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Acquiring accurate instantaneous channel state information (CSI) is a challenging aspect of massive multi-input multi-output (MIMO) communication. Utilizing statistical information, such as channel covariance matrix, to design statistical beamforming vectors is robust when compared to instantaneous CSI. In this paper, we propose a novel MIMO covariance shaping scheme over Riemannian manifolds. It serves as an effective statistical beamforming solution to a number of close proximity user equipment (UE) that are undergoing substantial channel correlation. Proposed algorithm exploits the Hermitian positive definite nature of covariance matrices lying over Riemannian manifold. We introduce Wasserstein distance function as a Riemannian metric to measure distances between channel covariance matrices. Furthermore, K-means clustering technique is utilized to effectively identify the optimal shape of effective optimal covariance matrices. Our findings suggest that maximizing the geodesic distance between covariance matrices ultimately leads to a corresponding increase in the network throughput, as determined by the beamforming vector used to shape the covariance matrices. Simulation results validate that the proposed solution converges faster than Euclidean-based state-of-the-art, while maintaining the same computational complexity. Finally, the sum rate performance asymptotically achieves full capacity for two-UE case and more than 96% of the upper bound exhaustive search benchmark for multi-UE scenario.

Intelligent reflecting surfaces (IRS) are considered one of the prominent technologies to be adopted in 6G and beyond networks. However, one of the main IRS limitations is restricting the communication to the reflective dimension, which means that users located behind the IRS surface cannot benefit from it. In this paper, we make use of intelligent omni-surfaces (IOS), as an alternative to IRS, in addition to unmanned aerial vehicles (UAV) to achieve secure communication in an Internet-of-Things (IoT) communication system. Specifically, we propose IOS-UAV assisted communication wherein an access point (AP) tries to send confidential information to a legitimate IoT device in presence of an eavesdropper. The UAV serves two fold; it acts as a friendly jammer that is trying to degrade the signal quality at the eavesdropper and as a source of energy to power up the IoT device via simultaneous wireless information and power transfer (SWIPT) to address the limited power available to it. We formulate an average secrecy rate maximization problem, which jointly optimizes the AP and UAV transmission powers, the UAV trajectory, IOS phase shifts and power splitting factor. The formulated problem is non-convex, therefore we exploit successive convex approximation (SCA) to tackle this issue. Simulation results show that the iterative SCA solution converges rapidly. Besides, the proposed scheme outperforms the traditional system without IOS by a gap of 140% in terms of the secrecy capacity.

Covariance matrices of spatially-correlated wireless channels in millimeter wave (mmWave) vehicular networks can be employed to design environment-aware beamforming codebooks. Such covariance matrices can be represented over non-Euclidean (i.e., curved surfaces) manifolds, thanks to their symmetric positive definite (SPD) structures. In this paper, we propose three learning models for channel covariance estimation over Riemannian manifolds. First, we propose an unsupervised Riemannian K-means clustering approach, where Log-Euclidean metric (LEM) is utilized as a distance metric among channel covariance matrices over the Riemannian manifold. Second, we propose a Riemannian Competitive Learning (RCL) model, which is an online clustering solution that is intended to reduce the learning time of the offline K-means models. Third, we apply a Riemannian Dictionary Learning (RDL) model that leverages the sparsity properties of mmWave channels, while estimating their channel covariance matrices. Simulation results show that the proposed Riemannian K-means, online RCL and RDL models can achieve 41%, 30% and 44% higher data rate, respectively, than their Euclidean-based counterparts. Furthermore, they require few training samples and hence fast construction of codebook design in dynamic environments, which leads to low latency communication.

In this paper, we propose two novel geometric machine learning (G-ML) methods for the wireless link scheduling problem in device-to-device (D2D) networks. In dynamic D2D networks (e.g., vehicular networks), obtaining a large number of training samples is time-consuming for real-time response, and acquiring accurate instantaneous channel state information (CSI) is challenging due to high mobility. Our goal is to efficiently represent D2D networks on Riemannian manifold and use G-ML with few training wireless network layouts and no CSI to approach the sum rate maximization performance as state-of-the-arts which is much needed in dynamic networks. To this aim, we first model the local graph around each D2D pair as a point through a set of regularized Laplacian matrices on the Riemannian manifold. We compute the Riemannian metric, e.g., Log-Euclidean metric (LEM), among D2D pairs, which are suitable measures of interference among these pairs. We use LEM in the geometric support vector machine (G-SVM) method to classify the link scheduling decisions in a supervised learning manner. Then we propose geometric $k$ -means clustering for unsupervised scheduling method for the case when no labeled training is available in some dynamic networks. Simulation results demonstrate that the proposed methods achieve promising performance for sum rate maximization against the existing state-of-the-arts approaches with only around a hundred training wireless network layouts for training and without using CSI.

