Simultaneous Beam and User Selection for the Beamspace mmWave/THz Massive MIMO Downlink

Beamspace millimeter-wave (mmWave) and terahertz (THz) massive MIMO constitute attractive schemes for next-generation communications, given their abundant bandwidth and high throughput. However, their user and beam selection problem has not been efficiently addressed yet. Inspired by this challenge, we develop low-complexity solutions explicitly. In contrast to the zero forcing in the prior art, we introduce the dirty paper coding (DPC) into the joint user and beam selection problem. We unveil the compelling properties of the DPC sum rate in beamspace massive MIMO, showing its monotonic evolution against the number of users and beams selected. We then exploit its beneficial properties for substantially simplifying the joint user and beam selection problem. Furthermore, we develop a set of algorithms striking unique trade-offs for solving the simplified problem, facilitating simultaneous user and beam selection based on partial beamspace channels for the first time. Additionally, we derive the sum rate bound of the algorithms and analyze their complexity. Our simulation results validate the effectiveness of the proposed design and analysis, confirming their superiority over prior solutions.

[1]  Xuemai Gu,et al.  Joint User Grouping and Beam Selection for Beamspace mmWave Multi-User MIMO System , 2022, IEEE Communications Letters.

[2]  Hongwen Yang,et al.  SLNR-Based Beamspace Precoding and Beam Selection for Wideband mmWave Massive MIMO , 2022, IEEE Communications Letters.

[3]  H. Vincent Poor,et al.  Maximizing the Geometric Mean of User-Rates to Improve Rate-Fairness: Proper vs. Improper Gaussian Signaling , 2021, IEEE Transactions on Wireless Communications.

[4]  Qianyun Zhang,et al.  On the Complexity Reduction of Beam Selection Algorithms for Beamspace MIMO Systems , 2021, IEEE Wireless Communications Letters.

[5]  N. Fonseca,et al.  Quasi-Optical Multi-Beam Antenna Technologies for B5G and 6G mmWave and THz Networks: A Review , 2021, IEEE Open Journal of Antennas and Propagation.

[6]  Nelson J. G. Fonseca,et al.  Circuit Type Multiple Beamforming Networks for Antenna Arrays in 5G and 6G Terrestrial and Non-Terrestrial Networks , 2021, IEEE Journal of Microwaves.

[7]  Erik G. Larsson,et al.  Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts , 2020, Science China Information Sciences.

[8]  Hongwen Yang,et al.  Low-Complexity Joint User and Beam Selection for Beamspace mmWave MIMO Systems , 2020, IEEE Communications Letters.

[9]  Octavia A. Dobre,et al.  A Prospective Look: Key Enabling Technologies, Applications and Open Research Topics in 6G Networks , 2020, IEEE Access.

[10]  Walid Saad,et al.  A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems , 2019, IEEE Network.

[11]  Lajos Hanzo,et al.  Beamspace Precoding and Beam Selection for Wideband Millimeter-Wave MIMO Relying on Lens Antenna Arrays , 2019, IEEE Transactions on Signal Processing.

[12]  Lajos Hanzo,et al.  Wideband Beamspace Channel Estimation for Millimeter-Wave MIMO Systems Relying on Lens Antenna Arrays , 2019, IEEE Transactions on Signal Processing.

[13]  Yuping Zhao,et al.  A Novel Two-Stage Beam Selection Algorithm in mmWave Hybrid Beamforming System , 2019, IEEE Communications Letters.

[14]  Wei Ni,et al.  Expeditious Estimation of Angle-of-Arrival for Hybrid Butler Matrix Arrays , 2019, IEEE Transactions on Wireless Communications.

[15]  Wei Ni,et al.  Efficient Angle-of-Arrival Estimation of Lens Antenna Arrays for Wireless Information and Power Transfer , 2019, IEEE Journal on Selected Areas in Communications.

[16]  Dezhong Peng,et al.  Massive MIMO Linear Precoding: A Survey , 2018, IEEE Systems Journal.

[17]  Lajos Hanzo,et al.  Joint Design of Beam Selection and Precoding Matrices for mmWave MU-MIMO Systems Relying on Lens Antenna Arrays , 2018, IEEE Journal of Selected Topics in Signal Processing.

[18]  K. V. Srinivas,et al.  A Beam Selection Algorithm for Millimeter-Wave Multi-User MIMO Systems , 2018, IEEE Communications Letters.

[19]  Shi Jin,et al.  Beamspace Channel Estimation in mmWave Systems Via Cosparse Image Reconstruction Technique , 2017, IEEE Transactions on Communications.

[20]  Akbar M. Sayeed,et al.  Low RF-Complexity Technologies to Enable Millimeter-Wave MIMO with Large Antenna Array for 5G Wireless Communications , 2016, IEEE Communications Magazine.

[21]  Lajos Hanzo,et al.  Millimeter-Wave Communications: Physical Channel Models, Design Considerations, Antenna Constructions, and Link-Budget , 2018, IEEE Communications Surveys & Tutorials.

[22]  Linglong Dai,et al.  Fast Channel Tracking for Terahertz Beamspace Massive MIMO Systems , 2017, IEEE Transactions on Vehicular Technology.

[23]  Shuangfeng Han,et al.  Reliable Beamspace Channel Estimation for Millimeter-Wave Massive MIMO Systems with Lens Antenna Array , 2017, IEEE Transactions on Wireless Communications.

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

[25]  Rui Zhang,et al.  Millimeter Wave MIMO With Lens Antenna Array: A New Path Division Multiplexing Paradigm , 2015, IEEE Transactions on Communications.

[26]  Jinho Choi,et al.  Beam Selection in mm-Wave Multiuser MIMO Systems Using Compressive Sensing , 2015, IEEE Transactions on Communications.

[27]  Christos Masouros,et al.  Low RF-Complexity Millimeter-Wave Beamspace-MIMO Systems by Beam Selection , 2015, IEEE Transactions on Communications.

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

[29]  N.D. Sidiropoulos,et al.  On downlink beamforming with greedy user selection: performance analysis and a simple new algorithm , 2005, IEEE Transactions on Signal Processing.