The fifth generation (5G) of the mobile networks is envisioned to feature two major service classes: ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB). URLLC applications require a stringent one-way radio latency of 1 ms with 99.999% success probability while eMBB services demand extreme data rates. The coexistence of the URLLC and eMBB quality of service (QoS) on the same radio spectrum leads to a challenging scheduling optimization problem, that is vastly different from that of the current cellular technology. This calls for the novel scheduling solutions which cross-optimize the system performance on a user-centric, instead of network-centric basis. In this paper, a null-space-based spatial preemptive scheduler for joint URLLC and eMBB traffic is proposed for the densely populated 5G networks. Proposed scheduler framework seeks for cross-objective optimization, where the critical URLLC QoS is guaranteed while extracting the maximum possible eMBB ergodic capacity. It utilizes the system spatial degrees of freedom in order to instantly offer an interference-free subspace for the critical URLLC traffic. Thus, a sufficient URLLC decoding ability is always preserved, and with the minimal impact on the eMBB performance. Analytical analysis and extensive system level simulations are conducted to evaluate the performance of the proposed scheduler against the state-of-the-art scheduler proposals from industry and academia. Simulation results show that the proposed scheduler offers extremely robust URLLC latency performance with a significantly improved ergodic capacity.INDEX TERMS 5G, radio resource management, scheduling, ultra-reliable low-latency communications (URLLC), enhanced mobile broadband (eMBB), MU-MIMO, preemptive, null space.

5G new radio is envisioned to support three major service classes: enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine type communications. Emerging URLLC services require up to one millisecond of communication latency with 99.999% success probability. Though, there is a fundamental trade-off between system spectral efficiency (SE) and achievable latency. This calls for novel scheduling protocols which cross-optimize system performance on usercentric; instead of network-centric basis. In this paper, we develop a joint multi-user preemptive scheduling strategy to simultaneously cross-optimize system SE and URLLC latency. At each scheduling opportunity, available URLLC traffic is always given higher priority. When sporadic URLLC traffic appears during a transmission time interval (TTI), proposed scheduler seeks for fitting the URLLC-eMBB traffic in a multiuser transmission. If the available spatial degrees of freedom are limited within a TTI, the URLLC traffic instantly overwrites part of the ongoing eMBB transmissions to satisfy the URLLC latency requirements, at the expense of minimal eMBB throughput loss. Extensive dynamic system level simulations show that proposed scheduler provides significant performance gain in terms of eMBB SE and URLLC latency.

The ultra-reliable and low-latency communication (URLLC) is the key driver of the current 5G new radio standardization. URLLC encompasses sporadic and small-payload transmissions that should be delivered within extremely tight radio latency and reliability bounds, i.e., a radio latency of 1 ms with 99.999% success probability. However, such URLLC targets are further challenging in the 5G dynamic time division duplexing (TDD) systems, due to the switching between the uplink and downlink transmission opportunities and the additional inter-cell cross-link interference (CLI). This paper presents a system level analysis of the URLLC outage performance within the 5G new radio flexible TDD systems. Specifically, we study the feasibility of the URLLC outage targets compared to the case with the 5G frequency division duplexing (FDD), and with numerous 5G design variants. The presented results therefore offer valuable observations on the URLLC outage performance in such deployments, and hence, introducing the state-of-the-art flexible-FDD technology.

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The fifth generation (5G) radio access technology is designed to support highly delaysensitive applications, i.e., ultra-reliable and low-latency communications (URLLC). For dynamic time division duplex (TDD) systems, the real-time optimization of the radio pattern selection becomes of a vital significance in achieving decent URLLC outage latency. In this study, a dual reinforcement machine learning (RML) approach is developed for online pattern optimization in 5G new radio TDD deployments. The proposed solution seeks to minimizing the maximum URLLC tail latency, i.e., min-max problem, by introducing nested RML instances. The directional and real-time traffic statistics are monitored and given to the primary RML layer to estimate the sufficient number of downlink (DL) and uplink (UL) symbols across the upcoming radio pattern. The secondary RML sub-networks determine the DL and UL symbol structure which best minimizes the URLLC outage latency. The proposed solution is evaluated by extensive and highly-detailed system level simulations, where our results demonstrate a considerable URLLC outage latency improvement with the proposed scheme, compared to the state-of-the-art dynamic-TDD proposals.