Robust Downlink Multi-Antenna Beamforming With Heterogenous CSI: Enabling eMBB and URLLC Coexistence

Abstract

Two of the main problems to achieve ultra-reliable low-latency communications (URLLC) are related to instantaneous channel state information (I-CSI) acquisition and the coexistence with other service modes such as enhanced mobile broadband (eMBB). The former comes from the non-negligible time required for accurate I-CSI acquisition, while the latter, from the heterogeneous and conflicting requirements of different nodes sharing the same network resources. In this paper, we leverage the I-CSI of multiple eMBB links and the channel measurement's history of a URLLC user for multi-antenna beamforming design. Specifically, we propose a precoding design that minimizes the transmit power of a base station (BS) providing eMBB and URLLC services with signal-to-interferenceplus-noise ratio (SINR) and outage constraints, respectively, by modifying existing I-CSI-based precoding schemes to account for URLLC channel history information. Moreover, we illustrate and validate the proposed method by adopting zero-forcing (ZF) and the transmit power minimization (TPM) precoding with SINR constraints. We show that the ZF implementation outperforms TPM in adverse channel conditions as in Rayleigh fading, while the situation is rapidly reversed as the channel experiences some line-of-sight (LOS). Finally, we determine the confidence levels at which the target outage probabilities are reached. For instance, we show that outage probabilities below 10 −3 are achievable with more than 99% confidence for both precoding schemes under favorable LOS conditions with 16 transmit antennas and 500 samples of URLLC channel history.

Extreme Value Theory-Based Robust Minimum-Power Precoding for URLLC

“…In recent years, several works have focused on the solution to minimum-power precoding design problems where the UEs have strict QoS requirements, e.g., SINR, outage probability, data rate [12]- [19]. For instance, the work in [13] proposed a worst-case MIMO precoding design to guarantee an SINR performance of the UE for every channel realization and channel estimation error, thus, robust against channel/estimation uncertainties.…”

AI-Assisted Dynamic Frame Structure With Intelligent Puncturing Schemes for 5G Networks

“…In [37], the authors proposed a spectrum partition scheme for maximizing the eMBB data rate and satisfying the URLLC latency and reliability. In [38], the authors proposed a precoding design in order to minimize the transmit power of a Base Station (BS) while satisfying the QoS of eMBB and URLLC users based on imperfect CSI. Joint Transmission (JT) and Orthogonal Transmission (OT) modes were investigated for satisfying the Block Error Rate (BLER) and data rate of URLLC and eMBB, respectively in [39].…”

Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication—A Tutorial

“…Precoding [79]): Assume that a critical data message arriving at the data queue of a multiantenna node A demands an urgent URLLC transmission to a single-antenna node B. Because of the stringent latency constraints, the transmission needs to be carried out immediately in one shot; thus, instantaneous CSI cannot be acquired at A.…”

“…Alternatively, and/or in addition, one might leverage a probabilistic upper bound of the sample mean to ensure ( 28) is met with a certain confidence level. Such bound can be designed exploiting the central limit theorem, as in [79]. In general, the confidence intervals narrow as L increases.…”

“…Assume that s ∈ C n is sparse, i.e., f (s) ≪ n, and needs to be estimated from m < n samples that are collected via a sensing matrix H ∈ C m×n as y = Hs + w (79) where y ∈ C m and w ∈ C m denote, respectively, the receive and noise signals. Then, compressed sensing problems are commonly formulated to find the sparsest signal, such that the estimated measurement noise power is upper bounded by a preconfigured noise level η as…”