Ieee access special section editorial: Cloud and big data-based next-generation cognitive radio networks

In cognitive radio networks (CRN), secondary users (SUs) are required to detect the presence of the licensed users, known as primary users (PUs), and to find spectrum holes for opportunistic spectrum access without causing harmful interference to PUs. However, due to complicated data processing, non-real-Time information exchange and limited memory, SUs often suffer from imperfect sensing and unreliable spectrum access. Cloud computing can solve this problem by allowing the data to be stored and processed in a shared environment. Furthermore, the information from a massive number of SUs allows for more comprehensive information exchanges to assist the

Dynamics Spectrum Sharing Environment Using Deep Learning Techniques

The recent fast expansion of mobile communication services has resulted in a scarcity of spectrum resources. The challenge of multidimensional resource allocation in cognitive radio systems is addressed in this work. Complicated and dynamic Spectrum Sharing SS systems might be vulnerable to a variety of possible security and privacy vulnerabilities, necessitating protection techniques that are adaptable, dependable, and scalable. Methods based on machine learning (ML) have repeatedly been proposed to overcome these challenges. We present a complete assessment of the current progress of ML-based SS approaches, the most crucial security challenges, and the accompanying protection mechanisms in this paper. We develop cutting-edge methodologies for improving the performance of SS communication systems in a variety of critical areas, such as ML-based cognitive radio networks (CRNs), ML-based database assisted SS networks, ML-based LTE-U networks, ML-based ambient backscatter networks, and other ML-based SS solutions. The results of the simulation trials show that the suggested strategy may successfully boost the user's incentive while reducing collisions. In terms of reward, the suggested strategy beats opportunistic multichannel ALOHA by around 10% and 30%, respectively, for the single SU and multi-SU scenarios.&nbsp

Game Theoretic Approach for Cognitive Radio Networks

Cognitive radio is a promising approach to make efficient use of spectrum by rapidly adapting the changes in wireless environment. In such networks, nodes are expected to be aware of their environment and must be able to learn from the outcomes of past decisions. Game theory which comprise of analytical tools designed to study interactive decision-making processes can be applied effectively to solve some of such complex problems. This paper discusses the significance of using game theory for cognitive radio networks and resource allocation, and various game theoretic approaches used for it. DOI: 10.17762/ijritcc2321-8169.15059

Secondary user pricing strategies in a cognitive radio environment

There has been a growing demand for spectrum availability due to inefficient management of the radio frequency spectrum and underutilization of all spectrum bands. Spectrum has been managed with the same approach for over the last decade and only recently due to the phenomenal growth in mobile and broadband communications has attention been given to it. Intelligent communication systems such as cognitive radio have been identified in assisting the need for the limited resource, wireless spectrum. If spectrum trading becomes commercially successful, it can provide great economic and social benefits for the service provider, primary and secondary users. In order to maintain viability of spectrum trading, a pricing strategy is necessary for secondary users, it is also imperative to find a game theory model that minimally impacts the primary users in terms of their service, however it should aid in decreasing the cost to the primary users. Game theory along with economic theory is used to analyse the relationships/cooperation between the users and service provider. This work contributes to the field of dynamic spectrum access and aims to compare pricing strategies of secondary users in terms of the revenue earned by the primary service providers as well as investigate the impact of regulations on said pricing strategies. The pricing strategies modelled and simulated in MATLAB include the market-equilibrium pricing strategy and the competitive pricing strategy. These two strategies are chosen as they are the most relevant in South Africa. The two pricing strategies are compared in terms of advantages and disadvantages as well the revenue earned by each of the primary services. The framework for testing is provided along with the test cases. The influence of telecommunication regulations and policy on the frameworks and results are discussed in detail as well as the impact of the telecommunication regulation and policy in South Africa

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Optimal resource allocation for GAA users in spectrum access system using Q-learning algorithm

Spectrum access system (SAS) is a three-tier layered spectrum sharing architecture proposed by the Federal Communications Commission (FCC) for Citizens Broadband Radio Service (CBRS) 3.5 GHz band. The available 150 MHz spectrum is dynamically shared among Incumbent Access (IA), Primary Access Licensees (PAL) and General Authorized Access (GAA) users. IA users are the highest priority federal military users, PAL users are the licensed users and the GAA users are the least priority unlicensed users. In this scenario, PAL operators are willing to give access to their idle spectrum to GAA users to generate extra revenue. SAS will ensure to protect IA users and PAL users from interference caused by lower-tier users. It is the responsibility of SAS to allocate resources to GAA users but the method to do so is left open. In this article, a novel auction algorithm based on Q-learning for dynamic spectrum access (SAS-QLA) is proposed. In SAS-QLA, multiple GAA users dynamically and intelligently bid using Q-learning to access PAL reserved idle channels. SAS will decide to allocate the channels to GAA users with maximum bidding offers. GAA users have their own quality of service (QoS) demands i.e., transmission rate, packet loss, bidding efficiency, and maintain the preference of available PAL reserved idle channels based on Q-learning considering the available QoS. The proposed scenario is also modeled as a knapsack NP-hard problem and solved using dynamic programming and distributed relaxation method. Numerical results demonstrate the effectiveness of the SAS-QLA algorithm in improving the bidding efficiency, maximizing the data rate per unit cost and spectrum utilization.Web of Science10608046079

