### Related Work  
There have been a number of studies addressing the question of network slicing at the air interface. Specifically, Ref. [15] focuses on two key features of slicing: traffic isolation and automated management. In doing so, a pre-emption-based prioritization (PP) scheme, \"merging\" the resource allocation and traffic prioritization schemes, is proposed. To evaluate or estimate the so-called PP scheme, a queuing system model analyzing the functioning of a single base station (BS) accommodating multiple services with different QoS requirements is given. Concretely, this paper considers each service-oriented slice to be assigned an overall share of radio resources, including guaranteed ones utilized by neighboring slices. As one key result, the proposed PP scheme can achieve 100% gain in terms of blocking probabilities with respect to a predefined baseline. In addition, Ref. [11] investigates the following features of slicing: flexible priority-based traffic isolation, fair QoS-aware resource allocation, and efficient usage of radio resources. The authors proposed a slicing scheme bolstering the traffic isolation and maintaining the efficient usage of radio resources, and represented it by utilizing a queuing model with three 5G services having uniform data rate requirements at one BS. This slicing scheme takes advantage of the complete partitioning and complete sharing policies' key features. In practice, this paper considers the users of each service-oriented slice to be ensured a minimum data rate, with the possibility to achieve higher data rates whenever radio resources are free. The proposed slicing scheme can achieve 90% gain in terms of average user satisfaction index, and reduce the session drop probability by an order of magnitude dependent on the baseline. Authors in [13] studied the industrial deployment of 5G with simultaneous support of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services. The effectiveness of coexistence strategies is achieved via explicit prioritization. In addition, the authors consider service strategies in which URLLC traffic can be offloaded onto device-to-device (D2D) connections with and without explicit reservation of a fraction of resources for direct connections. As a result, the authors concluded that a D2D-aware strategy, in which the BS explicitly reserves resources for direct connection, is significantly superior to strategies in which no explicit reservation is used, as well as the strategy without support for D2D connections. Recently, studies suggesting the use of machine learning (ML) for network slicing have started to appear. Specifically, Ref. [16] utilizes deep reinforcement learning (DRL) technology and proposes a hybrid hard-soft slicing framework, guaranteeing service level agreements (SLAs) and maximizing the spectrum efficiency given some isolation constraints. Technically, the paper considers the users of each service-oriented slice to be able to utilize the radio resources of a newly configured service-oriented slice neighboring existing ones. As the main result, the SLAs can be guaranteed all the time with the proposed hybrid slicing able to achieve near-optimal performance in terms of SLA satisfaction ratio, isolation degree, and spectrum efficiency. As the main drawback, the proposed slicing cannot satisfy mixed SLAs such as latency and reliability. The authors in [17] investigate the impact of traffic in one slice on QoS parameters experienced by the traffic in another slice. They develop a data-driven slicing and allocation model by using ML algorithms,where resources between network slices are intelligently redistributed in accordance with prescribed QoS parameters. The study in [18] considers an experimental 5G network prototype with the ability to configure radio resources for network slices using ML solutions based on real-time performance metrics.