Autonomous vehicles (AVs) require real-time, low-latency, and high-throughput communication to ensure safe and efficient operation. Fifth-generation (5G) and beyond communication networks, particularly New Radio (NR) operating at millimeter wave (mmWave) frequencies, offer promising solutions. However, higher frequencies introduce challenges like signal attenuation, blockage, and complexities of beam management. This paper provides a comprehensive survey of 5G and beyond communication networks, emphasizing their role in vehicle-to-everything (V2X) communication. It uniquely addresses the technical challenges of beam training and management, highlighting the application of artificial intelligence (AI) and machine learning (ML) techniques to optimize beam selection and reduce delays. Furthermore, the paper explores future directions, focusing on AI/ML-driven solutions to enhance beamforming and communication reliability in dynamic AV environments.

The conventional K‐Means clustering algorithm is widely used for grouping similar data points by initially selecting random centroids. However, the accuracy of clustering results is significantly influenced by the initial centroid selection. Despite different approaches, including various K‐Means versions, suboptimal outcomes persist due to inadequate initial centroid choices and reliance on common normalization techniques like min‐max normalization. In this study, we propose an improved algorithm that selects initial centroids more effectively by utilizing a novel formula to differentiate between instance attributes, creating a single weight for differentiation. We introduce a preprocessing phase for dataset normalization without forcing values into a specific range, yielding significantly improved results compared to unnormalized datasets and those normalized using min‐max techniques. For our experiments, we used five real datasets and five simulated datasets. The proposed algorithm is evaluated using various metrics and an external benchmark measure, such as the Adjusted Rand Index (ARI), and compared with the traditional K‐Means algorithm and 11 other modified K‐Means algorithms. Experimental evaluations on these datasets demonstrate the superiority of our proposed methodologies, achieving an impressive average accuracy rate of up to 95.47% and an average ARI score of 0.95. Additionally, the number of iterations required is reduced compared to the conventional K‐Means algorithm. By introducing innovative techniques, this research provides significant contributions to the field of data clustering, particularly in addressing modern data‐driven clustering challenges.

The selection of an optimal photodetector area is closely linked to the attainment of higher data rates in optical wireless communication receivers. If the photodetector area is too large, the channel capacity degrades due to lower modulation bandwidth of the detector. A smaller photodetector maximizes the bandwidth, but minimizes the captured signal power and the subsequent signal-to-noise ratio. Therein lies an opportunity in this trade-off to maximize the channel rate by choosing the optimal photodetector area. In this study, we have optimized the photodetector area in order to maximize the channel capacity of a free-space optical link for a diverse set of communication scenarios. We believe that the study in this paper in general—and the closed-form solutions derived in this study in particular—will be helpful to maximize achievable data rates of a wide gamut of optical wireless communication systems: from long range deep space optical links to short range indoor visible light communication systems.

The problem of ordered clustering in the context of decision‐making with multiple criteria has garnered significant interest from researchers in the field of management science and operational research. In real‐world scenarios, the datasets often exhibit imprecision or uncertainty, which can lead to suboptimal ordered‐clustering outcomes. However, the intuitionistic fuzzy c‐means (IFCM) clustering algorithm enhances the accuracy and effectiveness of decision‐making processes by effectively handling uncertain dataset information for clustering. Therefore, we propose a new clustering algorithm, called the generalized ordered intuitionistic fuzzy c‐means (G‐OIFCM), based on PROMETHEE and the IFCM clustering algorithm. Different from the classical IFCM clustering algorithm, we use positive flow (φ+(si) ∈ [0, 1]) and negative flow (φ−(si) ∈ [0, 1]) of PROMETHEE to generate ordered clusters within the intuitionistic environment. We define a new objective function based on the positive and negative flow of the PROMETHEE and IFCM clustering algorithm, whose properties are mathematically justified in terms of convergence and optimization. The performance of the proposed algorithm is evaluated using two different real‐world datasets to assess both the ordered clustering and the quality of partitioning. To demonstrate the effectiveness of G‐OIFCM, a comparison is conducted with three other algorithms: fuzzy c‐means (FCM), ordered fuzzy c‐means (OFCM), and an adaptive generalized intuitionistic fuzzy c‐means (G‐IFCM). The results demonstrate the effectiveness of G‐OIFCM in enhancing optimal ordered clustering and utility when dealing with uncertainty in datasets.

