matlab code

In this study, we will describe the concept of the based on fuzzy logic a CAC scheme on 5G -C-RAN. With the aid of call admission control (CAC), the network congestion may be avoided and along with that, it may avoid traffic congestion in 5G. Besides, the CAC scheme plays a vital role in the guaranteed QoS provision. The CAC algorithm's main role is to choose precisely whether an association might be acknowledged into an asset compelled network without abusing the help responsibilities shaped to the generally conceded associations. An efficient CAC scheme may optimize system utilization, and call blocking probability (CBP) as well as call dropping probability (CDP), etc in the 5G network. But, the traditional CAC scheme is not appropriate for 5G C-RAN. The main goal of the thesis is to utilize the fuzzy predictor approach in determining the performance of the 5G network. Another goal of the study is to present a scheme of CAC based on fuzzy logic in 5G C-RAN using pre-emption.

The power of 5G technology is that it may able to connect to all sort of devices and also refers as Wireless Broadband Technology. Another powerful feature of 5G technology is that it may offer a high speed along with the capability of handling a large volume of data. 5G technology makes utilization of tiny cells, so it may meet the growing demands of mobile users as well as may offer better connectivity at any time or any place. With the aid of 5G technology, any sort of IoT device may be connected and may be easily cope-up with such applications of IoT. We may attain high data rates as well as low latency owing to having optimized packet radio access as well as flexible bandwidth in the 5G technology. Since the users' channel quality may influence the transmission rates, so the user may obtain higher transmission rates along with better channel conditions. If the transmission rates are not appropriate for attaining the demand of users, then users may lose data due to queue congestion.

There is a load of devices such as IoT, Tablets, iPhone, Android, Wearable Devices, etc, with a lot of apps producing network requests in 5G networks. To avoid network congestion from such devices, the 5G network needs to cope with such greater traffic requests. In this case, there is a requirement to build an efficient scheme of CAC that could be embraced in 5G C-RAN for improving resource utilization as well as CBP while maintaining the needed QoS. In this study, we will study a scheme of CAC using fuzzy logic in 5G C-RAN using pre-emption.

In this paper, we will explain three techniques covering Cloud Bursting For Pre-Empted Connections, Fuzzy Logic Controller Structure, And For Preempted Connections – Queueing System. In the cloud bursting for pre-empted connections, the operators are allowed to extend dynamically their infrastructure by renting 3rd party sources. In the case of fuzzy controller, three inputs are involved: (a) Network Congestion Factor – Nc. (b) Available Capacity – Ac. (c) Effective Capacity – Ec. And, the yield is represented by Admittance Decision – Ad. The fuzzy controller involves three aspects covering Membership function, Fuzzy rule base, and the method of defuzzification. The next thing that has been covered in the methodology part is the M/M/c/K model.

In the cloud, the connections follow the Erlang B Model or M/M/c/K. In this case, the Poisson process aids in governing the request arrival at arrival rate λ, as well as the times of the service with parameter μ are exponentially distributed, as well as in the cloud processing the requests, there are c servers from the front of the queue. The system capacity is denoted by the K variable. In this case, the requests for connection greater than the queue length are dropped as well as the buffer is assumed to be of finite size. With the aid of the Markov Chain with continuous time, the model may be described.

In this paper, the research will let you know how the network congestion may be avoided with the aid of call admission control and how it may avoid congestion in traffic in 5G. Here, the study will explain how the CAC scheme will assist in deciding accurately whether a connection may be admitted into a resource-restricted network without the commitments of the service violation formed to the admitted already connections. In this thesis, we will estimate the performance of the 5G network via fuzzy logic CAC scheme.

Nowadays, a lot of improvements in the telecommunication sector are being witnessed owing to advancements in technologies. Starting from the 2G network to date to the 4G networks, a lot of improvements have been observed in the network. Now, it is time to launch of 5G network which is now about to launch and several firms are preparing themselves to launch it. The power of 5G technology is that it may able to connect to all sort of devices and also refers as Wireless Broadband Technology. Another powerful feature of 5G technology is that it may offer a high speed along with the capability of handling a large volume of data. 5G technology makes utilization of tiny cells, so it may meet the growing demands of mobile users as well as may offer better connectivity at any time or any place. With the aid of 5G technology, any sort of IoT device may be connected and may be easily cope-up with such applications of IoT.

We may attain high data rates as well as low latency owing to having packet radio access with optimized value as well as flexible bandwidth in the 5G technology. Until the rates of the transmission are provided, the data addressed to mobile users get stored at the base station in the queues in the 5G system. Since the users' channel quality may influence the transmission rates, so the user may obtain greater transmission rates along with better channel conditions. If the rates of the transmission are not appropriate for attaining the demand of users, then users may lose data due to congestion of the queue. So, the algorithms of the flow rate control may be applied at the base station to arriving network traffic flows, therefore offer more ample service to mobile users. When the flow rate control algorithm is applied to 5G technology, the smaller size queue may be attained at eNodeB, thus leading to obtaining lower loss rates as well as shorter waiting times for users.

Nowadays, the demands for high data rates and larger system capacities have much enhanced in high mobility environments over large coverage areas. This has resulted owing to the utilization of multimedia services as well as a load of mobile devices in recent years. So, it is becoming much hard to manage or control the radio access networks (RAN) owing to their growing size. Some of the major challenges for next-generation systems are optimizing resource utilization or maintaining the quality of service (QoS) non-real-time as well as for real-time. Millions of devices form the 5G cellular networks including IoT, Tablets, iPhones, Androids, Wearable Devices, etc, and such devices are further connected to the network with an immense app. Such devices need to avoid traffic congestion as well as network overload in the core network generating from traffic requests which the 5G network needs to cope with it. To solve the shortcomings of conventional RAN via pooling resources of BS to a centralized cloud, a concept of C-RAN was introduced in the next-generation technology that is 5G. In the BBU pool, BS processing resources are allocated dynamically to distinct virtual baseband units by introducing the concept of virtualization on general-purpose processors.

With the aid of call admission control (CAC), the network congestion may be avoided and along with that, it may prevent congestion in traffic in 5G. Besides, the CAC scheme plays a prime role in the guaranteed QoS provision. The CAC algorithm's main role is to decide accurately whether a connection may be admitted into a resource-restricted network without service commitments violating formed to the already admitted connections. An efficient CAC scheme may optimize system exploitation, call dropping probability (CDP), as well as call blocking probability (CBP), etc in the 5G network. But, the traditional CAC scheme is not suitable for 5G C-RAN.

