In order to increase energy efficiency over transmissions channels, common approach for optimization tasks is by means of channel’s second-order statistics. Actual channel modeling tools for 5G networks end with channel’s first-order statistics, although these metrics are not sufficient when channel conditions are rapidly changing, either in time, frequency or space. In this paper, we establish a tool for evaluation and comparision of energy efficiency of mobile radio channel using its second-order statistics, especially level crossing rate (LCR) and average fade durations (AFD), as they can implicitly pinpoint to transmission configurations that are energy efficient or, as oposit, become a waste of energy. Using both deterministic and stochastic channel modeling, we present results after simulations of Rayleigh channel for narrowband case and further extend it to passband cases, suitable for 5G scenario. We conclude about the energy efficiency of different transmission schemes used by the 5G physical layer observing LCR and AFD values.
With rise in device complexity and transmission rates, reliability in data recovery has become another critical issue requiring costly and computationally demanding mechanism. The popularity of artificial intelligence (AI) and its ubiquitousness have established the usefulness of design of data recovery schemes where device level complexity is less. Lower device complexity is being ensured by the use of AI driven data recovery. In this work, we focus on the design of such a mechanism where traditional process are replaced by a neuro-computing structure. The advantage is lower levels of device complexity but incorporation of a training latency. Experimental results have established the reliability of the proposed system.
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