Planning and Operation of Millimeter-wave Downlink Systems with Hybrid Beamforming

TL;DR

This paper investigates downlink radio resource management (RRM) in millimeter-wave systems with codebook-based hybrid beamforming in a single cell with valuable insights that show the importance of not neglecting the constraint and guides the design of low-complexity and high-performance online downlink RRM schemes.

Abstract

This paper investigates downlink radio resource management (RRM) in millimeter-wave systems with codebook-based hybrid beamforming in a single cell. We consider a practical but often overlooked multi-channel scenario where the base station is equipped with fewer radio frequency chains than there are user equipment (UEs) in the cell. In this case, analog beam selection is important because not all beams preferred by UEs can be selected simultaneously, and since the beam selection cannot vary across subchannels in a time slot, this creates a coupling between subchannels within a time slot. None of the solutions proposed in the literature deal with this important constraint. The paper begins with an offline study that analyzes the impact of different RRM procedures and system parameters on performance. An offline joint RRM optimization problem is formulated and solved that includes beam set selection, UE set selection, power distribution, modulation and coding scheme selection, and digital beamforming as a part of hybrid beamforming. The evaluation results of the offline study provide valuable insights that shows the importance of not neglecting the constraint and guide the design of low-complexity and high-performance online downlink RRM schemes in the second part of the paper. The proposed online RRM algorithms perform close to the performance targets obtained from the offline study while offering acceptable runtime.

Figures (7)

• Figure 1: Fully connected HBF architecture at the BS and UE
• Figure 2: An example of piece-wise constant rate function $f(\gamma)$ and two convex approximations $\tilde{f}(\gamma)$, a tight one and an upper bound (see Section \ref{['subsec:ratefunc']})
• Figure 3: (a) Quality of feasible solution, and (b) System performance vs. $K$ for different cases EPD/OPD and ZF-DBF/N-DBF. For (a) and (b): $N_b=128$, $N_u=16$, $B_b=32$, $B_u=4$.
• Figure 4: (a) Impact of ConB on system performance considering OPD, ZF-DBF, $N_b=128$, $N_u=16$, $B_b=32$, $B_u=4$, (b) System performance vs. $K$ for different ABF codebook sizes considering OPD, ZF-DBF, $U=10$, $N_b=128$, and $N_u=16$.
• Figure 5: (a) Impact of the number of preferred beams on system performance considering OPD, ZF-DBF (b) WF performance for ZF-DBF. For (a) and (b): $N_b=128$, $N_u=16$, $B_b=32$, $B_u=4$.

Theorems & Definitions (1)

• Remark 1