Electronic Engineering

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Application of cognitive radio based sensor network in smart grids for efficient, holistic monitoring and control.
(2018) Ogbodo, Emmanuel Utochukwu.; Dorrell, David George.; Abu-Mahfouz, Adnan M.
This thesis is directed towards the application of cognitive radio based sensor network (CRSN) in smart grid (SG) for efficient, holistic monitoring and control. The work involves enabling of sensor network and wireless communication devices for spectra utilization via the capability of Dynamic Spectrum Access (DSA) of a cognitive radio (CR) as well as end to end communication access technology for unified monitoring and control in smart grids. Smart Grid (SG) is a new power grid paradigm that can provide predictive information and recommendations to utilities, including their suppliers, and their customers on how best to manage power delivery and consumption. SG can greatly reduce air pollution from our surrounding by renewable power sources such as wind energy, solar plants and huge hydro stations. SG also reduces electricity blackouts and surges. Communication network is the foundation for modern SG. Implementing an improved communication solution will help in addressing the problems of the existing SG. Hence, this study proposed and implemented improved CRSN model which will help to ultimately evade the inherent problems of communication network in the SG such as: energy inefficiency, interference, spectrum inefficiencies, poor quality of service (QoS), latency and throughput. To overcome these problems, the existing approach which is more predominant is the use of wireless sensor network (WSNs) for communication needs in SG. However, WSNs have low battery power, low computational complexity, low bandwidth support, and high latency or delay due to multihop transmission in existing WSN topology. Consequently, solving these problems by addressing energy efficiency, bandwidth or throughput, and latency have not been fully realized due to the limitations in the WSN and the existing network topology. Therefore, existing approach has not fully addressed the communication needs in SG. SG can be fully realized by integrating communication network technologies infrastructures into the power grid. Cognitive Radio-based Sensor Network (CRSN) is considered a feasible solution to enhance various aspects of the electric power grid such as communication with end and remote devices in real-time manner for efficient monitoring and to realize maximum benefits of a smart grid system. CRSN in SG is aimed at addressing the problem of spectrum inefficiency and interference which wireless sensor network (WSN) could not. However, numerous challenges for CRSNs are due to the harsh environmental wireless condition in a smart grid system. As a result, latency, throughput and reliability become critical issues. To overcome these challenges, lots of approaches can be adopted ranging from integration of CRSNs into SGs; proper implementation design model for SG; reliable communication access devices for SG; key immunity requirements for communication infrastructure in SG; up to communication network protocol optimization and so on. To this end, this study utilized the National Institute of Standard (NIST) framework for SG interoperability in the design of unified communication network architecture including implementation model for guaranteed quality of service (QoS) of smart grid applications. This involves virtualized network in form of multi-homing comprising low power wide area network (LPWAN) devices such as LTE CAT1/LTE-M, and TV white space band device (TVBD). Simulation and analysis show that the performance of the developed modules architecture outperforms the legacy wireless systems in terms of latency, blocking probability, and throughput in SG harsh environmental condition. In addition, the problem of multi correlation fading channels due to multi antenna channels of the sensor nodes in CRSN based SG has been addressed by the performance analysis of a moment generating function (MGF) based M-QAM error probability over Nakagami-q dual correlated fading channels with maximum ratio combiner (MRC) receiver technique which includes derivation and novel algorithmic approach. The results of the MATLAB simulation are provided as a guide for sensor node deployment in order to avoid the problem of multi correlation in CRSN based SGs. SGs application requires reliable and efficient communication with low latency in timely manner as well as adequate topology of sensor nodes deployment for guaranteed QoS. Another important requirement is the need for an optimized protocol/algorithms for energy efficiency and cross layer spectrum aware made possible for opportunistic spectrum access in the CRSN nodes. Consequently, an optimized cross layer interaction of the physical and MAC layer protocols using various novel algorithms and techniques was developed. This includes a novel energy efficient distributed heterogeneous clustered spectrum aware (EDHC- SA) multichannel sensing signal model with novel algorithm called Equilateral triangulation algorithm for guaranteed network connectivity in CRSN based SG. The simulation results further obtained confirm that EDHC-SA CRSN model outperforms conventional ZigBee WSN in terms of bit error rate (BER), end-to-end delay (latency) and energy consumption. This no doubt validates the suitability of the developed model in SG.
