Initial Beam Association in Millimeter Wave Cellular Systems: Analysis and Design Insights

Abstract

Enabling the high data rates of millimeter wave (mmWave) cellular systems requires deploying large antenna arrays at both the basestations and mobile users. The beamforming weights of these large arrays need to be tuned to guarantee sufficient beamforming gains. Prior work on coverage and rate of mmWave cellular networks focused mainly on the case when base stations and mobile users beamfomring vectors are perfectly designed for maximum beamforming gains. Designing beamforming/combining vectors, though, requires training which may impact both the SINR coverage and rate of mmWave cellular systems. This paper characterizes and evaluates the performance of mmWave cellular networks while accounting for the beam training/association overhead. First, a model for the initial beam association is developed based on beam sweeping and downlink control pilot reuse. To incorporate the impact of beam training into system performance, a new metric, called the effective reliable rate, is defined and adopted. Using stochastic geometry, the effective reliable rate of mmWave cellular networks is derived for two special cases: with near-orthogonal control pilots and with full pilot reuse. Analytical and simulation results provide insights into the answers of three important questions: (i) What is the impact of beam association on mmWave network performance? (ii) Should orthogonal or reused control pilots be employed in the initial beam association phase? (iii) Should exhaustive or hierarchical search be adopted for the beam training phase? The results show that unless the employed beams are very wide or the system coherence block length is very small, exhaustive search with full pilot reuse is nearly as good as perfect beam alignment.

Bandwidth Partitioning and Downlink Analysis in Millimeter Wave Integrated Access and Backhaul for 5G

“…These mm-wave cellular network models specifically focus on the mm-wave propagation characteristics which signifantly differ from those of the sub-6 GHz [6], such as the severity of blocking of mm-wave signals by physical obstacles like walls and trees, directional beamforming using arXiv:1802.08776v2 [cs.IT] 11 Nov 2018 antenna arrays, and inteference being dominated by noise [25]. These initial modeling approaches were later extended to study different problems specific to mm-wave cellular networks, such as, cell search [26], antenna beam alignment [27], and cell association in the mm-wave spectrum [28]. With this brief introduction, we now shift our attention to the main focus of this paper which is IAB in mm-wave cellular networks.…”

Exploring the Trade-Off Between Privacy and Coordination in Millimeter Wave Spectrum Sharing

“…We assume a time division framework [13] in which each scheduling In the following, we assume that the association between BSs and UEs has been accomplished based on minimum UE-BS distance criterion. The association between one BS and one UE in a mmWave network involves a beam choosing stage for which a transmit beam is selected to communicate [14]. We assume an SNR maximization scheme where the beam index η u ∈ 1,N BS chosen by the b-th BS to serve its u-th UE is as follows:…”

Impact of Channel Models on the End-to-End Performance of Mmwave Cellular Networks

“…The module is equipped with different channel models [12]: there are two SCMs, i.e., the 3GPP [14] and the NYU channel models [6], and the possibility of using ray-tracing or measured channel traces. In addition to the available models, in this paper we introduce the MmWaveSimpleChannel class, which implements a channel model based on Nakagami fading and simplified beamforming, inspired to the widely-used models for mmWave cellular networks analysis in [15], [23], [26], [27].…”