Wireless Body Area Networks (WBANs) play a paramount role in enhancing the quality of health monitoring and personalized medicine. WBANs frequently necessitate novel green solutions like energy harvesting (EH) to sustain the implants over long periods of time. In this work, we consider an implanted patient present in an indoor environment having an RF power source. The implant can harvest power from the source for which a non-linear EH model is considered. We have first provided a hybrid channel model for off-to-in body communication, thereby calculating the harvested power at the implant. In addition to incorporating the effect of different floors, partition walls in the channel model, we have also investigated the impact of specific-absorption rate on human tissues by providing a novel relation between specific absorption rate and relative permittivity for various layers inside human body. The performance of the proposed model is analyzed in different indoor room scenarios for linear as well as non-linear EH scenarios, and compared with already existing ITU-R path loss models thereby including insights on channel fading. The proposed model is found to outperform the considered benchmark model.

In this paper, we focus on a UAV-aided Flying Ad hoc Network (FANET) with multiple unmanned aerial vehi-cles (UAVs) deployed for power-efficient data transmission to a ground control station (GCS) through multihopping. Our main objective is to enhance overall network performance by investigating the most effective multihop routing structure for efficient data dissemination. Considering the more realistic Rician fading channel for communication conditions, our goal is to maximize network throughput in a multihop UAV relay network by jointly optimizing multihop routing and power allocation (PA) to each UAV within a specified power budget. We reformulate the original optimization problem into two subproblems and propose an algorithm to derive the optimal solution. Additionally, we analyze the system's reliability in terms of packet loss probability (PLP). Numerical results demonstrate the effectiveness of our approach, offering novel insights in the context of Rician fading channels compared to traditional path loss channel models.

This paper considers a multi-UAV network with a ground station (GS) that uses multihop relaying structure for data transmission in a power-efficient manner. In order to increase the total network performance of a flying ad-hoc network (FANET) of UAVs, the objective is to investigate the best possible multihop routing structure for data dissemination. We formulate a problem to jointly optimize the multihop routing structure with the communication link selection for a given power budget so that the overall network throughput can be maximized. It appears that the formulated problem belongs to a class of nonconvex and integer optimization problems, thus making it NP-hard. To solve this problem efficiently, it is decoupled into two subproblems i) power allocation with known Bellman Ford-based multihop routing structure and ii) link selection problem. Further, these two subproblems are independently converted into convex problems by relaxation and solved in tandem for the best suboptimal solution to the main problem. Simulation results indicate that the proposed multihop routing schemes can achieve a significant improvement in network throughput compared to the other benchmark scheme.

Graphene-based intelligent reflecting surface (GIRS) has been proved to provide a promising propagation environment to enhance the quality of high frequency terahertz (THz) wireless communication. In this paper, we characterize GIRS for THz communication (GITz) using material specific parameters of graphene to tune the reflection of the incident wave at IRS. In particular, we propose a GITz design model considering the incident signal frequency material level parameters like conductivity, Fermi-level, patch width to control the reflection amplitude (RA) at the communication receiver. We have obtained the closed-form expression of RA for an accurate design and characterization of GIRS, which is incomplete in the existing research due to the inclusion of only phase-shift. The numerical simulation results demonstrate the effectiveness of the proposed characterization by providing key insights.

Intelligent reflecting surface (IRS) has emerged as a transforming solution to enrich wireless communications by efficiently reconfiguring the propagation environment. In this paper, a novel IRS circuit characterization model is proposed for practical beamforming design incorporating various electrical parameters of the meta-surface unit cell. Specifically, we have modelled the IRS control parameters, phase shift (PS) and reflection amplitude (RA) at the communication receiver, in addition to the circuit level parameter, variable effective capacitance C of IRS unit cell. We have obtained closed-form expressions of PS, RA and C in terms of transmission frequency of signal incident to IRS and various electrical parameters of IRS circuit, with a novel touch towards an accurate analytical model for a better beamforming design perspective. Numerical results demonstrate the efficacy of the proposed characterization thereby providing key insights.

Advent of unmanned aerial vehicle (UAV) as relay in ad-hoc networks has offered numerous promising communication solutions with both military and civilian applications. Additionally with more degrees of freedom, mobile UAV relaying provides better susceptibility to changing environment conditions thereby enhancing the network performance. However, optimization of UAV altitude is a primary concern in such flying ad hoc networks (FANETs). In this work, we consider a three node decode-and-forward (DF) FANET, and the aim is to minimize the average network outage probability subject to practical mobility constraints of maximum UAV velocity and minimum allowable UAV height, thereby finally optimizing the overall UAV trajectory (UT). In particular, for pre-determined initial and final relay positions, we first prove convexity of the optimization problem and then obtain the semi-closed-form globally optimal UT solution. The analysis is numerically validated along with the discussion of multiple optimal design insights. Results reveal the respective average performance gain of 56% and 37% over the two benchmark schemes.

In a cognitive radio environment, the optimality in channel selection by a secondary user (SU) is directly dependent on its spectrum‐sensing efficiency, and quality of experience (QoE) in terms of the channel‐switching frequency (CSF) and the interference caused to the primary users (PUs). Modelling the spectrum through statistical methods becomes, sometimes, difficult due to the lack of a‐priori information of the PU activity. This work proposes a framework for learning‐based prediction of the future idle times of the PUs thereby opportunistically allocating the channel with enhanced QoE of SUs. The idea is to minimise the spectrum‐sensing energy requirement by sensing only if the channel is predicted to be idle, thereby reducing the CSF and mitigating the SU–PU interference. Initially, the authors have tested the accuracy of the prediction approach in various traffic scenarios for a single PU channel case. Later, it is extended to the multiple channel case for a particular data traffic. Furthermore, a practical scenario has been considered where the efficacy of the proposed framework is validated for PU data traffic in GSM and ISM bands. The results highlight the practicability of prediction‐based opportunistic dynamic spectrum access with improvement in the SU QoE over random channel selection.