Ultra-dense Networks Research Articles

Performance of the Typical User in RIS-Assisted Indoor Ultra Dense Networks

Performance of the Typical User in RIS-Assisted Indoor Ultra Dense Networks

Interference Management Based on Clustering Method for Ultra-Dense Networks in Multicellular Network

One of solutions to address the increasing demands for data traffic by mobile users is to implement ultra dense network (UDN). UDN consists of many femtocells that are densely deployed on macrocellular communication networks. Since femtocells radius is very short and they are tightly packed, it faces interferences problem i.e., co-tier and cross-tier interferences. This paper proposes interference management technique based on clustering method for UDN multicellular communication networks at downlink transmission. It is purposed to reduce interference effects. To evaluate the proposed clustering method, two simulation scenarios have been designed; namely baseline system and system with the proposed clustering method. The scenarios applied is to randomly distribute femtocells following a uniform distribution in three macrocells areas. Through a clustering algorithm, adjacent femtocells are grouped into one cluster and assigned different frequency channels for each femtocell in that cluster. The same cluster pattern is repeated for all femtocells. Thus, interference effect is canceled within one cluster and reduced among clusters by widening the distance among femtocells that use the same frequency channels. Through simulation experiment, the proposed clustering method is evaluated and compared to the baseline system. The simulation results show that Signal to Interference plus Noise Ratio (SINR) of femtocells increases 0.44 dB, throughput increases 1.67%, Bit Error Rate (BER) reduces, and other parameters improve as well which include spectral efficiency, network energy, and average network energy. The proposed clustering method increases performances of the networks and provides better solution for transmission data speed in densely femtocell network.

Dynamic Resource Allocation Approach for Interference Mitigation based on NOMA in Network Operating in Both Uplink and Downlink

The new notion of Internet of Vehicles (IoV) supports Vehicle-to-Everything (V2X) communications, which play a very important role in 5G systems. Ultra-Dense-Network (UDN) and the new scheme Non-orthogonal multiple access (NOMA) are two important concepts to meet the requirements of V2X services with their remarkable capacity. However, the concept of NOMA technology, which allows multiple vehicles to be served by the same resource, can lead to interference between different active users. In addition, the extensive placement of small base stations (SBS) in UDN causes severe interference and degrades the network performance. To solve these problems, interference mitigation is widely used to avoid interference in NOMA-based UDN. In this document, we suggested a dynamic resource allocation method named Intra-cell-Inter-cell Interference Mitigation for V2X communication for UDN in uplink and downlink sense (Intra-Inter IMVN). Our algorithm aims to achieve five principals objectives, namely applying the same algorithm for both downlink and uplink modes, reducing the degree of interference, improving network performance in aspects like error rates and throughput, minimizing cost and delay when using this alternative, combining dynamic metrics to support many V2X scenarios and requirements, and dealing with two categories of interference such as inter-cell interference and intra-cell interference. Numerical results demonstrate that our suggested Intra-Inter IMVN approach offers higher performance compared to other existing algorithms as a function of various parameters.

Evaluation of the Influence of the Spatial Smoothing Procedure of the Correlation Matrix on the Efficiency of the 2D-MUSIC Method

The occurrence of correlations between radio signals arriving at the antenna array of the base station reduces the effectiveness of subspace methods for positioning user devices in wireless communication networks. For the purpose of radiation sources decorrelation, a spatial smoothing algorithm can be applied dividing the base station antenna array into sub-arrays and then averaging the signal correlation matrices obtained for these sub-arrays. In this paper, we investigate the impact of various ways of dividing a flat equidistant antenna array into subarrays on the performance of the 2D-MUSIC multiple signal classification method in the scenario of ultra-dense networks operating in the FR2 band.