Robust game-theoretic algorithms for distributed resource allocation in wireless communications

The predominant game-theoretic solutions for distributed rate-maximization algorithms in Gaussian interference channels through optimal power control require perfect channel knowledge, which is not possible in practice due to various reasons, such as estimation errors, feedback quantization and latency between channel estimation and signal transmission. This thesis therefore aims at addressing this issue through the design and analysis of robust gametheoretic algorithms for rate-maximization in Gaussian interference channels in the presence of bounded channel uncertainty. A robust rate-maximization game is formulated for the single-antenna frequency-selective Gaussian interference channel under bounded channel uncertainty. The robust-optimization equilibrium solution for this game is independent of the probability distribution of the channel uncertainty. The existence and uniqueness of the equilibrium are studied and sufficient conditions for the uniqueness of the equilibrium are provided. Distributed algorithms to compute the equilibrium solution are presented and shown to have guaranteed asymptotic convergence when the game has a unique equilibrium. The sum-rate and the price of anarchy at the equilibrium of this game are analyzed for the two-user scenario and shown to improve with increase in channel uncertainty under certain conditions. These results indicate that the robust solution moves closer to a frequency division multiple access (FDMA) solution when uncertainty increases. This leads to a higher sum-rate and a lower price of anarchy for systems where FDMA is globally optimal. A robust rate-maximization game for multi-antenna Gaussian interference channels in the presence of channel uncertainty is also developed along similar principles. It is shown that this robust game is equivalent to the nominal game with modified channel matrices. The robust-optimization equilibrium for this game and a distributed algorithm for its computation are presented and characterized. Sufficient conditions for the uniqueness of the equilibrium and asymptotic convergence of the algorithm are presented. Numerical simulations are used to confirm the behaviour of these algorithms. The analytical and numerical results of this thesis indicate that channel uncertainty is not necessarily detrimental, but can indeed result in improvement of performance of networks in particular situations, where the Nash equilibrium solution is quite inefficient and channel uncertainty leads to reduced greediness of users.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Game theory for cooperation in multi-access edge computing

Cooperative strategies amongst network players can improve network performance and spectrum utilization in future networking environments. Game Theory is very suitable for these emerging scenarios, since it models high-complex interactions among distributed decision makers. It also finds the more convenient management policies for the diverse players (e.g., content providers, cloud providers, edge providers, brokers, network providers, or users). These management policies optimize the performance of the overall network infrastructure with a fair utilization of their resources. This chapter discusses relevant theoretical models that enable cooperation amongst the players in distinct ways through, namely, pricing or reputation. In addition, the authors highlight open problems, such as the lack of proper models for dynamic and incomplete information scenarios. These upcoming scenarios are associated to computing and storage at the network edge, as well as, the deployment of large-scale IoT systems. The chapter finalizes by discussing a business model for future networks.info:eu-repo/semantics/acceptedVersio

Business model for rural connectivity using multi-tenancy 5G network slicing

Rural areas are often neglected while deploying newer mobile technologies. Hence, these place are digitally disconnected from the world. To overcome this challenge, 5G network slicing supporting multi-tenancy, also known as neutral host network, is studied to improve rural connectivity. The infrastructure provider (InP) deploys the rural 5G network and mobile network operators (MNOs) lease the slices from InP to serve their end-users. This aims to study the value network configuration (VNC) for the 5G network slicing architecture to understand the possible business model. As a result, three configurations are defined driven by micro-operator, MNO and community end-users respectively. The business models are constructed using SWOT analysis and business canvas models. The revenue streams for the proposed rural network are analyzed

Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications

From its introduction to its quindecennial, network coding has built a strong reputation for enhancing packet recovery and achieving maximum information flow in both wired and wireless networks. Traditional studies focused on optimizing the throughput of the system by proposing elaborate schemes able to reach the network capacity. With the shift toward distributed computing on mobile devices, performance and complexity become both critical factors that affect the efficiency of a coding strategy. Instantly decodable network coding presents itself as a new paradigm in network coding that trades off these two aspects. This paper review instantly decodable network coding schemes by identifying, categorizing, and evaluating various algorithms proposed in the literature. The first part of the manuscript investigates the conventional centralized systems, in which all decisions are carried out by a central unit, e.g., a base-station. In particular, two successful approaches known as the strict and generalized instantly decodable network are compared in terms of reliability, performance, complexity, and packet selection methodology. The second part considers the use of instantly decodable codes in a device-to-device communication network, in which devices speed up the recovery of the missing packets by exchanging network coded packets. Although the performance improvements are directly proportional to the computational complexity increases, numerous successful schemes from both the performance and complexity viewpoints are identified