Analyzing mmWave Bands From a Techno-Economic Perspective in 5G Networks

Background. The introduction of fifth-generation (5G) networks creates new opportunities for fast and continuous data exchange, but there are still some problems with the quality of voice services in such networks. With the rapid development of technology and the further spread of 5G, there is a need to understand the impact of key aspects of 5G on voice quality. This requires research that can systematically analyse the features of 5G networks that affect the quality of voice services. Objective. Identification of ways to improve the quality of voice services in 5G networks. Assessment of key indicators of voice service quality in 5G networks. Determination of the best option for the gradual transition to the Standalone mode and the use of VoNR technology in the fifth generation networks. Methods. Analysis of factors affecting the quality of voice services in fifth-generation networks. Analysis of well-known publications on the implementation of 5G networks. Comparison of the implementation of Non-Standalone and Standalone modes in the 5G network. Testing of the modern EVS codec, which provides an opportunity to improve the customer experience. Results. Confirmation that 5G networks can significantly improve the quality of voice services compared to previous mobile communication technologies such as 4G and 3G. Certain factors that may affect the quality of voice services and require additional attention when planning and deploying 5G networks are identified. The optimal steps for the transition to the Standalone mode and the use of VoNR technology in fifth-generation networks are determined. The main differences in the QoS architecture between LTE and 5G are identified, and the purpose of DRB flows for separating traffic types and services is established. Conclusions. It has been confirmed that 5G networks can significantly improve the quality of voice services compared to previous technologies such as 4G and 3G. This is possible due to the broadband capabilities of 5G networks, improved data transmission, low latency, the use of an advanced EVS codec and reduced response time. However, certain factors, such as network coverage, optimisation level and traffic characteristics, can affect the quality of voice services and require additional attention when planning and deploying 5G networks. The QoS management architecture consists of QoS flows, which allow separating packet assignment to flows (managed by the CN) from the assignment of DRB flows (managed by the RAN). As 5G networks are being rolled out gradually, it is important to properly integrate the 5G domain into the existing telecoms provider's network. The transition from Non-Standalone to Dual connectivity is a necessary step for the implementation of VoNR technology in Standalone mode. Using the modern EVS codec allows not only improving the customer experience, but also introducing new voice services.

Ensuring a seamless connection with various types of mobile user equipment (UE) items is one of the more significant challenges facing different generations of wireless systems. However, enabling the high-band spectrum – such as the millimeter wave (mmWave) band – is also one of the important factors of 5G networks, as it enables them to deal with increasing demand and ensures high coverage. Therefore, the deployment of new (small) cells with a short range and operating within the mmWave band is required in order to assist the macro cells which are responsible for operating long-range radio connections. The deployment of small cells results in a new network structure, known as heterogeneous networks (HetNets). As a result, the number of passthrough cells using the handover (HO) process will be dramatically increased. Mobility management (MM) in such a massive network will become crucial, especially when it comes to mobile users traveling at very high speeds. Current MM solutions will be ineffective, as they will not be able to provide the required reliability, flexibility, and scalability.Thus, smart algorithms and techniques are required in future networks. Also, machine learning (ML) techniques are perfectly capable of supporting the latest 5G technologies that are expected to deliver high data rates to upcoming use cases and services, such as massive machine type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low latency communications (uRLLC). This paper aims to review the MM approaches used in 5G HetNets and describes the deployment of AI mechanisms and techniques in ″connected mode″ MM schemes. Furthermore, this paper addresses the related challenges and suggests potential solutions for 5G networks and beyond.

