* This is the dataset of the accepted paper (August 6, 2018): R. Rajashekar, Marco Di Renzo, Lie-Liang Yang, K.V.S. Hari, and L. Hanzo, "A Finite Input Alphabet Perspective on the Rate-Energy Tradeoff in SWIPT over Parallel Gaussian Channels" * Paper Abstract: Simultaneous wireless information and power transfer (SWIPT) has gained significant popularity in the recent past owing to its applications in a wide range of use-cases. Although SWIPT has been fairly well investigated in the literature, the existing work has mainly focused on attaining the optimal rate energy (RE) trade-off assuming Gaussian input alphabet. However, practical systems operate with finite input alphabets such as QAM/PSK. We characterise the attainable RE trade-off in SWIPT systems employing finite input alphabet for transmission over parallel Gaussian channels of say orthogonal frequency division multiplexing subcarriers or multiple-input multiple-output streams. Some of the key results in the literature that assume Gaussian input alphabet are shown to be special cases of our results. Furthermore, we provide insights into our results with the aid of graphical illustrations, which throw light on the optimal power allocation policy for various energy harvesting constraints. Furthermore, we consider practically relevant time sharing and power splitting schemes operating with finite input alphabet and characterise their RE trade-off. Their optimal solutions in the asymptotic regime are obtained, which serve as low-complexity solutions suitable for practical implementation. Our simulation studies have demonstrated that the Gaussian input assumption significantly over-estimates the attainable RE trade-off, especially when the signal set employed is small. Furthermore, it is observed through numerical simulations that the proposed optimal power allocation performs significantly better than the power allocation based on the Gaussian input assumption. Specifically, as much as 30\% rate improvement is observed when employing the classic 4-QAM signal set. * Project: The financial support of the EPSRC projects EP/P034284/1, EP/N004558/1 and EP/L018659/1, as well as of the ERC's Advanced Fellow Award QuantCom is gratefully acknowledged. * This DOI contains the datasets of Figures 1 to 3 and 5 to 14 of the aforementioned paper. Each Fig#.fig file corresponds to the same numbered figure in the paper. Each .fig file has all the information required to generate the plot. To regenerate the results, just open the Fig#.fig file in Matlab. Exact values of each of the curves can be read from the property editor.