Generalized Vietoris Bisimulations

We introduce and study bisimulations for coalgebras on Stone spaces [14]. Our notion of bisimulation is sound and complete for behavioural equivalence, and generalizes Vietoris bisimulations [4]. The main result of our paper is that bisimulation for a $\mathbf{Stone}$ coalgebra is the topological closure of bisimulation for the underlying $\mathbf{Set}$ coalgebra

Robust Beamforming for IRS Aided MIMO Full Duplex Systems

In this paper, a novel robust beamforming for an intelligent reflecting surface (IRS) assisted FD system is presented. Since perfect channel state information (CSI) is often challenging to acquire in practice, we consider the case of imperfect CSI and adopt a statistically robust beamforming approach to maximize the ergodic weighted sum rate (WSR). We also analyze the achievable WSR of an IRS-assisted FD with imperfect CSI, for which the lower and the upper bounds are derived. The ergodic WSR maximization problem is tackled based on the expected Weighted Minimum Mean Squared Error (WMMSE), which is guaranteed to converge to a local optimum. The effectiveness of the proposed design is investigated with extensive simulation results. It is shown that our robust design achieves significant performance gain compared to the naive beamforming approaches and considerably outperforms the robust Half-Duplex (HD) system

IRS Assisted MIMO Full Duplex: Rate Analysis and Beamforming Under Imperfect CSI

Intelligent reflecting surfaces (IRS) have emerged as a promising technology to enhance the performance of wireless communication systems. By actively manipulating the wireless propagation environment, IRS enables efficient signal transmission and reception. In recent years, the integration of IRS with full-duplex (FD) communication has garnered significant attention due to its potential to further improve spectral and energy efficiencies. IRS-assisted FD systems combine the benefits of both IRS and FD technologies, providing a powerful solution for the next generation of cellular systems. In this manuscript, we present a novel approach to jointly optimize active and passive beamforming in a multiple-input-multiple-output (MIMO) FD system assisted by an IRS for weighted sum rate (WSR) maximization. Given the inherent difficulty in obtaining perfect channel state information (CSI) in practical scenarios, we consider imperfect CSI and propose a statistically robust beamforming strategy to maximize the ergodic WSR. Additionally, we analyze the achievable WSR for an IRS-assisted MIMO FD system under imperfect CSI by deriving both the lower and upper bounds. To tackle the problem of ergodic WSR maximization, we employ the concept of expected weighted minimum mean squared error (EWMMSE), which exploits the information of the expected error covariance matrices and ensures convergence to a local optimum. We evaluate the effectiveness of our proposed design through extensive simulations. The results demonstrate that our robust approach yields significant performance improvements compared to the simplistic beamforming approach that disregards CSI errors, while also outperforming the robust half-duplex (HD) system considerablyComment: arXiv admin note: substantial text overlap with arXiv:2308.0801

Ambient Backscatter Assisted Co-Existence in Aerial-IRS Wireless Networks

Ambient backscatter communication (AmBC) is an emerging technology that has the potential to offer spectral- and energy-efficient solutions for the next generation wireless communications networks, especially for the Internet of Things (IoT). Intelligent reflecting surfaces (IRSs) are also perceived to be an integral part of the beyond 5G systems to complement the traditional relaying scheme. To this end, this paper proposes a novel system design that enables the co-existence of a backscattering secondary system with the legacy primary system. This co-existence is primarily driven by leveraging the AmBC technique in IRS-assisted unmanned aerial vehicle (UAV) networks. More specifically, an aerial-IRS mounted on a UAV is considered to be employed for cooperatively relaying the transmitted signal from a terrestrial primary source node to a user equipment on the ground. Meanwhile, capitalizing on the AmBC technology, a backscatter capable terrestrial secondary node transmits its information to a terrestrial secondary receiver by modulating and backscattering the ambient relayed radio frequency signals from the UAV-IRS. We comprehensively analyze the performance of the proposed design framework with co-existing systems by deriving the outage probability and ergodic spectral efficiency expressions. Moreover, we also investigate the asymptotic behaviour of outage performance in high transmit power regimes for both primary and secondary systems. Importantly, we analyze the performance of the primary system by considering two different scenarios i.e., optimal phase shifts design and random phase shifting at IRS. Finally, based on the analytical performance assessment, we present numerical results to provide various useful insights and also provide simulation results to corroborate the derived theoretical results

