In this paper, the problems of relay selection and distributed beamforming are investigated for bi-directional dual-hop amplify-and-forward frequency-division duplex cooperative wireless networks. When using individual per-relay maximum transmission power constraint, it has been proven that the relay selection and beamforming optimization problem becomes NP hard and requires exhaustive search to find the optimal solution. Therefore, we propose a computationally affordable suboptimal multiple relay selection and beamforming optimization scheme based on the ℓ 1 norm squared relaxation. The proposed scheme performs the selection for the two transmission directions, simultaneously, while aiming at maximizing the aggregated SNR of the two communicating nodes. Furthermore, by exploiting the previous solutions to accelerate the algorithm's convergence, our proposed algorithm converges to a suboptimal solution compared to the exhaustive search technique with much less complexity.
This paper proposes a secure amplify-and-forward (AF) relaying scheme in unmanned aerial vehicle (UAV) networks while accounting for the power consumption limitations of UAVs. Since a UAV's battery life is limited, we propose selecting the UAV relays based on their probability of availability, which we model to be a function of the UAV's power consumption model. Then, physical layer security is achieved by partitioning the limited available relays into information relaying UAVs and cooperative jammers while aiming to maximize the secrecy rate of the network when an eavesdropper is located in the vicinity of the destination node. Meanwhile, the information bearing relays use beamforming to enhance the information delivery to the destination, and the cooperative jamming relays use precoded artificial-noise scheme to degrade the eavesdropping links' received signal-to-noise ratios. Simulation results show significant secrecy rate enhancements when the proposed schemes are adopted compared to conventional relaying scenarios, especially when cooperative jamming is deployed.
Medical data exchange between diverse e-health entities can lead to a better healthcare quality, improving the response time in emergency conditions, and a more accurate control of critical medical events (e.g., national health threats or epidemics). However, exchanging large amount of information between different e-health entities is challenging in terms of security, privacy, and network loads, especially for large-scale healthcare systems. Indeed, recent solutions suffer from poor scalability, computational cost, and slow response. Thus, this article proposes medical-edge-blockchain (MEdge-Chain), a holistic framework that exploits the integration of edge computing and blockchain-based technologies to process large amounts of medical data. Specifically, the proposed framework describes a healthcare system that aims to aggregate diverse health entities in a unique national healthcare system by enabling swift, secure exchange, and storage of medical data. Moreover, we design an automated patients monitoring scheme, at the edge, which enables the remote monitoring and efficient discovery of critical medical events. Then, we integrate this scheme with a blockchain architecture to optimize medical data exchanging between diverse entities. Furthermore, we develop a blockchain-based optimization model that aims to optimize the latency and computational cost of medical data exchange between different health entities, hence providing effective and secure healthcare services. Finally, we show the effectiveness of our system in adapting to different critical events, while highlighting the benefits of the proposed intelligent health system.
A major performance limiter in amplify-and-forward (AF) full-duplex (FD) relaying is the high level of residual self-interference (SI) due to the imperfect estimation of the effective self-interfering channel. One primary obstacle that contributes to the ineffectiveness of estimating the effective SI channel is the transceiver's radio-frequency (RF) impairments. In this paper, we consider the effect of in-phase/quadrature imbalance (IQI) on the process of SI cancellation in FD AF transceivers. We model the cumulative SI as a function of IQI, and thoroughly analyze the stability of the performance of the relay by analyzing the different transmission parameters and configurations that guarantee the stability of the system. In particular, depending on the RF impairments of the relay's transceiver, the wireless channel conditions, and the channel estimation accuracy, the relay's maximum amplification factor and transmission power that limit the residual SI power and prevent the system oscillations are analytically derived and verified by Monte-Carlo simulations. Finally, we show that the average SI power is bounded by a sum of scaled Gamma functions.
We derive an analytical expression of the error vector magnitude (EVM) in terahertz (THz) communication links affected by two performance limiters, namely antenna alignment error and the receiver's in-phase/quadrature imbalance (IQI). Through the sole use of elementary functions, we show how to compute the EVM in closed-form approximately, but accurately and efficiently. Furthermore, we show that antenna alignment errors and IQI can severely degrade the performance of THz communications. Investigations of the special and limiting cases of the regimes pertaining to high signal-to-noise ratio (SNR) and low antenna alignment error variance are carried out, where we also demonstrate the possible separation of the IQI and THz channel effects on the performance of the EVM.
This letter addresses the evaluation of the performance of a full-duplex (FD) jammer and intercepting node, where the FD node attempts to disrupt the communication link of an intruding unmanned aerial vehicle (UAV). The FD node's performance is evaluated by deriving an analytical expression of the probability of successfully interrupting the communication link between the UAV node and its control source (CS). Moreover, the analysis takes into account the channel characteristics between the interacting nodes. Simulations were conducted to examine the dynamics of the proposed system model by varying different key parameters. The conducted simulations verify the accuracy of our derived expressions, and the results highlight the importance of having low residual self-interference power in order to successfully intercept the communication link between the UAV node and its control source.
The usage of unmanned aerial vehicles (UAVs) in civil and military applications continues to increase due to the numerous advantages that they provide over conventional approaches. Despite the abundance of such advantages, it is imperative to investigate the performance of UAV utilization while considering their design limitations. This paper investigates the deployment of UAV swarms when each UAV carries a machine learning classification task. To avoid data exchange with ground-based processing nodes, a federated learning approach is adopted between a UAV leader and the swarm members to improve the local learning model while avoiding excessive air-to-ground and ground-to-air communications. Moreover, the proposed de-ployment framework considers the stringent energy constraints of UAVs and the problem of class imbalance, where we show that considering these design parameters significantly improves the performances of the UAV swarm in terms of classification accuracy, energy consumption and availability of UAVs when compared with several baseline algorithms.
In this paper, the problems of multiple relay selection and beamforming optimization are considered for a dual-hop amplify-and-forward relaying network under individual and total power constraints. The selection is performed based on maximizing the signal-to-noise ratio with computationally efficient algorithms. For the individual power constraint case, an iterative selection technique based on the use of the squared ℓ 1 -norm is proposed and investigated. We demonstrate that the proposed technique can converge to the same suboptimal solution proposed in the literature with up to 80% reduction in the number of iterations. Furthermore, we derive numerical suboptimal beamforming and relay selection solutions based on eigenvalue decomposition for both total and individual power constraints without the need for solving semidefinite programing problems.
