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.

Recently, drones have attracted considerable attention for sensing hostile areas. Multiple drones are deployed to communicate and coordinate sensing and data transfer in the Internet of Drones (IoD) network. Traditionally, multi-hop routing is employed for communication over long distances to increase the network’s lifetime. However, multi-hop routing over large-scale networks leads to energy imbalance and higher data latency. Motivated by this, in this paper, a novel framework of energy-efficient and latency-aware data routing is proposed for Small-World (SW)-IoD networks. We started with an optimization problem formulation in terms of network delay, energy consumption, and reliability. Then, the formulated mixed integer problem is solved by introducing the Small-World Characters (SWC) into the conventional IoD network to form the SW-IoD network. Here, the proposed framework introduces SWC by removing a few existing edges with the least edge weight from the traditional network and introducing the same number of long-range edges with the highest edge weight. We present the simulation results corresponding to packet delivery ratio, network lifetime, and network delay for the performance comparison of the proposed framework with state-of-the-art approaches such as the conventional SWC method, LEACH, Modified LEACH, Canonical Particle Multi-Swarm (PMS) method, and conventional shortest path routing algorithm. We also analyze the effect of the location of the ground control station, the velocity of the drones, and the different heights of layers on the performance of the proposed framework. Through experiments, the superiority of the proposed method is proven to be better when compared to other methods. Finally, the performance evaluation of the proposed model is tested on a network simulator (NS3).

The lifetime of a UAV-assisted wireless network is determined by the amount of energy consumed by the UAVs during flight, data collection, and transmission to the ground station. Routing protocols are commonly used for data transmission in a communication network. However, because of the mobility of UAVs, using a routing protocol with a single communication technology results in higher delay and more energy consumption in a UAV-assisted wireless network. To overcome this, we propose two reinforcement learning (RL) algorithms, Q-learning and deep Q-network (DQN), for energy-efficient data transmission over a hybrid BLE/LTE/Wi-Fi/LoRa UAV-assisted wireless network. We consider BLE, LTE, Wi-Fi, and LoRa for communication over a UAV-GS link. The RL algorithms take any random network as input and learn the best policy to output the network with less energy consumption. The reward/penalty is chosen in such a way that the network with the highest energy consumption is penalized and the one with the lowest is rewarded, thereby minimizing total network energy consumption. Based on learning, it creates a hybrid BLE/LTE/Wi-Fi/LoRa UAV-assisted wireless network by assigning the best communication technology to a UAV-GS link. Further, we compare the performance of proposed RL algorithms with a rule-based algorithm and random hybrid scheme. In addition, we propose a theoretical framework for constructing hybrid network for both free space and free space multipath path loss models. We demonstrate the performance comparison of the proposed work with the conventional shortest path routing algorithm in terms of network energy consumption and average network delay using extensive results. Finally, the effect of the velocity of the UAV and the number of packets on the performance of the proposed framework is analyzed.

Multihop data routing over a large-scale Internet of Things (IoT) network results in energy imbalance and poor data throughput performance. In addition, data transmission using a large number of hops causes more delay. In light of this, in this work, a novel method of energy and throughput management in a delay-constrained small-world unmanned aerial vehicle (UAV)-IoT network is proposed. The proposed small-world framework optimizes the number of hops required for the data transmission leading to improved energy efficiency and quality of service. The method introduces optimal long-range links between device pairs resulting in low average path length and high clustering coefficient which are called as small-world characteristics. Therefore, in this work, UAVs are deployed to collect the data from IoT devices and forward it to the ground station (GS) utilizing the small world framework. It is shown through results that the network delays corresponding to the proposed method, conventional routing method, low-energy adaptive clustering hierarchy (LEACH) protocol, modified LEACH protocol, and canonical particle multiswarm (CPMS) method are 789.39, 1602.53, 1000.92, 873.63, and 999.79 s, respectively. It is also observed that the number of dead UAVs in case of the proposed method is reduced when compared to other existing methods. It is also noticed that the proposed method results in 100% packet delivery ratio (PDR) dominating LEACH and modified LEACH protocols. Thus, it is shown that the proposed method outperforms the other shortest path methods in terms of network latency, lifetime, and PDR. Further, the effect of location of GS, velocities of UAVs, and hovering heights of UAVs is considered for the performance evaluation of the proposed method. The obtained results validate the significance of utilization of the proposed method over various network scenarios.

Inclusion of massive machine-type-communication (mMTC) devices in 5G cellular Internet of Things (IoT) has significantly raised the issue of network congestion. To address this challenge, a successive interference cancellation-rate splitting random access (SIC-RSRA) mechanism is proposed in this paper. Unlike traditional mechanisms, all selected mMTC devices are allowed to make a finite number of repeated message requests in randomly selected time slots within a radio frame. The gNodeB, on the other hand, applies both intra-slot SIC (utilizing RSRA) and inter-slot SIC to decode messages from a larger number of devices. For the proposed mechanism, the impact of increasing the number of devices as well as the received power difference is investigated. Through extensive simulations, we show that the proposed mechanism outperforms the other mechanisms in terms of number of RACH successes and number of supported devices.

The spectrum cartography finds applications in several areas such as cognitive radios , spectrum aware communications, machine-type communications, Internet of Things , connected vehicles, wireless sensor networks , and radio frequency management systems, etc. This paper presents a survey on state-of-the-art of spectrum cartography techniques for the construction of various radio environment maps (REMs). Following a brief overview on spectrum cartography, various techniques considered to construct the REMs such as channel gain map, power spectral density map, power map, spectrum map, power propagation map, radio frequency map, and interference map are reviewed. In this paper, we compare the performance of the different spectrum cartography methods in terms of mean absolute error , mean square error , normalized mean square error, and root mean square error . The information presented in this paper aims to serve as a practical reference guide for various spectrum cartography methods for constructing different REMs. Finally, some of the open issues and challenges for future research and development are discussed.

The cellular Internet-of-Things has resulted in the deployment of millions of machine type communication (MTC) devices under the coverage of a single gNodeB (gNB). These massive number of devices should connect to the gNodeB (gNB) via the random access channel (RACH) mechanism. Moreover, the existing RACH mechanisms are inefficient when dealing with such large number of devices. To address this issue, we propose the rate-splitting random access (RSRA) mechanism, which uses rate splitting and decoding in rate-splitting multiple access (RSMA), to improve the RACH success rate. The proposed mechanism divides the message into common and private messages and enhances the decoding performance. We demonstrate, using extensive simulations, that the proposed RSRA mechanism significantly improves the success rate of MTC in cellular IoT networks. We also evaluate the performance of the proposed mechanism with increasing number of devices and received power difference.

In this study, the authors propose a hybrid power line communication (PLC)/visible light communication (VLC)/radio frequency (RF) fronthaul with a fibre‐based wired backhaul system to support massive number of smart devices (SDs). Since a signal‐to‐noise ratio‐based access point (AP) association and bandwidth (BW) allocation for each SD do not necessarily improve the system capacity, they propose novel and efficient AP association and BW allocation strategies to maximise the sum rate capacity (SRC) of the hybrid system under consideration. An optimisation problem is formulated for the SRC with the AP association and BW allocation as the optimisation parameters and a hierarchical decomposition method is used to convert the non‐linear optimisation problem into a set of convex optimisation problems. Then, the proposed strategies are used to solve the optimisation problem in an iterative manner until the SRC converges to an optimal value. Further, an analytical approximation for the BW allocated to each SD for a given AP association is derived using the Lagrangian multiplier method. The performance of the proposed system is evaluated through extensive numerical results. Moreover, the effect of the increased number of SDs on the optimal SRC is analysed.