Symbiotic Radio based Spectrum Sharing in Cooperative UAV-IRS Wireless Networks

Ambient backscatter communication (AmBC) technology can potentially offer spectral- and energy-efficient solutions for future wireless systems. This paper proposes a novel design to facilitate the spectrum sharing between a secondary system and a primary system based on the AmBC technique in intelligent reflective surface (IRS)-assisted unmanned aerial vehicle (UAV) networks. In particular, an IRS-aided UAV cooperatively relays the transmission from a terrestrial primary source node to a user equipment on the ground. On the other hand, leveraging on the AmBC technology, a terrestrial secondary node transmits its information to a terrestrial secondary receiver by modulating and backscattering the ambient relayed radio frequency (RF) signals from the UAV-IRS. The performance of such a system setup is analyzed by deriving the expressions of outage probability and ergodic spectral efficiency. Finally, we present the numerical results to provide useful insights into the system design and also validate the derived theoretical results using Monte Carlo simulations

Short-Packet Communication Assisted Reliable Control of UAV for Optimum Coverage Range

The reliability of command and control (C2) operation of the UAV is one of the crucial aspects for the success of UAV applications beyond 5G wireless networks. In this paper, we focus on the short-packet communication to maximize the coverage range of reliable UAV control. We quantify the reliability performance of the C2 transmission from a multi-antenna ground control station (GCS), which also leverages maximal-ratio transmission beamforming, by deriving the closed-form expression for the average block error rate (BLER). To obtain additional insights, we also derive the asymptotic expression of the average BLER in the high-transmit power regime and subsequently analyze the possible UAV configuration space to find the optimum altitude. Based on the derived average BLER, we formulate a joint optimization problem to maximize the range up to which a UAV can be reliably controlled from a GCS. The solution to this problem leads to the optimal resource allocation parameters including blocklength and transmit power while exploiting the vertical degrees of freedom for UAV placement. Finally, we present numerical and simulation results to corroborate the analysis and to provide various useful design insights

Boosting Quantum Battery-Based IoT Gadgets via RF-Enabled Energy Harvesting

The search for a highly portable and efficient supply of energy to run small-scale wireless gadgets has captivated the human race for the past few years. As a part of this quest, the idea of realizing a Quantum battery (QB) seems promising. Like any other practically tractable system, the design of QBs also involve several critical challenges. The main problem in this context is to ensure a lossless environment pertaining to the closed-system design of the QB, which is extremely difficult to realize in practice. Herein, we model and optimize various aspects of a Radio-Frequency (RF) Energy Harvesting (EH)-assisted, QB-enabled Internet-of-Things (IoT) system. Several RF-EH modules (in the form of micro- or nano-meter-sized integrated circuits (ICs)) are placed in parallel at the IoT receiver device, and the overall correspondingly harvested energy helps the involved Quantum sources achieve the so-called quasi-stable state. Concretely, the Quantum sources absorb the energy of photons that are emitted by a photon-emitting device controlled by a micro-controller, which also manages the overall harvested energy from the RF-EH ICs. To investigate the considered framework, we first minimize the total transmit power under the constraints on overall harvested energy and the number of RF-EH ICs at the QB-enabled wireless IoT device. Next, we optimize the number of RF-EH ICs, subject to the constraints on total transmit power and overall harvested energy. Correspondingly, we obtain suitable analytical solutions to the above-mentioned problems, respectively, and also cross-validate them using a non-linear program solver. The effectiveness of the proposed technique is reported in the form of numerical results, which are both theoretical and simulations based, by taking a range of operating system parameters into account

MEC-assisted Low Latency Communication for Autonomous Flight Control of 5G-Connected UAV

Proliferating applications of unmanned aerial vehicles (UAVs) impose new service requirements, leading to several challenges. One of the crucial challenges in this vein is to facilitate the autonomous navigation of UAVs. Concretely, the UAV needs to individually process the visual data and subsequently plan its trajectories. Since the UAV has limited onboard storage constraints, its computational capabilities are often restricted and it may not be viable to process the data locally for trajectory planning. Alternatively, the UAV can send the visual inputs to the ground controller which, in turn, feeds back the command and control signals to the UAV for its safe navigation. However, this process may introduce some delays, which is not desirable for autonomous UAVs’ safe and reliable navigation. Thus, it is essential to devise techniques and approaches that can potentially offer low-latency solutions for planning the UAV’s flight. To this end, this paper analyzes a multi-access edge computing aided UAV and aims to minimize the latency of the task processing. More specifically, we propose an offloading strategy for a UAV by optimally designing the offloading parameter, local computational resources, and altitude of the UAV. The numerical and simulation results are presented to offer various design insights, and the benefits of the proposed strategy are also illustrated in contrast to the other baseline approaches