Here are lists of several reasons for the unsuitability of the conventional CAC scheme in the 5G network. First, due to the time-varying parameter nature (for instance – available power, channel conditions, direction, location, speed, etc.) and radio signals real-time processing, the traditional approach of CAC suffers uncertainties in the cellular network. If the network is indeed servicing incapable the request as well as incorrect rejection, then the traditional CAC scheme may lead to incorrect request admission. The static state of information in the network is assumed by such sort of CAC schemes. But, the network is dynamic as well as values measured keep altering in practice. Second – It is the standalone RAN base station on which the traditional CAC scheme is based whereas 5G will be based on centralized cloud BS’ss. Such BS’s have computation resources as well as unshared processing situated in the BS cell sites along they are preconfigured for peak load. Such BSs resources may not be shared for addressing varied traffic requirements on other cell sites, thus causing high CDP as well as CBP, or poor resource utilization. So, an efficient CAC scheme is needed in such cases. In cellular networks, the issue of uncertainties and imprecision may be solved through the method of intelligent schemes of the CAC based on intelligent decision-making that is also a promising solution. Without the requirement for complex modeling of mathematical, the schemes mimic the behavior cognitively of the human mind, thus making them suitable, flexible, less complex, or adaptive for coping with rapidly altering network situations of cellular networks in 5G.

In this study, a scheme of CAC in 5G C-RAN based on fuzzy logic using pre-emption has been presented. In this paper, the research will let you know how the network congestion may be avoided with the aid of call admission control and how it may avoid congestion in traffic in 5G. Here, the study will explain how the CAC scheme will assist in deciding accurately whether a connection may be admitted into a resource-restricted network without the commitments of the service violation formed to the admitted already connections. In this thesis, we will estimate the performance of the 5G network via fuzzy logic CAC scheme.

Here is the description of the problem statement. There is a load of devices such as IoT, Tablets, iPhone, Android, Wearable Devices, etc, with a lot of apps producing requests to the network in 5G. To avoid network congestion from such devices, the 5G network needs for coping with such higher requests of traffic. In this case, the question to be answered is how to build efficient schemes of the CAC that could be embraced in 5G C-RAN for improving resource utilization as well as CBP while maintaining the needed QoS.

Aim – The paper aims to utilize the fuzzy predictor approach in determining the performance of the 5G network. Another goal of the study is to present a scheme of CAC in 5G C-RAN in fuzzy logic-based using pre-emption.

Objectives – Various Objectives are:

· Understanding the concept of 5G

· Understanding the fuzzy logic approach

· Utilizing the proposed system for mitigating the congestions and thus maintaining the energy optimization

· To develop a simulated environment for attaining the goal of performance

· Using MATLAB for evaluating the performance and understanding how it happens.

Here are the contributions of the research areas:

a) In this study, a fuzzy logic-based scheme of the CAC in 5G C-RAN using pre-emption is proposed. The main task of fuzzy logic is to prevent uncertainties in RAN distributed systems caused by traditional schemes of the CAC.

b) Another technique is proposed called cloud bursting. NRT connections with Low priority as well as delay-tolerant are pre-empted as well as outsourced at a certain price penalty to a public cloud for accommodating the RT connections during congestion. Here, it is considered that the infinite processing capacity is carried out by the public cloud as such it will not be captured in the simulation or it may not get congested.

c) For validating the proposed scheme, a rigorous study of the simulation is conducted that exhibits a significant performance improvement.

Now, we will appraise you the organization of the thesis. This will aid you in knowing what would be covered in the thesis in the stepwise manner which is described as follows.

The section covers some of the theories or proposed systems that had been presented by various researchers. Here, we will come to know what advancements have been presented by various researchers in the past or present. Let present the details as follows.

5G technology is now about to come and many researchers are still working on it for getting improved services. There is a load of devices such as IoT, Tablets, iPhone, Android, Wearable Devices, etc, with a lot of apps producing network requests in 5G. To avoid network congestion from such devices, the 5G network needs for coping with such greater requests of traffic. With shared baseband processing, requests from mobile devices are processed as C-RAN has been assumed as a paradigm shift for 5G. The technique of call admission control has been recently overlooked by the community, despite it being one of the radio RRM methods for avoiding congestion in the network. There are two aspects such as overall system efficiency as well as for individual connections the quality of service (QoS), etc on which the CAC method in 5G c-RAN has an influence directly on the quality of service (QoS). In the subsequent sections, we will present some of the theories related to the concept that had been presented in the past or present. Here are some of the literature reviews of various researchers as follows.

Here are some of the latest theories related to the topic presented by various researchers are as follows.

(Al-Maitah et al., 2018) The researchers had described a hybrid approach in the 5G network for call admission control. There are several issues such as practical, technical, scientific issues, etc that may be solved by employing artificial intelligence. The techniques of AI such as evolutionary algorithms, genetic algorithms, fuzzy systems, or neural networks, etc that may be widely utilized for prediction, optimizations, or communication system management. In a cellular network, optimized results may be obtained with the aid of AI in the challenging task of traffic prediction, routing, handover, or call admission control, etc. In 5G, a significant need of it is accommodating the quality of service satisfaction as well as the great number of users as they are designed as heterogeneous networks. In offering the desired service quality, as well as call admission control plays a vital role. To optimize the cellular network, effective call admission control methods are required. Several methods of call admission control had proposed. In this thesis, the researcher had proposed a method for building a genetic neuro-fuzzy controller in 5G networks for call admission. With the aid of computer simulation, the performance of the proposed methods had been evaluated.

(Sigwele et al., 2017) The researcher had explained the call admission control based on fuzzy logic in 5G networks cloud radio access with pre-emption. There are millions of devices (for instance – IoT, Tablets, iPhone, Android, Wearable Devices, etc.) that will be connected to 5G networks. And, such devices will also generate an immense apps network request. To avoid network congestion from such devices, the 5G network needs to cope with such greater requests of the traffic. With shared baseband processing, requests from mobile devices are processed as C-RAN has been assumed as a paradigm shift for 5G. The technique of call admission control has been recently overlooked by the community, despite it being one of the RRM methods for avoiding congestion in the network. There are two aspects such as overall system efficiency as well as for individual connections the quality of service (QoS), etc on which the CAC method in 5G C-RAN has an influence directly on the QoS.

(Umoh et al., 2018) In this paper, the author had presented a method for controlling based on interval type-2 fuzzy logic of Mamdani fuzzy inference deployed for modeling connection admission control in 4G networks for improving quality of service. Based on major system parameters such as user mobility, signal strength, load, packet loss, or latency, etc, the researcher had assumed the suitable parameter selection for attaining connection admission control. In the proposed system, the author had also explored the utilization of approaches such as Wu-Mendel and Karnik0Mendal. In 4G networks for comparison purposes, the researcher had implemented a T1FLS connection admission control for guaranteed QoS. The author had utilized 4G network admission control synthetic datasets for the empirical comparison on the designed system.

(X Yang et al., 2007) With multiple classes of traffic, the researcher had solved the CAC in the multimedia cellular network using a novel learning approach. A form of algorithm of neuro-evolution had utilized by the researcher for obtaining the near-optimal CAC policy. While retaining an acceptable CBP, the technique guarantees that the CDP remains under a pre-defined upper bound. Since the info of the scheme, internal working is never known as the scheme is a black box learning approach.