The hybrid list decoding and Chase-like algorithm of Reed-Solomon codes.
(2005) Jin, Wei.; Xu, Hongjun.; Takawira, Fambirai.
Reed-Solomon (RS) codes are powerful error-correcting codes that can be found in a wide variety of digital communications and digital data-storage systems. Classical hard decoder of RS code can correct t = (dmin -1) /2 errors where dmin = (n - k+ 1) is the minimum distance of the codeword, n is the length of codeword and k is the dimension of codeword. Maximum likelihood decoding (MLD) performs better than the classical decoding and therefore how to approach the performance of the MLD with less complexity is a subject which has been researched extensively. Applying the bit reliability obtained from channel to the conventional decoding algorithm is always an efficient technique to approach the performance of MLD, although the exponential increase of complexity is always concomitant. It is definite that more enhancement of performance can be achieved if we apply the bit reliability to enhanced algebraic decoding algorithm that is more powerful than conventional decoding algorithm. In 1997 Madhu Sudan, building on previous work of Welch-Berlekamp, and others, discovered a polynomial-time algorithm for decoding low-rate Reed- Solomon codes beyond the classical error-correcting bound t = (dmin -1) /2. Two years later Guruswami and Sudan published a significantly improved version of Sudan's algorithm (GS), but these papers did not focus on devising practical implementation. The other authors, Kotter, Roth and Ruckenstein, were able to find realizations for the key steps in the GS algorithm, thus making the GS algorithm a practical instrument in transmission systems. The Gross list algorithm, which is a simplified one with less decoding complexity realized by a reencoding scheme, is also taken into account in this dissertation. The fundamental idea of the GS algorithm is to take advantage of an interpolation step to get an interpolation polynomial produced by support symbols, received symbols and their corresponding multiplicities. After that the GS algorithm implements a factorization step to find the roots of the interpolation polynomial. After comparing the reliability of these codewords which are from the output of factorization, the GS algorithm outputs the most likely one. The support set, received set and multiplicity set are created by Koetter Vardy (KV) front end algorithm. In the GS list decoding algorithm, the number of errors that can be corrected increases to tcs = n - 1 - lJ (k - 1) n J. It is easy to show that the GS list decoding algorithm is capable of correcting more errors than a conventional decoding algorithm. In this dissertation, we present two hybrid list decoding and Chase-like algorithms. We apply the Chase algorithms to the KV soft-decision front end. Consequently, we are able to provide a more reliable input to the KV list algorithm. In the application of Chase-like algorithm, we take two conditions into consideration, so that the floor cannot occur and more coding gains are possible. With an increase of the bits that are chosen by the Chase algorithm, the complexity of the hybrid algorithm increases exponentially. To solve this problem an adaptive algorithm is applied to the hybrid algorithm based on the fact that as signal-to-noise ratio (SNR) increases the received bits are more reliable, and not every received sequence needs to create the fixed number of test error patterns by the Chase algorithm. We set a threshold according to the given SNR and utilize it to finally decide which unreliable bits are picked up by Chase algorithm. However, the performance of the adaptive hybrid algorithm at high SNRs decreases as the complexity decreases. It means that the adaptive algorithm is not a sufficient mechanism for eliminating the redundant test error patterns. The performance of the adaptive hybrid algorithm at high SNRs motivates us to find out another way to reduce the complexity without loss of performance. We would consider the two following problems before dealing with the problem on hand. One problem is: can we find a terminative condition to decide which generated candidate codeword is the most likely codeword for received sequence before all candidates of received set are tested? Another one is: can we eliminate the test error patterns that cannot create more likely codewords than the generated codewords? In our final algorithm, an optimality lemma coming from the Kaneko algorithm is applied to solve the first problem and the second problem is solved by a ruling out scheme for the reduced list decoding algorithm. The Gross list algorithm is also applied in our final hybrid algorithm. After the two problems have been solved, the final hybrid algorithm has performance comparable with the hybrid algorithm combined the KV list decoding algorithm and the Chase algorithm but much less complexity at high SNRs.