The performance of the millimeter-wave (mmWave) and Sub-6 GHz frequency bands for 5G networks are compared in this work, particularly for urban scenarios. Considering that the 5G technology is evolving, these two bands have their features providing specific opportunities and issues for populous regions. It assesses the utilization of KPIs such as data rate, coverage, latency, and penetration capacity that is significant in meeting smart city connectivity needs; mmWave bands have high data rate and low latency suitable for near real-time applications like self-driving cars and telesurgery, but its restrictive coverage and sensitivity to physical obstacles limit it when used in large areas. On the other hand, Sub-6 GHz frequencies offer wider coverage and better penetration through barriers for lower data rates. They are more suitable for general broadband internet and many other applications with acceptable delay. This paper explains using band-wise mmWave’s and Sub-6 GHz’s pros and cons in detailed graphical analysis and case studies while presenting a novel mixed model for mmWave and Sub-6 GHz bands to uncover their best attributes. These bands, when integrated, will help 5G networks meet multifaceted needs of connectivity in cities, providing high-speed connections in dense areas and sustaining steady links across vast distances. This has implications for future urban network planning; it brings into focus issues of intelligent network management and energy-efficient sustainable formations of infrastructure. The findings from this study offer meaningful recommendations for enhancing the utilization of 5G in urban settings to support smart city projects and future smart economy development.

In today's world, the significance of reducing energy consumption globally is increasing, making it imperative to prioritize energy efficiency in 5th-generation (5G) networks. However, it is crucial to ensure that these energy-saving measures do not compromise the Key Performance Indicators (KPIs), such as user experience, quality of service (QoS), or other important aspects of the network. Advanced wireless technologies have been integrated into 5G network designs at multiple network layers to address this difficulty. The integration of emerging technology trends, such as machine learning (ML), which is a subset of artificial intelligence (AI), and AI's rapid improvements have made the integration of these trends into 5G networks a significant topic of research. The primary objective of this survey is to analyze AI's integration into 5G networks for enhanced energy efficiency. By exploring this intersection between AI and 5G, we aim to identify potential strategies and techniques for optimizing energy consumption while maintaining the desired network performance and user experience.

In order to satisfy the various coverage and capacity needs of next-generation wireless networks, the sub-6 GHz and millimeter-wave (mmWave) frequency bands must be integrated. However, because of their different propagation characteristics, it is still difficult to ensure effective user association between various bands. In order to dynamically balance users across sub-6 GHz and mmWave tiers, this article suggests an adaptive user association approach based on Signal and Power Threshold Adjustments (SPTA). The suggested approach maximizes network performance and minimizes service delays by optimizing user distribution using a mathematical model that takes path loss, received signal strength (RSS), and power control into account. The path loss equation is used to model the signal quality. Higher data speeds are ensured by using a threshold-based decision strategy, in which users are assigned to the mmWave band if the received power surpasses a predetermined threshold. For broader coverage, they stay in the sub-6 GHz rung otherwise. There is an expression for the utility function that maximizes system throughput while minimizing user discontent. According to simulation data, the suggested SPTA strategy outperforms static association methods in terms of throughput by an average of 35%. Additionally, while maintaining acceptable delay levels, mmWave utilization increases by 40% in high-traffic scenarios. In order to balance network load, the adaptive thresholding system dynamically reallocates users, showing notable gains in network efficiency, user satisfaction, and resource utilization. This work highlights the potential of adaptive user association driven by signal and power thresholds to support seamless connectivity in hybrid 5G and future 6G networks.

The fifth and sixth wireless generation networks (5G & 6G) will utilize the millimeter-wave (mm-wave) band of the frequency spectrum due to the wide bandwidth availability. This will enable future networks to support more devices with high data rates. While the mm-wave band offers large bandwidths, it presents new challenges for radio frequency (RF) system design to achieve high reliability, real-time operation, robustness, low-latency, compactness, high efficiency, and low power consumption. Beamforming is among the most paramount operations in wireless communication; it enables a variety of applications such as direction-finding, imaging, and interference rejection. This thesis presents analog beamforming techniques suitable for the mm-wave band using leaky-wave antennas (LWAs) and metasurfaces. While the emphasis of the thesis is on the mm-wave band, some concepts will be more conveniently developed and applied at microwave bands, while being readily scalable to mm-wave frequencies. LWAs are proposed for mm-wave applications including direction-finding, multiplexing/demultiplexing, and pattern synthesis. Metasurface transmitters are proposed for antenna near-field engineering to achieve far-field beamforming including beam-steering, difference-pattern generation, and polarization conversion. Reconfigurable metasurface reflectors are proposed for enhanced control of magnitude and phase, which enables versatile beam transformations in reflection such as beam-steering, side-lobe level (SLL) control, and multi-beam patterns. The proposed LWAs and metasurfaces can operate separately or simultaneously to achieve beamforming in wireless environments, thereby enabling smart control of electromagnetic (EM) waves in future wireless communication systems.

