The swarms of unmanned aerial vehicles (UAV) are nowadays finding numerous applications in different fields. While performing their missions, UAVs have to rely on external positioning information to maintain connectivity and communications between units in a swarm. However, some of the critical applications such as rescue missions are performed in locations, where this information is partially or fully not available, e.g., deep woods, mountains, indoors. In this paper, we propose a method for dynamic topology organization and maintenance in UAV swarms. In addition to the baseline functionality, we also design advanced features required for dynamic swarms merging and disjoining, making it suitable for practical applications. Specifically, the proposal is based on the virtual coordinates system allowing for the utilization of conventional geographical routing algorithms. We test the proposed algorithm in different swarm conditions to illustrate that: (i) it is insensitive to distance estimates up to at least 30% allowing for simple estimation techniques, (ii) the accuracy of the topology inference is at least 90% even under impairments caused by mobility and temporal loss of connectivity, and (iii) the impact of the developed merging algorithm for swarms lasts for multiple tens of time steps that correspond to just few seconds in practice. The set of developed algorithms can be utilized to ensure always connected topology in conditions where positioning information is partially or fully unavailable.

Radio frequency fingerprinting (RFF), a technique based on specific transmitter hardware impairments, has emerged as an effective solution for wireless device identification. In this paper, we present a flexible deep CNN-LSTM for RF feature extraction capable of handling inputs with varying lengths. We construct a channel-independent spectrogram by exploiting the amplitude and phase information of the received RF signals, ensuring the extractor’s resilience to channel variations. To evaluate the performance of the proposed approach, we utilize the open-source LoRa dataset consisting of 60 commercial off-the-shelf LoRa devices and a USRP N210 software-defined radio platform. The experimental results show that classifiers perform better when trained with RF templates generated from amplitude-phase spectrogram than amplitude-only spectrogram. This is due to the additional information present in the amplitude-phase channel-independent spectrogram.

A symbolic feature that integrates the space, air and ground network components for service in challenging and remote areas is being envisaged with continuity and high mobility of the 6G mobile system. Simultaneously providing sensing and connectivity over the radio signal becomes essential to support the management of low-space air crafts in the mobile system with limited spectrum resources. In this paper, we investigate the optimal joint radar and communications beamforming scheme with the presence of the clutter to support the low-space airborne vehicles, e.g. unmanned aerial vehicles or drones that are essential components of Non-Terrestrial Networks. The proposed scheme achieves the optimal signal-to-clutter-plus-noise ratio of the sensing function while maintaining the performance of the predefined communications. The novel application of approximations and rank-reduction algorithms in this work maximizes the joint radar and communications performance, for a system model similar to the one that is solved with a local optimum solution in a previous work. The numeric simulation results show that our approach maintains low complexity while guaranteeing the global optimum beamforming solution.

This paper studies a drone relay assisted cooperative wireless communication system. Specifically, a drone is used as the relay node to establish communication between the base station and an aerial mobile terminal under realistic channel models with the consideration of line-of-sight probability. The total transmission time is divided into smaller time slots and in each time slot the relay uses decode-and-forward protocol to forward the received information to the mobile terminal. Then, an optimization problem is formulated where the objective is to maximize the sum rate over the whole transmission time. The formulated problem is non-convex. However, we show that for several cases the global optimal solution can be achieved. Moreover, we develop a low-complexity algorithm to find suboptimal solutions for the other cases.

Dual functional radar communication (DFRC) is a promising approach that provides a viable solution for the problem of spectrum sharing between communication and radar applications. This paper studies a DFRC system with multiple communication users (CUs) and one radar target. The goal is to devise beamforming vectors at the DFRC transmitter in such a way that the radar received signal to noise ratio (SNR) is maximized while the minimum data rate requirements of the individual CUs are satisfied. Even though the formulated optimization problem is non-convex, it is shown that it can be solved optimally through semi-definite relaxation (SDR). Also, it is observed that there is no need to transmit dedicated probing signal for the radar detection.