(Mohammed et al., 2017) A call admission control algorithm along with a dynamic QoS-aware for mobile networks had explained by the researcher. In mobile broadband networks, one of the significant components of the scarce management of wireless network resources is the call admission control. In this paper, a method was proposed by the researcher for improving the resource utilization as well as ensuring QoS for all classes called QACAC-BR-BD). To find whether the number of resources attained from the degradation will be enough for employing DQACAC, a pre-bandwidth degradation method was employed by the DQACAC. When resources are insufficient, a dynamic BD mechanism by the algorithm for degrading distinct resources amount from existing connections. The proposed algorithm efficiency was assessed by conducting an extensive simulation demonstration. In terms of mitigating the new connections blocking probability, the proposed scheme outperforms what was indicated by the simulation results. Thus, observed an efficient utilization of resources.

(Kumar et al., 2019) For next-generation mobile multimedia networks, as well as call admission control based on Neuro-fuzzy was proposed by the researcher. To attain extremely consistent data transmission as well as wireless communication over the networks, wireless networks still require advancement in terms of user mobility, efficient bandwidth utilization, link availability, reliable traffic performance, etc as the demands of the mobile users are enhancing day by day. A high-speed network, as well as a call admission control scheme, is needed owing to the evolving demand for services of multimedia that not only guarantee optimum utilization of resources but also offer the quality of services for new as well as handoff calls. In this paper, a decent resource allocation approach was utilized for improving QoS which was the main goal of this integrated neural CAC scheme based on the fuzzy system. The method proposed by the researcher had integrated the self-training capacity of a NN and the semantic rule ability of fuzzy logic. As contrasted to fuzzy logic-based CAC as well as existing schemes of CAC, a neural fuzzy-based CAC may attain maximum resource utilization as well as minimal call dropping probabilities as concluded by the simulation results.

(Mamman et al., 2020) In this paper, the author had discussed an efficient dynamic call admission control for 4G Or 5G networks. As the 5G network is a flexible, reliable, efficient, or faster network, but the present 4G network does not allow the variety of services for the future need. The best certainties that describe the elementary principles of the smart cities of the upcoming 5G technology are the call admission control and the 5G network. Communication will be granted based on cost, speed, or latency through substantial CAC in the smart cities environment. Since the adaptive threshold value utilized for determining the network strength, so the present CAC algorithm suffers from performance deteriorates under the 4G network. For addressing performance deterioration, a novel CAC algorithm that utilizes dynamic threshold values in the 5G network for smart cities was proposed. The proposed algorithm efficacy was evaluated using the simulation and performed better as indicated through the simulation results.

(Aguodoh et al., 2018) The researcher had utilized the intelligent agent for improving an optimized call admission control in the 3G network. As a result of high bit error rate, inefficient, inference, or congestion to control the situations such as issues of not having free network had arisen. In this paper, the author had improved optimized call admission control using the intelligent agent. Using the following aspects such as designing a model for improving an optimized call admission, designing a membership function for improved call admission control, determining queueing priority scheme, determining channel allocation by reducing the bit error rate, determining the channel capacity, etc in the 3G network using the intelligent agent.

(Ojieabu et al., 2017) Based on the higher-order Markov model, the researcher had explained call admission utilizing probability techniques and constraints optimization. Distinct radio access technologies will operate together in the next-generation wireless network. One prime challenge in NGWN is RRM. One RRM method is the call admission control that plays an instrumental role to ensure the desired QoS working on distinct apps. In the prevailing circumstance, one of the big challenges to be addressed is the available channel capacity distribution amongst the multiple traffic with distinct bandwidth needs to guarantee the QoS needs of the traffic. In this study, the researchers had assumed three classes of traffic having distinct QoS needs. From the performance model, optimistic yields were attained.

(Roy et al., 2020) In this paper, the researcher had utilized call admission control for studying system resource management as well as the dynamicity of call management. In wireless or cellular networks, the key element in the guaranteed QoS provision is the call admission control. The author had presented a CAC scheme based on factors such as adaptively to traffic conditions or distributed control and with a focus on dynamic reservation schemes by considering handoff prioritization. For providing guaranteed QoS and better system resource utilization, the author had solved the basic drawback of the bandwidth reservation approach for a distinct type of call.

(Dewanand et al., 2017) In this paper, the author had proposed a system using call admission control for voice-over LTE. 4G technology is also called LTE (Long Term Evolution). Voice data may be transmitted over the LTE network with the aid of a new protocol called voice over LTE. Using call admission control in the voice-over LTE is an extension to the existing VoLTE protocol. When the total bandwidth amount of a system is reserved for high-priority calls then VoLTE CAC is utilized in cellular networks. The call admission control system assumes an ongoing call if a new call request arrives in a cell. During the handoff calls, the research yields the clarification for major call drops in communication, by decreasing call blocking probability as well as call dropping probability. In this thesis, the author had improved the CBP considerably over a large range of handoff rates the traffic model irrespective as well as a pattern of the mobility. Using call admission control, QoS needs in the network are dealt also with within the voice-over LTE.

A few highlights of the researches that have been explained in this section are as follows. The author had improved the CBP considerably over a greater range of handoff rates in the traffic model irrespective as well as mobility pattern. Using call admission control, QoS needs in the network are dealt also with within the voice-over LTE. To optimize the cellular network system, effective call admission control methods are required. Several methods of call admission control are proposed. The researcher had proposed a method for building a genetic neuro-fuzzy controller for call admission in 5G networks. To avoid network congestion from such devices, the 5G network needs to cope with such greater requests of the traffic. With shared baseband processing, requests are processed from mobile devices as C-RAN has been assumed as a paradigm shift for 5G. The researcher had utilized the intelligent agent for improving an optimized call admission control in the 3G network. The author had presented a CAC scheme based on factors such as adaptively to traffic conditions or distributed control and with a focus on dynamic reservation schemes by considering handoff prioritization.

The section covers the methodology part. It will explain what methodologies will be followed for attaining the goals. Under the methodology section, three techniques will be covered including Cloud Bursting For Pre-Empted Connections, Fuzzy Logic Controller Structure, And Queueing System For Preempted Connections, etc. Let's get into details of each technique which is as follows.

With the aid of the method of cloud bursting, the operators are allowed to extend their infrastructure dynamically by renting 3rd party sources. Figure 1 exhibits the model of cloud bursting. When the cloud is congested and a high priority RT connection arrives during the C-RAN infrastructure congestion of the operator, two things occur:

a) If no NRT connections for pre-empting in the C-RAN of the operator, then the RT connection is dropped.

b) If NRT connections with low priority are pre-empted from the C-RAN of the operator, then it burst into the public infrastructure of C-RAN for accommodating the RT connections with greater priority.

