The communication between two legitimate peers in the presence of an external eavesdropper is studied from a physical-layer security perspective in the context of free-space optical (FSO) communications. We discuss viable mechanisms to eavesdrop the communication and study the effect of random optical irradiance fluctuations inherent to FSO communications on the probability of achieving a secure transmission. We observe that the joint effect of laser-beam divergence and turbulence-induced fading on the received irradiance, under certain conditions, allows an external eavesdropper close to the legitimate receiver to compromise the communication. Interestingly, we also observe that an eavesdropper placed close to the legitimate transmitter can easily compromise the communication by taking advantage of the larger attenuation suffered by the signal when propagating through the FSO link.

Link adaptation (LA) process is a core feature for the downlink of 3GPP long-term evolution (LTE) and LTE-advanced (LTE-A). Through a channel quality indicator (CQI), the receiver suggests to the base station (BS) an appropriate modulation and coding scheme (MCS) according to the current channel conditions. In order to overcome any non-ideality in this process, the outer loop link adaptation (OLLA) algorithm is used to adaptively modify the mapping from signal-to-noise ratio (SNR) to CQI. OLLA basically modifies the measured SNR by an offset, according to whether data packets are received correctly or not, in order to adjust the average block error rate (aBLER) to a target. Although the OLLA technique has been extensively used, there exists a lack of analysis in the literature about its dynamics and convergence conditions. In this paper, a deep analysis of this algorithm has been carried out in order to cover this gap. From this analysis, we propose a new approach to the OLLA, the enhanced OLLA (eOLLA), which is able to adaptively modify its step size as well as to update its offset according to the reception conditions even if no data packets have been received. Thus, for LTE- and LTE-A-realistic scenarios, simulation results show that the proposed eOLLA outperforms the traditional OLLA, achieving a performance gain of up to a 15 % in terms of throughput.

Millimeter-wave communications have been recently proposed as a promising candidate to fulfill the connectivity requisites imposed over autonomous driving. The need for transmission capabilities of multiple gigabits per second for autonomous vehicles is the main driving factor for millimeter-wave technology. However, there are other paramount requirements, such as short delays, high reliability, security, and support for both unicast and broadcast communications, which must be carefully addressed before this technology becomes a reality. In this article we first review the state-of-the art modeling frameworks that are being considered for vehicular communications. Then we explore the key technologies that will enhance the suitability of millimeter-wave communications for autonomous driving, and we investigate the main challenges that need to be overcome. Finally, we envision that a combination of analog/ hybrid beamforming with a location-based beam search protocol, using full duplex radio and physical layer key generation, has the potential of fulfilling the requirements of autonomous driving technology.

LTE V2X is the response of the 3GPP standardization body to the high market expectations related to vehicular communications for safety and infotainment services. To fulfill the stringent requirements in terms of reliability associated with safety applications, geo-location based access (GLOC) has been proposed for direct Vehicle-to-Vehicle (V2V) communication. Such a scheme aims at maximizing the distance of co-channel transmitters (i.e. transmitter that use the same resources) while preserving a low latency when accessing the resources and a low overhead. In this paper we analyze, with the aid of stochastic geometry, the delivery of periodic and non-periodic broadcast messages with GLOC, taking into account path loss and fading as well as the random locations of transmitting vehicles. Analytical results include the average interference, average Binary Rate (BR), capture probability, i.e., the probability of successful message transmission, and Energy Efficiency (EE). Mathematical analysis reveals interesting insights about the system performance, which are validated thought extensive Monte Carlo simulations. In particular, it is shown that the capture probability is an increasing function with exponential dependence with respect to the transmit power and it is demonstrated that an arbitrary high capture probability can be achieved, as long as the number of access resources is high enough. Finally, to facilitate the system-level design of GLOC, it is obtained the optimum transmit power, which fulfill a given minimum capture probability constraint while maximizing the EE of the system.

Inter-cell interference is one of the main limiting factors in current heterogeneous cellular networks. Uplink fractional power control (FPC) is a well-known method that aims to cope with such a limiting factor and to save the battery life of the mobile terminals (MTs). In order to do that, the transmit power of each MT is adjusted as a function of a set of parameters that usually depend only on the link between MTs and serving base station (BS), such as the desired received power at the serving BS or the path loss between the MT and its serving BS. Contrary to these classical FPC schemes, in this paper, we use stochastic geometry to analyze a power control mechanism that keeps the interference generated by each MT under a given threshold. We also consider a maximum transmitted power and a partial compensation of the path loss. Our analysis reveals that such an interference aware method can reduce the average power consumption and increase the average spectral efficiency at once. In addition, the variance of the interference is reduced, thus improving the performance of adaptive modulation and coding schemes, since the interference can be better estimated.

With the speeding up of the fifth generation (5G) new radio (NR) worldwide commercialization, one of the paramount questions for operators and vendors is how to optimize the radio links, considering the widely diverse scenarios envisioned. One of the key pillars of 5G has been an unprecedented flexibility on the configuration of the radio access network (RAN) on scenarios that include cellular, vehicular, and industrial networks among others. This flexibility has its main exponent on link adaptation (LA), which has evolved into a multi-domain technique where a plethora of parameters, like numerology, bandwidth part, radio frequency beam, power, modulation and coding scheme (MCS) or multiple antenna precoding can be adapted to the instantaneous link conditions. Although such enhancements open the door to a significant performance improvement, they also pose many challenges to LA optimization. In this article, we first present the signaling aspects of NR technology for multi-domain LA and the challenges that need to be faced. Then, we explore the latest advances on LA for wireless networks. We envision a combination of machine learning (ML) tools with multi-domain LA as a key enabler for 5G and beyond networks. Finally, we investigate emerging ML approaches for LA and present a promising application of ML for LA that is assessed with simulations. With this scheme, the training is performed at the network side to relieve the user equipment (UE) to do such a complex task. It is shown with simulations that our ML approach outperforms the well-known outer loop link adaptation (OLLA) algorithm in terms of instantaneous block error rate (BLER), while reaching the same average spectral efficiency (SE). Interestingly, it is shown that the proposed scheme only requires 4 bits to represent the features used to train the model, which makes it suitable for implementation in real systems with limited feedback.