Journal
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
Volume 37, Issue 9, Pages 2117-2131Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2019.2929404
Keywords
Index modulation; SM-MIMO; machine learning; neural network; link adaptation
Funding
- National Science Foundation of China [61876033, 61671131]
- Foundation Project of National Key Laboratory of Science and Technology on Communications [9140C020108140C02005]
- Fundamental Research Funds for the Central Universities [ZYGX2015KYQD003]
- SSF project High-Reliable Low-Latency Industrial Wireless Communications
- EU Marie Sklodowska-Curie Actions Project High-Reliability Low-Latency Communications With Network Coding
- ERA-NET, SMART-MLA
Ask authors/readers for more resources
In this paper, we propose a novel framework of low-cost link adaptation for spatial modulation multiple-input multiple-output (SM-MIMO) systems-based upon the machine learning paradigm. Specifically, we first convert the problems of transmit antenna selection (TAS) and power allocation (PA) in SM-MIMO to ones-based upon data-driven prediction rather than conventional optimization-driven decisions. Then, supervised-learning classifiers (SLC), such as the K-nearest neighbors (KNN) and support vector machine (SVM) algorithms, are developed to obtain their statistically-consistent solutions. Moreover, for further comparison we integrate deep neural networks (DNN) with these adaptive SM-MIMO schemes, and propose a novel DNN-based multi-label classifier for TAS and PA parameter evaluation. Furthermore, we investigate the design of feature vectors for the SLC and DNN approaches and propose a novel feature vector generator to match the specific transmission mode of SM. As a further advance, our proposed approaches are extended to other adaptive index modulation (IM) schemes, e.g., adaptive modulation (AM) aided orthogonal frequency division multiplexing with IM (OFDM-IM). Our simulation results show that the SLC and DNN-based adaptive SM-MIMO systems outperform many conventional optimization-driven designs and are capable of achieving a near-optimal performance with a significantly lower complexity.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available