Resources In Cellular Networks Research Articles

Performance of hybrid transmission in a reconfigurable intelligent surface assisted D2D using NOMA

Recent research on Reconfigurable Intelligent Surface (RIS) aided communication and Non-Orthogonal Multiple Access (NOMA) technology shows enhanced spectral efficiency for 5 G and beyond wireless network systems. This paper evaluates the performance of a Device-to-Device (D2D) network where RIS is deployed in a suitable position and NOMA is employed to accommodate more number of users in the spectrum simultaneously. The D2D users exploit cellular network resources. Analytical models for outage, throughput, and energy-efficiency are developed considering spectrum sensing and a transmit power constraint. This work investigates the network performance in underlay, overlay, as well as hybrid modes through extensive simulations. The simulation model is designed based on the analytical models. Outage probability, network throughput, and energy efficiency are analysed to evaluate the D2D network performance. The simulation results shows incorporation of RIS and NOMA, network performance improves substantially. Increase in number of RIS elements also improves the network performance.

Mathematical Model of Heterogeneous Traffic in LTE Networks

With the introduction of multimedia mobile applications in cellular networks, effective methods have been used to increase throughput. The article defines methods for dividing cellular network resources through mathematical modeling for heterogeneous traffic. The numerical properties of the indicators of joint servicing of requests, the efficiency of joint transmission of real-time traffic and elastic data traffic have been studied. Explores key uses of traffic model resource bandwidth.

Resource-aware DBSCAN-based re-clustering in hybrid C-V2X/DSRC vehicular networks.

5G wireless networks are paying increasing attention to Vehicle to Everything (V2X) communications as the number of autonomous vehicles rises. In V2X applications, a number of demanding criteria such as latency, stability, and resource availability have emerged. Due to limited licensed radio resources in 5G cellular networks, Cellular V2X (C-V2X) faces challenges in serving a large number of cars and managing their network access. A reason is the unbalanced load of serving Base Stations (BSs) that makes it difficult to manage the resources of the BSs optimally regarding the frequency reuse in cells and its subsequent co-channel interference. It is while the routing protocols could help redirect the load of loaded BSs to neighboring ones. In this article, we propose a resource-aware routing protocol to mitigate this challenge. In this regard, a hybrid C-V2X/ Dedicated Short Range Communication (DSRC) vehicular network is considered. We employ cluster-based routing that enables many cars to interface with the network via some Cluster Heads (CH) using DSRC resources while the CHs send their traffic across C-V2X links to the BSs. Traditional cluster-based routings do not attend the resource availability in BSs that are supporting the clusters. Thus, our study describes an enhanced clustering method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) that re-clusters the vehicles based on the resource availability of BSs. Simulation results show that the proposed re-clustering method improves the spectrum efficiency by at least 79%, packet delivery ratio by at least 5%, and load balance of BSs by at least 90% compared to the baseline.

Spatial-Temporal Cellular Traffic Prediction for 5G and Beyond: A Graph Neural Networks-Based Approach

During the past decade, Industry 4.0 has greatly promoted the improvement of industrial productivity by introducing advanced communication and network technologies in the manufacturing process. With the continuous emergence of new communication technologies and networking facilities, especially the rapid evolution of cellular networks for 5 G and beyond, the requirements for smarter, more reliable, and more efficient cellular network services have been raised from the Industry 5.0 blueprint. To meet these increasingly challenging requirements, proactive and effective allocation of cellular network resources becomes essential. As an integral part of the cellular network resource management system, cellular traffic prediction faces severe challenges with stringent requirements for accuracy and reliability. One of the most critical problems is how to improve the prediction performance by jointly exploring the spatial and temporal information within the cellular traffic data. A promising solution to this problem is provided by Graph Neural Networks (GNNs), which can jointly leverage the cellular traffic in the temporal domain and the physical or logical topology of cellular networks in the spatial domain to make accurate predictions. In this paper, we present the spatial-temporal analysis of a real-world cellular network traffic dataset and review the state-of-the-art researches in this field. Based on this, we further propose a time-series similarity-based graph attention network, TSGAN, for the spatial-temporal cellular traffic prediction. The simulation results show that our proposed TSGAN outperforms three classic prediction models based on GNNs or GRU on a real-world cellular network dataset in short-term, mid-term, and long-term prediction scenarios.

