SCIENCE CHINA Information Sciences, Volume 63 , Issue 8 : 180303(2020) https://doi.org/10.1007/s11432-020-2937-y

## Multitask deep learning-based multiuser hybrid beamforming for mm-wave orthogonal frequency division multiple access systems

• AcceptedJun 3, 2020
• PublishedJul 15, 2020
Share
Rating

### Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61871321, 61901367), National Science and Technology Major Project (Grant No. 2017ZX03001012-005), and Shaanxi STA International Cooperation and Exchanges Project (Grant No. 2017KW-011).

### References

[1] Roh W, Seol J Y, Park J. Millimeter-wave beamforming as an enabling technology for 5G cellular communications: theoretical feasibility and prototype results. IEEE Commun Mag, 2014, 52: 106-113 CrossRef Google Scholar

[2] Bogale T E, Le L B. Massive MIMO and mmWave for 5G Wireless HetNet: Potential Benefits and Challenges. IEEE Veh Technol Mag, 2016, 11: 64-75 CrossRef Google Scholar

[3] Ayach O E, Rajagopal S, Abu-Surra S. Spatially Sparse Precoding in Millimeter Wave MIMO Systems. IEEE Trans Wireless Commun, 2014, 13: 1499-1513 CrossRef Google Scholar

[4] Sharma S K, Bogale T E, Le L B. Dynamic Spectrum Sharing in 5G Wireless Networks With Full-Duplex Technology: Recent Advances and Research Challenges. IEEE Commun Surv Tutorials, 2018, 20: 674-707 CrossRef Google Scholar

[5] Samimi M K, Rappaport T S. Ultra-wideband statistical channel model for non line of sight millimeter-wave urban channels. In: Proceedings of IEEE Global Communications Conference (Globecom), 2014. 3483--3489. Google Scholar

[6] Kwon G, Park H. A joint scheduling and millimeter wave hybrid beamforming system with partial side information. In: Proceedings of IEEE International Conference on Communications (ICC), 2016. Google Scholar

[7] He S, Wu Y, Ng D W K. Joint Optimization of Analog Beam and User Scheduling for Millimeter Wave Communications. IEEE Commun Lett, 2017, 21: 2638-2641 CrossRef Google Scholar

[8] Jiang Z, Chen S, Zhou S. Joint User Scheduling and Beam Selection Optimization for Beam-Based Massive MIMO Downlinks. IEEE Trans Wireless Commun, 2018, 17: 2190-2204 CrossRef Google Scholar

[9] Jiang J, Kong D. Joint User Scheduling and MU-MIMO Hybrid Beamforming Algorithm for mmWave FDMA Massive MIMO System. Int J Antennas Propagation, 2016, 2016: 1-10 CrossRef Google Scholar

[10] Qin Z, Ye H, Li G Y. Deep Learning in Physical Layer Communications. IEEE Wireless Commun, 2019, 26: 93-99 CrossRef Google Scholar

[11] Lu C, Xu W, Shen H. MIMO Channel Information Feedback Using Deep Recurrent Network. IEEE Commun Lett, 2019, 23: 188-191 CrossRef Google Scholar

[12] He Y F, Zhang J, Wen C K, et al. TurboNet: a model-driven DNN decoder based on max-log-map algorithm for turbo code. 2019,. arXiv Google Scholar

[13] Ruder S. An overview of multi-task learning in deep neural networks. 2017,. arXiv Google Scholar

[14] Sun Y, Wang X G, Tang X O. Deep learning face representation by joint identification-verification. 2014,. arXiv Google Scholar

[15] Alkhateeb A, El Ayach O, Leus G. Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems. IEEE J Sel Top Signal Process, 2014, 8: 831-846 CrossRef ADS arXiv Google Scholar

[16] Cai M M, Gao K, Nie D, et al. Effect of wideband beam squint on codebook design in phased-array wireless systems. In: Proceedings of IEEE Global Communications Conference (GLOBECOM), 2016. Google Scholar

[17] Wang B, Jian M, Gao F. Beam Squint and Channel Estimation for Wideband mmWave Massive MIMO-OFDM Systems. IEEE Trans Signal Process, 2019, 67: 5893-5908 CrossRef ADS arXiv Google Scholar

[18] Wang M, Gao F, Shlezinger N. A Block Sparsity Based Estimator for mmWave Massive MIMO Channels With Beam Squint. IEEE Trans Signal Process, 2020, 68: 49-64 CrossRef ADS arXiv Google Scholar

[19] Caruana R. Multitask learning. Machine Learning, 1997, 28: 41-75 CrossRef Google Scholar

[20] Nair V, Hinton G E. Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010. 807--814. Google Scholar

[21] Srivastava N, Hinton G F, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res, 2014, 15: 1532--4435. Google Scholar

[22] Kingma D, Ba J. Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR), 2014. Google Scholar

• Figure 1

The structure of multi-user hybrid beamforming scheme.

• Figure 2

The structure of multitask deep learning-based multiuser hybrid beamforming scheme.

• Figure 3

The structure of single task deep learning-based multiuser hybrid beamforming scheme.

• Figure 4

The sum-rate cumulative distribution function (CDF) of different schemes with SNR = 0 dB. (a) ${{N}_{\rm~RF}}=2$, ${{N}_{\rm~RB}}=2$; (b) ${{N}_{\rm~RF}}=2$, ${{N}_{\rm~RB}}=4$.

• Figure 5

The sum-rate of different scheme versus SNR. (a) ${{N}_{\rm~RF}}=2$, ${{N}_{\rm~RB}}=2$; (b) ${{N}_{\rm~RF}}=2$, ${{N}_{\rm~RB}}=4$.

