Unmanned aerial vehicles (UAVs)-assisted communication systems are considered as a promising technology in diverse verticals. This paper studies the deployment of UAVs in wireless sensor network (WSN) systems. Considering the energy-constrained nature of the wireless sensors, we have proposed a multi-UAV deployment algorithm that minimizes the maximum power transmitted among the sensor nodes (SN) for given minimum data collection rate, minimum data-transferring rate, maximum power and height constraints. The problem is divided into three subproblems in order to reduce the complexity involved in solving them as a single problem. The subproblems are UAV-SN association, 2D positioning of the UAVs and the altitude optimization of the UAVs. Each subproblem is optimized by fixing other parameters as constant. First, the UAV-SN association is addressed using a customized Gale–Shapley algorithm. Second, the 2D positions of the UAVs are optimized using a modified pattern search algorithm. Third, the altitudes of the UAVs are optimized through a customized inexact line search algorithm. Finally, we proposed a combined optimization algorithm that integrates the approaches of all three subproblems in the suitable hierarchy to provide an optimal or a near-optimal solution. In the combined optimization, the first and second subproblems are iteratively solved until the convergence. After that, the third subproblem is solved independently for each UAV. Moreover, the combined optimization gives the minimum number of UAVs required to serve all the SNs with the given rate and power constraints. The numerical simulation validates the efficacy of our proposed algorithms. The results indicate a significant performance gain compared to the benchmark methods in terms of the number of iterations for convergence, maximum transmission power requirement power and the minimum number of UAV requirements.
In this article, we consider a simultaneous lightwave information and power transfer-enabled indoor visible light/radio frequency (RF) hybrid communication system. In this system, several luminaries are mounted on the ceiling of a room and sensors receive lightwave power and information from luminaries. Each sensor harvests energy from lightwave signals and uses them for uplink communication using RF signals. In general, blockages due to the movement of humans are common in indoor systems which results in severe performance degradation. We consider blockages due to such human movements in our system. An optimization problem is formulated to maximize the uplink weighted sum rate considering uplink orthogonal multiple access (OMA) and non-OMA cases. To find the optimal beamforming matrix and time allocation parameters, a lightweight artificial neural network architecture is proposed. Our solution is capable of predicting the human blockages and accordingly optimizing the beamforming matrices and time allocation parameters leading to a near-optimal uplink sum rate. Specifically, up to 30% rate improvement is observed compared to zero-forcing beamforming when the number of blockages is more than five. Further, the use of NOMA for uplink results in up to 25% sum rate improvement.
One of the most challenging tasks in a disaster scenario is the detection and localization of victims with high accuracy and minimum delay, especially in out-of-coverage areas. In the event of a disaster that disrupts the cellular network infrastructure, emergency calls can be relayed to the core network via multi-hop D2D communications. In this paper, a localization system is proposed that uses radio measurements obtained through such D2D multi-hop assisted emergency calls to localize in-coverage and out-of-coverage devices. To address the uncertainty and gradual reception of data in real-time in this scenario, a dynamic constraint satisfaction-based Multi Victim Localization Algorithm (MVLA) is proposed. This algorithm locates multi-hop devices in a progressive propagation manner to provide fast and accurate updates on victim locations. Additionally, three modes of $\mathit{MVLA}$ , namely $\mathit{MVLA}_{recent}$ , $\mathit{MVLA}_{seq}$ , and $\mathit{MVLA}_{all}$ are proposed. Simulation results demonstrate that $\mathit{MVLA}_{all}$ has a lower localization error compared to $\mathit{MVLA}_{recent}$ and $\mathit{MVLA}_{seq}$ . Moreover, $\mathit{MVLA}_{all}$ , is compared with an existing particle filtering-based localization algorithm called RSSI Monte-Carlo Boxed Localization ( RSSI-MCL ) under an increasing number of emergency user devices and functional gNodeBs. Results show that $\mathit{MVLA}_{all}$ significantly outperforms the RSSI-MCL method in terms of localization accuracy and computational delay.
Motivated by the timely need for fresh updates in time-critical applications, this paper investigates the AoI-driven UAV trajectory optimization for a two-way relay network. The non-linear simultaneous wireless information and power transfer (SWIPT) power-splitting (PS) technique is used to provide the energy required for UAV communication considering hardware impairments. Amalgamating derived outage probability, AoI expression is obtained to evaluate data timeliness and its performance trade-offs. Then, we optimize the trajectory of the UAV while minimizing the AoI performance within the given discretized time slot. The impact of the system parameters, that is, size of the status update, PS factor, outage probability, and UAV's position, on the AoI performance are extensively investigated. Motivated by the pressing demand for real-time updates in time-critical applications, this paper explores the optimization of UAV trajectories driven by the age of information (AoI) for a simultaneous wireless information and power transfer (SWIPT) enabled two-way relay network. AoI is introduced as a novel performance metric aimed at quantifying the data freshness at the receiver's end. The paper investigates trade-offs between AoI and conventional performance metrics, employing them to determine optimal parameters for the proposed UAV-assisted communication system.image
The complexity of successive interference cancellation at the receiver’s end is a challenging issue in conventional non-orthogonal multiple access assisted massive wireless networks. The computational complexity of decoding increases exponentially with the number of users. Further, under realistic channel conditions, a synchronous non-orthogonal multiple access scheme is impractical in the uplink device-to-device communications. In this paper, an asynchronous non-orthogonal multiple access-based cyclic triangular successive interference cancellation scheme is proposed for a massive device-to-device network. The proposed scheme reduces the decoding complexity, energy consumption, and bit error rate of a superimposed signal received in an outband device-to-device network. More specifically, the scheme follows three consecutive stages; optimization, decoding, and re- transmission. In the optimization stage, a dual Lagrangian objective function is defined to maximize the number of data symbols decoded at the receiver by determining an optimal interference cancellation triangle, under the co-channel interference and data rate constraints. In the decoding stage, the data in the optimal interference cancellation triangle is decoded using a conventional triangular successive interference cancellation technique. Next, the remaining users’ data are decoded in sequential iterations of the proposed scheme, using the retransmissions from such users. Utilizing the successive interference cancellation characteristics, the performance of the proposed device-to-device network is defined in terms of energy efficiency, bit error rate, computational complexity, and decoding delay metrics. Moreover, the performance of the proposed decoding scheme is compared with the conventional triangular successive interference cancellation decoding scheme to demonstrate the superiority of the proposed scheme.