Energy-efficient BS sleeping, user association, and resource allocation in full-duplex ultra-dense networks

Full-duplex ultra-dense network (FD-UDN) is a promising technology in 5G mobile networks for handling the increase in network capacity. However, interference management and energy efficiency are two of the most important challenges that must be addressed. With the small base stations (SBSs) densification, employing sleeping strategy coupled with resource management becomes an effective approach to manage interference and power consumption in FD-UDN. To the best of our knowledge, this aspect has not been addressed in previous work. To this end, we develop a framework to optimize the BS sleeping and resource management with the aim of maximizing energy efficiency and maintaining the quality of service (QoS) requirements of users. The problem is formulated as a non-convex mixed-integer non-linear programming problem, which is difficult to handle. Employing the Dinkelbach method, the objective function of the problem is converted to an equivalent parametric subtractive form. Then, the problem is decoupled into two sub-problems: user association and resource allocation, as well as BSs on/off switching. The former is solved using the iterative reweighted lq-norm minimization (IRM) method, and the latter is solved using the Lagrangian dual method and the constrained concave-convex procedure (CCCP). The simulation results demonstrate that the proposed method is more effective than traditional ones in simultaneously improving the EE, reducing power consumption, and keeping fewer SBSs active, especially in high network loads.

Joint tensor completion anchor selection and coordinate transformation for high precision cooperative positioning in device-to-device systems

The synergistic amalgamation of device-to-device (D2D) communication between mobile terminals (MT) and ultra-dense networks in 5G systems enables cooperative positioning. In this paper, we propose a joint tensor completion, anchor selection, and coordinate transformation (JTCASCT) for high precision cooperative positioning in D2D systems. The positioning is achieved by mitigating the challenging issue of recovering the missing values from the corrupted distance between nodes and determining the global positions of unknown stationary nodes. Initially, a hankelized distance tensor structure called Euclidean distance tensor (EDT) is constructed from incomplete EDM. The missing values are recovered as tensor completion by exploiting its latent low rank structure. The optimization problem is solved using simple low rank tensor completion (SiLRTC) and high accuracy low rank tensor completion (HaLRTC) algorithms. The relative position of nodes is determined after applying multidimensionality scaling (MDS). To reduce the data dimension for coordinate transformation and to select the optimal combination of anchor nodes, we propose a low complex Kruskal–Wallis (KW) test. Finally, the global position of unknown nodes is computed using Helmert transformation (HT). Numerical evaluations demonstrate that the cooperative positioning accuracy is significantly improved with the proposed JTCASCT approach compared to the state-of-the-art techniques.

Fairness based dynamic channel assignment for in-building ultra dense networks

Fairness based dynamic channel assignment for in-building ultra dense networks

Residual Energy-Based Computation Efficiency Maximization in Dense Edge Computing Systems

With the rapid development of 5G, artificial intelligence, the internet of things (IoT) and other technologies, the number of intelligent terminal devices is growing explosively, bringing huge challenges to the existing communication network and cloud computing service mode. The dense edge computing system (DECS), which combines mobile edge computing (MEC) with an ultra-dense network (UDN), has the potential to significantly improve low latency of communications and enhance the quality of experience (QoE) of user equipments (UEs). In this paper, to achieve energy-efficient MEC, computation efficiency (CE) is maximized by jointly optimizing computation offloading, subchannel allocation and power allocation, which yields a challenging non-convex problem. Specially, due to the heterogeneity of UE battery capacities and residual energy, the residual energy of UEs should be taken into consideration in order to achieve better QoE. Therefore, we develop a residual energy-based computation efficiency (RECE) optimization scheme to maximize CE, where the optimization problem is divided into three subproblems. Firstly, the computation offloading subproblem is addressed by a many-to-one matching strategy. Secondly, the subchannel allocation subproblem is dealt with by adopting the graph coloring algorithm. Finally, the power allocation subproblem is solved by the concave–convex procedure (CCCP) method. The numerical results illustrate that UEs’ CE can be optimized based on their residual energy in the proposed RECE scheme. Additionally, compared to a scheme without considering UE residual energy, the system CE can be much enhanced, and the UE energy consumption can be significantly reduced in the RECE scheme.

