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HGS-Assisted Detection Algorithm for 4G and Beyond Wireless Mobile Communication Systems

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HGS-Assisted Detection Algorithm for 4G and Beyond Wireless Mobile Communication Systems

  1. 1. HGS-Assisted Detection Algorithm for 4G and Beyond Wireless Mobile Communication SystemsMahamod Ismail, Fares Sayadi, Rosdiadee Nordin Department of Electrical, Electronic & Systems Engineering Faculty of Engineering & Built Environment Universiti Kebangsaan Malaysia (UKM) APCC 2011
  2. 2. Presentation OutlinesA. IntroductionB. Research Motivation/ Problem StatementC. Objectives of StudyD. MethodologyE. Results & DiscussionF. Conclusion 2/22
  3. 3. Introduction OverviewWhy do we need to advanced MIMO OFDM CDMA Wireless Advanced Detectionprogressive data detection System System System Algorithmsalgorithm for MMC systems?What do we mean advanced Combination Subspacetransmitted data detection basedalgorithms for MMC systems? and MC-CDMA SystemSubspace and metaheuristic Time-variant Metaheuristic MIMO wireless assisteddefinition? channel Methods Innovative Hybrid Algorithms MMC Wireless Communication Systems 3/22
  4. 4. Research Motivation/ Problem Statementa) Problems: develop a detection method with low-probable error and faster convergence speed (in regard to the computational requirements) over realistic wireless channelsb) Computational complexities of the optimal detection algorithms disperform as polynomial function in the number of antennasc) Suboptimal algorithms suffer from a significant performance reduction and degrade the diversity gain as compared to optimum performance Thus, there is a strong demand for reduced-rank complexity data detection algorithms that can attain optimal performance in MMC wireless communication systems over realistic MIMO channels 4/22
  5. 5. Summary of Contributionsa) Novel adaptive and iterative CE and MUD algorithms for 4G applications and beyondb) Novel adaptive and reconfigurable detection algorithms to MOPS 5/22
  6. 6. ObjectivesThe general objectives of this research are:a) To Test, Implement & Evaluate the Performance of Optimal Data Detection Based on Heuristic Approachesb) To develop novel and flexible metaheuristic-assisted detection algorithm with fewer control parameters and faster convergence speed as well as intense trade- off between BER performance and computational complexity order in the development of MOPSd) To evaluate the performance of the novel adaptive and iterative detection algorithm, known as HGS for the MC-CDMA systems accompanied by extended multiple antennas on multimodal and multivariable problems via time-variant MIMO channel in both Uplink and Downlink scenarios 6/22
  7. 7. START Methodology Invoke system and channel model metaheuristic-basedalgorithms developments Create initial population Initialization Draw initial related parameter Process of received signal Training- Aided Step Draw initial parameters of new subsets Decision-Directed Adaptive Step Equilibrate process of residual error Process of decision variable/ MOP Stopping No criterion Yes END 7/22
  8. 8. Flowchart for the simulation of the proposed detection algorithm Start Detection Define and create parameters Subjective Based on Reconstruction Generate the received signals by multiple antennas BER & CCO as: (Adaptive & Comp./ Conv. CF & CR Iterative Processing) Input of sampling signal into selected algorithm Objective Based onExecute statement of request mast via related necessary calculation RER as: MMSE & Performance N-MSE dB. Evaluation Determine BER/N-MSE/flops/CF/CR, etc. Self-perception test/termination criterion Plot illustrations End 8/22
  9. 9. Simulation Parametersa) STBC MC-CDMA Systems: – Tx = 2, Rx = 1 – Subs/ user = 8 – Mobile users = 4, 6, 8b) Algo Param.: – No of gen = 30, Population size = 30 – Crossover prob. = 0.9 – Mutation prob. = 0.01c) Rayleigh Channel: – Carrier freq = 2 GHz – Delay spread = 1 µ sec. – No of resolvable paths = 5 9/22
  10. 10. Performance Comparison for Metaheuristic-assisted Detection Algorithms HGS labeled HE‘; MMSE-MUD labeled MD‘; GACE labeled GE’ 0 10 GE- 8 GE- 6 HE- 8 -1 GE- 4 10 HE- 6 HE- 4 MD- 4 BER -2 10 4.2 dB -3 10 2.5 dB -4 10 0 2 4 6 8 10 12 14 16 18 Eb/N0 (dB) HGS STBC MC-CDMA: K ∈ (4, 6, 8), M = 8, 𝑁 𝑡 = 2, 𝑁 𝑟 = 1 10/22
  11. 11. Computational complexity comparison for Metaheuristic-assisted Detection Algorithms -7 x 10 3 1-LS 1-SLS 2.5 SA WPSO GA 2 HGS CR 1.5 1 0.5 0 CR comparison for HGS algorithm: Parameters as defined in Table 6.1 11/22
  12. 12. ConclusionFor extended STBC MC-CDMA systems over realistic wireless channel:a) development of adaptive and reconfigurable metaheuristic-assisted algorithms as MOPS methods have been achieved successfullyb) the principal method is based on the hybridization of the proposed algorithms deployment 12/22
  13. 13. Conclusion (cont.) Contributions of the Research Worka) developed a much quicker detection algorithm as most probable adaptive and scalable solution using less common control parameters for extended MMC Systemsb) Brought specialist knowledge yielded to the next generation on the production possibility frontier as well as sufficient for practical and macroeconomic issues 13/22
  14. 14. Conclusion (cont.): Future worka) Utilizing Interleave Division Multiple Access scheme would enhance the overall performance as methods of realizing spectrum sharing.b) Applying an Independent Component Analysis based method would reduce the imperfection phenomena caused by the TVMCsc) Implementation metaheuristic-based algorithms with variable data rate could be utilize to estimate a near optimal solution 14/22
  15. 15. ACKNOWLEDGEMENTSThanks &Terima kasih:To Your Attention Questions? 15/22

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