## 2 Background for Location-Aware Beamforming in 5G Ultra-Dense Networks
If in UL of 5G NR measuring pilots, transmitted by each UE, can be accomplished by gNB, acquisition of CSI in DL is more complicated. Long-Term Evolution (LTE) standard performed channel estimation for conventional MIMO in DL in the following order: eNodeB (eNB) transmitted pilots, UE measured them and returned back CSI estimate. However, what was feasible in the networks of the previous generation LTE for conventional MIMO, is a challenge in 5G NR for massive MIMO due to the following circumstances [71]. Orthogonal pilots for mMIMO should occupy huge time-frequency resource in DL, proportional to the number of gNB AE, which is an order of magnitude greater than in eNB with conventional MIMO. Consequently, for FDD mode, UE also should occupy huge time-frequency resource in UL to feedback CSI estimate to gNB. If we talk about time division duplex (TDD) mode, problem can be alleviated due to reciprocity between the UL and DL, however it is still a challenge for scenarios with high mobility. Authors in [71] among research problems for mMIMO distinguish so called \"Non-CSI@TX operation:\", which means, that before a link with UE has been established, gNB has no way of knowing CSI. At the same time, one of the ways to solve this problem could be LAB approach. Comparison between beamforming and spatial multiplexing (SM) in mmWave MIMO wireless communication systems is performed in [72] and show, that SM and BF could work in tandem. However, SM is more effective in predominantly NLOS links with sufficient channel diversity or rank to support multiple parallel streams in bandwidth-limited high SNR regime, while BF is more effective in predominantly LOS links with inherent to mmWave high pathloss (PL) in power-limited low SNR regime. Measurement campaigns for 28 and 73 GHz revealed, that beams, properly aligned at both transmitter and receiver sides, are characterized with minimum number of MPC, root mean square (RMS) delay spread and propagation PL; this in turn, facilitates exploiting narrow beamwidth and single-carrier modulation without equalization. Among beam training authors in [72] distinguishes codebook-based, AOD based and long-term BF approaches, and each of these methods can be improved by knowing UE/gNB location in advance, so that to decrease beam space and speed up beam search during initial beam sweeping. Work [73] addresses implementation issues of large-scale antenna systems (LASS) in mmWave in general, and hybrid BF schemes in particular. Hybrid BF structure includes a much lower number of digital transceivers, than the total number of AE, thus each transceiver is connected to multiple AE, and the phase of AE is controlled through a network of analog phase shifters. Authors in [73] investigate optimal ratio between the number of transceivers and corresponding number of AE per each transceiver and seek reference signal design to obtain DL CSI, taking an assumption, that partial information about AOD and AOA is available. Simulation results of 32-element uniform linear array (ULA) revealed, that AOD estimation accuracy of RS with hybrid BF is limited by the main beam direction of analog BF on each transceiver. In particular [73], the further main beam direction of hybrid BF is away from the analog BF main beam direction, the smaller is the gain. Presented background reveals several problems in organizing directional links in 5G UDN and substantiates location-aware beamforming as a solution. Consider further briefly approaches for interference evaluation in 5G mmWave UDN. Investigation [74] consider deployment of mmWave gNB microcells in the areas with high UE density in addition to traditional sub-6 GHz macrocells. While sub-6 GHz Base Stations (BS) and UE are equipped with omni-directional antennas, mmWave gNB utilize directional transmission. Using stochastic geometry and assumption of UE distribution according to Poisson cluster process, authors in [74] develop comprehensive system level analytical framework and analyze various performance metrics, including interference characterization.