some malicious and selfish nodes that try compromise the routing protocol functionality and makes MANET vulnerable to security attacks. In this paper, we present a security-enhanced AODV (Ad hoc On-demand Distance Vector Routing) routing protocol called R-AODV (Reliant Ad hoc On-demand Distance Vector Routing). The implementation of this work is done by modified a trust mechanism known as direct and recommendations trust model and then incorporating it inside AODV which will allow AODV to not just find the shortest path, but instead to find a short path that can be trusted. This enhances security by ensuring that data does not go through malicious nodes that have been known to misbehave. The R-AODV protocol has been implemented and simulated on NS-2. Based on the simulation result, it can be shown that R-AODV does provide a more reliable data transfer compared to the normal AODV if there are malicious nodes in the MANET.

Minimum Variance Distortionless Response Beamformer with Enhanced Nulling Level Control via Dynamic Mutated Artificial Immune System

In smart antenna applications, the adaptive beamforming technique is used to cancel interfering signals (placing nulls) and produce or steer a strong beam toward the target signal according to the calculated weight vectors. Minimum variance distortionless response (MVDR) beamforming is capable of determining the weight vectors for beam steering; however, its nulling level on the interference sources remains unsatisfactory. Beamforming can be considered as an optimization problem, such that optimal weight vector should be obtained through computation. Hence, in this paper, a new dynamic mutated artificial immune system (DM-AIS) is proposed to enhance MVDR beamforming for controlling the null steering of interference and increase the signal to interference noise ratio (SINR) for wanted signals.

Energy efficiency of LTE macro base station

The growing energy consumption in wireless networks driven by dramatic increases in mobile users and network traffic, are putting mobile operators under immense challenges towards meeting the demands of both cost reduction and environment conservation. The network Energy Efficiency (EE) considers not only energy consumed by the base station (BS), but also the capacity and coverage of the network. In this paper we study the impact of modulation and coding schemes (MCS), bandwidth (BW) size and transmitted power on the energy efficiency of a LTE macro base station. Although it is very much expected that higher transmission power results in lower EE, the difference actually diminishes when cell size increases. At around 1200m it is found that the EE are almost equal for all transmission power considered. On the other hand, EE increases significantly as the BW increases. Similar effect on EE is observed when MCS changes from lower order to higher order scheme. In fact EE becomes more sensitive to MCS change at higher bandwidth.

Development of software planning tools for an intelligent traffic light wireless communication link using 5.8 GHz WLAN

This paper presents the development of software planning tools for wireless LAN link optimization as an intelligent traffic light system control to minimize waiting time of road users and reducing congestion at each junction, in Bandar Baru Bangi area. The software directory is based on combination of MatLab and MapInfo software, which gives the best grouping parameters to build up the software development. Among the requirements, the traffic light site selections must be line-of-sight (LOS) field strength prediction for either point to point or point to multi points. The results presented include two-dimensional plots for creating the RF LOS; design parameters representing the height and location for each traffic light depending on K-factor of the area

Dynamic characterized genetic algorithm for adaptive beam forming in WCDMA system

In this paper, a modified genetic algorithm named as dynamic characterized genetic algorithm (DCGA) is proposed. The new DCGA is then integrated into adaptive beam forming technique to reduce the power usage of adaptive antenna at WCDMA base station (node B) for serving instantaneous mobile needs. As contradict to conventional GA, DCGA is able to adapt itself in chromosome representation and number of crossover points in order to reduce unnecessary searching space and thus, increase convergence rate efficiently. Power usage at node B is used as fitness function to compare the performance of DCGA and GA. Simulation result has shown that DCGA converges faster and is superior in adaptive beam forming in the aspect of power usage at node B as compared to conventional GA.

