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Low complexity partial sampled MMSE channel estimation for downlink OFDMA IEEE 802.16e system
Galih S.a,b, Karlina R.a, Irawan A.c, Adiono T.a, Kurniawan A.a, Iskandara
a School of Electronics and Informatics, Bandung Institute of Technology, Indonesia
b Department of Informatics, Widyatama University, Indonesia
c Versatile Silicon, PAU Building, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]Channel estimation is one of key problems in IEEE 802.16e Orthogonal Frequency Division Multiplexing Access (OFDMA) downlink system. Minimum Mean Square Error (MMSE) channel estimation has been known as a superior performance channel estimation. However, this algorithm has high computational complexity. In this paper, we present low complexity partial-sampled MMSE channel estimation for compromising between complexity and performance. We reduced MMSE channel estimation complexity by partially sampling the MMSE weight matrix. The simulation results show that the bit error rate (BER) performance significantly improved over the least square channel estimation and has comparable BER performance with MMSE channel estimation at low SNR. Depending the sixze of sampling, significant decrease 57 % to 64 % in computational complexity can be achieved. ©2009 IEEE.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]BER performance,Bit error rate performance,Downlink systems,IEEE 802.16e,Key problems,Least square channel estimations,Low complexity,Low SNR,Minimum mean-square error,Mobile WiMAX,Orthogonal frequency division multiplexing access,Simulation result,Weight matrices[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Channel estimation,Mobile WiMAX,OFDMA[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ISPACS.2009.5383875[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]