Drones are imperative for the 5G architecture as a mobile source to expand network coverage and support seamless services, particularly through enabling device-to-device (D2D) communication. Such deployment of drones in D2D settings raises various security threats in drone communication. While the existing D2D communication security standard within the 4G cellular architecture may address some of these issues, the standard includes heavy traffic toward the network core servers. If this security standard is to be adopted in the 5G D2D security services with the same traffic load, it may negatively impact the 5G network performance. Therefore, this paper proposes a lightweight proxy signature-based authentication mechanism for a swarm of drones compatible with the 5G D2D standard mechanisms. This paper proposes a distributed delegation-based authentication mechanism to reduce the traffic overhead toward the 5G core network. In this scheme, the legitimate drones are authorized as proxy delegated signers to perform authentication on behalf of the core network. Furthermore, we propose a mechanism to elect and relocate a new leader relay drone from the existing drone swarm. We implemented the proposed authentication algorithm in the 5G D2D-based communication package over NS-3 while performing the computational calculations on a RaspberryPi3 device to mimic the drone calculation process and delays. The performance of the proposed authentication shows a promising reduction in the authentication time and shows lightweight and reliable compatibility.

Spectrum sharing between different technologies has become a necessity as the RF spectrum has become more congested than ever due to the increasing number of connected devices and applications all wishing to access the spectrum simultaneously. Vehicular networks are one of many scenarios of spectrum sharing as vehicles are expected to share the same spectrum for radar sensing and communication purposes. Channel assignment among radar sensors and communication transceivers in automotive systems is crucial for the success of future vehicular networks. This paper proposes an optimization framework for the channel assignment to multiple automotive radars and communication transceivers aiming at maximizing the number of served vehicles under hard quality of service requirements. The channel assignment problem is formulated as an integer linear optimization with binary variables. A heuristic solution that is based on ordered sequential channel assignment (OSCA) is proposed and is shown to achieve at least 92% of the solution obtained from branch-and-cut based techniques with much less computational complexity and run-time; at most 2% of the run-time of branch-and-cut.

We consider intermittently connected vehicular networks (ICVNs) in which base stations (BSs) are installed along a highway to connect moving vehicles with internet. Due to the deployment cost, it is hard to cover the entire highway with BSs. To minimize the outage time in the uncovered area (UA), several store-carry-forward (SCF) schemes have been proposed in which a vehicle is selected to act as a relay by buffering data to be relayed to a target vehicle in the UA. In this paper, we propose a full-duplex (FD) SCF scheme that exploits the relay's ability to simultaneously receive and transmit in order to improve the effective communication time (ECT) with the target vehicle and accordingly deliver more data to it in the UA. The optimal power allocation (PA) that maximizes the ergodic capacities of the links is determined and the amount of data that can be buffered inside the BS coverage and can be delivered by each relay candidate to the target vehicle in the UA is found. Since the relay cannot deliver more data in the UA than what can be buffered inside the coverage, the performance of each relay candidate is limited by the minimum of the two amounts and hence is used as a relay selection (RS) criteria. To reduce the computational complexity, an alternative RS scheme that selects the relay candidate that offers the highest ECT is proposed. As compared to half-duplex SCF schemes, simulation results show that the proposed FD schemes are capable of delivering significantly higher amount of data to the target vehicle in the UA.

