The escalating demand for mobile data has driven the need to explore innovative solutions for enhancing the capacity of cellular networks beyond Fifth-Generation (5G) standards. One promising approach is the deployment of small cells, low-power base stations with limited coverage. Additionally, Unmanned Aerial Vehicles (UAVs) can serve as flying base stations, providing connectivity when terrestrial infrastructure is compromised during emergencies. Nonetheless, the uncontrolled transmit power of small cells and UAVs can cause increased interference and energy consumption, negatively impacting overall network performance. To address these challenges, efficient power control strategies are crucial for optimizing performance and ensuring a high-quality user experience. In this paper, we propose a social-assisted bipartite graph technique for managing macro user transmit power. Initially, we construct an interference-aware bipartite graph based on users' Signal-to-Aggregate Interference Ratio (SAIR). We then reduce interference by minimizing the number of edges through social connections, adjusting transmit power, and improving system throughput. Our simulation results demonstrate that this technique significantly enhances system throughput and energy efficiency compared to traditional techniques.

Incorporating Device-to-Device (D2D) communication and Unmanned Aerial Vehicles (UAVs) into next-generation cellular networks is crucial for addressing the growing demand for high-data-rate applications. However, increasing communication distances in both cellular and D2D environments raises transmission power requirements, leading to higher energy consumption and reduced network efficiency. Additionally, in the conventional technique, UAVs communicate directly with each UE individually, further exacerbating the energy demands of UAVs. To overcome these challenges, this paper presents an energy-efficient clustering technique designed to minimize power consumption for both UEs and UAVs. Hypergraph-based clustering enables adaptive grouping by leveraging received signal strength, while the Whale Optimization Algorithm (WOA) selects central users based on UE distance, residual energy, and connectivity metrics. This structured clustering strategy enhances system throughput and optimizes energy efficiency. Extensive simulations validate the proposed model, demonstrating significant improvements in energy efficiency, reduced computational complexity, and superior network performance compared to existing methods.

Proximal spectrum sharing allows users to connect to nearby base stations (BSs) of other operators based on SINR, improving spectral efficiency and user experience. However, it raises challenges in load balancing and fair revenue distribution. To address this, we propose a game-theoretic framework for joint user association and dynamic pricing. Users choose BSs by maximizing a utility that considers SINR and price, while operators adjust prices based on load to optimize revenue. The interaction is modeled as a two-stage game: an ordinal potential game for user association and a concave optimization for pricing. We prove convergence to a Nash Equilibrium, ensuring stability and fairness. Our framework ensures fair, stable, and efficient coexistence—maximizing throughput while preserving operator incentives. Extensive simulations validate its effectiveness across throughput, load distribution, user satisfaction, and operator revenue.

The evolution toward Sixth-Generation (6G) wireless networks demands high spectral efficiency, user connectivity, and efficient resource utilization. While dense macro base station deployment improves capacity, it also increases co-channel interference and operational costs. Femtocells serve as a cost-effective means to enhance indoor coverage and spectrum reuse. However, trust and performance concerns often discourage femtocell owners from sharing access, leading to under-utilization. Social ties can enable trusted access and help overcome this limitation. Additionally, Non-Orthogonal Multiple Access (NOMA) improves spectral efficiency by allowing multiple users to share a single subchannel, thereby increasing throughput. Motivated by this, we propose a social-aware, NOMA-enabled heterogeneous network framework to enhance the system throughput by integrating two layers of collaboration: (i) inter-operator collaboration and (ii) social ties among users. Furthermore, a novel social-aware Rate-Difference-Aware Pairing (RDAP) algorithm is proposed to pair the NOMA users. Subsequently, we derive the bounds on power allocation and Successive Interference Cancellation (SIC) imperfection to ensure that the NOMA user rates dominate their Orthogonal Multiple Access (OMA) counterparts. Through extensive simulations, we show the superiority of the proposed social-aware RDAP algorithm over the conventional and socialaware OMA/NOMA baselines, including existing NOMA pairing methods, in terms of throughput, energy efficiency, and spectral efficiency, while reducing user blocking. The impact of power allocation and SIC imperfections is also systematically evaluated.

To enhance user safety and experience, next-generation transportation system needs to understand and classify drivers’ behavior accurately. For instance, reckless or unsafe behavior may lead to road accidents. Traditional driver behavior monitoring methods require local data to be uploaded to a central server for model training, which is impractical due to the large data size and privacy concerns. This paper presents Federated Learning (FL) as a method for enabling local training on clients while keeping their data private and minimizing communication overhead. However, the diversity in drivers’ skills, habits, and unique preferences adds significant complexity to modeling driver behavior. To address this, we introduce pFedCI, a novel personalized FL framework based on cognitive intelligence for monitoring driver behavior. In this framework, each client retains a locally trained centralized model, which fine-tunes the global model to create distinct and personalized models. A key innovation of the pFedCI framework is its ability to allow clients to choose the optimization algorithm during each training round adaptively. Additionally, the server optimizes the global model to ensure its generalizability. Extensive experiments on the UAH and KIA Soul datasets show that pFedCI significantly outperforms previous FL methods. Moreover, we demonstrate that pFedCI achieves better generalization across various state-of-the-art models and clients with diverse features.