• Figure 6

The elapsed time of different schemes.

•

Algorithm 1 Generation of the training data

Require:${\boldsymbol~x}_{\rm~in}$.

Output:$\mathcal{R}$, $\mathcal{B}$. Initialization: $~\mathcal{M}\in~O\left(~{{K}_{\rm~all}}\times~{{N}_{c}}~\right)$, $~\mathcal{U}\in~O\left(~{{N}_{c}}\times~{{N}_{\rm~RB}}~\right)$, $\mathcal{R}\in~O\left(~{{K}_{\rm~all}}\times~{{N}_{\rm~RB}}~\right)$, $\mathcal{B}\in~O\left(~{{K}_{\rm~all}}\times~{{N}_{c}}~\right)$, the selected MU-MIMO user set ${{\Omega~}_{S}}={{\emptyset}}$.

According to the best beam index of each user, set the corresponding element of $\mathcal{M}$ to 1, e.g., if ${{\boldsymbol~B}_{k}}=n_c$, then $\mathcal{M}(k,n_c)=1$.

Assume there are $Q_{{n}_{c}}$ possible schemes of resource allocation for users with the same best beam. Exploiting the exhaustive search algorithm to find the user scheduling scheme with the maximum sum rate. for ${{n}_{c}}=1:{{N}_{c}}$ for ${q}=1:{Q_{{n}_{c}}}$ $\left~\{~k^*~\right~\}=~{\mathop~{\arg~\max~}\nolimits_{q}}~{\sum_{n=1}^{N_{\rm~RB}}}~\Big(~{\frac{{{{\left\|~{{{\overline~{\boldsymbol~H}~}_{k,n}}}~\right\|}^2}}}{{{\sigma~^2}}}}~\Big)$, $\mathcal{U}({{n}_{c},n})={{k}^{*}}$ and ${\mathcal~R}({n_c},n)~=~1$, end end

After resource allocation, each beam is regarded as a virtual OFDMA user multiplexing the whole frequency resource. Then, the integrated channel of each virtual OFDMA user is merged as follows, $\Omega~=\{~{{\tilde{\boldsymbol~H}}_{{{n}_{1}}}},{{\tilde{\boldsymbol~H}}_{{{n}_{2}}}},\ldots,{{\tilde{\boldsymbol~H}}_{{{N}_{c}}}}~\}$ and ${{\tilde~{\boldsymbol~H}}_{{n_c}}}~=~{[~{{{\boldsymbol~H}_{{\cal~U}({n_c},1)}}|{{\boldsymbol~H}_{{\cal~U}({n_c},2)}}|\cdots~|{{\boldsymbol~H}_{{\cal~U}({n_c},{N_{\rm~RB}})}}}~]_{{N_r}~\times~{N_{{t}}}}}$ is the channel matrix of a user allocated in the $n$th RB for the ${n}_{c}$th virtual OFDMA user. When a frequency resource is not been allocated to any user, ${\boldsymbol~H}_{\mathcal{U}({{n}_{c}},{n}})$ is equal to zero matrix.

Select ${N}_{\rm~RF}$ virtual OFDMA users to maximizes sum-rate. for ${{n}_{c}}=1:{{N}_{\rm~RF}}$ if $~{n_c}~=~\mathop~{\max~}\nolimits_{{{\tilde~H}_{{n_c}}}~\in~\Omega~}~{\log~_2}\Big(~{1~+~\frac{{{{\|~{{\boldsymbol~U}_{{n_c}}^{\rm~H}{{{\tilde~{\boldsymbol~H}}}_{{n_c}}}{{\boldsymbol~V}_{{n_c}}}}~\|}^2}}}{{{\delta~^2}~+~\sum\nolimits_{j~\in~{\Omega~_s},{n_c}~\in~\Omega~}~{{{\|~{{\boldsymbol~U}_j^{\rm~H}{{{\tilde~{\boldsymbol~H}}}_{{n_c}}}{{\boldsymbol~V}_j}}~\|}^2}}~}}} +~\sum\nolimits_{j~\in~{\Omega~_s}}~{\frac{{{{\|~{{\boldsymbol~U}_j^{\rm~H}{{{\tilde~{\boldsymbol~H}}}_j}{{\boldsymbol~V}_j}}~\|}^2}}}{{{\delta~^2}~+~{{\|~{{\boldsymbol~U}_{{n_c}}^{\rm~H}{{{\tilde~{\boldsymbol~H}}}_j}{{\boldsymbol~V}_{{n_c}}}}~\|}^2}~+~\sum\nolimits_{i~\in~{\Omega~_s},i~\ne~j}~{{{\|~{{\boldsymbol~U}_i^{\rm~H}{{{\tilde~{\boldsymbol~H}}}_j}{{\boldsymbol~V}_i}}~\|}^2}}~}}}~~\Big)$, where ${\boldsymbol~U}$ and ${\boldsymbol~V}$ are the left unitary matrix and the right unitary matrix of the singular value decomposition of virtual OFDMA user ${{\tilde~{\boldsymbol~H}}}_{n_c}$, respectively. then ${\cal~B}(:,{n_c})~=~1$, $\Omega~~\leftarrow~\Omega~\backslash~\{~{{\tilde~{\boldsymbol~H}}_{{n_c}}}\}$, ${\Omega~_s}~\leftarrow~{\Omega~_s}~\cup~\{~{{\tilde~{\boldsymbol~H}}_{{n_c}}}\}~$. end

Citations

• #### 0

Altmetric

Copyright 2020  CHINA SCIENCE PUBLISHING & MEDIA LTD.  中国科技出版传媒股份有限公司  版权所有