Design and Analysis of OFDM System for Powerline Based Communication

Research on digital communication systems has been greatly developed in the past few years and offers a high quality of transmission in both wired and wireless communication environments. Coupled with advances in new modulation techniques, Orthogonal Frequency Division Multiplexing (OFDM) is a well-known digital multicarrier communication technique and one of the best methods of digital data transmission over a limited bandwidth [1]. In this paper, design and analysis of OFDM system for powerline based communication is proposed. In doing so, MATLAB and embedded Digital Signal Processing (DSP) systems are used to simulate the operation of virtual transmitter and receiver. The performance of the system design is then analysed by adding noise (additive white Gaussian noise, Powerline coloured background noise and Middleton Class A noise) in an attempt to corrupt the signal. In this paper results will show that performance is improved by using lower order modulation formats e.g. Binary Phase Shift Keying (BPSK), QPSK, etc. compared to the higher modulation schemes e.g. 64 Quadrature Amplitude Modulation (QAM); as they offer lower data rates but are more robust in the presence of noise. The performance study of OFDM scheme is also examined with and without presence of noise and application of forward error correction (FEC)

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Simulations of Implementation of Advanced Communication Technologies

Wireless communication systems have seen significant advancements with the introduction of 3G, 4G, and 5G mobile standards. Since the simulation of entire systems is complex and may not allow evaluation of the impact of individual techniques, this thesis presents techniques and results for simulating the performance of advanced signaling techniques used in 3G, 4G, and 5G systems, including Code division multiple access (CDMA), Multiple Input Multiple Output (MIMO) systems, and Low-Density Parity Check (LDPC) codes. One implementation issue that is explored is the use of quantized Analog to Digital Converter (ADC) outputs and their impact on system performance. Code division multiple access (CDMA) is a popular wireless technique, but its effectiveness is limited by factors such as multiple access interference (MAI) and the near far effect (NFE). The joint effect of sampling and quantization on the analog-digital converter (ADC) at the receiver\u27s front end has also been evaluated for different quantization bits. It has been demonstrated that 4 bits is the minimum ADC resolution sensitivity required for a reliable connection for a quantized signal with 3- and 6-dB power levels in noisy and interference-prone environments. The demand for high data rate, reliable transmission, low bit error rate, and maximum transmission with low power has increased in wireless systems. Multiple Input Multiple Output (MIMO) systems with multiple antennas at both the transmitter and receiver side can meet these requirements by exploiting diversity and multipath propagation. The focus of MIMO systems is on improving reliability and maximizing throughput. Performance analysis of single input single output (SISO), single input multiple output (SIMO), multiple input single output (MISO), and MIMO systems is conducted using Alamouti space time block code (STBC) and Maximum Ratio Combining (MRC) technique used for transmit and receive diversity for Rayleigh fading channel under AWGN environment for BPSK and QPSK modulation schemes. Spatial Multiplexing (SM) is used to enhance spectral efficiency without additional bandwidth and power requirements. Minimum mean square error (MMSE) method is used for signal detection at the receiver end due to its low complexity and better performance. The performance of MIMO SM technique is compared for different antenna configurations and modulation schemes, and the MMSE detector is employed at the receiving end. Advanced error correction techniques for channel coding are necessary to meet the demand for Mobile Internet in 5G wireless communications, particularly for the Internet of Things. Low Density Parity Check (LDPC) codes are used for error correction in 5G, offering high coding gain, high throughput, low latency, low power dissipation, low complexity, and rate compatibility. LDPC codes use base matrices of 5G New Radio (NR) for LDPC encoding, and a soft decision decoding algorithm is used for efficient Frame Error Rate (FER) performance. The performance of LDPC codes is assessed using a soft decision decoding layered message passing algorithm, with BPSK modulation and AWGN channel. Furthermore, the effects of quantization on LDPC codes are analyzed for both small and large numbers of quantization bits