This document presents a novel hybrid algorithm called HGS for detecting transmitted data in 4G and beyond mobile communication systems. The HGS algorithm uses metaheuristic approaches to achieve optimal detection performance with low complexity. It aims to develop adaptive detection methods that have faster convergence speeds and require fewer parameters over time-varying MIMO channels. Simulation results show the HGS algorithm achieves better bit error rate than other detection methods like MMSE-MUD and GACE, with lower computational complexity. Future work could involve applying the metaheuristic approaches to systems using interleaved division multiple access or independent component analysis.

1. HGS-Assisted Detection Algorithm for 4G and Beyond
Wireless Mobile Communication Systems
Mahamod Ismail, Fares Sayadi, Rosdiadee Nordin
Department of Electrical, Electronic & Systems Engineering
Faculty of Engineering & Built Environment
Universiti Kebangsaan Malaysia (UKM)
APCC 2011

2. Presentation Outlines
A. Introduction
B. Research Motivation/ Problem Statement
C. Objectives of Study
D. Methodology
E. Results & Discussion
F. Conclusion
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3. Introduction
Overview
Why do we need to advanced MIMO OFDM CDMA
Wireless Advanced Detection
progressive data detection System System
System Algorithms
algorithm for MMC systems?
What do we mean advanced Combination
Subspace
transmitted data detection based
algorithms for MMC systems? and
MC-CDMA
System
Subspace and metaheuristic Time-variant Metaheuristic
MIMO wireless assisted
definition? channel
Methods
Innovative Hybrid
Algorithms
MMC Wireless
Communication
Systems
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4. Research Motivation/ Problem Statement
a) Problems: develop a detection method with low-probable error and faster
convergence speed (in regard to the computational requirements) over realistic
wireless channels
b) Computational complexities of the optimal detection algorithms disperform as
polynomial function in the number of antennas
c) 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
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5. Summary of Contributions
a) Novel adaptive and iterative CE and MUD algorithms for 4G
applications and beyond
b) Novel adaptive and reconfigurable detection algorithms to MOPS
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6. Objectives
The general objectives of this research are:
a) To Test, Implement & Evaluate the Performance of Optimal Data Detection
Based on Heuristic Approaches
b) 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 MOPS
d) 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
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7. START
Methodology
Invoke system and channel model
metaheuristic-based
algorithms 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
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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 on
Execute 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
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9. Simulation Parameters
a) STBC MC-CDMA Systems:
– Tx = 2, Rx = 1
– Subs/ user = 8
– Mobile users = 4, 6, 8
b) Algo Param.:
– No of gen = 30, Population size = 30
– Crossover prob. = 0.9
– Mutation prob. = 0.01
c) Rayleigh Channel:
– Carrier freq = 2 GHz
– Delay spread = 1 µ sec.
– No of resolvable paths = 5
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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
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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
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12. Conclusion
For extended STBC MC-CDMA systems over realistic wireless channel:
a) development of adaptive and reconfigurable metaheuristic-assisted
algorithms as MOPS methods have been achieved successfully
b) the principal method is based on the hybridization of the proposed
algorithms deployment
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13. Conclusion (cont.)
Contributions of the Research Work
a) developed a much quicker detection algorithm as most probable adaptive and
scalable solution using less common control parameters for extended MMC
Systems
b) Brought specialist knowledge yielded to the next generation on the production
possibility frontier as well as sufficient for practical and macroeconomic issues
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14. Conclusion (cont.):
Future work
a) 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 TVMCs
c) Implementation metaheuristic-based algorithms with variable data rate
could be utilize to estimate a near optimal solution
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15. ACKNOWLEDGEMENTS
Thanks &Terima kasih:
To Your Attention
Questions?
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