To broaden the application scenario and reduce energy consumption, we propose an energy-efficient fixed-gain (FG) amplify-and-forward (AF) relay assisted orthog- onal frequency-division multiplexing with index modulation (OFDM-IM) scheme in this thesis. The proposed system needs neither instantaneous channel state informa- tion (CSI) nor performing complicated processing at the relay node. It operates based on a new design of power allocation that minimizes the sum of transmit power at both source and relay node, given an outage probability constraint. Considering the actual situation and combining with the characteristics of normalization research, the pro- posed scheme can be discussed in two scenarios regarding to whether the subcarriers are interfered with by fading and noise independently. Based on the consistency of statistical CSI for each subcarrier, through a series of problem transformation and simplification, this thesis converts the original power allocation problem to a relaxed version and solve the relaxed problem using the convex optimization techniques. To reveal the computing efficiency of the proposed power allocation scheme, we analyze its computational complexity. Numerical simulations substantiate that the proposed optimization scheme has a neglectable loss compared with the brute force search, while the computational complexity could be considerably reduced. As for the sce- nario about the independence of statistical CSI for each subcarrier, an approach of artificial neural network (ANN) based on deep learning is incorporated into the sys- tem, enabling the proposed scheme to achieve a high accuracy comparing perfect optimization scheme. In the processing of power minimization, this study utilizes the adaptive moment estimation (Adam) method to implement back-propagation learn- ing and achieve the power allocation needed.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/662604 |
| Date | 04 1900 |
| Creators | Zhou, Jiusi |
| Contributors | Alouini, Mohamed-Slim, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Shihada, Basem, Amin, Osama |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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