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Physical Layer Abstraction for Ultra-Reliable Communications in 5G Multi-Connectivity Networks

The fifth generation (5G) of mobile communication will enable new use-cases such as self driving cars, smart automation and mission critical applications, which require ultra-reliable communications. Multi-Connectivity (MC) is a promising approach to achieve high reliability in wireless networks. In order to enable MC and efficiently utilize radio resources, dynamic link adaptation is required for choosing appropriate modulation schemes and number of links. This can be achieved by using an effective link quality metric such as effective signal-to-noise ratio (SNR) and mapping it to the packet error rate, a process referred to as physical layer abstraction (PLA). In this paper, we investigate and compare the performance of existing PLA methods especially exponential effective SNR mapping (EESM) and received bit information rate (RBIR), for OFDM-based MC systems. Furthermore, we propose a new robust PLA method, called enhanced EESM (eEESM). The eEESM minimizes the efforts of optimizing tuning parameter by fitting the variations in tuning parameter to known curves as a function of channel and diversity order. Simulation results show that eEESM outperforms the state of the art PLAs for different channel conditions, modulation and diversity orders.