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Centralized radio access network (C-RAN) is a network architecture that is emerging as a key technology enabler for 5G mobile networks as capacity demands for mobile traffic continue to proliferate. Essentially, C-RAN involves separating the remote radio heads from baseband units to be processed in the cloud. A systematic design of C-RAN involves mapping of individual baseband signal processing tasks to general purpose cloud infrastructures, such as the microserver to reduce the energy footprint. In this paper, we start with mapping the lowest protocol stack, i.e., the physical layer (PHY), which is characterized by strict latency and dynamic data rate. To achieve this, we explore the use of machine intelligence for energy-efficient mapping of PHY signal processing on microservers. Fundamental to this approach is: 1) the use of principal component analysis to represent workload from multi-dimensional hardware performance statistics, demonstrating 99.88% correlation with the critical PHY processing latency and 2) the use of deep learning to model latency and predict dynamic workload for on-demand resource allocation, resulting in up to 36% reduction in hardware usage. These principles are built into a cross-layer run-time framework, which adapts resource allocation in response to time-varying data rate, guaranteeing latency, and improving energy efficiency by up to 48% (average 28%).

This work explores the potentiality of millimeter waves (mmW) as physical layer in industrial wireless networks. Innovative models and a link design method are proposed to achieve reliable communication, at a distance of tens of meters for a single hop, even in harsh environments. By exploiting the worldwide-free band of several GHz, available around 60 GHz, mmW links allow to achieve a performance boosting of up to two orders of magnitude, w.r.t. conventional sub-6-GHz wireless links, in indoor industrial environments. Time slotted channel hopping and frequency-diversity can be implemented with a large number of channels, and with high bit rate (several Mb/s per channel). This allows for robust networking of high data-rate sensors, such as cameras, radars, or laser scanners. Featuring a low bit error rate, mmW communication allows for low-latency link and large number of hops in networks with a large radius. Finally, it ensures interference separation from operating frequencies of electrical machines, switching converters, and other industrial wireless networks (e.g., 802.11 or 802.15). Implementation results for key HW blocks in low-cost technologies show the feasibility of mmW communication nodes with low-power and compact size.

In this paper, the impact of the jamming signal on the secrecy performance of Energy Harvesting (EH) enabled dual-hop amplify-and-forward relaying network is investigated. First, the security outage probability analysis is studied for conventional networks under a single passive eavesdropper attack. Then, the outage performance analysis in two cases regarding energy harvesting is investigated. Moreover, the proposed work enhances Physical Layer (PHY) security performance of two-hop relaying model using Cooperative Jamming Dual-Hop Techniques (CJDH). For this purpose, new closed-form expressions are derived for the outage probability of CJDH model in the presence of interference over Rayleigh fading channels. A power allocation optimization problem for energy harvesting protocol is formulated and solved for enhancing the system security. The derived analytical formulas herein are supported by numerical and simulation results to clarify the main contributions of the paper.