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![Cell Coverage Divided into Concentric Areas [41]](profile/Aderemi-Atayero/publication/307572538/figure/fig2/AS:405683540381697@1473733901079/Cell-Coverage-Divided-into-Concentric-Areas-41.png)
![Fig. 4. Cell Coverage Divided into Concentric Areas [41]](profile/Aderemi-Atayero/publication/307572538/figure/fig2/AS:405683540381697@1473733901079/Cell-Coverage-Divided-into-Concentric-Areas-41_Q320.jpg)
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... region of interest is divided into three concentric regions, with each region corresponding to a Modulation and Coding Scheme (MCS). Specifically, 64QAM is used in the inner region, 16QAM is used in the middle region and QPSK is used in the outer region (see Fig.4). Users in the same region experience the same SINR, thus, they are assigned the same CQI. ...
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... Though the problem of admission control has been extensively studied [14,15], previous admission control algorithms do not take monitoring overhead into account, which might because the measurement overhead in admission control problems is hard to quantify. The requirement to dynamically adjust measurement frequency is also challenging. ...
5G is expected to provide a high-performance and highly efficient network to prominent industry verticals with ubiquitous access to a wide range of services with orders of magnitude of improvement over 4G. Network slicing, which allocates network resources according to users’ specific requirements, is a key feature to fulfil the diversity of requirements in 5G network. However, network slicing also brings more orchestration and difficulty in monitoring and admission control. Although the problem of admission control has been extensively studied, those research take measurements for granted. Fixed high monitoring frequency can waste system resources, while low monitoring frequency (low level of observability) can lead to insufficient information for good admission control decisions. To achieve efficient admission control in 5G, we consider the impact of configurable observability, i.e. control observed information by configuring measurement frequency, is worth investigating. Generally, we believe more measurements provide more information about the monitored system, thus enabling a capable decision-maker to have better decisions. However, more measurements also bring more monitoring overhead. To study the problem of configurable observability, we can dynamically decide what measurements to monitor and their frequencies to achieve efficient admission control. In the problem of admission control with configurable observability, the objective is to minimize monitoring overhead while maintaining enough information to make proper admission control decisions. In this thesis, we propose using the Deep Reinforcement Learning (DRL) method to achieve efficient admission control in a simulated 5G end-to-end network, including core network, radio access network and four dynamic UEs. The proposed method is evaluated by comparing with baseline methods using different performance metrics, and then the results are discussed. With experiments, the proposed method demonstrates the ability to learn from interaction with the simulated environment and have good performance in admission control and used low measurement frequencies. After 11000 steps of learning, the proposed DRL agents generally achieve better performance than the threshold-based baseline agent, which takes admission decisions based on combined threshold conditions on RTT and throughput. Furthermore, the DRL agents that take non-zero measurement costs into consideration uses much lower measurement frequencies than DRL agents that take measurement costs as zero.
... But, the resource allocation mechanism used in packet-switched LTE network differs from that of traditional cellular wireless communication networks. Call blocking performances for packetswitched networks providing voice service has been previously discussed in [16]- [18]. To make it possible to estimate the impact of HARQ on blocking performance we have assumed all voice packets generated from a single voice call require equal number of RBs during the full duration of the call. ...
... That requires different amount of physical resources for voice transmission which further affects to the VoLTE cell capacity [3]. In general, call blocking performance depends on cell capacity [15]. Therefore, variation in voice codec bit rate would also affect VoLTE call blocking performance in an LTE cell. ...
... Call blocking probability (CBP) is the probability of a new call being discarded by the eNB. In traditional cellular network, the original form of the Erlang B model allows us to find the call blocking probability as given in the following expression [15,[19][20]. ...
... As LTE is a packet-switched network, the resource allocation mechanism differs from the traditional circuit-switched based cellular mobile network. Blocking performance in packetswitched network is previously discussed in [15,19]. These prior works have not taken into account the codec dependent variation in voice payload, codec overhead and different layer specific overheads before the air interface of the VoLTE traffic. ...
... 2 In general, there are two categories of CAC systems in mobile cellular networks, namely deterministic CAC and stochastic CAC. 3 In 2G mobile network uses either FDMA (frequency division multiple access) or TDMA (time division multiple access), frequencies and time slots are managed. Hence for CAC, the GSM network allocates both frequency and time slots to each new user. ...
The International Telecommunications Union defines in its IMT‐2020 recommendations three types of use of 5G services: mMTC (massive Machine‐type Communications), eMBB (enhanced Mobile Broadband), and uRLLC (ultra‐Reliable Low Latency Communications). The mMTC service allows a considerable number of machines and devices to communicate while guaranteeing a good quality of service. The eMBB service allows very high data throughput, even at the cell border. The uRLLC service is used for ultra‐reliable communication for critical needs requiring very low latency. These services are provided separately in a given cell. However, the number of connected objects is starting to increase rapidly as well as the bit rates and energy consumption. The 5G network must make it possible to provide access to a vast number of users of its different service categories. Call admission control (CAC) techniques focus more on availability in terms of bit rate and coverage. In this article, we suggest an algorithm for modeling CAC in an area served by the three categories of services in a 5G access network, mainly based on minimum energy consumption. This technique will allow connected objects that consume low energy to connect to the network with an adequate quality of service and enable the development of the Internet of Things.