As the world pushes toward the use of greener technology and minimizes energy waste, energy efficiency in the wireless network has become more critical than ever. The next-generation networks, such as 5G, are being designed to improve energy efficiency and thus constitute a critical aspect of research and network design. The 5G network is expected to deliver a wide range of services that includes enhanced mobile broadband, massive machine-type communication and ultra-reliability, and low latency. To realize such a diverse set of requirement, 5G network has evolved as a multi-layer network that uses various technological advances to offer an extensive range of wireless services. Several technologies, such as software-defined networking, network function virtualization, edge computing, cloud computing, and small cells, are being integrated into the 5G networks to fulfill the need for diverse requirements. Such a complex network design is going to result in increased power consumption; therefore, energy efficiency becomes of utmost importance. To assist in the task of achieving energy efficiency in the network machine learning technique could play a significant role and hence gained significant interest from the research community. In this paper, we review the state-of-art application of machine learning techniques in the 5G network to enable energy efficiency at the access, edge, and core network. Based on the review, we present a taxonomy of machine learning applications in 5G networks for improving energy efficiency. We discuss several issues that can be solved using machine learning regarding energy efficiency in 5G networks. Finally, we discuss various challenges that need to be addressed to realize the full potential of machine learning to improve energy efficiency in the 5G networks. The survey presents a broad range of ideas related to machine learning in 5G that addresses the issue of energy efficiency in virtualization, resource optimization, power allocation, and incorporating enabling technologies of 5G can enhance energy efficiency.

To cope with massive growth over the past decade experienced by mobile network due to the popularity of smartphones and tablets, 5G networks are anticipated to use larger amounts of electromagnetic spectrum, spanning all the way from the microwave (sub 3GHz) to the millimetre wave (mmWave) bands (3–300 GHz). However, moving to the mmWave band will increase the exposure of users to high power/frequency radiation. This paper discusses the regulatory requirements at mmWave band. In this work, the performance of Cloud-Radio Access Network (C-RAN) network with uniformly distributed Remote Radio Heads (RRHs) was investigated and compared with default network architecture. This work provides insights on how to minimise the exposure to mmWave radiation in C-RAN network using Low-Power Nodes (LPN) by adopting Distributed Base Station (DBS) architecture. DBS can use remotely located low power antennas to extend the coverage of mmWave base stations. The results show that this architecture can significantly reduce the exposure to mmWave radiation without compromising the quality of service of C-RAN network, where the data throughput improvement is shown as the performance metric.

Integrating the ultra-high capacity millimeter wave (mmWave) band into the existing cellular network is one of the main challenges towards a unified multi-Gbps 5G network. In this paper, a novel hierarchical multi-band heterogeneous network (HetNet) is proposed to efficiently overlay mmWave small cells (SCs) over the legacy LTE eNB. By which, a medium coverage band is used to precisely control the access over the small coverage mmWave band via the proposed concept of sub-clouds; while, the large coverage LTE eNB only controls the access over the distributed sub-clouds. Without loss of generality, Wi-Fi band is used as the medium coverage band in this paper. Consequently, a novel concept of 2C/U plane splitting is introduced. The C1 plane carried by the LTE eNB controls the access between the LTE eNB and the sub-clouds. Whereas, the C2 plane carried by the medium coverage (Wi-Fi) band controls the access among the mmWave SCs inside each sub-cloud. By this distributed control, associated signaling/processing load on both core network (CN) and LTE eNB can be highly relaxed. Analytical and numerical analysis confirm performance superiority of the proposed LTE/Wi-Fi/mmWave HetNet over the conventional LTE/mmWave HetNet in terms of handover (HO) signaling delays and mmWave band usability.

