Next-generation Cellular Networks Research Articles

Spectrum occupancy prediction using LSTM models for cognitive radio applications

ABSTRACT In recent days, mobile traffic prediction has become a prominent solution for spectrum management-related operations for the next-generation cellular networks in Cognitive Radio (CR) applications. To achieve this, the binary dataset has been created from the captured data by monitoring the spectrum activities of nine different Long Term Evolution (LTE) frequency channels. We propose a Long Short Term Memory (LSTM) based Spectrum Occupancy Prediction (SOP) approach for modelling infrastructure-based cellular traffic systems. The different types of LSTM models, such as Convolutional, Convolutional Neural Network (CNN), Stacked, and Bidirectional have been generated via offline training and tested for the created binary datasets. Moreover, the prediction performance evaluation of the generated LSTM models has been calculated using Mean Absolute Error (MAE). The pro- posed LSTM-based SOP model has achieved 2.5% higher prediction accuracy than the Auto-Regressive Integrated Moving Average (ARIMA) statistical model, accurately aligning the traffic trend with the actual samples.

A Comprehensive Survey on Machine Learning Methods for Handover Optimization in 5G Networks

One of the key features of mobile networks in this age of mobile communication is seamless communication. Handover (HO) is a critical component of next-generation (NG) cellular communication networks, which requires careful management since it poses several risks to quality-of-service (QoS), including a decrease in average throughput and service disruptions. Due to the dramatic rise in base stations (BSs) and connections per unit area brought about by new fifth-generation (5G) network enablers, such as Internet of things (IoT), network densification, and mm-wave communications, HO management has become more challenging. The degree of difficulty is increased in light of the strict criteria that were recently published in the specifications of 5G networks. In order to address these issues more successfully and efficiently, this study has explored and examined intelligent HO optimization strategies using machine learning models. Furthermore, the significant goal of this review is to present the state of cellular networks as they are now, as well as to talk about mobility and home office administration in 5G alongside the overall features of 5G networks. This work presents an overview of machine learning methods in handover optimization and of the various data availability for evaluations. In the final section, the challenges and future research directions are also detailed.

DHT-based GFDM and STC-GFDM systems for beyond 5G wireless networks

This manuscript endeavours an exploration of Generalized Frequency Division Multiplexing (GFDM) as a promising alternative to the Orthogonal Frequency Division Multiplexing (OFDM) in next-generation cellular networks. GFDM’s exceptional performance in minimizing out-of-band emissions (OOBE) and maximizing spectral efficiency (SE) makes it a compelling candidate, especially when integrated with Multi-Input Multi-Output (MIMO) technology to enhance reliability through spatial diversity. However, ensuring GFDM’s resilience against frequency-selective channels (FSCs), especially in low-latency scenarios, is crucial.This research investigates Discrete Hartley Transform (DHT) based GFDM and Space-Time Coded GFDM (STC-GFDM) systems, proposing a comprehensive framework to enhance performance metrics such as Bit Error Rate (BER) and Achievable Rate (AR). Further enhancements are achieved through a Minimum BER-Power Allocation (MBER-PA) strategy, optimizing BER and AR in QAM-based GFDM and STC-GFDM systems employing a DHT precoding scheme. Analytical expressions for BER and AR are derived and validated via Monte Carlo simulations. Additionally, a comparative analysis against traditional GFDM systems is conducted, evaluating metrics including Peak-to-Average Power Ratio (PAPR), BER, and AR.

User plane acceleration service for next-generation cellular networks

User plane acceleration service for next-generation cellular networks

Artificial Intelligence Assisted Enhanced Energy Efficient Model for Device-to-Device Communication in 5G Networks

