A Hybrid Statistical and Prioritised Unequal Error Protection Scheme for IEEE 802.11n LDPC Codes

The combination of powerful error correcting codes such as (LDPC) codes and Quadrature Amplitude Modulation (QAM) has been widely deployed in wireless communication standards such as the IEEE 802.11n and DVB-T2. Recently, several Unequal Error Protection schemes which exploit non-uniform degree distribution of bit nodes in irregular LDPC codes have been proposed. In parallel, schemes that exploit the inherent UEP characteristics of the QAM constellation have also been developed. In this paper, a hybrid UEP scheme is proposed for LDPC codes with QAM. The scheme uses statistical distribution of source symbols to map the systematic bits of the LDPC encoded symbols to the QAM constellation. Essentially, systematic symbols having highest probabilities of occurrence are mapped onto the low power region of the QAM constellation and those with a low probability of occurrence are mapped onto the high power region. The decrease in overall transmission power allows for an increased spacing between the QAM constellation points. Additionally, the scheme uses the distribution of the bit node degree of the LDPC code-word to map the parity bits having the highest degree onto prioritised QAM constellation points. Simulations with the IEEE 802.11n LDPC codes revealed that the proposed scheme can provide gains of up to 0.91 dB in Eb/No compared with other UEP schemes for a range of Bit Error Rate (BER) values

Performance Of IEEE 802.11 OFDM With Multiple Frequency Transforms And Pulse Shaping Schemes

Orthogonal Frequency Division Multiplexing (OFDM) is employed in various communication systems such as the IEEE 802.11 wireless standards, in which both frequency transform, Fast Fourier Transform (FFT) and pulse shaping filter, Square Root Raised Cosine (SRRC) are used. The main contribution of this paper is the analysis of the performance of different combinations of frequency transforms and pulse shaping schemes for the 802.11n standard. The frequency transforms which have been used are: Fast Fourier Transforms (FFT), Discrete Wavelet Transforms (DWT) and Discrete Hartley Transform (DHT). The pulse shaping filters are the Raised Cosine (RC), SRRC and Flipped Exponential Pulse (FEXP). The IEEE 802.11 WLAN system with Additive White Gaussian (AWGN) has been used as the modelling environment. The results showed that the DWT-based OFDM system has a better performance than the DHT and FFT schemes and upon comparing the pulse shaping filters, the SRRC filter outperforms the FEXP and RC filters

Performance Analysis of a Real-Time Adaptive Prediction Algorithm for Traffic Congestion

Traffic congestion is a major factor to consider in the development of a sustainable urban road network. In the past, several mechanisms have been developed to predict congestion, but few have considered an adaptive real-time congestion prediction. This paper proposes two congestion prediction approaches are created. The approaches choose between five different prediction algorithms using the Root Mean Square Error model selection criterion. The implementation consisted of a Global Positioning System based transmitter connected to an Arduino board with a Global System for Mobile/General Packet Radio Service shield that relays the vehicles position to a cloud server. A control station then accesses the vehicles position in real-time, computes its speed. Based on the calculated speed, it estimates the congestion level and it applies the prediction algorithms to the congestion level to predict the congestion for future time intervals. The performance of the prediction algorithms was analysed, and it was observed that the proposed schemes provide the best prediction results with a lower Mean Square Error than all other prediction algorithms when compared with the actual traffic congestion states

Analysing Transportation Data with Open Source Big Data Analytic Tools

Big data analytics allows a vast amount of structured and unstructured data to be effectively processed so that correlations, hidden patterns, and other useful information can be mined from the data. Several open source big data analytic tools that can perform tasks such as dimensionality reduction, feature extraction, transformation, optimization, are now available. One interesting area where such tools can provide effective solutions is transportation. Big data analytics can be used to efficiently manage transport infrastructure assets such as roads, airports, bus stations or ports. In this paper an overview of two open source big data analytic tools is first provided followed by a simple demonstration of application of these tools on transport dataset

SYMBOL LEVEL DECODING FOR DUO-BINARY TURBO CODES

This paper investigates the performance of three different symbol level decoding algorithms for Duo-Binary Turbo codes. Explicit details of the computations involved in the three decoding techniques, and a computational complexity analysis are given. Simulation results with different couple lengths, code-rates, and QPSK modulation reveal that the symbol level decoding with bit-level information outperforms the symbol level decoding by 0.1 dB on average in the error floor region. Moreover, a complexity analysis reveals that symbol level decoding with bit-level information reduces the decoding complexity by 19.6 % in terms of the total number of computations required for each half-iteration as compared to symbol level decoding