... It should be noted that the interruption of a communication in progress (dropping call) is more troublesome than the blocking call from the point of view of the users. Call Admission Control (CAC) is a technique that controls the admittance or otherwise of new or handover calls [3]. ...
In this paper, we discuss the Call admission control (CAC) issue in 5G network and the state of the art in this field. Which study will be conducted to suggest and develop an algorithm of admission control modeling in the case of New Radio access namely NR 5G. It will also tackle the issue of handover and more generally mobility management, power control and interference. All this will be done taking into consideration and ensuring acceptable QoS and QoE as well as an energy saving and power efficiency. We focus, in our study, on the development of an algorithm for the improvement of call admission control based essentially on a minimal consumption of energy. In the algorithm we consider one base station (RRH) in ultra dense network environment by considering Heterogeneous network and CRAN systems (H-CRAN).
... As it is reported in [40,45], in this work, we have assumed that the UEs those are generating CQI in the range of 1-6 form a group in Region-3 and considered as poor case scenario, in such case QPSK modulation is used for voice packet transmission. Similarly, UEs generating CQI in the range of 7-9 form a group in Region-2 and considered as average case scenario, in such case 16QAM modulation is used by eNB for voice packet transmission. ...
In this study, the authors introduce analytical models for region‐based Voice over Long Term Evolution (VoLTE) cell capacity estimation. Using the authors' proposed analytical models, they estimate cell capacity in different regions by using several user‐specific voice traffic characteristics such as Voice Activity Factor (VAF) and Silent Insertion Descriptor (SID), and different advanced techniques like Packet bundling and Transmission Time Interval (TTI) bundling. It also incorporates Hybrid automatic repeat request (HARQ) retransmission. In this work, the cell is divided into multiple regions, each corresponding to the area in which user equipment generates a certain level of Channel Quality Indication (CQI) value based on Signal to Interference plus Noise Ratio (SINR). This proposed model for region‐based VoLTE capacity estimation optimises the network resource utilisation and enhances the number of concurrent active VoLTE users within the cell in each region. In this work, they have defined packet bundling and TTI bundling factors for appropriate regions to optimally allocate the radio resources for capacity estimation. Numerical results obtained using their proposed model considering different radio specific parameters are found improving the VoLTE cell capacity significantly. Further, the impact of HARQ on region‐based VoLTE cell capacity is also obtained.
... Keywords: smart campus, broadband internet access, data bit rate, mobile communication, knowledge management Availability of these data will help network administrators to determine optimal network latency towards efficient deployment of high-speed broadband communication networks in smart campuses [9][10][11]. ...
Efficient broadband Internet access is required for optimal productivity in smart campuses. Besides access to broadband Internet, delivery of high speed and good Quality of Service (QoS) are pivotal to achieving a sustainable development in the area of education. In this data article, trends and patterns of the speed of broadband Internet provided in a Nigerian private university campus are largely explored. Data transmission speed and data reception speed were monitored and recorded on daily basis at Covenant University, Nigeria for a period of twelve months (January – December, 2017). The continuous data collection and logging were performed at the Network Operating Center (NOC) of the university using SolarWinds Orion software. Descriptive statistics, correlation and regression analyses, Probability Density Functions (PDFs), Cumulative Distribution Functions (CDFs), Analysis of Variance (ANOVA) test, and multiple comparison post-hoc test are performed using MATLAB 2016a. Extensive statistical visualizations of the results obtained are presented in tables, graphs, and plots. Availability of these data will help network administrators to determine optimal network latency towards efficient deployment of high-speed broadband communication networks in smart campuses.
... In order to avoid network congestion, as well as to ensure QoS for the UEs, cellular networks adopt call admission control (CAC) algorithms. As a part of the radio resource management, the objective of a CAC algorithm is to satisfy the QoS requirement of ongoing and new calls considering the maximum network capacity [3]. A CAC algorithm decides whether to accept or rejecr an incoming call based on the availability of radio resources [4] [5]. ...
In cellular networks, call admission control (CAC) prevents network congestion, and guarantees the quality of service (QoS) to existing user equipments (UEs) by rejecting new calls considering the availability of bandwidth or policy reasons. These rejected calls may cause degradation of the overall network throughput. However, collaboration of CAC with a load-balancing algorithm can mitigate the number of rejected calls by triggering a load-balancing algorithm on demand. A load-balancing algorithm handovers some of the UEs from a fully occupied cell to its neighboring cells and make the occupied cell free enough to accept new calls. In this paper, we propose an algorithm for collaborating CAC with load-balancing in ultra-dense small-cell networks. Simulation results show that collaboration of CAC with load-balancing ensures required QoS for the UEs and maximizes the network throughput.
... As a result, with limited bandwidth resources, a large data volume will cause congestion in NAN. In commercial cellular network (CCN), previous literature have clearly given the insight that the Admission Control (AC) mechanisms can effectively prevent congestion by limiting the number of connections in the system [4]- [6]. However, different SG services with different QoS requirements will have different traffic patterns. ...
... Several approaches about AC techniques are discussed in previous literature. [4] is a survey of different AC schemes for LTE. In [5], an AC scheme is proposed based on greedy choice with bandwidth availability aware defragmentation algorithm. ...