There is an urgent need to support high data rate among connected vehicles for both safety and infotainment purposes. However, high rate for vehicular communications is impacted by several challenges such as optimal beam tracking for multiple simultaneous-transmitting vehicles in highly-dynamic environments. In this paper, we aim to maximize the overall network throughput for multi-vehicular communications. We propose a reinforcement learning (RL) approach called combinatorial multi-armed bandit (CMAB) framework, where multiple arms (i.e., actions) form a super arm and can be played together, to handle the beam selection problem in a vehicular network. More specifically, we propose an adaptive combinatorial Thompson sampling algorithm, namely adaptive CTS, that CMAB embodies for appropriate selection of simultaneous beams in a high-mobility vehicular environment. The proposed approach applies the smart exploration-exploitation trade-off for the fast selection of beams. As the proposed adaptive CTS scheme produces a higher complexity over the search space which increases exponentially with the number of user, we also propose a sequential Thompson sampling (TS) algorithm where beam selection is performed in a one-by-one manner. We analyze the regret bounds for the proposed beam tracking algorithms. The performance of the proposed strategies are evaluated for multi-vehicle millimeter-wave (mmWave) simulation environments. Our results suggest that the proposed sequential approach performs almost similar to the simultaneous adaptive CTS scheme for tracking optimal beams in a multi-vehicular network with much reduced complexity. Simulation results also show that both of our proposed strategies approach the optimal achievable rate achieved by the genie-aided solution.

In this article, we propose a resource allocation (RA) scheme for vehicular communication networks (VCNs). The proposed scheme exploits the spectral efficiency of full-duplex (FD) communications to fulfill the reliability constraints of vehicle-to-vehicle (V2V) links and the high capacity requirements of vehicle-to-infrastructure (V2I) links. Also, it is capable of coping with the fast variations of the channels due to the high mobility. Based on the links requirements, the RA problem is formulated as a non-convex problem, which is solved in two steps. First, the optimal power allocation (PA) is obtained by solving a system of linear equations. Second, the channel assignment (CA), which turns out to be a maximum weight bipartite matching problem, is solved using the Hungarian method. Also, a heuristic hybrid scheme, which combines the proposed FD scheme and the half-duplex (HD) scheme that optimally finds the RA, is proposed. Compared to the optimal HD-based scheme, simulation results show that the proposed FD scheme always offers higher sum of the V2I links' capacities except for the case in which V2V links require low transmission rates, while the hybrid scheme ensures higher performance for all potential cases.

An integrated access and backhaul (IAB) network architecture can enable flexible and fast deployment of the next-generation cellular networks. However, mutual interference between access and backhaul links, small inter-site distance, and spatial dynamics of user distribution pose major challenges in the practical deployment of the IAB networks. To tackle these problems, we leverage the flying capabilities of unmanned aerial vehicles (UAVs) as hovering IAB-nodes and propose an interference management algorithm to maximize the overall sum rate of the IAB network. In particular, we jointly optimize the user and base station associations, the downlink power allocations for access and backhaul transmissions, and the spatial configurations of the UAVs. We consider two spatial configuration modes of the UAVs, distributed UAVs and drone antenna array (DAA), and show how they are intertwined with the spatial distribution of ground users. Our numerical results show that the proposed algorithm achieves an average of 2.9× and 6.7× gains in the received downlink signal-to-interference-plus-noise ratio (SINR) and overall network sum rate, respectively. Finally, the numerical results reveal that UAVs can not only be used for coverage improvement but also for capacity boosting in the IAB cellular networks.

We propose a new sparsity-aware framework to model and mitigate the joint effects of narrow-band interference (NBI) and impulsive noise (IN) in hybrid powerline and unlicensed wireless communication systems. The proposed mitigation techniques, based on the principles of compressive sensing, exploit the inherent (non-contiguous or contiguous) sparse structures of NBI and IN in the frequency and time domains, respectively. For the non-contiguous NBI and IN, we develop a multi-level orthogonal matching pursuit recovery algorithm that exploits prior knowledge about the sparsity level at each receive antenna and powerline to further reduce computational complexity without performance loss. In addition, for the non-contiguous asynchronous NBI scenario, we investigate the application of time-domain windowing to enhance the NBI's sparsity and, hence, improve the NBI mitigation performance. For the contiguous NBI and IN scenario, we estimate the NBI and IN signals by modeling their burstiness as block-sparse vectors with and without prior knowledge of the bursts' boundaries. Moreover, we show how to exploit the spatial correlations of the NBI and IN across the receive antennas and powerlines to convert a non-contiguous NBI and IN problem to a block-sparse estimation problem with much lower complexity. Furthermore, we investigate a Bayesian linear minimum mean square error-based approach for estimating both non-contiguous and contiguous NBI and IN based on their second-order statistics to further improve the estimation performance. Finally, our numerical results illustrate the superiority of the joint processing of our proposed NBI and IN sparsity-based mitigation techniques compared to separate processing of the wireless and powerline received signals.