An unequal error protection scheme for non-binary LDPC using statistical QAM and prioritized mapping

Low Density Parity Check (LDPC) codes are among the most popular channel codes used nowadays because of their ability to achieve near channel capacity performances. However, with the ever-increasing demand for reliable transmission of data at higher data rates, there is a need to narrow down the gap between the performance of LDPC codes and the channel capacity. LDPC codes and Quadrature Amplitude Modulation (QAM) have been widely deployed in wireless communication standards such as the IEEE 802.11n and Digital Video Broadcasting-Second Generation Terrestrial (DVB-T2). Recently, several Unequal Error Protection (UEP) schemes have been used to enhance the performance of LDPC codes. In this paper an UEP scheme is proposed for Non- Binary LDPC codes with QAM. The scheme uses the statistical distribution of the source symbols to obtain a more efficient statistical QAM constellation. Additionally, it uses the degree distribution of the nodes of the LDPC codeword to achieve prioritized QAM mapping. Simulations revealed that the proposed scheme can provide Eb/N0 gains of up to 0.78 dB and 1.24 dB with 16-QAM and 64-QAM respectively in the range BER≤10-2

Performance of modified and low complexity pulse shaping filters for IEEE 802.11 OFDM transmission

The most commonly used multicarrier modulation method in 802.11 wireless communication systems is Orthogonal Frequency Division Multiplexing (OFDM). However, OFDM is easily affected by Inter Symbol Interference (ISI). In this paper, different pulse shaping filters are employed in the OFDM system and three new filters are presented to further reduce ISI effects. The first one is a Modified Parametric Exponential Pulse (MPEXP) filter which employs a different transfer function that is implemented at the transmitter side. The second one is a Better Than Modified Flipped Exponential Pulse (BTMFEXP) filter which is derived by modifying the transfer function of Modified Flipped Exponential Pulse (MFEXP). The third one is a hybrid of BTMFEXP and MPEXP (HBTMFPEXP). Lastly, low complexity forms of the MFEXP and BTMFEXP, derived from Taylor Series, are implemented. The OFDM system was tested with the existing and proposed pulses over AWGN and Fading channels. The proposed filters show better Bit Error Rate (BER) performance. Under a high ISI level, BTMFEXP and HBTMFPEXP showed a gain of 0.23 and 0.57 dB respectively over MFEXP with AWGN channel. With a fading channel at high ISI level, the proposed BTMFEXP and HBTMFPEXP demonstrated a gain of 2.33 and 5.33 dB respectively over MFEXP

PERFORMANCE ANALYSIS OF A REAL-TIME ADAPTIVE PREDICTION ALGORITHM FOR TRAFFIC CONGESTION

Traffic congestion is a major factor to consider in the development of a sustainable urban road network. In the past, several mechanisms have been developed to predict congestion, but few have considered an adaptive real-time congestion prediction. This paper proposes two congestion prediction approaches are created. The approaches choose between five different prediction algorithms using the Root Mean Square Error model selection criterion. The implementation consisted of a Global Positioning System based transmitter connected to an Arduino board with a Global System for Mobile/General Packet Radio Service shield that relays the vehicle’s position to a cloud server. A control station then accesses the vehicle’s position in real-time, computes its speed. Based on the calculated speed, it estimates the congestion level and it applies the prediction algorithms to the congestion level to predict the congestion for future time intervals. The performance of the prediction algorithms was analysed, and it was observed that the proposed schemes provide the best prediction results with a lower Mean Square Error than all other prediction algorithms when compared with the actual traffic congestion states.

PERFORMANCE ANALYSIS OF A REAL-TIME ADAPTIVE PREDICTION ALGORITHM FOR TRAFFIC CONGESTION

Traffic congestion is a major factor to consider in the development of a sustainable urban road network. In the past, several mechanisms have been developed to predict congestion, but few have considered an adaptive real-time congestion prediction. This paper proposes two congestion prediction approaches are created. The approaches choose between five different prediction algorithms using the Root Mean Square Error model selection criterion. The implementation consisted of a Global Positioning System based transmitter connected to an Arduino board with a Global System for Mobile/General Packet Radio Service shield that relays the vehicle’s position to a cloud server. A control station then accesses the vehicle’s position in real-time, computes its speed. Based on the calculated speed, it estimates the congestion level and it applies the prediction algorithms to the congestion level to predict the congestion for future time intervals. The performance of the prediction algorithms was analysed, and it was observed that the proposed schemes provide the best prediction results with a lower Mean Square Error than all other prediction algorithms when compared with the actual traffic congestion states.