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The fifth generation of mobile networks (5G) represents the latest development in mobile communications. It has been designed to support several types of data traffic and to meet more performance requirements than ever before. These characteristics make 5G very attractive for current but also novel public and private industries and services. However, because of coverage, regulatory, business, and security reasons, many of these novel applications can only be deployed as part of a private network. The cost of licensed frequencies makes such approach prohibitive for many stakeholders, and therefore unlicensed frequency bands represent a more affordable option. Even so, private 5G networks for use in globally unlicensed frequency bands do not yet exist. In this paper we present the first end-to-end private 5G network operating in a globally unlicensed frequency band, using general purpose computers, open-source software and software-defined radio. We evidence its working and show that the choice of the hardware can significantly affect the performance of the network., Comment: arXiv admin note: This version has been removed by arXiv administrators as the submitter did not have the right to agree to the license at the time of submission

Wireless data communications are always facing the risk of eavesdropping and interception. Conventional protection solutions which are based on encryption may not always be practical as is the case for wireless IoT networks or may soon become ineffective against quantum computers. In this regard, Physical Layer Security (PLS) presents a promising approach to secure wireless communications through the exploitation of the physical properties of the wireless channel. Cooperative Friendly Jamming (CFJ) is among the PLS techniques that have received attention in recent years. However, finding an optimal transmit power allocation that results in the highest secrecy is a complex problem that becomes more difficult to address as the size of the wireless network increases. In this paper, we propose an optimization approach to achieve CFJ in large Wi-Fi networks by using a Reinforcement Learning Algorithm. Obtained results show that our optimization approach offers better secrecy results and becomes more effective as the network size and the density of Wi-Fi access points increase.

The broadcasting nature of the wireless medium makes exposure to eavesdroppers a potential threat. Physical Layer Security (PLS) has been widely recognized as a promising security measure complementary to encryption. It has recently been demonstrated that PLS can be implemented using off-the-shelf equipment by spectrum-programming enhanced Software-Defined Networking (SDN), where a network controller is able to execute intelligent access point (AP) selection algorithms such that PLS can be achieved and secrecy capacity optimized. In this paper, we provide a basic system model for such implementations. We also introduce a novel secrecy capacity optimization algorithm, in which we combine intelligent AP selection with the addition of Friendly Jamming (FJ) by the not-selected AP.