In the literature, machine learning (ML) has been implemented at the base station (BS) and user equipment (UE) to improve the precision of downlink channel state information (CSI). However, ML implementation at the UE can be infeasible for various reasons, such as UE power consumption. Motivated by this issue, we propose a CSI learning mechanism at BS, called CSILaBS, to avoid ML at UE. To this end, by exploiting channel predictor (CP) at BS, a light-weight predictor function (PF) is considered for feedback evaluation at the UE. CSILaBS reduces over-the-air (OTA) feedback overhead, improves CSI quality, and lowers the computation cost of UE. Besides, in a multiuser environment, we propose various mechanisms to select the feedback by exploiting PF while aiming to improve CSI accuracy. We also address various ML-based CPs, such as NeuralProphet (NP), an ML-inspired statistical algorithm. Furthermore, inspired to use a statistical model and ML together, we propose a novel hybrid framework composed of a recurrent neural network and NP, which yields better prediction accuracy than individual models. The performance of CSILaBS is evaluated through an empirical dataset recorded at Nokia Bell-Labs. The outcomes show that ML elimination at UE can retain performance gains, for example, precoding quality.

Channel state information (CSI) is of pivotal importance as it enables wireless systems to adapt transmission parameters more accurately, thus improving the system's overall performance. However, it becomes challenging to acquire accurate CSI in a highly dynamic environment, mainly due to multi-path fading. Inaccurate CSI can deteriorate the performance, particularly of a massive multiple-input multiple-output system. This paper adapts machine learning for CSI prediction. Specifically, we exploit time-series models of deep learning (DL) such as recurrent neural network (RNN) and bidirectional long-short term memory. Further, we use NeuralProphet (NP), a recently introduced time-series model, composed of statistical components, e.g., autoregressive and Fourier terms, for CSI prediction. Inspired by statistical models, we also develop a novel hybrid framework comprising RNN and NP to achieve better prediction accuracy. The proposed channel predictors performance is evaluated on a real-time dataset recorded at the Nokia Bell-Labs campus in Stuttgart, Germany. Numerical results show that DL brings performance gain when used with statistical models and showcases robustness.

Unmanned aerial vehicles (UAVs) evolution has gained an unabated interest for the use in several applications, such as agriculture, aerial surveillance, goods delivery, disaster recovery, intelligent transportation. The main features of this technology are high coverage, strong line-of-sight (LoS) links, promising throughput, cost-effective and flexible deployment. Currently, the Third Generation Partnership Project (3GPP) is working on the specification of release-17 (R-17) new radio (NR) for non-terrestrial networks (NTN). Therefore, owing to the drastic increase of UAV technology, in this paper, we propose channel state information (CSI) compression and its recovery with the aid of machine learning (ML)-based twin channel predictors. Due to the characteristic of gaining higher LoS communication paths in UAV network, the proposed strategy can bring potential benefits such as over-the-air (OTA)-overhead reduction, minimizing mean-squared-error (MSE) of a channel and maximizing precoding gain. Simulation-based results corroborate the validity of the proposed strategy, which can reap benefits in multiple factors.

In wireless communication, accurate channel state information (CSI) is of pivotal importance. In practice, due to processing and feedback delays, estimated CSI can be outdated, which can severely deteriorate the performance of the communication system. Besides, to feedback estimated CSI, a strong compression of the CSI, evaluated at the user equipment (UE), is performed to reduce the over-the-air (OTA) overhead. Such compression strongly reduces the precision of the estimated CSI, which ultimately impacts the performance of multipleinput multiple-output (MIMO) precoding. Motivated by such issues, we present a novel scalable idea of reporting CSI in wireless networks, which is applicable to both time-division duplex (TDD) and frequency-division duplex (FDD) systems. In particular, the novel approach introduces the use of a channel predictor function, e.g., Kalman filter (KF), at both ends of the communication system to predict CSI. Simulation-based results demonstrate that the novel approach reduces not only the channel mean-squared-error (MSE) but also the OTA overhead to feedback the estimated CSI when there is immense variation in the mobile radio channel. Besides, in the immobile radio channel, feedback can be eliminated, which brings the benefit of further reducing the OTA overhead. Additionally, the proposed method provides a significant signal-to-noise ratio (SNR) gain in both the channel conditions, i.e., highly mobile and immobile.

The staggering growth of the number of vehicles worldwide has become a critical challenge resulting in tragic incidents, environment pollution, congestion, etc. Therefore, one of the promising approaches is to design a smart vehicular system as it is beneficial to drive safely. Present vehicular system lacks data reliability, security, and easy deployment. Motivated by these issues, this paper addresses a drone-enabled intelligent vehicular system, which is secure, easy to deploy and reliable in quality. Nevertheless, an increase in the number of operating drones in the communication networks makes them more vulnerable towards the cyber-attacks, which can completely sabotage the communication infrastructure. To tackle these problems, we propose a blockchain-based registration and authentication system for the entities such as drones, smart vehicles (SVs) and roadside units (RSUs). This paper is mainly focused on the blockchain-based secure system design and the optimal placement of drones to improve the spectral efficiency of the overall network. In particular, we investigate the association of RSUs with the drones by considering multiple communication-related factors such as available bandwidth, maximum number of links a drone can support, and backhaul limitations. We show that the proposed model can easily be overlaid on the current vehicular network reaping benefits of secure and reliable communications.

Small cell networks (SCNs) offer a cost-effective coverage solution to wireless applications demanding high data rates. However in SCNs, a challenging problem is the proper management of backhaul links to small cell base stations (SCBSs). To make a good backhaul link, perfect line-of-sight (LoS) communication between the SCBSs and the core network plays a vital role. In this study, we use the idea of employing unmanned aerial vehicles (UAVs) to provide connectivity between SCBSs and the core network. We focus on the association of SCBSs with UAVs by considering multiple communication-related factors including data rate limit and available bandwidth resources of the backhaul. In particular, we address the optimum placement of UAVs to serve a maximum number of SCBSs while considering available resources using unsupervised \textit{k}- means algorithm. Numerical results show that the proposed approach outperforms the conventional approach in terms of associated SCBSs, bandwidth consumption, available link utilization, and sum- rate maximization.