Abstract
Precoding and post-processing are necessary technical steps for information recovery of multiple-input multiple-output (MIMO) systems, which can effectively suppress interference between data streams and improve system capacity and resource utilization. However, it is not trivial to design the precoders for multiuser MIMO system and the complexity of the traditional precoding algorithms is usually very high. Deep learning sheds new light to overcome this challenge via data-driven solutions. In this paper, we study the intelligent information transmission technique for a multiuser MIMO broadcast channel network based on deep learning (DL). We propose a DL-based intelligent transceiver structure in this work. The proposed structure is composed of a DL network at the transmitter that played the role of precoder and a post-decoding DL network with a radio transformer network (RTN) at the receiver. Given the channel state information at the transmitter, the proposed intelligent transceiver is trained through the symbols drawn from a discrete constellation by decreasing the mean-squared error (MSE) loss. Simulation results show the proposed intelligent structure is capable of suppressing the inter-stream and inter-user interference adaptively through the training.
This work was supported in part by the National Key R&D Program of China under grant 2019YFB2102600, the National Natural Science Foundation of China (NSFC) under Grants 61701269, 61832012, 61771289 and 61672321, the Shandong Provincial Natural Science Foundation under Grant ZR2017BF012, the Key Research and Development Program of Shandong Province under Grants 2019JZZY010313 and 2019JZZY020124, the program for Youth Innovative Research Team in University of Shandong Province under grant 2019KJN010, the Pilot Project for Integrated Innovation of Science, Education and Industry of Qilu University of Technology (Shandong Academy of Sciences) under Grant 2020KJC-ZD02.
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References
Chen, Q., Cai, Z., Cheng, L., Gao, H.: Low-latency data aggregation scheduling for cognitive radio networks with non-predetermined structure. IEEE Trans. Mob. Comput. 20, 2412–2426 (2020)
Cai, Z., Ji, S., He, J., Wei, L., Bourgeois, A.G.: Distributed and asynchronous data collection in cognitive radio networks with fairness consideration. IEEE Trans. Parallel Distrib. Syst. 25(8), 2020–2029 (2013)
Cai, Z., Ji, S., He, J., Bourgeois, A.G.: Optimal distributed data collection for asynchronous cognitive radio networks. In: 2012 IEEE 32nd International Conference on Distributed Computing Systems, pp. 245–254. IEEE (2012)
Lu, J., Cai, Z., Wang, X., Zhang, L., Li, P., He, Z.: User social activity-based routing for cognitive radio networks. Pers. Ubiquit. Comput. 22(3), 471–487 (2018)
Andrews, J.G., et al.: What will 5G be? IEEE J. Sel. Areas Commun. 32(6), 1065–1082 (2014)
Larsson, E.G., Edfors, O., Tufvesson, F., Marzetta, T.L.: Massive MIMO for next generation wireless systems. IEEE Commun. Mag. 52(2), 186–195 (2014)
Chen, S., Sun, S., Xu, G., Su, X., Cai, Y.: Beam-space multiplexing: practice, theory, and trends, from 4G TD-LTE, 5G, to 6G and beyond. IEEE Wirel. Commun. 27(2), 162–172 (2020)
Dong, A., Zhang, H., Yuan, D., Zhou, X.: Interference alignment transceiver design by minimizing the maximum mean square error for MIMO interfering broadcast channel. IEEE Trans. Veh. Technol. 65(8), 6024–6037 (2015)
Dong, A., Zhang, H., Wu, D., Yuan, D.: QoS-constrained transceiver design and power splitting for downlink multiuser MIMO SWIPT systems. In: 2016 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2016)
Zhao, N., et al.: Secure transmission via joint precoding optimization for downlink MISO NOMA. IEEE Trans. Veh. Technol. 68(8), 7603–7615 (2019)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)
Liang, Y., Cai, Z., Yu, J., Han, Q., Li, Y.: Deep learning based inference of private information using embedded sensors in smart devices. IEEE Netw. 32(4), 8–14 (2018)
O’Shea, T., Hoydis, J.: An introduction to deep learning for the physical layer. IEEE Trans. Cogn. Commun. Netw. 3(4), 563–575 (2017)
Liu, Z., Zhang, L., Ding, Z.: An efficient deep learning framework for low rate massive MIMO CSI reporting. IEEE Trans. Commun. 68, 4761–4772 (2020)
Ma, X., Gao, Z.: Data-driven deep learning to design pilot and channel estimator for massive MIMO. IEEE Trans. Veh. Technol. 69(5), 5677–5682 (2020)
Kim, H., Kim, S., Lee, H., Jang, C., Choi, Y., Choi, J.: Massive MIMO channel prediction: Kalman filtering vs. machine learning. IEEE Trans. Commun. 69, 518–528 (2020)
Shao, M., Ma, W.-K.: Binary MIMO detection via homotopy optimization and its deep adaptation. arXiv preprint arXiv:2004.12587 (2020)
Zhang, J., Dong, A., Yu, J.: Intelligent dynamic spectrum access for uplink underlay cognitive radio networks based on q-learning. In: Yu, D., Dressler, F., Yu, J. (eds.) WASA 2020. LNCS, vol. 12384, pp. 691–703. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59016-1_57
O’Shea, T.J., Erpek, T., Clancy, T.C.: Deep learning based MIMO communications. arXiv preprint arXiv:1707.07980 (2017)
He, H., Jin, S., Wen, C.-K., Gao, F., Li, G.Y., Xu, Z.: Model-driven deep learning for physical layer communications. IEEE Wirel. Commun. 26(5), 77–83 (2019)
Song, J., Häger, C., Schröder, J., O’Shea, T., Wymeersch, H.: Benchmarking end-to-end learning of MIMO physical-layer communication. arXiv preprint arXiv:2005.09718 (2020)
O’Shea, T.J., Pemula, L., Batra, D., Clancy, T.C.: Radio transformer networks: Attention models for learning to synchronize in wireless systems. In: 2016 50th Asilomar Conference on Signals, Systems and Computers, pp. 662–666. IEEE (2016)
Zhang, H., Dong, A., Jin, S., Yuan, D.: Joint transceiver and power splitting optimization for multiuser MIMO SWIPT under MSE QoS constraints. IEEE Trans. Veh. Technol. 66(8), 7123–7135 (2017)
Cui, W., Dong, A., Cao, Y., Zhang, C., Yu, J., Li, S.: Deep learning based MIMO transmission with precoding and radio transformer networks. Procedia Comput. Sci. 187, 396–401 (2021)
Gulli, A., Pal, S.: Deep Learning with Keras. Packt Publishing Ltd. (2017)
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Dong, A. et al. (2021). A Deep Learning Based Intelligent Transceiver Structure for Multiuser MIMO. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12939. Springer, Cham. https://doi.org/10.1007/978-3-030-86137-7_58
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