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Fibre optics is the best solution for the deployment of 5G fronthaul, but it is expensive and time-consuming. The use of a common fibre access network for fixed and mobile services results in cost reduction, quality of experience improvement, and synergy and convergence of the resources. Accordingly, in this study, a practical dual-purpose techno-economic model (FTTx + 5G) was introduced to evaluate its Capital Expenditures (CAPEX). Some technical, financial, and geographical data were also used for computation and comparing the costs in different situations by the integer linear programming method. Nomenclature Notation P e unit price of e P λ unit price of λ on WDM link for B λ preparation B λ prepared bandwidth by λ P Tre cost of a meter trenching and fibre cabling O e output bandwidth capacity of e e i equal to '1' if e type is available at the point i q e i quantity of e at the point i G i j equal to '1' if i and j path exists in a network graph F i j equal to '1' if the fibre is available between i and j l i j length of the selected route (m) between i and j q λ i j quantity of the prepared λ between i and j k e protection coefficient, '2' for mobile and '1' for the fixed network

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In this paper, a reversible data hiding scheme has been proposed which is based on correlation of subsample images. The proposed method modifies the blocks of sub-sampled image to prepare vacant positions for data embedding. The PSNR of the stego image produced by the proposed method is guaranteed to be above 47.5 dB, while the embedding capacity is at least, almost 6.5 times higher than that of the Kim et al. techniques with the same PSNR. This technique has the capability to control the capacity-PSNR. Experimental results support that the proposed method exploits the correlation of blocked sub-sampled image outperforms the prior works in terms of larger capacity and stego image quality. On various test images, the authors demonstrate the validity of the proposed method by comparing it with other existing reversible data hiding algorithms.