5G/5G+ network management employing AI-based continuous deployment

Purpose:The paper proposes a novel concept of deployment process dedicated to continuous development, integration, testing, and upgrading the software-defined AI/ML-based functionalities in 5G/5G+ cellular networks, reliable and minimally invasive for network infrastructure, users, and service quality, automating DevOps and performance management towards the zero-touch networks. The idea is demonstrated by the continuous deployment framework, where a virtual cluster of representative cellular network resources coexists synchronously with the operator network serving the thousands-to-millions of end-users. Performance KPIs measured in this twinning subnetwork is the time series exhibiting the complex interrelations governing a temporal state of the whole system. Methods:One of the crucial problems is the detection when the changes of configuration of individual base station impacted its performance, which was realized by change points identification using an enhanced-PELT (e-PELT) algorithm. This yielded a sequence of complex data patterns (between successive change points) into sections of unique waveforms corresponding to stable network conditions. The e-PELT scheme includes a peak removal and an ACPO module for automatic penalty search. Results:e-PELT algorithm, validated against experimental data in comparative studies, outperforms the AMOC, Binary Segmentation, and BOCPD schemes, increasing the F1-score by more than 12% and the Accuracy by 10% over the PELT baseline. Conclusion:The results obtained in a CD cluster with fixed and automated penalty search show that e-PELT provides outstanding results, proving that the proposed approach can be successfully applied to analyze data sequences from different 5G network configurations. The concept of AI/ML-featured Continuous Deployment Framework dedicated to cellular networks can impact the development and integration of new telecommunication features, including possible extension for the role of standardized NWDAF module in customer networks, enhanced with the application of methodology outlined in the paper.

Enhanced Resource Allocation in D2D Communications With NOMA and Unlicensed Spectrum

Device-to-device (D2D) communications with non- orthogonal multiple access (NOMA) have been proved in enhancing the network capacity and reducing the burden of the traditional cellular network. Due to reusing the same resources, the underlying D2D communication may cause high interference to cellular users, and thus reducing the network throughput. To alleviate this issue, we consider moving D2D users to the unlicensed band. In this case, D2D users can reuse the resources in cellular networks or share the unlicensed band with WiFi users. However, a critical question is how to effectively perform NOMA user clustering and determine D2D pairs that should use unlicensed band and how to guarantee the quality service of users at both networks. Our objective is to maximize the network throughput by investigating a joint resource allocation problem of user clustering, power control, and D2D mode selection. Since the formulated problem is a mixed-integer nonlinear programming problem, finding an optimal solution is a great challenge. To solve this problem, we propose to use a swarm intelligence approach, namely whale optimization algorithm in solving our optimization problem with an efficient solution. Through simulation results, we show that our proposed scheme attains a better performance than other alternative schemes.

Dynamic Channel Allocation Algorithm for Heterogeneous Cellular Networks in Power Communication

In order to solve the problems of low efficiency and high noise in the application of modern channel resource allocation methods, an anti-interference dynamic channel allocation algorithm for heterogeneous cellular networks in power communication is proposed. First, the heterogeneous cellular network model of power communication and the architecture of power multi-channel transmission platform are constructed. Then, according to the receiving and noise characteristics of communication resources, the anti-interference algorithm of power communication resources under multi-channel transmission is designed to obtain the communication index of power communication heterogeneous cellular network, and the anti-interference dynamic allocation algorithm of power communication heterogeneous cellular network resources is realized, Finally, the comparison method is used to prove the practicability of the method in this paper. The experimental results show that the average value of communication resources after interference suppression is 950 Mb, the average signal to noise ratio is 10.5 dB, and the resource allocation time is less than 3 min after 5000 iterations, which is superior to the comparison method. The method has good channel anti-interference ability and channel resource allocation efficiency, and has certain application value.

Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks

The motivations for deploying energy and spectral-efficient network architectures are the high energy consumption and the need for more spectral resources in modern cellular networks. The key method to solve the energy efficiency EE maximization problem of the downlink non-orthogonal multiple access (NOMA)-based massive MIMO system is to decouple it into user pairing and efficient power allocation problems. This work studies the performance of three main pairing methods in NOMA-based networks: Hungarian, Gale–Shapley, and correlation-based approaches. Firstly, we provide a mathematical analysis for EE of downlink NOMA in a massive MIMO system for the non-line of sight (NLoS) channel model with perfect successive interference cancellation (SIC). Finally, the sequential convex programming (SCP) approach is used to tackle the power allocation problem. Simulation results show that the Hungarian algorithm for pairing plus SCP for power allocation (Hungarian algorithm-SCP) achieves the highest energy efficiency among all the three pairing algorithms with an identical performance to joint user and resource block association with power allocation (joint user-RB PA) algorithm but with much lower computational complexity and outperforms the NOMA SCP greedy algorithm (NOMA-SCP-GA).

A comprehensive study of handover mechanism with minimal resources in 5G cellular networks: architecture and challenges

A comprehensive study of handover mechanism with minimal resources in 5G cellular networks: architecture and challenges

Hungarian Mechanism based Sectored FFR for Irregular Geometry Multicellular Networks

The growing demands for mobile broadband application services along with the scarcity of the spectrum have triggered the dense utilization of frequency resources in cellular networks. The capacity demands are coped accordingly, however at the detriment of added inter-cell interference (ICI). Fractional Frequency Reuse (FFR) is an effective ICI mitigation approach when adopted in realistic irregular geometry cellular networks. However, in the literature optimized spectrum resources for the individual users are not considered. In this paper Hungarian Mechanism based Sectored Fractional Frequency Reuse (HMS-FFR) scheme is proposed, where the sub-carriers present in the dynamically partitioned spectrum are optimally allocated to each user. Simulation results revealed that the proposed HMS-FFR scheme enhances the system performance in terms of achievable throughput, average sum rate, and achievable throughput with respect to load while considering full traffic.

Secure transmission of W-band millimeter-wave based on CNN and dynamic resource allocation.

A secure transmission scheme of a W-band millimeter-wave radio-over-fiber (RoF) system based on cellular neural network (CNN) and dynamic resource allocation is proposed, in which 7-D CNN chaotic system is composed to heighten the anti-deciphering ability of a secure system, and the spectrum resources are optimized by dynamic allocation. The analyses show that the key space of our scheme can reach approximately 10112. The encrypted W-band millimeter-wave signal is successfully transmitted in the filter-OFDM RoF system of 50-km SSMF and 5 m wireless channel, and the experimental results illustrate that our proposed scheme can enhance the bit error ratio performance by approximately 0.3 dB compared with the traditional one.

Resource Allocation and Optimization in Device-to-Device Communication 5G Networks

The next-generation wireless networks are expected to provide higher capacity, system throughput with improved energy efficiency. One of the key technologies, to meet the demand for high-rate transmission, is device-to-device (D2D) communication which allows users who are close to communicating directly instead of transiting through base stations, and D2D communication users to share the cellular user chain under the control of the cellular network. As a new generation of cellular network technology, D2D communication technology has the advantages of improving spectrum resource utilization and improving system throughput and has become one of the key technologies that have been widely concerned in the industry. However, due to the sharing of cellular network resources, D2D communication causes severe interference to existing cellular systems. One of the most important factors in D2D communication is the spectrum resources utilization and energy consumption which needs considerable attention from research scholars. To address these issues, this paper proposes an efficient algorithm based on the idea of particle swarm optimization. The main idea is to maximize the energy efficiency based on the overall link optimization of D2D user pairs by generating an allocation matrix of spectrum and power. The D2D users are enabled to reuse multiple cellular user’s resources by enhancing their total energy efficiency based on the quality of service constraints and the modification of location and speed in particle swarm. Such constraint also provides feasibility to solve the original fractional programming problem. Simulation results indicate that the proposed scheme effectively improved the energy efficiency and spectrum utilization as compared with other competing alternatives.

A Multi-Dimensional Intelligent Multiple Access Technique for 5G Beyond and 6G Wireless Networks

The ever-growing wireless applications and their diverse Quality of Service (QoS) requirements bring the challenge of tailored QoS provisioning with limited radio resources in future cellular networks. While resource constraint is ubiquitous, different communication equipment in cellular networks could experience very different constraints in the multi-dimensional resource domains. To achieve stringent yet diverse QoS with limited resources, a novel multi-dimensional intelligent multiple access (MD-IMA) scheme is proposed in this paper to exploit disparate resource constraints among heterogeneous equipment for 5G beyond and 6G networks. With the assist of real-time data analysis, real-time QoS requirements, and resource availability of the related equipment are first determined in the proposed MD-IMA. Based on this, multiple access (MA) scheme is then intelligently adapted accordingly for each equipment in multi-dimensional resource domain to maximize the overall system requirement with operational constraints. The resource allocation in the MD-IMA system is further formulated as an optimization problem. To solve this non-convexity optimization of high computational complexity, the overall optimization is divided into several sub-problems and a joint optimization algorithm is adopted. Simulation results demonstrate the system energy efficiency performance gain of proposed MD-IMA over traditional MA is around 15% - 18%.

A Palm-Jacobaeus Loss Formula for Multi-Service Systems with Separated Resources

This article presents a method to determine the blocking probability (non-availability) for strictly determined multi-service resources that belong to a group of multi-service resources. The dependencies obtained during the process correspond to the Palm-Jacobaeus formula derived for the group of resources servicing single-service traffic. The approach to determining the blocking probability is based on the determination of the availability of resources at the occupancy level of allocation units. The analytical results are compared with the obtained results of the simulation experiments for a number of selected parameters of multi-service groups of resources. The results of the present study indicate a high accuracy of the proposed solutions. The elaborated extension of the Palm-Jacobaeus formula can be used in the modeling of separated wireless resources in cellular networks.

Application of D2D Communication System Based on Android and JXTA on the Internet of Vehicles

This paper introduces the development prospect of the Internet of vehicles. By combining D2D technology with cellular networks, the problem of the increasingly scarce spectrum resources of traditional cellular networks is solved. D2D Communication System based on Android and JXTA is realized by designing network topology and network settings, peer discovery and instant messaging modules and applied on the Internet of Vehicles to achieve an intelligent transportation system with high security and reliability.

Spectrum allocation by wave based adaptive differential evolution algorithm

Resource allocation is very important in mobile communication. This paper studies the assignment of spectrum in cellular networks operated on orthogonal frequency division multiple access (OFDMA) system. Both network utility and fairness among all linked users are taken to measure quality of service in cellular networks. The problem is expressed as a maximization optimization model. Wave based adaptive differential evolution (WADE) algorithm is proposed to for spectrum allocation. The WADE algorithm adapts algorithmic parameters in wave propagation manner to accelerate convergence process of the algorithm. Simulation results show that WADE is more efficient than other compared algorithms. Moreover, it is effective to allocate spectrum resources in OFDMA-based cellular networks.