In the era of 5G networks, dense base station deployments are crucial to meet escalating mobile data demands, yet they bring about co-channel interference and high costs. Inter-operator collaborations offer a solution by enabling multiple mobile network operators to share base stations and licensed spectrum. Additionally, deploying small cells like femtocells indoors enhances spectrum reuse and system throughput. However, reluctance among femtocell owners to share resources due to trust and performance concerns leads to under-utilization. To address this, researchers propose using social network data to enable more users to access a femtocell based on user-friendship or trust levels. Our research analyses the impact of operator and user collaborations to share base stations and maximize resource usage. We propose a metric to enhance the cell selection, surpassing the bitrate-based method. Simulating various collaboration scenarios, we demonstrate the significance of such collaborations in improving the cellular network’s throughput, energy efficiency, and offloading efficiency.

Akshay Singh, Rahul Thakur. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Device-to-Device (D2D) communication and Un-manned Aerial Vehicle (UAV) femtocells play crucial roles in addressing the growing data demands of Fifth-Generation (5G) cellular networks. The conventional technique, where the UAV serves each User Equipment (UE) individually, increases energy consumption and reduces energy efficiency. Therefore, it is imperative to develop efficient clustering techniques that simultaneously improve system performance and energy efficiency. This paper introduces a novel clustering technique aimed at minimizing energy consumption in UAV-enabled D2D cellular networks. The proposed technique leverages hypergraph theory for cluster formation and employs the Particle Swarm Optimization (PSO) algorithm to select a central user within each cluster. The selection of an optimal central user takes into account various factors, including distance, residual energy, and degree centrality of the UEs. Through simulations, the proposed technique demonstrates superior performance in terms of system throughput, energy consumption, and energy efficiency when compared to traditional techniques.

Device-to-Device (D2D) is a novel communication architecture that is expected to significantly improve the performance of next-generation 5G cellular networks by efficient reuse of licensed spectrum. However, excessive and uncontrolled spectrum reuse comes with the cost of co-channel interference, which deteriorates the overall network’s performance. To maximize the gains of D2D deployments, it is crucial to develop interference-aware spectrum reuse techniques. This paper proposes a social-aware clustering technique to maximize spectrum reuse and minimize co-channel interference for parallel D2D transmissions. Furthermore, we propose a Particle Swarm Optimisation based transmit power control scheme to maximize the system throughput and energy efficiency. Our simulation results show a significant improvement in network performance for different activation probabilities.

To meet the ever-increasing mobile data demands, spectrum reuse via small cells and Device-to-Device (D2D) communication are envisioned to substantially enhance the Fifth-Generation (5G) cellular networks’ capacity. However, co-channel interference is a penalty for excessive and unregulated spectrum reuse, which results in overall network performance. Therefore, exploring interference-aware spectrum reuse strategies is essential to leverage the benefits of small cells and D2D communications. In this work, we aim to maximize the throughput of a UAV-assisted small cell network by proposing a socio-hypergraph coloring-based subchannel assignment technique. Our proposed technique considers the cumulative interference experienced by the User Equipments (UEs) while assigning subchannels. Additionally, we consider the strength of social ties between UEs to adjust the transmit power of cellular UEs to limit interference on D2D users. The simulation results show that our proposed technique performs the best in terms of system throughput as compared to the existing techniques.

After providing ubiquitous and high-speed network connectivity to mobile users, cellular operators are exploring unique domains to extend the reach of cellular networks. In this direction, the use of Unmanned Aerial Vehicles (UAVs) has received significant interest from both industry and academia. UAVs equipped with a transceiver module can act as relays and/or base stations to extend coverage and provide line-of-sight connectivity to mobile users, especially during emergencies such as earthquakes and floods. To reap the gains of UAV-based cellular networks, deployment and operational parameters of UAVs such as altitude and transmit power need to be carefully controlled. In this paper, we propose two algorithms for independently adjusting the altitude and transmit power of UAVs to maximize the system throughput. These algorithms are based on Particle Swarm Optimization and are shown to quickly converge to a better solution when compared to the traditional fixed altitude and fixed transmit power approaches.

To handle unprecedented mobile data demands in the next-generation 5G networks, dense deployments of base stations is the most promising solution. However, dense deployments not only leads to co-channel interference but also the underutilization of wireless resources in most scenarios. One way to maximize the gains of such deployments is to allow inter-operator collaborations where multiple operators can share their base stations and licensed spectrum with each other users. In addition to outdoor base stations, ultra-dense deployment of small cells such as femtocells inside homes/offices/public areas can further improve frequency reuse and system throughput. Femtocells are usually owned by end-users who are often reluctant to share them with other users due to trust and performance concerns. Hence, apart from operator collaboration, it is necessary to have collaborations among end-users to share femtocells. In this direction, we propose a unique cellular communication paradigm called Socio-Cellular Network along with an efficient cell selection scheme to facilitate operator and user collaborations to share base stations. Our simulation results show that collaborations among operators and end-users via social networks help to improve the performance of cellular networks in terms of throughput and energy efficiency.

