## 1 Introduction  
Fifth-generation (5G) and beyond networks will have multi-service communication. These multi-services are not only about higher data rate applications such as enhanced mobile broadband (eMBB) but are also designed to serve machine type communication (MTC) [1]. MTC addresses two main scenarios: massive MTC (mMTC) and ultra-reliable low-latency communication (URLLC) [2]. Massive MTC deals with the massive number of MTC devices in a given area, while URLLC is a novel service class aiming to address stringent latency and reliability requirements [3]. There are several potential URLLC applications, such as factory automation [4], process automation [4], intelligent transport systems [4], automated guided vehicles (AGV) [5] and smart grids [6] which require high reliability (e.g., frame error rates of \(10^{-5}\) to \(10^{-9}\), respectively depending upon the application) while guaranteeing very low latency [6, 7]. URLLC can be studied from two aspects: ultra-reliable communication (URC) and low latency communication (LLC). The author in [8] introduces URC over the long term, in which the required latency is \(>\)10 ms, and URC in the short term, referring to problems with very stringent latency requirements, below 10 ms. In this work, we focus on URC over the short term that delivers a message from source to destination with guaranteed high-reliability [9]. Some challenges related to achieving ultra-reliability are link degradation,-fading channel, shadowing, and interference which increases the probability of outage in wireless communication communication [10; 11]. From the ultra-reliable communication perspective, all these challenges add further complexity to the physical layer design [12]. Hence, in the next-generation wireless services, ensuring ultra-reliability for mission-critical applications is a challenging task. One of the identified solutions to overcome this challenge and enhance system reliability is multi-connectivity (MC). The basic idea of MC is to serve or send the packets to the user via multiple links through the spatial or frequency domain. Multi-connectivity adopts spatial diversity with the concept of cloud radio access network (CRAN) to ensure ultra-reliability through cooperative solutions [13]. In the CRAN, a large amount of signal processing and computing is performed in a distributed manner; base band units (BBUs) act as virtual base stations (BSs) responsible for resource allocation and centralized baseband processing, while remote radio heads (RRH) change baseband signal to radio frequency signal for ahead transmission to the users [14; 15]. CRAN has high-speed optical fiber links in the front haul that can support low latency and high-capacity communication, both of which lead to improved network performance [16]. Motivated by the above concerns, the aim of this paper is to achieve ultra-reliable region operation through interference management by the cooperation/coordination of RRH for downlink transmission through CRAN using multiple spatially distributed RRH connections. This kind of framework with edge-enabled multi-coordinated transmission schemes is beneficial, for example, in an industrial environment where various types of devices are present such as robots, AGV, sensors, and actuator devices which rely on URC [17]. We exploit MC through the maximum ratio transmission (MRT) and transmit antenna selection (TAS) scheme, or by reducing interference through silencing in an interference-limited downlink cellular network with RRHs' cooperation, and analyze the performance of different diversity schemes to ensure URC for critical MTC. Machine learning techniques are also identified as a potential candidate for radio resource management for wireless networks for some critical applications [18; 19]. However, due to the novelty of the critical MTC network, the main limitation of applying machine learning tools in these scenarios is the lack of an appropriate and adequate dataset for their precise application.