Abstract
A novel custom auto-encoder Complex Valued Convolutional Neural Network (AE-CVCNN) model is proposed and implemented using MATLAB for multiple-input-multiple output (MIMO) wireless networks. The proposed model is applied on two dierent generalized spatial modulation (GSM) schemes: the single symbol generalized spatial modulation SS - GSM and the multiple symbol generalized spatial modulation (MS-GSM). GSM schemes are used with Massive-MIMO to increase both the spectrum eciency and the energy eciency. On the other hand, GSM schemes are subjected to high computational complexity at the receiver to detect the transmitted information. High computational complexity slows down the throughput and increases the power consumption at the user terminals. Consequently, reducing both the total spectrum eciency and energy eciency. The proposed CNN framework achieves constant complexity reduction of 22.73% for SSGSM schemes compared to the complexity of its traditional maximum likelihood detector (ML). Also, it gives a complexity reduction of 14.7% for the MS-GSM schemes compared to the complexity of its detector. The performance penalty of the two schemes is at most 0.5 dB. Besides to the proposed custom AE CV-CNN model, a dierent ML detector0s formula for "SS -GSM" schemes is proposed that achieves the same performance as the traditional ML detector with a complexity reduction of at least 40% compared to that of the traditional ML detector. In addition, the proposed AE-CV-CNN model is applied to the proposed ML detector,and it gives a complexity reduction of at least 63.6% with a performance penalty of less than 0.5 dB. An interesting result about applying the proposed custom CNN model on the proposed ML detector is that the complexity is reduced as the spatial constellation size is increased which means that the total spectrum eciency is increased by increasing the spatial constellation size without increasing the computational complexity.
Publication Date
4-2018
Document Type
Thesis
Student Type
Graduate
Degree Name
Electrical Engineering (MS)
Department, Program, or Center
Electrical Engineering (KGCOE)
Advisor
Ferat Sahin
Advisor/Committee Member
Gill Tsouri
Advisor/Committee Member
Sohail A. Dianat
Recommended Citation
Marseet, Akram, "Application of Convolutional Neural Network Framework on Generalized Spatial Modulation for Next Generation Wireless Networks" (2018). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/9745
Campus
RIT – Main Campus