We are experiencing an explosive growth in the number of consumer devices, including smartphones, tablets, web-based computers such as Chromebooks, and wearable devices. For this class of devices, energy efficiency is a first-class concern due to the limited battery capacity and thermal power budget. We find that data movement is a major contributor to the total system energy and execution time in consumer devices. The energy and performance costs of moving data between the memory system and the compute units are significantly higher than the costs of computation. As a result, addressing data movement is crucial for consumer devices. In this work, we comprehensively analyze the energy and performance impact of data movement for several widely-used Google consumer workloads: (1) the Chrome web browser; (2) TensorFlow Mobile, Google's machine learning framework; (3) video playback, and (4) video capture, both of which are used in many video services such as YouTube and Google Hangouts. We find that processing-in-memory (PIM) can significantly reduce data movement for all of these workloads, by performing part of the computation close to memory. Each workload contains simple primitives and functions that contribute to a significant amount of the overall data movement. We investigate whether these primitives and functions are feasible to implement using PIM, given the limited area and power constraints of consumer devices. Our analysis shows that offloading these primitives to PIM logic, consisting of either simple cores or specialized accelerators, eliminates a large amount of data movement, and significantly reduces total system energy (by an average of 55.4% across the workloads) and execution time (by an average of 54.2%).

With a wide range of applications, Machine-to-Machine (M2M) communication has become an emerging technology for connecting generic machines to the Internet. To ensure ubiquity in connections across all machines, it is necessary to have a standard infrastructure, such as 3GPP LTE-A network infrastructure, that facilitates such type of communications. However, owing to the huge scale of machines to be deployed in near future and the nature of data transactions, ensuring ubiquitous connections among all the machines will be difficult. Solutions that not only maintain connectivity but also route machine data in a cost effective manner are the need of the hour. In this context, it has been suggested that Device-to-Device (D2D) communication can play a very important role in expanding network coverage and routing the data between source-destination machine pairs. In this paper, we conduct a feasibility study to highlight the impact of multi-hop D2D communication in increasing the network coverage and average rate of a Machine Type Communication (MTC) device. We present a stochastic geometry based framework to analyze the coverage probability and average data rate of a three-hop M2M network deployed along with User Equipments (UEs) and conduct extensive simulations to study the system performance. Our simulation results show that the three-hop M2M network formed from out-of-range MTC devices and UEs can significantly improve the coverage and average rate of the entire network. Due to the mobility of users in the network, design of robust routing mechanisms in such a time evolving network becomes difficult. Hence, we suggest the use of space-time graph built from the predicted user locations to design a cost efficient multi-hop D2D topology that enables routing of MTC data to its destination.

Use of femtocells for indoor and office environment has proved to be an effective solution to handle ever increasing mobile data demands. Femtocell helps improving network capacity in an energy efficient manner without significantly burdening the operator with huge capital and operational expenditure. Since extremely dense femtocell deployments are expected in near future, it is of interest to look into their energy efficiency measures. In this paper, we analyse the energy efficiency aspect of cell selection scheme for femtocell networks. Cell selection scheme defines the criteria on which mobile users associate themselves with base stations. Hence, it plays a crucial role in system load balancing and total energy consumption. We suggest a unique cell selection scheme that assigns mobile users to femtocell base stations considering the capacity improvement obtained per unit increase in transmit power. Our proposed scheme shows an improvement in network performance in terms of both system capacity and energy efficiency. Additionally, we suggest the use of power spreading over subchannels to keep overall interference minimum while maximizing spectrum utilization.

Femtocells have proved to be an effective solution for handling the ever increasing demands for wireless data without incurring additional deployment costs. Deployment of these low cost, miniature base stations not only improves network robustness but also facilitates efficient location specific services for mobile users. However, dense deployment of femtocells comes with the cost of increased interference. To handle this additional interference without significantly affecting spectrum efficiency, smart assignment of wireless resources is necessary among femtocells. In this paper, we suggest a technique to intelligently reuse the available wireless resources among interfering femtocells so as to improve spectrum reuse and energy efficiency. Additionally, the suggested technique also shows improvement in system blocking for all possible deployment scenarios. Obtained results are verified using extensive simulations.

To improve benefits of macrocell offloading in femto-assisted cellular networks, concept of cell biasing has been proposed. Cell biasing attempts to offload users from macrocell by modifying cell selection criteria. This is done by adding a positive bias to the measured signal from femtocells before performing cell selection. While the macrocell offloaded users may experience lower signal quality from femtocells, they are benefited by receiving higher bandwidth. From users' point of view, it is desirable that user equipments receive highest possible bitrate from the target base station. However, cell biasing only considers received signal strength to make cell selection decisions. The bitrate received at a user equipment is directly proportional to available bandwidth and user load at target base station. In this paper, we propose an enhanced cell selection scheme that considers scheduling opportunities available at femtocell by analyzing the current load and femtocell specific constraints such as maximum user count and minimum signal strength. Obtained results show that our work provides the best performance in terms of both system throughput and energy efficiency among all compared cell selection schemes.