6 research outputs found

    Intelligent Processing in Wireless Communications Using Particle Swarm Based Methods

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    There are a lot of optimization needs in the research and design of wireless communica- tion systems. Many of these optimization problems are Nondeterministic Polynomial (NP) hard problems and could not be solved well. Many of other non-NP-hard optimization problems are combinatorial and do not have satisfying solutions either. This dissertation presents a series of Particle Swarm Optimization (PSO) based search and optimization algorithms that solve open research and design problems in wireless communications. These problems are either avoided or solved approximately before. PSO is a bottom-up approach for optimization problems. It imposes no conditions on the underlying problem. Its simple formulation makes it easy to implement, apply, extend and hybridize. The algorithm uses simple operators like adders, and multipliers to travel through the search space and the process requires just five simple steps. PSO is also easy to control because it has limited number of parameters and is less sensitive to parameters than other swarm intelligence algorithms. It is not dependent on initial points and converges very fast. Four types of PSO based approaches are proposed targeting four different kinds of problems in wireless communications. First, we use binary PSO and continuous PSO together to find optimal compositions of Gaussian derivative pulses to form several UWB pulses that not only comply with the FCC spectrum mask, but also best exploit the avail- able spectrum and power. Second, three different PSO based algorithms are developed to solve the NLOS/LOS channel differentiation, NLOS range error mitigation and multilateration problems respectively. Third, a PSO based search method is proposed to find optimal orthogonal code sets to reduce the inter carrier interference effects in an frequency redundant OFDM system. Fourth, a PSO based phase optimization technique is proposed in reducing the PAPR of an frequency redundant OFDM system. The PSO based approaches are compared with other canonical solutions for these communication problems and showed superior performance in many aspects. which are confirmed by analysis and simulation results provided respectively. Open questions and future Open questions and future works for the dissertation are proposed to serve as a guide for the future research efforts

    A Novel PAPR Reduction in Filter Bank Multi-Carrier (FBMC) with Offset Quadrature Amplitude Modulation (OQAM) Based VLC Systems

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    The peak to average power ratio (PAPR) is one of the major problem with multicarrier-based systems. Due to its improved spectral efficiency and decreased PAPR, Filter Bank Multicarrier (FBMC) has recently become an effective alternative to the orthogonal multiplexing division (OFDM). For filter bank multicarrier communication/offset quadrature amplitude modulation-Visible light communication (FBMC/OQAM-VLC) systems is proposed a PAPR reduction technique. The suggested approach overlaps the proposed FBMC/OQAM-based VLC data signal with the existing signals. Non-redundant signals and data signals do not overlap in the frequency domain because data signals are scattered on odd subcarriers whereas built signals use even subcarriers. To reduce the effects of large-amplitude signal reduction, the suggested technique converts negative signals into positive signals rather than clipping them off as in conventional FBMC-based VLC systems. The PAPR reduction and bit error rate (BER) are realized using a scaling factor in the transformed signals. Complementary cumulative distribution function(CCDF) and BER are used to calculate the performance of the proposed approach. The presented study found that FBMC/OQAM-VLC systems to achieve a good trade-off between PAPR reduction and BER

    On PAPR Reduction of OFDM using Partial Transmit Sequence with Intelligent Optimization Algorithms

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    In recent time, the demand for multimedia data services over wireless links has grown up rapidly. Orthogonal Frequency Division Multiplexing (OFDM) forms the basis for all 3G and beyond wireless communication standards due to its efficient frequency utilization permitting near ideal data rate and ubiquitous coverage with high mobility. OFDM signals are prone to high peak-to-average-power ratio (PAPR). Unfortunately, the high PAPR inherent to OFDM signal envelopes occasionally drives high power amplifiers (HPAs) to operate in the nonlinear region of their characteristic leading out-of-band radiation, reduction in efficiency of communication system etc. A plethora of research has been devoted to reducing the performance degradation due to the PAPR problem inherent to OFDM systems. Advanced techniques such as partial transmit sequences (PTS) and selected mapping (SLM) have been considered most promising for PAPR reduction. Such techniques are seen to be efficient for distortion-less signal processing but suffer from computational complexity and often requires transmission of extra information in terms of several side information (SI) bits leading to loss in effective data rate. This thesis investigates the PAPR problem using Partial Transmit Sequence (PTS) scheme, where optimization is achieved with evolutionary bio-inspired metaheuristic stochastic algorithms. The phase factor optimization in PTS is used for PAPR reduction. At first, swarm intelligence based Firefly PTS (FF-PTS) algorithm is proposed which delivers improved PAPR performance with reduced searching complexity. Following this, Cuckoo Search based PTS (CS-PTS) technique is presented, which offers good PAPR performance in terms of solution quality and convergence speed. Lastly, Improved Harmony search based PTS (IHS-PTS) is introduced, which provides improved PAPR. The algorithm has simple structure with a very few parameters for larger PTS sub-blocks. The PAPR performance of the proposed technique with different parameters is also verified through extensive computer simulations. Furthermore, complexity analysis of algorithms demonstrates that the proposed schemes offer significant complexity reduction when compared to standard PAPR reduction techniques. Findings have been validated through extensive simulation tests

    Intelligent Circuits and Systems

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    ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.  This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering
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