Device-to-device (D2D) communications promise spectral and energy efficiency, total system capacity, and excellent data rates. These improvements in network performance led to much D2D research, but it revealed significant difficulties before their full potential could be realized in 5G networks. D2D communication in 5G networks can bring about performance gains regarding spectral and energy efficiency, total system capacity, and data rate. The major challenge in the 5G network is to meet latency, bandwidth, and traffic density requirements. In addition, the next generation of cellular networks must have increased throughput, decreased power consumption, and guaranteed Quality of Service. This potential, however, is associated with substantial difficulties. To address these challenges and improve the system capabilities of D2D networks, a deep learning-based Improved D2D communication (DLID2DC) model has been proposed. The proposed model is explicitly intended for 5G networks, using the exterior public cloud to replace automation with an explainable artificial intelligence (XAI) method to analyze communication needs. The communicated needs allow a selection of methodologies to transfer machine data from the remote server to the smart devices. The model utilizes deep learning algorithms for resource allocation in D2D communication to maximize the utilization of available spectrum resources. Experimental tests prove that the DLID2DC model brings about better throughput, lower end-to-end delay, better fairness, and improved energy efficiency than traditional methods.

Environment-Aware Codebook for Reconfigurable Intelligent Surface-Aided MISO Communications

Reconfigurable intelligent surface (RIS) is a revolutionary technology that can customize the wireless channel and improve the energy efficiency of next-generation cellular networks. This letter proposes an environment-aware codebook design by employing the statistical channel state information (CSI) for RIS-assisted multiple-input single-output (MISO) systems. Specifically, first of all, we generate multiple virtual channels offline by utilizing the location information and design an environment-aware reflection coefficient codebook. Thus, we only need to estimate the composite channel and optimize the active transmit beamforming for each reflection coefficient in the pre-designed codebook, while simplifying the reflection optimization substantially. Moreover, we analyze the theoretical performance of the proposed scheme. Finally, numerical results verify the performance benefits of the proposed scheme over the cascaded channel estimation and passive beamforming as well as the existing codebook scheme in the face of channel estimation errors, albeit its significantly reduced overhead and complexity.

Orchestrating Energy-Efficient vRANs: Bayesian Learning and Experimental Results

Virtualized base stations (vBS) can be implemented in diverse commodity platforms and are expected to bring unprecedented operational flexibility and cost efficiency to the next generation of cellular networks. However, their widespread adoption is hampered by their complex configuration options that affect in a non-traditional fashion both their performance and their power consumption requirements. Following an in-depth experimental analysis in a bespoke testbed, we characterize the vBS power cost profile and reveal previously unknown couplings between their various control knobs. Motivated by these findings, we develop a Bayesian learning framework for the orchestration of vBSs and design two novel algorithms: (i) BP-vRAN, which employs online learning to balance the vBS performance and energy consumption, and (ii) SBP-vRAN, which augments our optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient, i.e., converge an order of magnitude faster than state-of-the-art Deep Reinforcement Learning methods, and achieve optimal performance. We demonstrate the efficacy of these solutions in an experimental prototype using real traffic traces.

Semi-Integrated-Sensing-and-Communication (Semi-ISaC): From OMA to NOMA

The new concept of semi-integrated-sensing-and-communication (Semi-ISaC) is proposed for next-generation cellular networks. Compared to the state-of-the-art, where the total bandwidth is used for integrated sensing and communication (ISaC), the proposed Semi-ISaC framework provides more freedom as it allows that a portion of the bandwidth is exclusively used for either wireless communication or radar detection, while the rest is for ISaC transmission. To enhance the bandwidth efficiency (BE), we investigate the evolution of Semi-ISaC networks from orthogonal multiple access (OMA) to non-orthogonal multiple access (NOMA). First, we evaluate the performance of an OMA-based Semi-ISaC network. As for the communication signals, we investigate both the outage probability (OP) and the ergodic rate. As for the radar echoes, we characterize the ergodic radar estimation information rate (REIR). Then, we investigate the performance of a NOMA-based Semi-ISaC network, including the OP and the ergodic rate for communication signals and the ergodic REIR for radar echoes. The diversity gains of OP and the high signal-to-noise ratio (SNR) slopes of the ergodic REIR are also evaluated as insights. The analytical results indicate that: 1) Under a two-user NOMA-based Semi-ISaC scenario, the diversity order of the near-user is equal to the coefficient of the Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> fading channels ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> ), while that of the far-user is zero; and 2) The high-SNR slope for the ergodic REIR is based on the ratio of the radar signal’s duty cycle to the pulse duration. Our simulation results show that: 1) Semi-ISaC has better channel capacity than the conventional ISaC; and 2) The NOMA-based Semi-ISaC has better channel capacity than the OMA-based Semi-ISaC.

