GBLinks: GNN-Based Beam Selection and Link Activation for Ultra-Dense D2D mmWave Networks

In this paper, we consider the problem of joint beam selection and link activation across a set of communication pairs to effectively control the interference between communication pairs via inactivating part communication pairs in ultra-dense device-to-device (D2D) mmWave communication networks. The resulting optimization problem is formulated as an integer programming problem that is nonconvex and NP-hard problem. Consequently, the global optimal solution, even the local optimal solution, cannot be generally obtained. To overcome this challenge, this paper resorts to design a deep learning architecture based on graph neural network to finish the joint beam selection and link activation, with taking the network topology information into account. Meanwhile, we present an unsupervised Lagrangian dual learning framework to train the parameters of GBLinks model. Numerical results show that the proposed GBLinks model can converges to a stable point with the number of iterations increases, in terms of the sum rate. Furthermore, the GBLinks model can reach near-optimal solution through comparing with the exhaustive search scheme in small-scale ultra-dense D2D mmWave communication networks and outperforms GreedyNoSched and the SCA-based method. It also shows that the GBLinks model can generalize to varying scales and densities of ultra-dense D2D mmWave communication networks. Index Terms Millimeter wave communication, graph neural networks, beam selection, link activation, interference channel. S. He, S. Xiong, W. Zhang, and J. Ren are with the School of Computer Science and Engineering, Central South University, Changsha 410083, China. S. He is also with the Purple Mountain Laboratories, Nanjing 210096, China. (email: {shiwen.he.hn, shaowen.xiong, renju}@csu.edu.cn, sunbirdcsu@Outlook.com). Y. Yang is also with the Purple Mountain Laboratories, Nanjing 210096, China. (email: yangyiting@pmlabs.com.cn). Y. Huang is with the National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing 210096, China. He is also with the Purple Mountain Laboratories, Nanjing 210096, China. (email: huangym@seu.edu.cn).

[1]  Robert W. Heath,et al.  An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems , 2015, IEEE Journal of Selected Topics in Signal Processing.

[2]  Yongming Huang,et al.  Hybrid Precoder Design for Cache-Enabled Millimeter-Wave Radio Access Networks , 2019, IEEE Transactions on Wireless Communications.

[3]  Prabhu Babu,et al.  Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning , 2017, IEEE Transactions on Signal Processing.

[4]  Pascal Van Hentenryck,et al.  Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods , 2020, AAAI.

[5]  Jiguo Yu,et al.  Efficient Link Scheduling in Wireless Networks Under Rayleigh-Fading and Multiuser Interference , 2020, IEEE Transactions on Wireless Communications.

[6]  Alejandro Ribeiro,et al.  Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks , 2019, IEEE Transactions on Signal Processing.

[7]  Wenwu Zhu,et al.  Deep Learning on Graphs: A Survey , 2018, IEEE Transactions on Knowledge and Data Engineering.

[8]  Matti Latva-aho,et al.  Weighted Sum-Rate Maximization in Wireless Networks: A Review , 2012, Found. Trends Netw..

[9]  Chin-Sean Sum,et al.  Beam Codebook Based Beamforming Protocol for Multi-Gbps Millimeter-Wave WPAN Systems , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[10]  Yuanming Shi,et al.  LORM: Learning to Optimize for Resource Management in Wireless Networks With Few Training Samples , 2018, IEEE Transactions on Wireless Communications.

[11]  Robert W. Heath,et al.  Spatially Sparse Precoding in Millimeter Wave MIMO Systems , 2013, IEEE Transactions on Wireless Communications.

[12]  Markku J. Juntti,et al.  Achieving Energy Efficiency Fairness in Multicell MISO Downlink , 2015, IEEE Communications Letters.

[13]  Santiago Segarra,et al.  Unfolding WMMSE Using Graph Neural Networks for Efficient Power Allocation , 2021, IEEE Transactions on Wireless Communications.

[14]  Derrick Wing Kwan Ng,et al.  Power Efficient Resource Allocation for Full-Duplex Radio Distributed Antenna Networks , 2015, IEEE Transactions on Wireless Communications.

[15]  Pedro M. Castro,et al.  Tightening piecewise McCormick relaxations for bilinear problems , 2015, Comput. Chem. Eng..

[16]  Robert W. Heath,et al.  Device-to-Device Millimeter Wave Communications: Interference, Coverage, Rate, and Finite Topologies , 2015, IEEE Transactions on Wireless Communications.