Since the RT connections are delay-sensitive, so they are never outsourced to the public cloud. But, the NRT connections may be outsourced to the public cloud. For some NRT connections outsourcing, an agreement is done between the public cloud operator as well as the operator as well as charged some price as well. The NRT connection is forwarded for the public cloud along with a certain price penalty if it arrives as well as the cloud of the operator is congested. In this case, the charging manager charged for certain requests.

[Sigwele et al., 2017]

In the case of the system, three inputs are taken by the fuzzy controller: (a) Network Congestion Factor – Nc. (b) Available Capacity – Ac. (c) Effective Capacity – Ec. And, the output is represented by Admittance Decision – Ad. Here is the structure of the proposed fuzzy logic controller which is described as follows.

Membership Functions – To keep up the simplicity in the system, selected membership functions here are Triangular Membership Functions as well as Trapezoidal Membership Functions. For linguistic parameters (Input as well as Output), the membership functions are represented in figure 2. Based on the usually utilized membership functions values in diverse literature, the values of the membership functions have been selected in this study. The terms set for Nc, Ac, St, Ec, and Ad for the fuzzy controller are defined as follows:

Table 1: Terms Set Defining For Fuzzy Controller

[Sigwele et al., 2017]

The value of the variable exhibited is derived from figure 2 that we had already explained the concept.

Table 2: Fuzzy Rule Base For Fuzzy Controller

Defuzzification Technique – In this case, a method called Center of Gravity (COG) for converting the membership degrees with output linguistic parameters into numerical values. In this case, the adopted method is the COG method, and along with that the membership functions exploited are simple trapezoidal as well as triangular shapes with low computational complexity as well as may be represented as the equation (1).

In the cloud, the connections follow the Erlang B Model or M/M/c/K. In M/M/c/K model, the Poisson process aids in governing the request arrival at arrival rate λ, as well as the times of the service with parameter μ are exponentially distributed, as well as in the cloud processing the requests, there are c servers from the front of the queue. The system capacity is denoted by the K variable. In this case, the connection requests higher than the queue length are dropped as well as the buffer is assumed to be of finite size. With the aid of the Markov Chain with continued time, the model may be described. Mathematically, we may present the (ρ) the server utilization as:

The value of the variable (ρ) needs to keep less for keeping the queue to be stable and if it is not that then the length of the queue will grow without bound. Mathematically, the probability may be written for a system with n connections as follows:

And, the probability (πn) may be written for a cloud system with n connections as follows:

Response Time – The time amount spends by a connection in both service and queue is called response time. Mathematically, the average response time may be represented as follows:

Using the Erlang B formula, the probability may be written for an arriving connection is blocked as follows:

Here, we have explained three techniques covering Cloud Bursting For Pre-Empted Connections, Fuzzy Logic Controller Structure, And Queueing System For Preempted Connections. In the cloud bursting for pre-empted connections, the operators are allowed to extend dynamically their infrastructure by renting 3rd party sources. In the case of fuzzy controller, three inputs are involved: (a) Network Congestion Factor – Nc. (b) Available Capacity – Ac. (c) Effective Capacity – Ec. And, the yield is represented by Admittance Decision – Ad. The fuzzy controller involves three aspects covering Membership function, Fuzzy rule base, and the method of defuzzification. The next thing that has been covered in the methodology part is the M/M/c/K model. In this case, the Poisson process aids in governing the request arrival at arrival rate λ, as well as the service times with parameter μ are distributed exponentially, as well as in the cloud processing the requests, there are c servers from the front of the queue.

In this section, we will cover a few concepts related to the thesis involving fuzzy theory, 5G mobile networks, cloud radio access networks (C-RAN) along with call admission control. Such concepts are necessary to introduce as these will aid in understanding the fundamental theory as well as basic significance in the study. Under the fuzzy theory, we will explain fuzzy logic, its significance, several methods of fuzzy logic. Next, we will explain in detail the 5G technology covering what is 5G, its features, and architecture. In subsequent sections, we will describe in detail Call Admission Control and C-RAN, etc.

What is Fuzzy Logic? – An approach to process variables that aid in processing multiple values via the same variable. Fuzzy logic makes the best possible decision given the input by considering all available info as it is designed to solve issues. Fuzzy logic aids in obtaining an array of accurate conclusions since it solves issues with an open, imprecise spectrum of data.

Fuzzy Logic Architecture – It is comprised of four parts which are as follows:

· Rule Base – to control the system of decision-making, the experts offer a distinct set of rules as well as the if-then conditions. For the design as well as fuzzy controllers tuning, the recent update in fuzzy theory offers diverse techniques that even mitigate the number of the set rules of fuzzy.

· Fuzzification – It aids in converting the crisp numbers into fuzzy sets. The sensors assist in measuring the crisp inputs and further processing is done by passing them into the control system.

· Inference Engine – The degree of the match between the rules as well as fuzzy input is determined by the inference engine.

· Defuzzification – For converting the fuzzy sets into a crisp value, the defuzzification process is performed. Require to choose the best-suited method if it is utilized with an expert system as there are several methods available.

[Alemneh et al., 2020]

Membership Function – A graphical representation for mapping each point in the input space to membership value between 0 well as 1. Trapezoidal or triangular fuzzifier, Singelton fuzzifier, or Gaussian fuzzifier are three types of fuzzifiers.

What is Fuzzy Control? – The approach aids to deal with any sort of uncertainties. It may emulate human deductive thinking. It is designed for giving acceptable reasoning but may not be designed for giving accurate reasoning. In other words, it is a method for embodying human-like thinkings into a control system.

Different Fuzzy Control Methods – Using two distinct methodologies, the systems of the fuzzy interface may be designed are Mamdani and Sugeno.

· Mamdani – Among the first control systems, this most commonly utilized method was built using fuzzy set theory. The output variable to be a fuzzy set is expected by this inference method. It is also called the Singelton output mechanism as it uses a single linguistic variable.

· Sugeno – Like the Mamdani method, the first two-part pf Sugeno method is similar naming applying the fuzzy operator, and fuzzifying the inputs. Parameters optimization enhances linear techniques, and effectiveness in controlling and calculations, etc are some of its advantages.

Pros – Several advantages of fuzzy logic are as follows:

· Offers an effective solution for complex issues.

· May modify easily for improving or altering system performance.

· Since no precise inputs are needed, so robust mostly.

· Offers utilized for practical or commercial purposes.

· Easy to understand the structure of the fuzzy logic.

Cons – Several shortcomings of Fuzzy logic are as follows:

· Most of the time accuracy in Fuzzy Logic is compromised as it works on precise or imprecise data.

· Since we do not get every time the mathematical description of the process, so proof of its features is impossible or hard in major cases.

· Fuzzy logic may not aid in solving a given problem as there is no systematic approach.

What is 5G Technology? – Owing to having very high bandwidth, the 5G technology has changed the means to utilize mobile phones. Here, 5G refers to 5th generation mobile technology. All advanced features are included in the 5G technology, thus making it more powerful and will be high in demand in years to come. Broadband internet access may be attained using this technology by hooking with the 5G technology cell phone.