Performance Analysis of GFDM System in 5G Cellular Networks

Orthogonal Frequency Division Multiplexing (OFDM) is employed in the current 4G cellular technologies. Because of its resistance to multipath propagation channels, OFDM is a popular multicarrier modulation technique. Although OFDM is one of the effective approaches for a 4G network, out-of-band radiation, inter-symbol interference (ISI), and inter-carrier interference make this method unsuitable for 5G. (ICI). A new approach or next-generation cellular network, i.e., very high data rate, low latency, greater throughput, more device connectivity, and improved device-to-device communication, should be used to deliver effective and dependable communication. The system introduces generalized frequency division multiplexing (GFDM). In GFDM, a single cyclic prefix is utilized throughout the frame and an adjustable pulse-shaping filter can be used to monitor the transmitted signal’s out-of-band emission. Using this ISI, the multipath channel can be controlled. In the proposed system, with large signal constellations, higher-order modulation schemes, and advanced receiver structure, a very high data rate can be achieved. By using various iterative receiver structures the performance of the GFDM system in 5G cellular networks can be analyzed.

Thermal radiation mode: A deployment perspective for 5G New Radio

The 5G New Radio (NR) technology is under a standardization process by the 3rd Generation Partnership Project to provide an outline for a new radio interface for the next generation of cellular networks. The aims of 5G networks include not only providing greater capacity and coverage but also supporting advanced services, such as enhanced mobile broadband, ultrareliable low-latency communication (URLLC), and massive machine type communication.

Predictive Quality of Service in Cellular Networks: Challenges, Framework, and Application in Vehicular Communications

Predictive quality of service will allow next-generation cellular networks to improve the management of the existing services and support more demanding use cases. In this article, we discuss the main challenges on the way to practical realizations of predictive quality of service, from fundamental considerations related to prediction horizons and handling uncertainty to practical aspects on data collection and implications of the architectural choice. We also present a general methodology for predictive quality of service that addresses some of the main challenges and may be used to realize predictive quality of service at scale. As a case study, we apply the framework to a vehicular communications setting using a data-set acquired from a deployed private LTE network.

Toward Enabling Performance-Guaranteed Networking in Next-Generation Cellular Networks

A blueprint for ultra-low latency in 5G cellular networks is designed to enable ultra-reliable low-latency communication services that require fast delivery of small data units (e.g., packets or frames). However, futuristic applications that are envisioned to be time-critical demand much more than what this blueprint can handle because their data units are typically very large. As the data size increases, the latency is greatly affected by how much data the network can transmit per unit time (i.e., bandwidth). This impact of bandwidth on latency brings the need to guarantee the bandwidth required to keep the latency within the desired time. In 5G, network slicing is introduced to guarantee performance, but how to handle the inherent nature of changing data unit size and radio channel quality over time remains an open question. In this article, for a detailed understanding, we first discuss end-to-end latency at the application level from the perspective of first-byte delay and transmission delay for the remaining bytes. We then investigate how recent techniques relate to end-to-end latency reduction, and present challenging issues caused by their inherent natures. To handle the issues, we propose a new design for next-generation (6G) cellular networks to provide performance guarantees, and discuss open issues in our network design.

Reconfigurable Logic Design of CORDIC Based FFT Architecture for 5G Communications