Due to their flexibility and low cost deployment, unmanned aerial vehicles (UAV) will most likely act as base stations and backhaul relays in the next generation of wireless communication systems. However, these UAVs—in the untethered mode—can only operate for a finite time due to limited energy they carry in their batteries. In free-space optical communications, one solution is to transport both data and energy from the source to the UAV through the laser beam—a concept known as simultaneous lightwave information and power transfer (SLIPT). In this study, we have analyzed the SLIPT scheme for laser-powered decode-and-forward UAV relays in an optical wireless backhaul. The major goal of this study is to optimally allocate the received beam energy between the decoding circuit, the transmitting circuit and the rotor block of the relay in order to maximize a quality-of-service metric such as maximum achievable rate, outage or error probabilities. As expected, we note that the optimal power allocation depends heavily on the source-relay and relay-destination channel conditions. In the final part of this study, we have maximized the operational time of the UAV relay given that the maximum achievable rate stays above a certain threshold in order to meet a minimum quality-of-service requirement.

The relay positions or hop distances in a multi-hop relaying scheme is an important parameter that can be optimized in order to mitigate the angle-of-arrival variance or pointing error in a free-space optical (FSO) backhaul link. In this study, we have optimized the relay positions for amplify-and-forward and decode-and-forward relays in a multi-hop unmanned aerial vehicle-based relaying scheme for FSO. Particularly, we have shown that a significant performance improvement may be achieved by optimizing the outage probability as a function of the hop distance for amplify-and-forward relays when the relays are constrained by a finite power gain. Additionally, we have discovered that for a low signal-to-noise ratio channel, the optimal hop distance of a particular hop for decode-and-forward relays is inversely proportional to angle-of-arrival variance in that hop.

Acquisition and tracking systems form an important component of free-space optical communications due to directional nature of the optical signal. Acquisition subsystems are needed in order to search and locate the receiver terminal in an uncertainty/search region with very narrow laser beams. In this paper, we have proposed and analyzed two adaptive search schemes for acquisition systems that perform better-for the low probability of detection-than the spiral scanning approach. The first of these schemes, the adaptive spiral search, provides a better acquisition time performance by dividing the search region into a number of smaller subregions, and prioritizing search in regions of higher probability mass. The second technique-the shotgun approach-searches the region in a random manner by sampling the search region according to a Gaussian distribution. The adaptive spiral scheme outperforms the shotgun approach in terms of acquisition time, especially if the number of search subregions is large enough. However, a higher pointing accuracy is required by the adaptive spiral search in order to search the region precisely. On the other hand, the shotgun scanning approach does not require such stringent pointing accuracy.

Free-space optical (FSO) communications will play an important role in the backhaul of future generation of wireless networks in order to support high data rates. Because of narrow beamwidth inherent to an optical signal, acquisition and tracking form an important component of any FSO communication system. In this study, we have analyzed the optimization of received power allocation between tracking and data channels in an FSO receiver. In this regard, we have carried out a detailed analysis of the error statistics of the centroid estimator that is used for tracking the beam with the help of an array. The objective function that is optimized (minimized) are the probability of error and the probability of outage, and the optimization of power allocation is carried out as a function of parameters such as noise power, pointing error variance, pointing error correlation coefficient, and the threshold of outage. We have analyzed the optimization concerning the lognormal and exponentiated Weibull fading scenarios as well. We learn that the optimal power allocation is a function of the received signal-to-noise ratio: a lower signal-to-noise ratio dictates that a higher fraction of received power should be diverted to the tracking channel and vice versa.

Pointing and acquisition are an important aspect of free-space optical communications because of the narrow beamwidth associated with the optical signal. In this paper, we have analyzed the pointing and acquisition problem in free-space optical communications for photon-counting detector arrays and Gaussian beams. In this regard, we have considered the maximum likelihood detection for detecting the location of the array, and analyzed the one-shot probabilities of missed detection and false alarm using the scaled Poisson approximation. Moreover, the upper/lower bounds on the probabilities of missed detection and false alarm for one complete scan are also derived, and these probabilities are compared with Monte Carlo approximations for a few cases. Additionally, the upper bounds on the acquisition time and the mean acquisition time are also derived. The upper bound on mean acquisition time is minimized numerically with respect to the beam radius for a constant signal-to-noise ratio scenario. Finally, the complementary distribution function of an upper bound on acquisition time is also calculated in a closed form. Our study concludes that an array of smaller detectors gives a better acquisition performance (in terms of acquisition time) as compared to one large detector of similar dimensions as the array.