[17]  Kin K. Leung,et al.  Adaptive Federated Learning in Resource Constrained Edge Computing Systems , 2018, IEEE Journal on Selected Areas in Communications.

[18]  Chuan Zhang,et al.  Deep Neural Hybrid Beamforming for Multi-User mmWave Massive MIMO System , 2019, 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[19]  Tony Q. S. Quek,et al.  Constrained Deep Learning for Wireless Resource Management , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[20]  Jianyue Zhu,et al.  Beamforming Design for Multiuser uRLLC With Finite Blocklength Transmission , 2020, IEEE Transactions on Wireless Communications.

[21]  Philip S. Yu,et al.  A Comprehensive Survey on Graph Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Xinlei Chen,et al.  Device-to-Device Communications Enabled Energy Efficient Multicast Scheduling in mmWave Small Cells , 2017, IEEE Transactions on Communications.

[23]  Geoffrey Y. Li,et al.  Learning to Branch: Accelerating Resource Allocation in Wireless Networks , 2019, IEEE Transactions on Vehicular Technology.

[24]  Iain B. Collings,et al.  Energy Efficient Hybrid Beamforming for Multi-User Millimeter Wave Communication With Low-Resolution A/D at Transceivers , 2020, IEEE Journal on Selected Areas in Communications.

[25]  Yuanming Shi,et al.  Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis , 2021, IEEE Journal on Selected Areas in Communications.

[26]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[27]  Geoffrey Ye Li,et al.  Graph Embedding-Based Wireless Link Scheduling With Few Training Samples , 2019, IEEE Transactions on Wireless Communications.

[28]  Walid Saad,et al.  A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks , 2021, IEEE Transactions on Wireless Communications.

[29]  Jun Zhang,et al.  Hybrid Beamforming for 5G and Beyond Millimeter-Wave Systems: A Holistic View , 2019, IEEE Open Journal of the Communications Society.

[30]  Yuanming Shi,et al.  A Graph Neural Network Approach for Scalable Wireless Power Control , 2019, 2019 IEEE Globecom Workshops (GC Wkshps).

[31]  Mengyao Ge,et al.  Mobility-Aware Multi-User MIMO Link Scheduling for Dense Wireless Networks , 2018, 2018 IEEE International Conference on Communications (ICC).

[32]  Ha H. Nguyen,et al.  Joint Optimization of Cooperative Beamforming and Relay Assignment in Multi-User Wireless Relay Networks , 2014, IEEE Transactions on Wireless Communications.

[33]  Robert W. Heath,et al.  Energy-Efficient Hybrid Analog and Digital Precoding for MmWave MIMO Systems With Large Antenna Arrays , 2015, IEEE Journal on Selected Areas in Communications.

[34]  Hyun-Ho Choi,et al.  Learning-Based Joint Optimization of Transmit Power and Harvesting Time in Wireless-Powered Networks With Co-Channel Interference , 2020, IEEE Transactions on Vehicular Technology.

[35]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[36]  Wonjin Sung,et al.  Construction of a Generalized DFT Codebook Using Channel-Adaptive Parameters , 2017, IEEE Communications Letters.

[37]  N. Sidiropoulos,et al.  Learning to Optimize: Training Deep Neural Networks for Interference Management , 2017, IEEE Transactions on Signal Processing.

[38]  Weihua Zhuang,et al.  A Survey of Millimeter-Wave Communication: Physical-Layer Technology Specifications and Enabling Transmission Technologies , 2021, Proceedings of the IEEE.

[39]  Hien Quoc Ngo,et al.  Cell-Free Massive MIMO for Wireless Federated Learning , 2019, IEEE Transactions on Wireless Communications.

[40]  Xiaohu You,et al.  Beam Alignment and Tracking for Millimeter Wave Communications via Bandit Learning , 2020, IEEE Transactions on Communications.

[41]  H. Vincent Poor,et al.  Scheduling Policies for Federated Learning in Wireless Networks , 2019, IEEE Transactions on Communications.

[42]  Pascal Van Hentenryck,et al.  Constraint-Based Lagrangian Relaxation , 2014, CP.

[43]  Wei Cui,et al.  Spatial Deep Learning for Wireless Scheduling , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[44]  Yongming Huang,et al.  Joint Optimization of Analog Beam and User Scheduling for Millimeter Wave Communications , 2017, IEEE Communications Letters.

[45]  Chunmei Xu,et al.  Joint User Scheduling and Beam Selection in mmWave Networks Based on Multi-Agent Reinforcement Learning , 2020, 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM).