Beam-align: distributed user association for mmWave networks with multi-connectivity

TL;DR

The paper tackles the problem of optimal, per-user multi-connectivity in mmWave networks with beamforming. It formulates an optimal UA problem that maximizes throughput while ensuring user rate requirements, revealing that the optimal MC degree is user- and context-dependent. To enable practical deployment, it introduces BEAM-ALIGN, a distributed heuristic with $O(|\mathcal{U}| \log|\mathcal{U}|)$ complexity that relies only on local misalignment information and can approach optimal performance. The study demonstrates robustness of the approach under blockers, rain, and clustering, and discusses limitations and avenues for future work in adaptive beamwidths and overhead modeling.

Abstract

Since the spectrum below 6 GHz bands is insufficient to meet the high bandwidth requirements of 5G use cases, 5G networks expand their operation to mmWave bands. However, operation at these bands has to cope with a high penetration loss and susceptibility to blocking objects. Beamforming and multi-connectivity (MC) can together mitigate these challenges. But, to design such an optimal user association scheme leveraging these two features is non-trivial and computationally expensive. Previous studies either considered a fixed MC degree for all users or overlooked beamforming. Driven by the question what is the optimal degree of MC for each user in a mmWave network, we formulate a user association scheme that maximizes throughput considering beam formation and MC. Our numerical analysis shows that there is no one-size-fits-all degree of optimal MC; it depends on the number of users, their rate requirements, locations, and the maximum number of active beams at a BS.Based on the optimal association, we design BEAM-ALIGN: an efficient heuristic with polynomial-time complexity O(|U|log|U|), where |U| is the number of users. Moreover, BEAM-ALIGN only uses local BS information - i.e. the received signal quality at the user. Differing from prior works, BEAM-ALIGN considers beamforming, multiconnectivity and line-of-sight probability. Via simulations, we show that BEAM-ALIGN performs close to optimal in terms of per-user capacity and satisfaction while it outperforms frequently-used signal-to-interference-and-noise-ratio based association schemes. We then show that BEAM-ALIGN has a robust performance under various challenging scenarios: the presence of blockers, rain, and clustered users.

Figures (8)

• Figure 1: A mmWave scenario in which users and BSs have directional antennas and users can connect to multiple BSs. Links might be blocked by e.g. a building or another user. The beamforming gain depends on the boresight angle $\phi_{ij}$, geographical angle $\zeta_{ij}$ and misalignment angle $\alpha_{ij}$ of user $i$ and BS $j$. A user can only have a connection with a BS if the link-SNR is higher than $\gamma_{\text{min}}$.
• Figure 2: Capacity, satisfaction level, and distribution of the number of connections under different beamwidths, $k = \infty$.
• Figure 3: Capacity, satisfaction level and number of active beams under SC and MC, $\theta^b = 10\degree$.
• Figure 4: Probability of connecting to the red BS. The beamwidth is $\theta^b = 10\degree$, which means that the boresight angle of the beams are at $0, 10, 20, 30, \ldots$ degrees.
• Figure 5: Histogram of the misalignment of all links in the optimal UA scheme for $\lambda_U = 250$, $\theta^b = 10\degree$ and $k = \infty$. The red line denotes the threshold $\sigma(\theta^b)$, which is twice the standard deviation of the misalignment.