The growing demand for mobile data strains the licensed spectrum, prompting the need for efficient offloading strategies. New Radio in Unlicensed Spectrum (NR-U) leverages 5 GHz and 6 GHz bands but faces coexistence challenges with WiFi and Bluetooth due to interference. This paper proposes three reinforcement learning-based models to optimize Channel Occupancy Time (COT) in NR-U networks. The first uses Qlearning for fair coexistence with WiFi under Listen-BeforeTalk (LBT) rules. The second enhances energy efficiency by jointly optimizing COT and power control. The third applies Dyna-Q+ to accelerate learning and adapt to dynamic traffic loads. Simulations over 500 scenarios show all models achieve Jain’s Fairness Index above 0.9, ensuring equitable spectrum sharing. The energy-efficient model reduces power consumption by up to 20% without compromising performance. These results demonstrate the potential of intelligent learning-based approaches for robust and efficient NR-U and WiFi coexistence in shared spectrum environments.

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.

In this paper, we address the problem of reconfiguration of network slices (NSs) to handle network node failures and mitigate service interruptions while satisfying the diverse service requirements of users and multiple industry verticals in beyond 5G and 6G networks. First, we formulate the latency- and reliability-aware NS reconfiguration problem as an integer linear programming (ILP) to minimize resource consumption. Then, to deal with the computational time complexity of ILP, we propose a matching algorithm-based heuristic solution. Through extensive simulations, we show that our proposed algorithm provides a nearoptimal solution in polynomial time with an optimality gap of 2.5% and consumes fewer resources while satisfying stringent latency and reliability service requirements.

By bringing the processing and storage capabilities of the cloud closer to the end devices, fog computing (FC) enhances the Quality of Service (QoS) for latency-critical Internet of Things (IoT) applications, such as autonomous driving, haptics, and augmented reality (AR). To facilitate the processing and storage of data packets, the fog nodes in the underlying FC-enabled IoT network (FC-IoTN) are to be provisioned with storage and processing resources. Existing resource provisioning solutions focus mainly on latency sensitivity and cost efficiency. They also operate under the assumption that these fog nodes are completely reliable and energy efficient. In reality, this is not true. The fog nodes are not 100% reliable. Neither are they energy efficient. In this study, we propose a novel resource provisioning framework for the fog nodes that considers reliability and energy efficiency, in addition to latency sensitivity and cost efficiency. We first give an analytical framework to model the failures and recoveries in a fog node and use this modeling to provision resources in the fog nodes such that the resultant resource provisioning is optimal in terms of cost and energy consumption. Further, to understand the effect of latency, reliability, cost, and energy on resource provisioning, we analyze and decode the interplay between these factors during resource provisioning in fog nodes. We finally show the efficacy of our approach over the scenario that does not consider reliability and energy efficiency while provisioning resources. Without affecting the latency sensitivity and reliability of the system, our framework achieves an enhancement of 35%, and 37% in terms of cost and energy consumption, respectively, over a nonoptimized framework.

Network Functions Virtualization (NFV) allows flexibility, scalability, agility, and easy manageability of networks by leveraging the features of virtualization and cloud computing technologies. However, softwarization of network functions imposes many challenges. Reliability and latency are major challenges in NFV-enabled 5G networks that can lead to customer dissatisfaction and revenue loss. In general, redundancy is used to improve the reliability of communication services. However, redundancy requires the same amount of additional resources and thus increases cost. In this article, we address the reliability-aware, delay guaranteed, and resource efficient Service Function Chain (SFC) placement problem in softwarized 5G networks. First, we propose a novel SFC subchaining method to enhance the reliability of an SFC without backups. If reliability requirement is not met after subchaining method, we add backups to VNFs to meet the reliability requirement. Then, we formulate the reliable SFC placement problem as an Integer Linear Programming (ILP) problem in order to solve it optimally. Owing to high computational complexity of the ILP problem for solving large input instances, we propose a modified stable matching algorithm to provide near-optimal solution in polynomial time. By extensive simulations we show that our proposed solutions consume lesser physical resources compared to state-of-the-art solutions for provisioning reliable communication services.

To meet the ever increasing data rate demand in cellular network, the 3rd Generation Partnership Project (3GPP) introduced Licensed Assisted Access (LAA) with Listen Before Talk (LBT) mechanism to utilize the unlicensed spectrum to supplement the licensed LTE users. However, this leads to interference to already incumbent technologies in unlicensed spectrum such as WiFi. Furthermore, multi-carrier aggregation has been proposed to further increase the capacity in unlicensed spectrum. As WiFi primarily works on the principle of energy detection in the channel, in this paper, we first develop an analytical model to measure the performance of LAA and WiFi coexistence in unlicensed spectrum which supports multi-carrier aggregation which employs SINR based collision that allows the possibility of concurrent transmissions. Further, to reduce the interference to WiFi, we propose a coexistence method named Carrier Aggregation with Transmit Power Control (CATPC) which dynamically adjusts the LAA transmit power. CATPC provides a better opportunity for WiFi devices, better channel utilization, and energy efficient transmission by LAA. Through extensive simulations we demonstrate the effectiveness of CATPC when compared to other coexistence methods proposed in the literature.