Boosting Vehicle-to-Cloud Communication by Machine Learning-Enabled Context Prediction

The exploitation of vehicles as mobile sensors acts as a catalyst for novel crowdsensing-based applications such as intelligent traffic control and distributed weather forecast. However, the massive increases in Machine-type Communication (MTC) highly stress the capacities of the network infrastructure. With the system-immanent limitation of resources in cellular networks and the resource competition between human cell users and MTC, more resource-efficient channel access methods are required in order to improve the coexistence of the different communicating entities. In this paper, we present a machine learning-enabled transmission scheme for client-side opportunistic data transmission. By considering the measured channel state as well as the predicted future channel behavior, delay-tolerant MTC is performed with respect to the anticipated resource-efficiency. The proposed mechanism is evaluated in comprehensive field evaluations in public Long Term Evolution (LTE) networks, where it is able to increase the mean data rate by 194% while simultaneously reducing the average power consumption by up to 54%.

A Two-Stage Energy-Efficient Approach for Joint Power Control and Channel Allocation in D2D Communication

A large number of mobile multimedia terminals are prominent features of smart cities. Device-to-device (D2D) communication takes advantage of the limited bandwidth resources of cellular networks to accommodate more mobile devices. However, when D2D pairs reuse cellular users channels, serious interference leads to energy consumption, which dissatisfies the requirements of green communication. This paper focuses on energy efficiency maximization of D2D communication under the constraints of both D2D pairs and cellular users quality of service. The formulated resource allocation problem is NP-hard, which is usually difficult to solve within polynomial time. To make the problem easy to handle, we divide it into power control and channel allocation sub-problems. In particular, we propose a power control algorithm based on the Lambert W function to maximize the energy efficiency of a single D2D pair. The preference values of D2D pairs and cellular users are calculated using the power control results, respectively. A channel allocation scheme based on the Gale-Shapley algorithm utilizes preference values to match two sides, which aims at maximizing the signal to interference plus noise ratio of cellular users and the energy efficiency of D2D pairs. The simulation results show that the proposed algorithm could not only guarantee the transmission rate of cellular users but also improve the system and D2D pairs energy efficiency.

Non-Orthogonal Resource Sharing Optimization for D2D Communication in LTE-A Cellular Networks: A Fractional Frequency Reuse-Based Approach

To handle the fast-growing demand for high data rate applications, the capacity of cellular networks should be reinforced. However, the available radio resources in cellular networks are scarce, and their formulation is expensive. The state-of-the art solution to this problem is a new local networking technology known as the device-to-device (D2D) communication. D2D communications have great capability in achieving outstanding performance by reusing the existing uplink cellular channel resources. In D2D communication, two devices in close proximity can communicate directly without traversing data traffic through the evolved-NodeB (eNB). This results in a reduced traffic load to the eNB, reduced end-to-end delay, and improved spectral efficiency and system performance. However, enabling D2D communication in an LTE-Advanced (LTE-A) cellular network causes severe interference to traditional cellular users and D2D pairs. To maintain the quality of service (QoS) of the cellular users and D2D pairs and reduce the interference, we propose a distance-based resource allocation and power control scheme using fractional frequency reuse (FFR) technique. We calculate the system outage probability, total throughput and spectrum efficiency for both cellular users and D2D pairs in terms of their signal-to-interference-plus-noise ratio (SINR). Our simulation results show that the proposed scheme reduces interference significantly and improves system performance compared to the random resource allocation (RRA) and resource allocation (RA) without sectorization scheme.

Study on D2D Relay Transmission Algorithm in Cellular Networks

The introduction of Device-to-Device (D2D) communication technology can effectively improve the utilization ratio of spectrum resources of cellular networks. However, when D2D communication multiplexes cellular spectrum resources, the cellular signal and D2D signal would not be obtained by cellular user or D2D receiver due to the interference between cellular signal and D2D signal. To solve this problem, a D2D relay transmission algorithm based on complex field network coding (CFNC) is proposed in this paper. Specifically, as CFNC is adopted in the relay node to encode the received cellular signal and D2D signal, cellular signal and D2D signal are recovered respectively by the D2D receiver and the cellular user with maximum likelihood detection. Performance analyses and simulation results show that, compared with the traditional D2D transmission algorithm, the proposed algorithm can significantly improve the system achievable rate, and moreover reduce the outage probability.