Optimized digital signal processing algorithms applied to radio communications.
(1992) Carter, Alan James Auchmuty.; Broadhurst, Anthony D.
The application of digital signal processing to radio communications has come of age with the advent of low power, high speed microprocessors and over the past five years, various transceiver architectures, utilizing this new technology have been extensively researched. Due to the flexible nature of a software based transceiver, a myriad of possible applications exist and currently the emphasis is on the development of suitable algorithms. The principal aim of this research is the derivation of optimized digital signal processing algorithms applicable to three separate areas of radio communications. Optimized, as used by the author within this dissertation, implies a reasonable compromise between performance, complexity and numerical processing efficiency. This compromise is necessary since the algorithms are applied to a portable transceiver where power consumption, size and weight are limited. The digital signal processing algorithms described by this research is as follows:- 1. The derivation and assessment of a multirate speech amplitude modulation demodulator which exhibits low distortion (typically less than 2%) for a wide range of modulation indices, carrier frequency offsets and deviations. The demodulator is processing efficient and requires only five multiplications and five decisions for every output sample. 2. The derivation and assessment of a low sampling rate speech frequency modulation demodulator for signals whose bandwidth exceed quarter the sampling frequency. The demodulator exhibits low distortion (typically less than 2%) and is processing efficient requiring eighteen multiplications and three decisions for every output sample. 3. The derivation and assessment of a multirate single-sideband suppressed carrier automatic frequency control system which is a combination of a simple second order adaptive line enhancer and a digital phase-locked loop. The processing efficient automatic frequency control system is suited for low signal to noise power conditions, in both stationary and mobile communication channels.
Transmit antenna selection algorithms for quadrature spatial modulation.
(2016) Naidu, Suvigya.; Pillay, Narushan.
The use of multiple-input multiple-output (MIMO) systems has become increasingly popular due to the demand for high data rate transmissions. One such attractive MIMO system is spatial modulation (SM). SM is an ideal candidate for high data rate transmission as it is able to achieve a high spectral efficiency, whilst maintaining a relatively low receiver complexity. SM completely avoids inter-channel interference and the need for inter-antenna synchronisation. Furthermore, SM requires the existence of only one radio frequency chain. However, the need to increase the spectral efficiency achieved by SM is a topic which continues to garner interest. Quadrature spatial modulation (QSM) was introduced as an innovative SM-based MIMO system. QSM maintains the aforementioned advantages of SM, whilst further increasing the spectral efficiency of SM. However, similar to SM, the need to improve the reliability (error performance) of QSM still exists. One such strategy is the application of a closed-loop technique, such as transmit antenna selection (TAS). In this dissertation, Euclidean distance-based antenna selection for QSM (EDAS-QSM) is proposed. A substantial improvement in the average error performance is demonstrated. However, this is at the expense of a relatively high computational complexity. To address this, we formulate an algorithm in the form of reduced-complexity Euclidean distance-based antenna selection for QSM (RCEDAS-QSM) that is used for the computation of EDAS-QSM. RCEDAS-QSM yields a significant reduction in the computational complexity, whilst preserving the error performance. To further address computational complexity, four sub-optimal, low-complexity, TAS schemes for QSM are investigated, viz. capacity optimised antenna selection for QSM (COASQSM), TAS for QSM based on amplitude and antenna correlation (TAS-A-C-QSM), lowcomplexity TAS for QSM based on amplitude and antenna correlation using the splitting technique (LCTAS-A-C-QSM) and TAS based on amplitude, antenna correlation and Euclidean distance for QSM (A-C-ED-QSM). Amongst the sub-optimal algorithms, A-C-ED-QSM provides superior error performance. While the computational complexity of A-C-ED-QSM is higher than the other sub-optimal, lowcomplexity schemes, there is a significant reduction in the computational complexity compared to the optimal RCEDAS-QSM. However, this is at the expense of error performance. Hence, clearly a trade-off exists between error performance and computational complexity, and is investigated in detail in this dissertation.

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