Sasthi C. Ghosh

Reconfigurable intelligent surface (RIS) is a cutting-edge communication technology that has been proposed as a viable option for beyond fifth-generation wireless communication networks. This paper investigates various group selection strategies in the context of grouping-based self-sustainable RIS-aided device-to-device (D2D) communication with spatially correlated wireless channels. Specifically, we consider both power splitting (PS) and time switching (TS) configurations, of the self-sustainable RIS to analyze the system performance and propose appropriate bounds on the choice of system parameters. The analysis takes into account a simplified linear energy harvesting (EH) model as well as a practical non-linear EH model. Based on the application requirements, we propose various group selection strategies at the RIS. Notably, each strategy schedules the k-th best available group at the RIS based on the end-to-end signal-to-noise ratio (SNR) and also the energy harvested at a particular group of the RIS. Accordingly, by using tools from high order statistics, we derive analytical expressions for the outage probability of each selection strategy. Moreover, by applying the tools from extreme value theory, we also investigate an asymptotic scenario, where the number of groups available for selection at an RIS approaches infinity. The nontrivial insights obtained from this approach is especially beneficial in applications like large intelligent surface-aided wireless communication. Finally, the numerical results demonstrate the importance and benefits of the proposed approaches in terms of metrics such as the data throughput and the outage (both data and energy) performance.

Reconfigurable intelligent surfaces (RISs) offer a viable way to improve the performance of the multi-hop device-to-device (D2D) communication. However, due to the substantial propagation and penetration losses of the millimeter waves (mmWaves), a direct line of sight (LoS) link and close proximity of a device pair are required for a high data rate. Static obstacles like trees and buildings can easily impede the direct LoS connectivity between a device pair. Hence, RIS placement plays a crucial role in establishing an indirect LoS link between them. Therefore, in this work, we propose a set cover-based RIS deployment strategy for both single and double RIS-assisted D2D communication. In particular, we have demonstrated that permitting reflections via two consecutive RISs can greatly lower the RIS density in the environment, preventing resource waste and enabling the service of more obstructed device pairs. After the RIS deployment, for information transfer, we also propose an energy-efficient group selection criteria. Moreover, we prove that sometimes double reflections are more beneficial than single reflection, which is counter-intuitive. Numerical results show that our approach outperforms a random and a recent deployment strategy.

One primary focus of next generation wireless communication networks is the millimeterwave (mmWave) spectrum, typically considered in the 30 GHz to 300 GHz frequency range. Despite their promise of high data rates, mmWaves suffer from severe attenuation while passing through obstacles. Unmanned aerial vehicles (UAVs) have been proposed to offset this limitation on account of their additional degrees of freedom, which can be leveraged to provide line of sight (LoS) transmission paths. While some prior works have proposed analytical frameworks to compute the LoS probability for static ground users and a UAV, the same is lacking for mobile users on the ground. In this paper, we consider the popular Manhattan point line process (MPLP) to model an urban environment, within which a ground user moves with a known velocity for a small time interval along the roads. We derive an expression for the expected duration of LoS between a static UAV in the air and a mobile ground user, and validate the same through simulations. To demonstrate the efficacy of the proposed analysis, we propose a simple user association algorithm that greedily assigns the UAVs to users with the highest expected LoS time, and show that it outperforms the existing benchmark schemes that assign the users to the nearest UAVs with LoS without considering the user mobility.

In unmanned aerial vehicle (UAV) assisted millimeter wave (mmWave) communication, appropriate user-UAV association is crucial for improving system performance. In mmWave communication, user throughput largely depends on the line of sight (LoS) connectivity with the UAV, which in turn depends on the mobility pattern of the users. Moreover, different traffic types like enhanced mobile broadband (eMBB) and ultra reliable low latency communication (URLLC) may require different types of LoS connectivity. Existing user-UAV association policies do not consider the user mobility during a time interval and different LoS requirements of different traffic types. In this paper, we consider both of them and develop a user association policy in the presence of building blockages. First, considering a simplified scenario, we have analytically established the LoS area, which is the region where users will experience seamless LoS connectivity for eMBB traffic, and the LoS radius, which is the radius of the largest circle within which the user gets uninterrupted LoS services for URLLC traffic. Then, for a more complex scenario, we present a geometric shadow polygon-based method to compute LoS area and LoS radius. Finally, we associate eMBB and URLLC users, with the UAVs from which they get the maximum average throughput based on LoS area and maximum LoS radius respectively. We show that our approach outperforms the existing discretization based and maximum throughput based approaches.