What 5G technology Offers? – In the mobile market, the technology of 5G is going to be a new mobile revolution. You may access worldwide cellular phones with the aid of this technology and now the user is proficient in getting access to German phones as a local phone. Within the latest mobile operating system, the 5G can tie together infinite data broadcast as well as unrestricted call volumes as this technology has extraordinary data capabilities. Since 5G may offer priceless handsets to their customers and also may handle the best technologies, so it has a bright future. High connectivity is attainable in 5G owing to the utilization of the switch and router technology. Within the building, the internet access to nodes is distributed by the 5G and that may be implemented with the union of wired as well as wireless connections.

Features – Here are some of the features of 5G technology which are as follows:

· It offers to enhance and available connectivity just about the world.

· It offers a virtual private network (VPN) and provides speed up to 25 Mbps

· Offers a better and best solution through its remote management features.

· Its high-quality service based on policy to prevent error

· Offer subscriber supervision tools for fast action

· A more effective as well as attractive feature is its advanced billing interfaces.

5G Mobile Network Architecture – The figure is shown below exhibits the system model for 5G mobile systems. This model proposes the design of network architecture and it is based on the All-IP model for mobile interoperability as well as wireless networks. The model is comprised of several independent radio access technologies as well as a user terminal. To the outside internet world, each of the radio access methods is seen as the IP link within each of the terminals. But in the case of the mobile terminal, there needs to be a distinct radio interface for each radio access technology (RAT). For instance – To have the architecture to be functional, we need to have distinct accesses and along with that, they need to be active at the same time if we intend to have access to four distinct RATs.

[Devi et al., 2017]

What is C-RAN? – It is a centralized system that enables real-time virtualization capabilities, collaborative radio technology support, and large-scale deployment, etc. It may alternatively stand for collaborative or centralized. It has a focal handling framework in the cloud. It is probably going to be one of the key arrangements empowering huge scope 5G organized MIMO frameworks.

It is paving the way towards ultra-reliable communications as it may be applied for improving the resiliency as well as the availability of the wireless networks.

Components of C-RAN – It is comprised of three main parts including Fronthaul, Remote Radio Unit (RRU) network, and Base-Band Unit (BBU) pool. Figure 5 represents the overview of the C-RAN architecture which is as follows.

· BBU Pool – It is comprised of multiple BBU nodes that having high storage and computational capabilities. It is located at a centralized site. The main responsibility of the BBU is to process the resources and allocate them dynamically to RRUs based on the current network requirements.

· RRU Network – It is utilized for connecting wireless devices in traditional cellular networks. It is a wireless network.

· Fronthaul – It may be realized using distinct technologies that incorporate millimeter-wave communication, cellular communication, or optical fiber communication, etc. It handles the needs of multiple RRUs by providing high bandwidth links between a BBU and a set of RRUs.

[Silva et al., 2018]

C-RAN System Structure – It includes three sections which are as follows:

· Fully Centralized – It would move all network, MAC, or physical layers into BBU. All the functions of managing as well as processing resources may be handled by the BBU.

· Partially Centralized – Here, the MAC layer is performed at RRU, and the physical layer is performed at BBU. Since the physical layer takes a major computation burden of C-RANs, so this will reduce simply the overhead of RRUs-BBUs communication.

· Hybrid Centralized – In this case, the physical layer operation is done at RRU while others are done at the BBU layer.

Advantages – Here are some of the benefits of the C-RAN as follows:

· Attains faster speeds than distributed RANs

· It can reuse infrastructure or pool resources

· Supports a great number of mobile users as well as wireless standards

· Develops a more flexible, scalable, and simplified

· It has poor, cooling, or heating needs

· Offers higher spectrum efficiency.

· Resource Virtualization

What is the CAC scheme? – With the aid of call admission control (CAC), the network congestion may be avoided and along with that, it may avoid traffic congestion in 5G. Besides, the CAC scheme plays a vital role in the guaranteed QoS provision. The CAC algorithm's primary job is to choose precisely whether an association might be acknowledged into an asset limited organization without disregarding the assistance responsibilities shaped to the generally conceded associations.

Role of CAC in 5G Network – Here are lists of several reasons for the unsuitability of the conventional CAC scheme in the 5G network.

· First, due to the time-varying parameter nature (for instance – available power, channel conditions, direction, location, speed, etc.) and real-time processing of radio signals, the traditional approach of CAC suffers uncertainties in the cellular network. If the network is indeed incapable of servicing the request as well as incorrect rejection, then the traditional CAC scheme may lead to incorrect request admission. The static state of information in the network is assumed by such sort of CAC schemes. But, the network is dynamic as well as values measured keep altering in practice.

· Second – It is the standalone RAN base station on which the traditional CAC scheme is based whereas 5G will be based on centralized cloud BS’ss. Such BS’s have computation resources as well as unshared processing situated in the BS cell sites along they are preconfigured for peak load. Such BSs resources may not be shared for addressing varied traffic requirements on other cell sites, thus causing high CDP as well as CBP, or poor resource utilization. So, an efficient CAC scheme is needed in such cases

In cellular networks, the issue of uncertainties and imprecision may be solved through the method of intelligent schemes of the CAC based on intelligent decision-making that is also a promising solution. Without require for complex modeling of mathematical, the schemes mimic the behavior cognitively of the human mind, thus making them suitable, flexible, less complex, or adaptive for coping with rapidly altering network situations of cellular networks in 5G.

Thus, we have presented detailed descriptions of the following concepts – fuzzy theory, 5G mobile networks, cloud radio access networks (C-RAN) along call admission control. Fuzzy logic makes the best possible decision given the input by considering all available info as it is designed to solve issues. Fuzzy logic aids in obtaining an array of accurate conclusions since it solves issues with an open, imprecise spectrum of data. In the mobile market, the technology of 5G is going to be a new mobile revolution. You may access worldwide cellular phones with the aid of this technology and now the user is proficient in getting access to German phones as a local phone. Within the latest mobile operating system, the 5G can tie together infinite data broadcast as well as unrestricted call volumes as this technology has extraordinary data capabilities. C-RAN is a centralized system that enables real-time virtualization capabilities, collaborative radio technology support, and large-scale deployment, etc. It has a focal handling framework in the cloud. The primary part of the calculation of the CAC is to choose precisely whether an association might be acknowledged into an asset limited organization without administration responsibilities infringement framed to the generally conceded associations.

In this section, we will explain the proposed system along with a simulation of the system. In the proposed CAC scheme, we will explain the C-RAN architecture and fuzzy logic-based scheme of the CAC. Under fuzzy logic-based CAC scheme, three techniques will be covered including Cloud Bursting Technique for Pre-empted Connections, Structure of Fuzzy Logic Controller, and Queueing System for Pre-empted Connections, etc. Under the simulation, we will study the simulation environment and its included parameters for the study.

