In this paper, a survey of the literature of the past 15 years involving machine learning (ML) algorithms applied to self-organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of self-organizing networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this paper also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future.

Recent advances on wireless energy transfer (WET) make it a promising solution for powering future Internet-of-Things (IoT) devices enabled by the upcoming sixth-generation (6G) era. The main architectures, challenges and techniques for efficient and scalable wireless powering are overviewed in this article. Candidates enablers, such as energy beamforming (EB), distributed antenna systems (DASs), advances on devices' hardware and programmable medium, new spectrum opportunities, resource scheduling, and distributed ledger technology are outlined. Special emphasis is placed on discussing the suitability of channel state information (CSI)-limited/free strategies when powering simultaneously a massive number of devices. The benefits from combining DAS and EB, and from using average CSI whenever available, are numerically illustrated. The pros and cons of the state-of-the-art CSI-free WET techniques in ultralow power setups are thoroughly revised, and some possible future enhancements are outlined. Finally, key research directions toward realizing WET-enabled massive IoT networks in the 6G era are identified and discussed in detail.

We analyze the physical layer (PHY) security of a communication scheme consisting of a multiple antenna transmitter with a single radio frequency (RF) chain using transmit antenna selection (TAS) and a single antenna receiver, in the presence of a sophisticated multiple antenna eavesdropper. We develop closed-form expressions for the analysis of the secrecy outage probability, and we show that the PHY security can be considerably enhanced when multiple antennas are available at the legitimate transmitter. Moreover, a single RF chain multiple antenna transmitter reduces cost, complexity, size and power consumption at the expense of a slight loss in performance with respect to a multiple RF chain transmitter.

Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network.

Low power wide area network (LPWAN) technologies are increasingly catching the attention of the Internet-of-Things market and have brought the need for reliable knowledge about the performance of such networks. This paper is concerned with the performance and scalability of LoRa networks, a leading LPWAN technology. Several recently published articles have analyzed the ability of LoRa networks to scale, i.e., their ability to support increased traffic and number of nodes. This paper proposes to employ message replication and gateways with multiple receive antennas to achieve, respectively, time and spatial diversity. The paper presents the proposed schemes and evaluates them through theoretical analysis and computer simulations. Results show that LoRa networks are highly sensitive to the increase in user and traffic density, but both message replication and multiple antennas can enhance performance. Message replication has an optimum number of message copies for each network configuration, and its utilization is more beneficial in low-density networks, while the use of multiple receive antennas at the gateway is always beneficial.

We analyze and optimize a wireless system with energy transfer in the downlink and information transfer in the uplink, under quasi-static Nakagami-m fading. We consider ultrareliable communication scenarios representative of the fifth generation of wireless systems, with strict error and latency requirements. The error probability and delay are investigated, and an approximation for the former is given and validated through simulations. The numerical results demonstrate that there are optimum numbers of channels uses for both energy and information transfer for a given message length.