Cognitive Map approach for mobility path optimization using multiple objectives genetic algorithm

This paper describes the evolutionary planning strategies for mobile robot to move along the streamlined collision-free paths in a known static environment. The Cognitive Map method is combined with genetic algorithm to derive the mobile robot optimal moving path towards its goal functions. In this study, multi-objectives genetic algorithm (MOGA) is utilized due to there are more than one objective need to be achieved while planning for the robot moving path. Goal-factor and obstacle-factor are the key parameters incorporated in the MOGA fitness functions. The simulation results showed that the hybrid Cognitive Map approach with MOGA is capable of navigating a robot situated among non-moving obstacles. The proposed hybrid method demonstrates good performance in planning and optimizing mobile robot moving path with stationary obstacles and goal.

Energy-aware load adaptive framework for LTE heterogeneous network

One of the main approaches for improving the network energy efficiency EE is through the introduction of load adaptive techniques, where the networks components/subsystems are switched off when the network is lightly loaded. Optimising such a dynamic operation in a heterogeneous network HetNet remains an active topic of research. In this paper, a traffic load-adaptive model that aims to evaluate the EE of base stations in Long Term Evolution LTE HetNet is presented. First, a model that simulates the load-adaptive power consumption behaviour of LTE HetNet is developed. In this regard, a load adaptation factor is introduced to assess the networks EE performance. The model also adapts and predicts the achievable data rate of each base station with respect to the traffic load. Our study shows that the fully load-adaptive LTE HetNet can significantly improve networks EE up to 10%, 40%, and 80% for high, medium, and low loads, respectively, as compared to the conventional non load-adaptive HetNet. In addition, we show that the full adaptive network operation can achieve significant EE gains under a realistic daily traffic profile up to 86%. The proposed evaluation model is essential to assess the network EE and can be used in future studies that focus on improving the adaptation level of the already installed network equipments. Copyright ©2014 John Wiley & Sons, Ltd.

Prediction of PVT properties in crude oil systems using support vector machines

Calculation of reserves in an oil reservoir and the determination of its performance and economics require good knowledge of its physical properties. Accurate determination of the pressure-volume-temperature (PVT) properties such as the bubble point pressure (Pb) and the oil formation volume factor (Bob) are important in the primary and subsequent development of an oil field. This paper proposes Support Vector Machines (SVMs) as a novel machine learning technique for predicting outputs in uncertain situations using the ɛ-Support Vector Regression (ɛ-SVR) method. The objective of this research is to investigate the capability of SVRs in modeling PVT properties of crude oil systems and solving existing Artificial Neural Network (ANN) drawbacks. Three datasets used for training and testing the SVR prediction model were collected from distinct published sources. The ɛ-SVR model incorporates four input features from the datasets: (1) solution gas-oil ratio, (2) reservoir temperature, (3) oil gravity and, (4) gas relative density. A comparative study is carried out to compare ɛ-SVR performance with ANNs, nonlinear regression, and different empirical correlation techniques. The results obtained reveal that the ɛ-SVR once successfully trained and optimized is more accurate, reliable, and outperforms the other existing approaches such as empirical correlation for estimating crude oil PVT properties.

WCDMA downlink capacity improvement by using smart antenna

Heterogeneous type of users in modern mobile communication expect network service provider providing high-speed data, multimedia and voice services. However, capacity has always been an issue to any generation of mobile communication technologies as it is always inversely proportionate to QoS (quality of service) in nature. Hence, in order to increase the system capacity without jeopardizing the QoS, smart antenna was proposed to increase the spectral efficiency of the wireless channel. Many research works have shown that the system capacity of modern mobile communication can be improved by employing smart antenna, A dynamic radio network simulator was developed in Visual C++reg to study and estimate the downlink capacity of a WCDMA system with smart antenna. Capacity system by using different smart antenna strategies was studied in this research. Simulation was done based on single micro cell environment with considering interference from the first tier. User mobility is taken into account to provide a combined evaluation of radio resource management (RRM). Capacity system expressed in downlink outage under various simulation scenarios was represented in this paper