Using four C’s, C-RAN can be described as follows: Cloud-Computing real-time, Collaborative Radio, Central Processing, Clean, etc. It is also a paradigm shift for RANs next-generation such as 5G. In this study, the adopted C-RAN architecture is exhibited in figure 6. The concept of C-RAN separates the antenna as well as the radio parts from the digital baseband parts as well as pools multiple baseband units in a central office called the BBU pool. Such only digital BSs called vBBUs are linked through low latency, high bandwidth fiber for remoting radio heads (RRHs). For housing, the BBUs, GPPs such as ARM or X86 processors are utilized as well as utilizing cloud computing concept of the virtualization with multiple vBBU are provisioned dynamically in accordance to traffic desires.

CAC Scheme Based on Fuzzy Logic

Since vigor just as straightforwardness of the fuzzy scheme, it is utilized for performing CAC in 5G C-RAN in this paper. With a capacity to deliver exact arrangements from certain or inexact information, the techniques of fuzzy logic resemble human decision-making. The technique of fuzzy logic may process any number of inputs and it does not need precise inputs along it avoids computational complexities or uncertainties brought by many CAC schemes. Based on natural language, a fuzzy scheme includes a standard-based or straightforward methodology for settling control issues instead of endeavoring to show a framework numerically. On the standalone BBUs, basebands signals from multiple cells are no longer processed in the proposed scheme, but they are processed on GPPs using the cloud computing concept in the cloud. So, we may define GPPs as follows – it is software-enabled multiple radio signal from distinct cells to be processed in one computer platform. Using virtualization technology, we ay attain such things wherein hardware parts are abstracted from software components. Now, we may define vBBUs as – it aids in processing baseband signal of specific cell traffic. It is also provisioned dynamically to service traffic requests from cells. In the case of the cell, the traffic demand is mapped into baseband resource processing such that every traffic of RRH is serviced by its vBBU.

The proposed CAC plot dependent on fuzzy for 5G-RAN is shown in figure 7 that is situated inside the cloud regulator in the BBU pool. There are different modules present in the models involving the C-RAN foundation of the administrator for ordinary preparing of solicitations when the clog is low just as an outsider public C-RAN framework for dealing with demands for the C-Ran of the administrator during the blockage. Contingent upon the size of the association demand and the sort of administration, the handled association demands in the public foundation are charged a specific cost by the charging supervisor. In the C-RAN infrastructure of the operator, the resource estimator estimates the available capacity and also further indicates whether the cloud is congested or not. The proposed model also includes the fuzzy controller that performs the CAC decisions for incoming requests from users. In the case of the proposed system, three inputs are taken by the fuzzy controller: (a) Network Congestion Factor – Nc. (b) Available Capacity – Ac. (c) Effective Capacity – Ec. And, the output is represented by Admittance Decision – Ad. The admittance decision is to either accept a request, reject a request, as well as preempt some low priority requests as well as outsource them to a public cloud. There are RT and NRT are two traffic which divides the traffic requests which exhibited as follows.

· NRT Classes – It incorporates TCP-based and buffered streaming services such as point to point, ftp, email or wen browsing, etc. It is also called non-GBR.

· RT Classes – It is delay sensitive service. It incorporates real-time gaming, video call, live streaming, VoIP. It is also called GBR (Guaranteed Bit Rate).

[Sigwele et al., 2017]

Cloud Bursting Technique for Pre-empted Connections

With the aid of the method of cloud bursting, the operators are allowed to extend dynamically their infrastructure by renting 3rd party sources. Figure 1 exhibits the model of cloud bursting. When the cloud is congested and a high-priority RT connection arrives during the congestion of the C-RAN infrastructure of the operator. You may find the complete details in the methodology section.

Structure of Fuzzy Logic Controller

In this system, three inputs are taken by the fuzzy controller: (a) Network Congestion Factor – Nc. (b) Available Capacity – Ac. (c) Effective Capacity – Ec. And, the output is represented by Admittance Decision – Ad. Here is the structure of the fuzzy logic controller which is described as follows – Membership functions, Fuzzy Rule Base, and Defuzzification method. You may find the complete details in the methodology section.

Queueing System for Pre-empted Connections

In the cloud, the connections follow the Erlang B Model or M/M/c/K and in this case, the Poisson process aids in governing the request arrival at arrival rate λ, as well as the service times with parameter μ are exponentially distributed, as well as the requests in the cloud processing, there are c servers from the front of the queue. The system capacity is denoted by the K variable. In this case, the connection requests higher than the queue length are dropped as well as the buffer is assumed to be of finite size. With the aid of the Markov Chain with continuous time, the model may be described. You may find the complete details in the methodology section.

For the simulation, we have utilized the MATLAB of the Matrix laboratory. This software is a high-performance language for technical computing. It is designed specifically for engineers as well as scientists to analyze and design systems, or products that transform our world. Using this simulation environment, we will estimate the performance of the system. For this, we will set some parameters for simulations that you may find the details in the next section.

For evaluating the proposed system performance, four schemes will be utilized which is described as follows:

· The proposed scheme of CAC based on fuzzy logic with pre-emption on C-RAN

· It is based on fuzzy logic without pre-emption on C-RAN

· CAC scheme without fuzzy logic on C-RAN

· CAC scheme with standalone BBUs on distributed serving individual BSs.

In this study, we will exploit MATLAB for simulating the proposed framework. The following four traffic classes or service types are assumed in this study for simulation as well as performance evaluation as exhibited in table 3.

· WWW or web browsing as NRT service

· FTP as NRT service

· Conventional video as RT service

· VoIP as RT service

[Sigwele et al., 2017]

In this study, the values of MBR were taken as the values for Ec. The proposed framework was applied to multiple traffic classes, and four traffic classes were estimated for simplicity. During the simulation, 100 calls were produced for each class of traffic as well and the value (λ) was varied with every simulation. 500s were kept the simulation time. Figure 2 exhibits the function of membership for the inputs as well as the output of the fuzzy controller. It is considered that the network operator functioning the private cloud agrees with the operator of the public cloud that incorporates the SLA (Service Level Agreement) involving the price. In the public cloud, the price of accepting a connection request is considered to be 10 percent of what the private C-RAN operator will form if the request is processed. It needs to be noticed the QoS, duration, and size of the request, etc may shape the premise of how much the solicitation might be priced. But, it will be assumed in the future as well as in this study, only 10% is mitigated by the public cloud.

Here, we have learned the architecture of C-RAN covering four C’s such as Cloud-Computing real-time, Collaborative Radio, Central Processing, Clean, etc. The proposed CAC scheme based on fuzzy for 5G-RAN is also explained here covering three techniques. In this paper, we have also learned the simulation environment and its included parameters for the study as well.

Here, we will discuss the obtained output that we have attained through the simulation which is as follows. Figures 8, 9, and 10 exhibits a contrast of the input terms combination (St, Ac, Ec) and Ad – output if the fuzzy rules are applied as exhibited in table 2. As observed through the figures, the admittance Ad mitigates if the value of Ec enhances. This means that the admittance becomes Accept if the value Ec is low of a specific service.

