HBF MU-MIMO with Interference-Aware Beam Pair Link Allocation for Beyond-5G mm-Wave Networks

TL;DR

The paper tackles residual spatial interference in hybrid beamforming MU-MIMO mm-wave networks by introducing IABA, an interference-aware, 5G-NR-compliant BPL allocation scheme. It combines a site-specific, 3D ray-tracing based system model with a centralized or distributed CSI-RS monitoring approach to select secondary beam pair links that maximize network throughput while satisfying SINR and power constraints. Key contributions include a detailed multi-cell evaluation using realistic channel data, a rigorous IABA algorithm with complexity analysis, and extensive performance results showing that IABA can outperform 5G-NR baseline and even approach or exceed fully digital MU-MIMO under practical CSI signaling. The findings demonstrate that interference-aware BPL allocation is essential for realizing the full potential of beyond-5G mm-wave SDMA, especially as antenna arrays scale and networks densify, making IABA a viable path for practical deployments.

Abstract

Hybrid beamforming (HBF) multi-user multiple-input multiple-output (MU-MIMO) is a key technology for unlocking the directional millimeter-wave (mm-wave) nature for spatial multiplexing beyond current codebook-based 5G-NR networks. In order to suppress co-scheduled users' interference, HBF MU-MIMO is predicated on having sufficient radio frequency chains and accurate channel state information (CSI), which can otherwise lead to performance losses due to imperfect interference cancellation. In this work, we propose IABA, a 5G-NR standard-compliant beam pair link (BPL) allocation scheme for mitigating spatial interference in practical HBF MU-MIMO networks. IABA solves the network sum throughput optimization via either a distributed or a centralized BPL allocation using dedicated CSI reference signals for candidate BPL monitoring. We present a comprehensive study of practical multi-cell mm-wave networks and demonstrate that HBF MU-MIMO without interference-aware BPL allocation experiences strong residual interference which limits the achievable network performance. Our results show that IABA offers significant performance gains over the default interference-agnostic 5G-NR BPL allocation, and even allows HBF MU-MIMO to outperform the fully digital MU-MIMO baseline, by facilitating allocation of secondary BPLs other than the strongest BPL found during initial access. We further demonstrate the scalability of IABA with increased gNB antennas and densification for beyond-5G mm-wave networks.

Figures (11)

• Figure 1: Example codebook with 16 beams for a sector antenna covering the steering range of $\phi \in (-45^\circ,~45^\circ)$.
• Figure 2: Illustration of a mm-wave network, with an example scenario of a gNB simultaneously serving $N_u=6$ UEs. The gNB is using a fully-connected HBF architecture with $N_{sec}=4$ sector panels each equipped with $N_t$ antenna elements and $N^g_{RF,sec}=4$ RF chains, i. e. the gNB can simultaneously serve up to $N^g_{RF}=16$ UEs . For clarity, we only illustrate two out of the four gNB sector panels, with four UEs being served by sector #$1$ and two UEs served by sector #$2$. The UEs use a CBF architecture with $N_{sec}=4$ sector panels, each equipped with $N_r$ antenna elements, and $N^u_{RF}=1$ RF chain.
• Figure 3: 5G-NR-compliant operation for IABA: (i) SSB-based initial beam sweep for BPL selection, (ii) scheduling of NZP CSI-RS for BPL monitoring, (iii) NZP CSI-RS scheduling for MU-MIMO channel/interference measurements.
• Figure 4: Illustration of the urban site in Seoul, showing the building layout and an example gNB ($\bullet$) and UE ($\times$) deployment over the network study area (green box); $\lambda_{gNB}=64$$\text{gNBs/km}^2$ and $\lambda_{UE}=1000$$\text{UEs/km}^2$.
• Figure 5: SINR, INR and SNR distribution for the different BPL allocation approaches, assuming $n_q=\infty$, $N_{CSI-RS}=\infty$, i. e. ideal channel estimation and CSI-RS BPL monitoring, and our baseline network of $\lambda_{gNB}=64$ BSs/km$^2$, $\lambda_{UE}=1000$ UEs/km$^2$, $N_t=256/N_r=16$ elements.