In order to meet the exponential rise in mobile data demand, it is proposed to supplement existing licensed bandwidth with the unlicensed spectrum, where a part of cellular data demand is served in the unlicensed spectrum. This deployment of LTE technology in unlicensed spectrum is termed as LTE in unlicensed spectrum (LTE-U). However, deploying LTE technology in the unlicensed 2.4/5 GHz band will greatly cause interference to the already existing technologies such as WiFi and Zigbee due to the stark differences in their channel access mechanisms. For LTE-U to become a reality, it is necessary for LTE to fairly coexist with the existing technologies. Therefore, in this paper, we propose a Q-Learning based Dynamic Frame Selection (DFS) algorithm which ensures fair coexistence between LTE-U and WiFi technologies. Furthermore, we propose the use of reduced power subframe to limit interference to the co-channel users and increase the channel utilization. By extensive simulation we show the effectiveness of our proposed DFS algorithm when compared to existing algorithms in the literature.

A smart city aims at providing effective services to its citizens and administrators through the Internet of things (IoT) paradigm. A typical smart city architecture consists of a large topology with heterogeneous data generating sensors and communication technologies with support for multiple applications such as traffic management, smart energy, smart homes, and industrial IoT. Each application has a unique data generation rate and latency constraint based on the criticality of the application. In traditional IoT architecture, the data is processed in the Cloud which results in high propagation delay and hence high latency. To alleviate this problem, a decentralized and distributed paradigm called Fog Computing (FC) is gaining importance in latency critical IoT networks. The Fog layer is an intelligent layer in which the intermediate gateways are smart and have processing and storage resources. Smart processing of data at the fog layer results in significant reduction in latency. However, the smartness of gateways comes with a price. Therefore, placement of these resourceful gateways has to be done in a cost efficient manner considering latency constraints of all the applications supported in the system. In this paper, we propose a Hungarian method based Topology Construction (HTC) algorithm to identify the optimal locations and resources for the gateways such that over all system cost is minimized and latency constraints are met. We perform exhaustive simulations to show the effectiveness of our algorithm by comparing with existing models.

With advancements in mobile communications and technologies, Almost Blank Subframe (ABS) has found a lot of applications. It has been applied in heterogeneous networks to optimize various performance metrics, in LTE Licensed Assisted Access (LTE-LAA) for harmonious coexistence of LTE with WiFi in unlicensed spectrum, in enabling D2D communication in licensed spectrum, and so on. In any scenario, optimizing ABS density is very important in order to maximize the resource utilization, and hence other performance metrics. In this paper, we propose a generalized framework to find optimal ABS density that can be applied in any wireless network and application which balances the shortage and under-utilization of resources at various entities. To the best of our knowledge there does not exist such a framework which can be applied across various wireless networks and applications. We also propose models to apply our framework in heterogeneous networks and LTE-LAA. Through extensive simulations we show that the results obtained using our framework is the optimal solution.

Internet of Things (IoT) is a novel communication paradigm where devices, identified by unique addresses and belonging to different networks, are connected to the Internet. Communication between these devices and the Internet is facilitated with the help of IoT gateways. To have maximum coverage of network with reduced overall deployment cost, optimal placement of gateways is necessary. Owing to the high cost involved with IoT gateways, devices known as Solution Specific Gateways can be utilized along with IoT gateways for cost-effective gateway placement. In this paper, we propose a Network Intersection based Candidate Gateway Location Selection algorithm called NewIoTGateway-Select which determines the minimum number of gateways to be placed in a given Smart City scenario. NewIoTGateway-Select algorithm computes the candidate locations for gateway placement and selects optimal locations from them, in polynomial time. Additionally, our algorithm takes care of gateway failures and link failures by using k-coverage-connectivity based fault tolerance scheme. Our simulation results show that NewIoTGateway-Select algorithm gives better performance than Grid based Candidate Location Selection (GCLS) and Default Gateway Assignment strategy.

Internet of Things (IoT) is considered to be the next revolution in the field of wireless communications. This concept involves mapping of physical world to virtual (cyber) world. It is achieved by interconnecting devices having sensing capabilities using unique addressing scheme and passing their collective information to the IoT cloud. However at present, these devices belonging to different technologies are connected to the Internet using IoT gateways. With the upcoming of a large number of IoT devices connecting to the Internet, there exists a requirement of deploying a large number of IoT gateways in the network which is not cost effective. Therefore, in this paper, we make use of low cost Solution Specific GateWays (SSGWs). As voluminous data is generated by these IoT devices, routing in the IoT network becomes a challenge. In this paper, we propose parallel opportunistic routing in the IoT network using varied kinds of wireless technologies used by SSGWs. The simulation results show that our proposed algorithm performs better in terms of average network throughput and average packet delay when compared to normal routing using single technology.

As the mobile data traffic continues to grow rapidly, spectrum availability is a major concern. Furthermore, the fixed spectrum allocation to the mobile network operators (MNOs) and lack of free spectrum result in poor user quality of experience (QoE) and inefficient spectral resource utilization. Inter-operator spectrum sharing is a solution to overcome the spectral shortage, and this is achieved either by an MNO leasing other MNO's spectrum or by sharing a common pool of MNOs' spectrum. Although both approaches bring benefits, they have further challenges. We propose a novel spectrum sharing paradigm called inter-operator proximal spectrum sharing (IOPSS), where a base station (BS) intelligently offloads users to the neighboring BSs based on spectral proximity to enhance the users' QoE and spectral resource utilization. Users requesting high service rates can be served by using carrier aggregation. We demonstrate the IOPSS's benefits using a continuous-time Markov chain-based analytical model of a BS. A generic IOPSS Q-learning framework (IOPSS-QLF) for a BS to dynamically determine its load-based spectral needs and efficiently share its spectrum resulting in a self-organizing spectrally efficient network of BSs is proposed. The effectiveness of IOPSS-QLF is demonstrated using extensive simulations.

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.