Beam Allocation and Power Optimization for Energy-Efficiency in Multiuser mmWave Massive MIMO System

This paper studies beam allocation and power optimization scheme to decrease the hardware cost and downlink power consumption of a multiuser millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. Our target is to improve energy efficiency (EE) and decrease power consumption without obvious system performance loss. To this end, we propose a beam allocation and power optimization scheme. First, the problem of beam allocation and power optimization is formulated as a multivariate mixed-integer non-linear programming problem. Second, due to the non-convexity of this problem, we decompose it into two sub-problems which are beam allocation and power optimization. Finally, the beam allocation problem is solved by using a convex optimization technique. We solve the power optimization problem in two steps. First, the non-convex problem is converted into a convex problem by using a quadratic transformation scheme. The second step implements Lagrange dual and sub-gradient methods to solve the optimization problem. Performance analysis and simulation results show that the proposed algorithm performs almost identical to the exhaustive search (ES) method, while the greedy beam allocation and suboptimal beam allocation methods are far from the ES. Furthermore, experiment results demonstrated that our proposed algorithm outperforms the compared the greedy beam allocation method and the suboptimal beam allocation scheme in terms of average service ratio.

[1]  Wei Yu,et al.  Fractional Programming for Communication Systems—Part I: Power Control and Beamforming , 2018, IEEE Transactions on Signal Processing.

[2]  Junyuan Wang,et al.  Beam allocation and performance evaluation in switched-beam based massive MIMO systems , 2015, 2015 IEEE International Conference on Communications (ICC).

[3]  Akbar M. Sayeed,et al.  Beamspace MIMO for high-dimensional multiuser communication at millimeter-wave frequencies , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).

[4]  Xiang-Gen Xia,et al.  Joint Power Allocation and Beamforming for Non-Orthogonal Multiple Access (NOMA) in 5G Millimeter Wave Communications , 2017, IEEE Transactions on Wireless Communications.

[5]  C. I Seven fundamental rethinking for next-generation wireless communications , 2017, APSIPA Transactions on Signal and Information Processing.

[6]  Theodore S. Rappaport,et al.  Millimeter Wave Mobile Communications for 5G Cellular: It Will Work! , 2013, IEEE Access.

[7]  Shuangfeng Han,et al.  Large-scale antenna systems with hybrid analog and digital beamforming for millimeter wave 5G , 2015, IEEE Communications Magazine.

[8]  Tao Jiang,et al.  Beam Selection for mmWave Massive MIMO Systems Under Hybrid Transceiver Architecture , 2018, IEEE Communications Letters.

[9]  Fredrik Tufvesson,et al.  5G: A Tutorial Overview of Standards, Trials, Challenges, Deployment, and Practice , 2017, IEEE Journal on Selected Areas in Communications.

[10]  Xiaohu You,et al.  Utility-Energy Efficiency Oriented User Association With Power Control in Heterogeneous Networks , 2018, IEEE Wireless Communications Letters.

[11]  Shajahan Kutty,et al.  Beamforming for Millimeter Wave Communications: An Inclusive Survey , 2016, IEEE Communications Surveys & Tutorials.

[12]  George K. Karagiannidis,et al.  A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends , 2017, IEEE Journal on Selected Areas in Communications.

[13]  Erik G. Larsson,et al.  Massive MIMO for next generation wireless systems , 2013, IEEE Communications Magazine.

[14]  Gang Chuai,et al.  User Selection and Power Allocation in Massive multiuser MIMO Systems , 2019, 2019 IEEE Globecom Workshops (GC Wkshps).

[15]  Meixia Tao,et al.  Resource Allocation in Spectrum-Sharing OFDMA Femtocells With Heterogeneous Services , 2014, IEEE Transactions on Communications.

[16]  T. Aaron Gulliver,et al.  Intelligent Outage Probability Prediction for Mobile IoT Networks Based on an IGWO-Elman Neural Network , 2021, IEEE Transactions on Vehicular Technology.

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

[18]  Geoffrey Ye Li,et al.  Energy-Efficient Design of Indoor mmWave and Sub-THz Systems With Antenna Arrays , 2016, IEEE Transactions on Wireless Communications.

[19]  Lin Dai,et al.  Low-Complexity Beam Allocation for Switched-Beam Based Multiuser Massive MIMO Systems , 2016, IEEE Transactions on Wireless Communications.

[20]  Junyuan Wang,et al.  Exploiting Low Complexity Beam Allocation in Multi-User Switched Beam Millimeter Wave Systems , 2019, IEEE Access.

[21]  Junyuan Wang,et al.  On the Performance of Beam Allocation Based Multi-User Massive MIMO Systems , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[22]  Linglong Dai,et al.  Near-Optimal Beam Selection for Beamspace MmWave Massive MIMO Systems , 2016, IEEE Communications Letters.

[23]  Junyuan Wang,et al.  Frequency Reuse of Beam Allocation for Multiuser Massive MIMO Systems , 2018, IEEE Transactions on Wireless Communications.