In this paper, we propose an online power allocation (PA) scheme to maximize the time averaged sum rate for multiple downlink receivers with energy harvesting transmitter. The transmitter uses non-orthogonal multiple access (NOMA) to transmit data to multiple users. For fairness, we impose a power order constraint on the power allocation among the users. The decisions of total transmit power and power allocation for different users in a given time slot are obtained with the help of Lyapunov optimization technique. The proposed scheme does not require any statistical information of the channel states and the harvested energy. Furthermore, only one Bisection search is required for the implementation of proposed scheme and hence its computational complexity is small. Simulation results are used to show the efficacy of the proposed scheme.

In this paper, we study an energy harvesting mobile computing (wireless) communication system in which mobile computing nodes and base stations are distributed according to Poisson point processes. The mobile computing nodes use power splitting protocol to harvest energy from the downlink transmissions while decoding the downlink information. The harvested energy is then used for computing and uplink transmission. In this setting, our target is to minimize the overall energy consumption in the network while satisfying the quality of service constraints on communication and computational aspects of the network. The formulated optimization problem is not a convex optimization problem and is difficult to solve. Therefore, we relax the original problem and propose a computationally efficient solution that can be implemented with Bisection search over a single variable. Finally, numerical results are provided to demonstrate the effectiveness of the proposed solution.

This paper studies a cooperative wireless powered communication network system. The communication between uplink users and hybrid access point is supported by energy harvesting relays. In contrast to the exiting works, we assume that the uplink users also harvest energy from the energy signal transmitted by the hybrid access point. Furthermore, it is assumed that the relays employ decode and forward relaying protocol for relaying the users information to the access point. We propose an optimization problem whose aim is to maximize the average sum throughput of the system. It is proved that the proposed optimization problem is convex. Karush Kuhn Tucker (KKT) conditions are used to find the optimal time allocations for the energy signal transmission and information signal transmissions. The proposed scheme for finding the optimal time allocation does not require instantaneous channel state information therefore significant reduction in the feedback information can be achieved by using the proposed scheme. The simulation results show that the proposed time allocation can significantly improve the performance of the system.

A sum inverse energy efficiency (SIEE) minimization problem is solved. Compared with conventional sum energy efficiency (EE) maximization problems, minimizing SIEE achieves a better fairness. The paper begins by proposing a framework for solving sum-fraction minimization (SFMin) problems, then uses a novel transform to solve the SIEE minimization problem in a multiple base station (BS) system. After the reformulation into a multi-convex problem, the alternating direction method of multipliers (ADMM) is used to further simplify the problem. Numerical results confirm the efficiency of the transform and the fairness improvement of the SIEE minimization. Simulation results show that the algorithm convergences fast and the ADMM method is efficient.

The integration of simultaneous wireless information and power transfer (SWIPT) and mobile-edge computing (MEC) technologies is emerging as a promising technique to overcome the performance limits of ultra-low power devices (ULPD) due to their low battery capacities and their limited computation capabilities in the Internet of Things (IoT) era. In this paper, we propose an online resource allocation algorithm for multiuser SWIPT-based MEC systems with the aim of maximizing the proportional fairness computational utility function subject to the stability of task and energy queues. Lyapunov optimization framework is used to jointly optimize the amounts of time allocated for energy harvesting, information decoding and offloading, the transmission power for offloading and CPU-cycles frequencies for local computing. Moreover, rigorous performance analysis has been done to prove the asymptotic optimality of our proposed algorithm. Simulation results are also presented to demonstrate the gains of our proposed algorithms over alternative online approaches and the impact of different network parameters on the performance of our algorithm.

In this letter, we propose an online power allocation (PA) method to maximize energy efficiency (EE) in energy harvesting systems with realistic battery constraints for two receivers. The optimization problem in this system configuration is challenging since it has a non-convex fractional form and the information of channel quality and the harvested energy can only be obtained causally in practice. To overcome these issues, we design time-average EE maximization through Lyapunov optimization techniques where the transmission power is computed with current battery information and channel fading. Moreover, based on random matrix theory, we present a simplified online EE algorithm. Numerical experiments verify that the proposed PA outperforms conventional online approaches with much reduced computational complexity.