There are numerous goals in next-generation cellular networks (5G), which is expected to be available soon. They want to increase data rates, reduce end-to-end latencies, and improve end-user service quality. Modern networks need to change because there has been a significant rise in the number of base stations required to meet these needs and put the operators’ low-cost constraints to the test. Because it can withstand interference from other wireless networks, and Adaptive Complex Multicarrier Modulation (ACMM) system is being looked at as a possible choice for the 5th Generation (5G) of wireless networks. Many arithmetic units need to be used on the hardware side of multicarrier systems to do the pulse-shaping filters and inverse FFT. The main goal of this study is to adapt complex multicarrier modulation (ACMM) for baseband transmission with low complexity and the ability to change it. We found that this is the first reconfigurable architecture that lets you choose how many subcarriers a subband has while still having the same amount of hardware resources as before. Also, under the new design with a single selection line, it selects from a set of filters. The baseband modulating signal is evaluated and tested using a Field-Programmable Gate Array (FPGA) device. This device is available from a commercial source. New technology outperforms current technology in terms of computational complexity, simple design, and ease of implementation. Additionally, it has a higher power spectrum density, spectral efficiency, a lower bit error rate, and a higher peak to average power ratio than existing technology.

Edge Computing Platform with Efficient Migration Scheme for 5G/6G Networks

Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability, availability, and ultra-low latency. The requirements of such networks are the main challenges that can be handled using a range of recent technologies, including multi-access edge computing (MEC), artificial intelligence (AI), millimeter-wave communications (mmWave), and software-defined networking. Many aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service (QoS). This article considers developing a complex MEC structure for fifth and sixth-generation (5G/6G) cellular networks. Furthermore, we propose a seamless migration technique for complex edge computing structures. The developed migration scheme enables services to adapt to the required load on the radio channels. The proposed algorithm is analyzed for various use cases, and a test bench has been developed to emulate the operator’s infrastructure. The obtained results are introduced and discussed.

Resource Allocation and Mode Selection in 5G Networks Based on Energy Efficient Game Theory Approach

With the advent of next-generation cellular networks, energy efficiency is becoming increasingly important. To tackle this issue, this paper investigates energy efficiency in D2D-enabled heterogeneous cellular networks. Boosting the longterm energy efficiency of wireless 5G communication networks is being explored through mode selection and resource allocation. The study proposed a three-stage process for energy-efficient mode selection and resource allocation. The process starts with cellular users who switch to D2D emitting a beacon and cellular users within close proximity reacting to it. A proposed auction mechanism will be enacted inside the group in the second state ( in this paper, the group size will be four). Next, each cellular user was classified according to SINR values, distance, and battery life, so that they could dynamically transition between standard cellular mode and D2D mode. For stage three, direct-hop hybrid D2D communication, we developed a TAMM double auction game model that efficiently splits resources. To identify the true bidders in our game model, we compute the median and mode values of the ASK and BID values received by both seller and buyer cellular users. A simulation study shows that the proposed method is energy-efficient in a heterogeneous network enabled by D2D.

D2D-assisted user-centric adaptive video transmission in next generation cellular networks

Next-generation cellular networks need to provide seamless connectivity with higher data rates, increased capacity, and enhanced network coverage. As multimedia service demands in various heterogeneous devices grow rapidly compared to the underlying network’s capacity and bandwidth, the adaptation in multimedia streaming services is essential for providing satisfactory Quality of Experience (QoE). This paper develops a Device-to-Device (D2D)-assisted Utility-based Adaptive Multimedia (video) Streaming scheme (UAMS) using D2D communication in a 5th Generation (5G) cellular network where low-battery users may extend their streaming duration by spending lower reception energy with the help of D2D-assisted communication. The adaptation algorithm considers four utility functions: quality, power consumption, packet error ratio, and remaining battery of the user devices to adapt the bitrate dynamically and augment viewers’ experience. We formulate an optimization problem to maximize the joint utility function to provide the best adaptive multimedia content selected for transmission to the end-users either directly or via D2D Relay Nodes (DRNs) in every scheduling interval. We use a graph theoretic approach for choosing the best DRNs. Extensive simulations show the efficacy of the proposed scheme in terms of saved battery energy, churn rate, and QoE metrics compared to a few well-known existing schemes in the literature that do not use D2D communication.