5G networks are the next generation of mobile internet connectivity. Mobile data traffic may reach 30 Exabyte's per month. Microwave bands may reach saturation state to deliver the increment of data rate. Millimeter(mmwave) band is a promising band between 30 to 300 GHz. The main advantages of mmwave band are its small antenna radius and high attenuation. Handover in 5G mmwave is challenging because of its short cell radius where user equipment may perform a greater number of handovers meanwhile increasing handover delay. This paper presents the work of modification of x2 handover mechanism in 5G mmwave network. It mainly deals with reducing handover delay in 5G mmwave network considering different trajectories such as Horizontal trajectory and combination of horizontal and vertical.

In order to exploit millimeter wave (mmWave) bands for 5G communications, challenges mainly due to blockage and strong dependency on beamforming operation need to be overcome. In this work, we address some of the fundamental issues related to the use of mmWave bands for 5G communications, from propagation channel characteristics and modeling to the implications on system and network architecture design. A statistical mmWave channel model considering cluster-level spatial/temporal correlation is proposed. System-level simulation results based on the proposed model demonstrate the channel characteristics and system performance. Following that, key techniques in enabling mmWave system operation are discussed. Although we believe that mmWave bands can be utilized in 5G networks, significant efforts and time are needed in order to complete the air interface, device and network design which is shown to be quite different from the existing cellular design.

Radiofrequency (RF) electromagnetic fields (EMF) radiation exposure to human, most especially the mobile device users has been a serious concern in wireless network. Therefore there is a need to address and afterwards increase the health awareness regarding the radiation exposure of wireless networks to the public. This paper investigates the effect of minimizing the exposure index (EI) and specific absorption rate (SAR) induced in fifth generation (5G) wireless networks and its impact on the quality of service (QoS) of the users in the network. With the aim of minimizing the EI on mobile user, we propose a power control algorithm that solves an optimization problem formulated to minimize the EI while guaranteeing the QoS requirement of users. Given that the radiated SAR and EI are characterized by power density in the wireless network, the proposed algorithm controls the transmit and received powers subject to interference, power and QoS constraints. Simulation results show that the proposed scheme reduces the effect of both SAR and EI on users in 5G network while satisfying the required QoS in terms of the data rate. Furthermore, the results reveal that both SAR and EI are tolerable and fall within the threshold set by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) when compared with the ICNIRP standard.

ABSTRACTMobile edge computing (MEC) facilitates storage, cloud computing, and analysis capabilities near to the users in 5G communication systems. MEC and deep learning (DL) are combined in 5G networks to enable automated network management that provides resource allocation (RA), energy efficiency (EE), and adaptive security, thereby reducing computational costs and enhancing user services. A hybrid quantum‐classical convolutional neural network (HQCCNN) with simplicial attention network (SAN) is presented in the study that allocates appropriate resources for various users in the network. First, the green anaconda optimization (GAO) algorithm is used to optimize the objective function for effective RA. Consequently, the neural network receives the optimized objective functions to allocate resources. In the study, the suggested HQCCNN‐GAO model assesses the degree of need for every user and, based on those needs, allots resources to every user in the 5G network while preserving higher throughput and EE. Throughput, latency, mean square errors, processing time, bit error rates, and EE are used to measure the proposed model's efficiency. A few of the RA models that are now in use are contrasted with the outcomes of the suggested method. From the obtained outcomes, it is noticed that the suggested model provides a low latency of 0.08 s and a high throughput of 790 kbps for a range of network users.

—In this paper, we propose a Q-Learning approach for load balancing in Software Defined Networks to reduce the number of Unsatisfied Users in a 5G network. This solution integrates Q-Learning techniques with a fairness function to improve the user experience at peak traffic conditions. With typical high rates offered by 5G and future networks single user behavior shall have a significant impact on the Quality of Service (QoS) on the rest of the users. Therefore, we are in need of responsive networks based on their utilization and on the number of users occupied. In this paper we classify users into different groups and normalize the resources to provide the best QoS. The simulation results verify the improvement in terms of the number of Unsatisfied Users and of the connections dropped. Additionally, it enhances per-flow resource allocation while avoiding over-utilization of certain network resources. In a nutshell, this proposal will serve any future network with high traffic conditions to deliver the best QoS to their end users.

Case study for 3G/UMTS services and billing methods