These days, unmanned aerial vehicle (UAV)-based millimeter wave (mmWave) communication systems have drawn a lot of attention due to the increasing demand for faster data rates. Given the susceptibility of mmWave signals to obstacles and high propagation loss of mmWaves, ensuring line-of-sight (LoS) connectivity is critical for maintaining robust and efficient communication. Furthermore, UAVs have limited power resource and limited capacity in terms of number of users it can serve. Most significantly different users have different delay requirements and they keep moving while interacting with the UAVs. In this paper, first, we have provided an efficient solution for the optimal movement of the UAVs, by taking into account the energy efficiency of the UAVs as well as the mobility and delay priority of the users. Next, we have proposed a greedy solution for the optimal user-UAV assignment. After that, the numerical results show how well the suggested solution performs in comparison to the current benchmarks in terms of delay suffered by the users, number of unserved users, and energy efficiency of the UAVs.

Reconfigurable intelligent surfaces (RISs) is a novel communication technology that has been recently presented as a potential candidate for beyond fifth-generation wireless communication networks. In this paper, we propose a priority-aware user traffic-dependent grouping-based multihop routing scheme for a RIS-assisted millimeter wave (mmWave) device-to-device (D2D) communication network with spatially correlated channels. Specifically, the proposed scheme exploits the priority of the users (based on their respective delay-constrained applications) and the aspect of spatial correlation in the narrowly spaced reflecting elements of the RISs. Here, based on the other users in the neighborhood, their respective traffic characteristics, and the already deployed RISs in the surroundings, we establish a multihop connection for information transfer from one of the users to its intended receiver. In this context, we take into account the impact of considering practical discrete phase shifts at the RIS patches instead of its ideal continuous counterpart. Moreover, we also claim and demonstrate that the existing classic least remaining distance (LRD)-based approach is not always the optimal solution. Finally, numerical results demonstrate the advantages of the proposed strategy and that it significantly outperforms the existing benchmark schemes in terms of system performance metrics such as data throughput, energy consumption, as well as energy efficiency.

This paper studies a graph-based sensor deployment approach in wireless sensor networks (WSNs). Specifically, in today's world, where sensors are everywhere, detecting various attributes like temperature and movement, their deteriorating lifetime is indeed a very concerning issue. In many scenarios, these sensors are placed in extremely remote areas, where maintenance becomes challenging. As a result, it is not very wise to depend on a single sensor to obtain data from a particular terrain or place. Hence, multiple sensors are deployed in these places, such that no problem arises if one or few of them fail. In this work, this problem of intelligent placement of sensors is modelled from the graph theoretic point of view. We propose a new sensor deployment approach here, which results in lesser sensor density per unit area and less number of sensors as compared to the existing benchmark schemes. Finally, the numerical results also support our claims and provide insights regarding the selection of parameters that enhance the system performance.

The channel estimation (CE) overhead for unstructured multipath-rich channels increases linearly with the number of reflective elements of reconfigurable intelligent surface (RIS). This results in a significant portion of the channel coherence time being spent on CE, reducing data communication time. Furthermore, due to the mobility of the user equipment (UE) and the time consumed during CE, the estimated channel state information (CSI) may become outdated during actual data communication. In recent studies, the timing for CE has been primarily determined based on the coherence time interval, which is dependent on the velocity of the UE. However, the effect of the current channel condition and pathloss of the UEs can also be utilized to control the duration between successive CE to reduce the overhead while still maintaining the quality of service. Furthermore, for muti-user systems, the appropriate coherence time intervals of different users may be different depending on their velocities. Therefore CE carried out ignoring the difference in coherence time of different UEs may result in the estimated CSI being detrimentally outdated for some users. In contrast, others may not have sufficient time for data communication. To this end, based on the throughput analysis on outdated CSI, an algorithm has been designed to dynamically predict the next time instant for CE after the current CSI acquisition. In the first step, optimal RIS phase shifts to maximise channel gain is computed. Based on this and the amount of degradation of SINR due to outdated CSI, transmit powers are allocated for the UEs and finally the next time instant for CE is predicted such that the aggregated throughput is maximized. Simulation results confirm that our proposed algorithm outperforms the coherence time-based strategies.