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.

We analyze the impact of arbitrary dependence between the forward and backward links in backscatter communication systems. Specifically, we quantify the effect of positive and negative dependence between these fading links on channel capacity by using Copula theory. The benefits of this approach are highlighted over the classical framework of linear dependence based on Pearson’s correlation coefficient, which is also analyzed. Results show that for a fixed transmit power budget, capacity grows with positive dependence as well as with fading severity in the low signal-to-noise ratio (SNR) regime. Conversely, fading dependence becomes immaterial in the high SNR regime.

In this paper we investigate the application of free space optical (FSO) communications, energy harvesting, and unmanned aerial vehicles (UAVs) as key technology enablers of a cost-efficient backhaul/fronthaul framework for 5G and beyond (5G+) networks. This novel approach is motivated by several facts. First, the UAVs, acting as relay nodes, represent an easy-to-deploy and adaptive network that can provide line-of-sight between the base stations and the gateways connected to the core network. Second, FSO communications offer high data rates between the UAVs and the network nodes, while avoiding any potential interference with the 5G radio access networks. Third, energy harvesting in the optical domain has the potential to extend the UAVs’ battery life. Nevertheless, the presence of atmospheric turbulence, atmospheric attenuation, and pointing errors in the FSO links severely degrades their performance. For this reason an accurate yet tractable modelling framework is required to fully understand whether an UAV-FSO backhaul/fronthaul network with energy harvesting can be applied. To this end, we consider a composite channel attenuation model that includes the effect of turbulence fading, pointing errors, and atmospheric attenuation. Using this model, we derive analytical closed-form expressions of the average harvested energy as a function of the FSO link parameters. These expressions can be used to improve energy harvesting efficiency in FSO link design. We have applied our proposed expressions to evaluate the energy harvested in vertical FSO links for a variety of real scenarios under a modified on-off keying (OOK) scheme optimized for energy harvesting. From the simulations carried out in this paper, we demonstrate that significant values of harvested energy can be obtained. Such performance enhancement can complement the existing deployment charging stations.