Fakhrul Alam

Adaptive energy detection for cognitive radio: An experimental study

A cognitive radio (CR) is able to sense spectral environment over a wide range of frequencies, and provide opportunistic access to frequency bands temporarily unoccupied by an incumbent. Accurate channel sensing is the first important task for a CR, and energy detector is often used for this purpose. While a normal energy detector works well with well chosen window size based on prior knowledge about possible primary users, it often fails with signals that are narrow compared to the detector window, or if only a fraction of the signal is inside the detector window. We propose an adaptive energy detector that can adjust its detection window, and evaluate such detectors performance using experimental results obtained through a real time implementation.

Profiling Delay and Throughput Characteristics of Interactive Multimedia Traffic over WLANs Using OPNET

Performance statistics of WLANs in published literatures are limited only to homogeneous traffic. However, mixed traffic scenario is prevailing in WLANs. This paper is an effort to characterize WLANs behaviour in transmitting mixed traffic. OPNET was used to simulate a typical WLANs scenario. It was found that tuning the CFP and superframe size dynamically will achieve better end-to-end delay and throughput profiles.

Performance comparison between pilot symbol assisted and blind beamformer-RAKE receivers at the reverse link of third generation CDMA system

A beamformer-RAKE receiver allows processing of the signal in both the spatial and temporal domain by combining an adaptive antenna array with a RAKE. This can significantly improve the system performance along the reverse link of a wideband code division multiple access (WCDMA) system by providing multiple access interference (MAI) suppression and multipath diversity. We compare the performance of two different beamformer-RAKE receivers that employ the minimum mean square error (MUSE) and the maximum signal to interference and noise ratio (MSINR) criteria to form the beams in the spatial domain. The pilot symbol assisted (PSA) technique and the blind code gated algorithm (CGA) were selected to exploit the aforementioned criteria.

Indoor localization of mobile robot with visible light communication

This paper focuses on the implementation of visible light communication (VLC) with an autonomous mobile robot to provide indoor localization. Recent research on VLC, mostly in the form of simulations, offers the opportunity to conduct real life experiments and test the theory. Accurate indoor positioning is achieved by employing multi-frequency method with received signal strength (RSS) to calculate the distance of the robot from each LED installed above the robot in a plane parallel to the plane of the robot base. Multi-frequency method consists of each LED transmitting its location ID at a different frequency. It is demonstrated that the receiver is able to separate each location ID from simultaneous data transmission with a bandpass filter.

Matsuoka Neuronal Oscillator for Traffic Signal Control Using Agent-based Simulation

Matsuoka neuronal oscillator is proposed to control the traffic signals of an isolated four-phase signalized intersection. The oscillator is a model of central pattern generator (CPG) and has seen various applications in humanoid robots. Matsuoka oscillator was chosen for the traffic signal control because of its stable and predictable rhythmic outputs that exploit autonomously the dynamics of the road system. In this paper, the dynamics of Matsuoka oscillator was described in a set of first-order differential equations and simluated in an agent-based modelling environment. This novel signal control algorithm was validated in a Application Programming Interface (API) function by AIMSUN (Advanced Interactive Microscopic Simulator for Urban and Non-Urban Networks). The results were compared to the performance of the existing traffic system and have shown the potential capability of the proposed algorithm in reductions of vehicle delay time and queues.

Effects of Varying Superframe Duration on Jitter in Interactive Multimedia Traffic Transmission over WLANs

Variation of superframe duration could be a promising way to improve the performance of a wireless local area network (WLAN) carrying interactive multimedia traffic. Most of the studies, however, looked into DCF-based performance improvements of WLAN taking delay and throughput as the performance metric for homogenous traffic. This study looks into the jitter profiles of PCF-based WLAN in transmitting interactive multimedia traffic in a heterogeneous setup. In this mixed traffic environment, the effects on jitter, following variation of superframe and contention free period durations are investigated by simulating a typical mixed traffic WLANs scenario. Results of simulations show that selecting and tuning the CFP and superframe sizes will attain better jitter profiles.

Comparison of low complexity algorithms for MSNR beamforming

We investigate eigen-beamforming resulting from the maximum signal to noise ratio (MSNR) criterion. MSNR beamforming criterion leads to a simple eigenvalue problem (SE) so that the weight vector is the principal eigenvector of the covariance matrix of the desired signal. We employ a beamformer-RAKE receiver at the uplink of a wideband code division multiple access (WCDMA) system. The receiver exploits the MSNR based beamforming for spatial processing. There are different algorithms like the power method, the Lagrange multiplier method and the conjugate gradient method to solve the SE. We compare these three algorithms in terms of computational complexity and bit error rate (BER) performance in multipath propagation environment.

Gaussian Process Model Predictive Control of an Unmanned Quadrotor

The Model Predictive Control (MPC) trajectory tracking problem of an unmanned quadrotor with input and output constraints is addressed. In this article, the dynamic models of the quadrotor are obtained purely from operational data in the form of probabilistic Gaussian Process (GP) models. This is different from conventional models obtained through Newtonian analysis. A hierarchical control scheme is used to handle the trajectory tracking problem with the translational subsystem in the outer loop and the rotational subsystem in the inner loop. Constrained GP based MPC are formulated separately for both subsystems. The resulting MPC problems are typically nonlinear and non-convex. We derived a GP based local dynamical model that allows these optimization problems to be relaxed to convex ones which can be efficiently solved with a simple active-set algorithm. The performance of the proposed approach is compared with an existing unconstrained Nonlinear Model Predictive Control (NMPC) algorithm and an existing constrained nonlinear GP based MPC algorithm. In the first comparison, simulation results show that the two approaches exhibit similar trajectory tracking performance. However, our approach has the advantage of incorporating constraints on the control inputs. In the second comparison, simulation results demonstrate that our approach only requires 20% of the computational time for the existing nonlinear GP based MPC.

Gaussian Process based Model Predictive Control for Linear Time Varying systems

Two main issues associated with Model Predictive Control (MPC) are learning the unknown dynamics of the system and handling model uncertainties. In this paper, unknown Linear Time-Varying (LTV) system with external noise is represented by using probabilistic Gaussian Process (GP) models. In this way, we can explicitly evaluate model uncertainties as variances. As a result, it is possible to directly take obtained variances into account when planing the policy. In addition, through using analytical gradients that are available during the GP modelling process, the optimization problem in GP based MPC can be solved faster. The performance of proposed approach is demonstrated by simulations on trajectory tracking problem of a LTV system.

Particle swarm optimization for convolved Gaussian process models

Convolved Gaussian process (CGP) is a type Gaussian process modelling technique applicable for multiple-input multiple-output systems. It employs convolution processes to construct a covariance function that models the correlation between outputs. Modelling using CGP involves learning the hyperparameters of the latent function and the smoothing kernel. Conventionally, learning involves the maximization of the log likelihood function of the training samples using conjugate gradient (CG) or particle swarm optimization (PSO) methods. We propose to use PSO to minimize the model error. In this way, a clearer direct indication of the quality of the current solution during the optimization process can be obtained. Simulation results on a dynamical system show that our method is able to learn appropriate CGP models and achieve better predictive performance compared with CG when the searching space is not well defined.