Anomaly detection in multi-tiered cellular networks using LSTM and 1D CNN

Self-organizing networks (SONs) are considered as one of the key features for automation of network management in new generation of mobile communications. The upcoming fifth-generation mobile networks and beyond are likely to offer new advancements for SON solutions. In SON concept, self-healing is a prominent task which comes along with cell outage detection and cell outage compensation. Next-generation cellular networks are supposed to have ultra-dense deployments which make cell outage detection critical and harder for network maintenance. Therefore, by imitating the ultra-dense multi-tiered scenarios, this study scrutinizes femtocell outage detection with the help of long short-term memory and one-dimensional convolutional neural networks by using time sequences of key performance indicator parameters generated in user equipment. In both the proposed schemes, probable outage-related anomalies in femto access points (FAP) are detected and classified within predetermined time sequence intervals. Moreover, aggregation decision methods are also incorporated into the proposed framework for boosting cell outage detection procedure on FAP level. Our findings show that proposed deep learning approaches outperform existing feed-forward neural networks, and on the average, in more than 80% of the cases the outage states of the femtocells are correctly predicted among healthy and three anomalous states.

Genetic Approach for Joint Transmission Grouping in Next-Generation Cellular Networks.

Coordinated multipoint joint transmission (JT) is one of the critical downlink transmission technologies to improve network throughput. However, multiple cells in a JT group should have the same user data to transmit simultaneously, resulting in a considerable backhaul burden. Even when cells are already equipped with caches in fifth-generation networks, JT groups, without effectively utilizing the caching data, still cause unnecessary backhaul data traffic. In this article, we investigate the JT grouping problem with the consideration of caches at cells. Then, we propose a genetic approach to solve the above problem with the objective of minimizing the amount of backhaul data traffic subject to the data-rate requirement of each user. The simulation results show that our proposed generic algorithm can significantly decrease the backhaul bandwidth consumption compared to the two baselines.

Novel Channel/QoS Aware Downlink Scheduler for Next-Generation Cellular Networks

Downlink schedulers play a vital part in the current and next-generation wireless networks. The next generation downlink scheduler should satisfy the demand for different requirements, such as dealing with ultra-dense networks and the need to run real-time (RT) and non-real-time (nRT) applications, with a high quality of service (QoS). Many researchers have developed various schedulers for these, but none have introduced one scheduler to target them all. This paper introduces a novel channel/QoS aware downlink scheduler algorithm, called Advanced Fair Throughput Optimized Scheduler (AFTOS), for ultra-dense networks. AFTOS is a multi-QoS scheduler that aims to maximize system spectrum efficiency and user throughput with enhanced fairness, delay, and packet loss ratio (PLR). It is capable of handling RT and nRT traffic. We developed two new policies, called Adjusted Largest Weighted Delay First (ALWDF) and Fair Throughput Optimized Scheduler (FTOS), for RT and nRT traffic. Then, we joint them to introduce our novel downlink scheduler Advanced Fair Throughput Optimized Scheduler (AFTOS). For evaluating the suggested algorithm, we undertook experiments to decide the ideal parameter value for the proposed approaches and compared the proposed solution to current best practices. The findings prove that the AFTOS algorithm can achieve its objectives, outperforming the alternative techniques.

A Game Theoretic Analysis for Power Management and Cost Optimization of Green Base Stations in 5G and Beyond Communication Networks

Due to the exponential increase in the number of users, the next-generation cellular networks are resource-constrained in power and bandwidth. Power consumption is one of the critical consideration for the next-generation wireless networks, therefore, management of available resources is essential to achieve power efficiency. With the growing incentive to ‘go green’ and to reduce the carbon footprint, the fifth generation (5G) and beyond wireless networks will derive power from renewable sources to solve the energy efficiency problems. This work focuses on integrated regulation of the traditional, i.e., the grid-based and the renewable, i.e., the solar-based power supplies for the 5G and beyond 5G green base stations (BSs) in a smart city scenario. We propose a pricing model for suppliers to charge the BSs for electricity consumption when the renewable power supply cannot meet their total energy requirements. We propose a game-theoretic analysis for cost optimization by proposing two games, i.e., the power control game and the best supplier game. Each BS acts as a game player and has some actions like power reduction and supplier selection to reduce the total energy costs. We also provide the game transition profiles for the BSs. Furthermore, the Nash Equilibrium’s existence is verified for each of these games and an optimal cost solution is proposed for the green BSs.