000			03642ntm a2200337 i 4500
003			MY-KuUP
005			20260525115338.0
006			t||||fr|||| 000 0
007			ta
008			260525t20262026my a|||fr|||| 000 0 eng d
020			_aTHE0010525 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFTKEE .N87 2026 r Thesis
100	1		_aNura Abdalrhman Alhajalsharief Mahmoud, _eauthor.
245	1	0	_aEnhancing a channel estimation for fbmc/oqam using deep neural network / _cNura Abdalrhman Alhajalsharief Mahmoud
264		1	_aKuantan, Pahang : _bUMPSA, _c2026
264		4	_c© 2026
300			_axiii, 143 pages : _billustrations ; _c30 cm. + _e1 CD-ROM.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Electrical & Electronics Engineering Technology
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang - 2026
504			_aInclude bibliographical reference
520	3		_aFilter Bank Multicarrier with Offset Quadrature Amplitude Modulation (FBMC/OQAM) is a promising multicarrier modulation technique for sixth-generation (6G) networks that offers high spectral efficiency and cyclic prefix (CP) free transmission at reduced out-of-band emissions (OOBE) and resilience to asynchronous environments. However, this technique complicates the critical process of channel estimation (CE) in wireless communication due to the interference from a lack of orthogonality in the imaginary domain. Its performance in terms of system capacity, spectral efficiency, and data rates are enhanced through the integration of massive multiple-input multiple-output (m-MIMO), yet, at the reliance on accurate channel state information (CSI). While the least-squares (LS) estimator as a commonly employed interference approximation method (IAM) is computationally efficient, it suffers poor accuracy towards noise, multipath fading, and condition of low signal-to-noise ratio (SNR). With this in mind, the focus is shifted to deep neural networks (DNN) for an outstanding likelihood against the pinpointed shortcomings as a reliable and simplified solution to enhanced wireless CE performance. This is supported by the distributive technicalities of the graphics processing unit (GPU) for DNN applications in actual settings. The current study, therefore, introduces an improved IAM-LS (Interference approximation method) channel estimator based on the DNN approach (M-IAM-LS-DNN) for enhanced CE performance. The proposed model was trained and tested through MATLAB simulations under Rayleigh, and Rician fading channel and additive white Gaussian noise (AWGN) across various SNR levels and mobility scenarios including pedestrian (Ped A), road-to-vehicular (RTV), and vehicular (Veh A, Veh B) channels. Obtained results on the implementation of M-IAM-LS-DNN further confirm a 10% reduction in normalized mean square error (NMSE) and a 33% improvement in bit error rate (BER) against the conventional M-IAM-LS. The suitability of the proposed approach for applications is then substantiated by 8% and 12% lower computational complexities for the three and two hidden layers layouts, respectively. This has been achieved by eliminating the complex matrix computations with a concurrent improvement of BER, NMSE, and overall system capacity upon m-MIMO following integration with m-MIMO.
610	2	0	_aFaculty of Electrical & Electronics Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS
999			_c104889 _d104895