But the admittance becomes pre-empt if the value Ec enhances. And, for Available capacity Ac, the admittance value becomes higher if Ac is not enough. This means that there is pre-emption of NRT connections. The admittance tends to accept as the value of Ac enhances (to enough). Lastly, for service type St, the admittance reduces from pre-emption for accepting if the value of St enhances from NRT to RT since the RT connections are accepted and the NRT requests are pre-empted.

[Sigwele et al., 2017]

Figure 11 shown below exhibits the offered traffic load vs the blocking probability. It has been observed that the blocking probability increases as the offered traffic enhance for all the schemes. Since the power of computing of the baseband is restricted, so the CAC and the CBP distributed RAN is greater than all the other schemes as each cell is covered with limited capacity by a single BBU.

Owing to uncertain and improper admission control scheme decision-making without fuzzy logic, the CAC C-RAN blocking probability with no fuzzy scheme also actions poorly with blocking probability higher than the threshold at 40 percent offered traffic load.

As fuzzy logic prevents uncertainties as well as imprecisions if performing admission control, so the without pre-emption fuzzy C-RAN performs 90% traffic load differentiated to the past two plans. Likewise, since they are sent to a public cloud as such more associations are acknowledged in the framework rather than association demands being hindered, the fuzzy C-RAN with pre-emption plot performs better compared to all the rest with a hundred percent traffic beneath the obstructing likelihood edge of 5%.

[Sigwele et al., 2017]

For distinct traffic arrival rates, the resource utilization in the private CRAN is exhibited in figure 12. Since more requests are being occupied and processed the available capacity, so the resource utilization also increases in the cloud as the arrival rate enhances. In the cloud, the BBUs are shared as well as a single BBU may process requests from myriad cells, so the fuzzy C-RAN without pre-emption as well as the fuzzy C-RAN with pre-emption have the same but greater exploitation of the resource than all the other schemes.

It may observe that pre-emption does not influence the utilization of the resource. In the case of the CAC distributed scheme of the RAN, BBUs are standalone as well as BBU resource processing is shared for addressing varied traffic requirements in the cell site, so the C-RAN scheme with no fuzzy has greater exploitation than the distributed scheme of CAC of RAN.

[Sigwele et al., 2017]

In the case of the C-RAN system, the graph between the response time as well as offered traffic load is shown in figure 13. Since high requests take greater time to be processed, so the response time increases as the offered traffic enhances. Since the pre-empted NRT connections are forwarded to the public cloud incurring high delays, so it takes more time to be processed but this does not influence the NRT connections with pre-empted since they are delay tolerant. Since the new RT connections are processed not in the public cloud but the private cloud, so they have a less response time as well as are delay-sensitive.

For peak traffic periods, the operator revenue is exhibited in figure 14. The observed blocking probability for the CAC distributed RAN scheme is 0.5 meaning revenue is 50 percent at peak traffic periods. In this case, the smaller revenue is owing to a greater blocking probability. At peak traffic, 20% blocking probability is carried by the CAC scheme with no fuzzy, thus leading to a revenue of 80%. At peak traffic, 10% blocking probability is carried by the CAC C-RAN with fuzzy without pre-emption scheme, thus leading to a revenue of 90%. Whereas, C-RAN fuzzy with the scheme of pre-emption has observed more revenue which is 95% because more requests are admitted in both public or private in this case.

[Sigwele et al, 2017]

Figure 15 exhibits the total throughput of the network for distinct traffic loads as well as it may be exhibited that the network throughput enhances as the traffic load rises for all schemes. For the entire network, the throughput is expected to be larger and is estimated at the BBU pool. During low and peak traffic, a higher throughput is observed for the fuzzy C-RAN with a scheme of pre-emption than all the other schemes with 900 and 1600 Mbps.

Since the public cloud is providing more computing resources uring the cloud bursting technique, so more connections are being accepted. During low and peak traffic, a throughput of 880 and 1550 Mbps is observed for the fuzzy C-RAN without pre-emption as well as outperforms the RAN scheme of the CAC-distributed by 25.7%. if we talk about the C-RAN with no fuzzy, then it performs 8.6% better than the distributed scheme of the CAC. This scheme performs poorly than the rest schemes with 700 as well as 1400 Mbps during low as well as peak traffic since it has a great blocking probability owing to restricted resources of the computing of the baseband.

In this study, we have studied the scheme of the CAC concept based on fuzzy logic. The power of 5G technology is that it may able to connect to all sort of devices and also refers as Wireless Broadband Technology. Another powerful feature of 5G technology is that it may offer a high speed along with the capability of handling a large volume of data. 5G technology makes utilization of tiny cells, so it may meet the growing demands of mobile users as well as may offer better connectivity at any time or any place. With the aid of 5G technology, any sort of IoT device may be connected and may be easily cope-up with such applications of IoT. We may attain high data rates as well as low latency owing to having optimized packet radio access as well as flexible bandwidth in the 5G technology.

Since the users' channel quality may influence the transmission rates, so the user may obtain higher rates of transmission along with better conditions of the channel. If the rates of the transmission are not appropriate for attaining the demand of users, then users may lose data due to congestion in the queue. So, the algorithms of the flow rate control may be applied at the base station for arriving network traffic flows, therefore offer more ample service to mobile users. When the flow rate control algorithm is applied to 5G technology, the smaller size queue may be attained at eNodeB, thus leading to obtaining lower loss rates as well as shorter waiting times for users.

With the aid of call admission control (CAC), the network congestion may be avoided and along with that, it may prevent congestion in traffic in 5G. Besides, the CAC scheme plays a major role in the guaranteed QoS provision. The CAC algorithm's main role is to decide precisely whether a connection may be admitted into a network with resource-restricted without the service commitments violation formed to the already admitted connections. An efficient scheme of the CAC may optimize system exploitation, call dropping probability (CDP), as well as call blocking probability (CBP), etc in the 5G network. But, the traditional CAC scheme is not suitable for 5G C-RAN. The main goal of the thesis is to utilize the fuzzy predictor approach in determining the performance of the 5G network. Another goal of the study is to present a fuzzy logic-based scheme of the CAC in 5G C-RAN using pre-emption.

In this study, a fuzzy logic-based scheme of the CAC in 5G C-RAN using pre-emption is proposed. The main task of fuzzy logic is to prevent uncertainties in distributed systems of RAN caused by conventional schemes of CAC. Another technique is proposed called cloud bursting. NRT connections delay-tolerant with Low priority are pre-empted as well as outsourced at a certain price penalty to a public cloud for accommodating the RT connections during congestion. Here, it is considered that the infinite processing capacity is carried out by the public cloud as such it will not be captured in the simulation or it may not get congested. For validating the proposed scheme, a rigorous study of simulation is conducted that exhibits a significant performance improvement.

Here are lists of several reasons for the unsuitability of the conventional CAC scheme in the 5G network. First, due to the time-varying parameter nature (for instance – available power, channel conditions, direction, location, speed, etc.) and radio signals real-time processing, the traditional approach of CAC suffers uncertainties in the cellular network. If the network is indeed servicing incapable the request as well as rejection incorrectly, then the traditional CAC scheme may lead to incorrect request admission. The static state of information in the network is assumed by such sort of CAC schemes. But, the network is dynamic as well as values measured keep altering in practice.

Second – It is the standalone RAN base station on which the traditional CAC scheme is based whereas 5G will utilize the centralized cloud BS’s. Such BS’s have computation resources as well as unshared processing placed in the BS cell sites along, they are preconfigured for peak load. Such BSs resources may not be shared for addressing varied traffic requirements on other cell sites, thus causing high CDP as well as CBP, or poor resource utilization. So, an efficient CAC scheme is needed in such cases. In cellular networks, the issue of uncertainties and imprecision may be solved through the method of intelligent schemes of CAC based on intelligent decision-making that is also a promising solution. Without the requirement of the model of complex mathematical, the schemes mimic the behavior cognitively of the human mind, thus making them suitable, flexible, less complex, or adaptive for coping with rapidly altering network situations in 5G.

In this study, we have explained three techniques covering Cloud Bursting for Pre-Empted Connections, Fuzzy Logic Controller Structure, And Queueing System For Pre-empted Connections. In the cloud bursting for pre-empted connections, the operators are allowed to extend dynamically their infrastructure by renting 3rd party sources. In the case of fuzzy controller, three inputs are involved: (a) Network Congestion Factor – Nc. (b) Available Capacity – Ac. (c) Effective Capacity – Ec. And, the result is represented by Admittance Decision – Ad. The fuzzy controller involves three aspects covering Membership function, Fuzzy rule base, and the method of defuzzification. The next thing that has been covered in the methodology part is the M/M/c/K model wherein the Poisson process aids in governing the request arrival at arrival rate λ, as well as the service times with parameter μ are distributed exponentially, as well as the requests in the cloud, there are c servers from the front of the queue.

[1] Sigwele, T., Pillai, P., Alam, A.S. and Hu, Y.F., 2017. Fuzzy logic-based call admission control in 5G cloud radio access networks with preemption. EURASIP Journal on Wireless Communications and Networking, 2017(1), pp.1-10.

[2] Al-Maitah, M., Semenova, O.O., Semenov, A.O., Kulakov, P.I. and Kucheruk, V.Y., 2018. A hybrid approach to call admission control in 5G networks. Advances in Fuzzy Systems, 2018.

[3] Ampririt, P., Ohara, S., Qafzezi, E., Ikeda, M., Matsuo, K. and Barolli, L., 2021. An Integrated Fuzzy-based Admission Control System (IFACS) for 5G Wireless Networks: Its implementation and performance evaluation. Internet of Things, 13, p.100351.

[4] Alhammadi, A., Roslee, M., Alias, M.Y., Shayea, I., Alriah, S. and Abas, A.B., 2019, July. Advanced handover self-optimization approach for 4G/5G HetNets using weighted fuzzy logic control. In 2019 15th International Conference on Telecommunications (ConTEL) (pp. 1-6). IEEE.

[5] Jadhav, V.S. and Kolekar, U.D., 2019. Fuzzy-based decisive approach for call admission control in the LTE networks. Evolutionary Intelligence, pp.1-18.

[6] Sigwele, T., Pillai, P. and Hu, Y.F., 2015, July. Elastic call admission control using fuzzy logic in virtualized cloud radio base stations. In International Conference on Wireless and Satellite Systems (pp. 359-372). Springer, Cham.

[7] Alkhawlani, M. and Ayesh, A., 2008. Access network selection based on fuzzy logic and genetic algorithms. Advances in Artificial Intelligence, 2008.

[8] Khan, A.A., Abolhasan, M., Ni, W., Lipman, J. and Jamalipour, A., 2019. A hybrid-fuzzy logic-guided genetic algorithm (H-FLGA) approach for resource optimization in 5G VANETs. IEEE Transactions on Vehicular Technology, 68(7), pp.6964-6974.

[9] Arora, Y., 2020. Call Admission Control Decision Maker for Smart City Applications. International Journal of Information Technology (IJIT), 6(5).

[10] Palle, S. and Shankar, S., 2021. Mobility prediction assisted call admission control model for mobile heterogeneous cellular network. International Journal of Communication Networks and Distributed Systems, 26(2), pp.219-244.

[11] Cardoso, E., Silva, K. and Francês, R., 2017, June. Intelligent handover procedure for heterogeneous LTE networks using fuzzy logic. In 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC) (pp. 2163-2168). IEEE.

[12] Roose, A.I., Yahya, S. and Al-Rizzo, H., 2017. Fuzzy-logic control of an inverted pendulum on a cart. Computers & Electrical Engineering, 61, pp.31-47.

[13] Devi, R., Jha, R.K., Gupta, A., Jain, S., and Kumar, P., 2017. Implementation of an intrusion detection system using adaptive neuro-fuzzy inference system for 5G wireless communication network. AEU-International Journal of Electronics and Communications, 74, pp.94-106.

[14] Silva, K.C., Becvar, Z., Cardoso, E.H. and Francês, C.R., 2018, April. Self-tuning handover algorithm based on fuzzy logic in mobile networks with dense small cells. In 2018 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6). IEEE.

[15] Bharathi, M.L., Basha, R.F.K., Ramanathan, S.K. and Harikrishnan, R., 2020. Fuzzy Logic controlled Maximum Power Point Tracking for SEPIC converter fed DC drive-A hybrid Power Generation System. Microprocessors and Microsystems, p.103371.

[16] Abdelbari, A. and Haci, H., 2019, August. Fuzzy Logic-Based User Scheduling Scheme for 5G Wireless Networks and Beyond. In International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions (pp. 429-435). Springer, Cham.

[17] Sodhro, A.H., Luo, Z., Sangaiah, A.K. and Baik, S.W., 2019. Mobile edge computing-based QoS optimization in medical healthcare applications. International Journal of Information Management, 45, pp.308-318.

[18] Zhang, L., Ge, L., Su, X. and Zeng, J., 2017, April. Fuzzy logic-based vertical handover algorithm for trunking system. In 2017 26th Wireless and Optical Communication Conference (WOCC) (pp. 1-5). IEEE.

[19] Alemneh, E., Senouci, S.M. and Messous, M.A., 2020. An energy-efficient adaptive beaconing rate management for pedestrian safety: A fuzzy logic-based approach. Pervasive and Mobile Computing, 69, p.101285.

[20] Mahmood, A.M., Al-Yasiri, A. and Alani, O.Y., 2018. Latency reduction by dynamic channel estimator selection in C-RAN networks using fuzzy logic. Computer Networks, 138, pp.44-56.