A Fairness-based Cell Selection Mechanism for Ultra-Dense Networks (UDNs)

A typical 5G Ultra-Dense Network (UDN) comprises different types of Base Stations (BSs) in its structure. Dense deployment of small-cell BSs within a macrocell BS's coverage offers significant benefits, as the distance between a User Equipment (UE) and its small-cell BS is shorter with robust signals. Thus, the network capacity will increase dramatically. However, selecting an appropriate small-cell BS for a particular UE becomes a challenge in 5G UDNs. This study proposed a mechanism to address the cell selection problem and maximize fairness among UEs when making the cell selection decision. The proposed mechanism considered different parameters. The load balance for each small-cell BS was considered to fairly distribute UEs and avoid traffic congestion. Moreover, the signal strength was considered with the achievable data rate for all small-cell BSs to stimulate idle small-cell BSs to be in operating mode. A simulation was carried out in MATLAB to evaluate the proposed mechanism. Signal-to-Interference-Ratio (SINR) and Signal Strength (SS) -based strategies were also simulated for comparison. The proposed solution outperformed the other schemes in terms of fairness, as the UEs attached to the system were fairly distributed among small-cell BSs. Furthermore, the proposed mechanism achieved the best radio resource distribution in terms of fairness compared to the two other schemes.

Ultra dense satellite-enabled 6G networks: Resource optimization and interference management

With the evolution of the sixth generation (6G) mobile communication technology, ample attention has gone to the integrated terrestrial-satellite networks. This paper notes that four typical application scenarios of integrated terrestrial-satellite networks are integrated into ultra dense satellite-enabled 6G networks architecture. Then the subchannel and power allocation schemes for the downlink of the ultra dense satellite-enabled 6G heterogeneous networks are introduced. Satellite mobile edge computing (SMEC) with edge caching in three-layer heterogeneous networks serves to reduce the link traffic of networks. Furthermore, a scheme for interference management is presented, involving quality-of-service (QoS) and co-tier/cross-tier interference constraints. The simulation results show that the proposed schemes can significantly increase the total capacity of ultra dense satellite-enabled 6G heterogeneous networks.

Optimizing Performance of Worst Case User in Ultra-dense Networks Utilizing Deep Q-learning

Abstract: This paper defines, analyzes, and improves the performance of the worst-case user in ultradense networks Background: In Ultra-Dense Networks (UDNs), where the Base Stations are distributed with a very high density, the users are possibly near the cells’ intersection. These users are called the Worst-Case Users (WCU) and usually experience very low performance. Objectives: Thus, improving the WCU performance is an urgent problem to secure the service requirement of future cellular networks. Methods: In this paper, the performance of the WCU is analyzed in UDNs with a maximum power algorithm and under the wireless environment with Stretched Path Loss model and Rayleigh fading. To improve the WCU data rate, the Deep Q Networks with and without Multi-Input-Multi-output (MIMO) are utilized in this paper. Results: The simulation results show that a system–based Deep Q Learning can dramatically improve the WCU performance compared to the system with the maximum power algorithm. In addition, the deployment of the MIMO technique in a system–based Deep Q-learning only has benefits in bad channel conditions. Conclusion: In any channel condition, utilization of Deep Q Learning is a suitable solution to improve the WCU performance. Furthermore, if the user experiences a good channel condition, the MIMO technique can be used with Deep Q Learning to obtain further performance improvement.

Analysis and optimization of validation procedure in blockchain-enhanced wireless resource sharing and transactions

To ensure the security of resource and intelligence sharing in 6G, blockchain has been widely adopted in wireless communications and applications. Although blockchain can ensure the traceability and non-tamperability of data in the concatenated blocks, it cannot guarantee the honest behaviors of users in the application before the generation of transactions. Thus, additional technologies are required to ensure that the source of blockchain data is reliable. In this paper, the detailed procedure is designed for the application-oriented task validation in the blockchain-enhanced computing resource sharing and transactions in ultra dense networks (UDN). The corresponding queuing model is built and analyzed with the consideration of the wireless re-transmission and the probability of malicious deception by users. Based on the analysis results, the UDN deployment is optimized to save network cost while ensuring latency performance. Numerical results verify our analysis, and the optimized system deployment including the number and service capacities of both base stations and mobile edge computing (MEC) servers are also given with various system settings.

Performance Analysis and Optimization of Worst Case User in CoMP Ultra Dense Networks

In the cellular system, the Worst Case User (WCU), whose distances to three nearest BSs are the similar, usually achieves the lowest performance. Improving user performance, especially the WCU, is a big problem for both network designers and operators. This paper works on the WCU in terms of coverage probability analysis by the stochastic geometry tool and data rate optimization with the transmission power constraint by the reinforcement learning technique under the Stretched Pathloss Model (SPLM). In analysis, only fast fading from the WCU to the serving Base Stations (BSs) is taken into the analysis to derive the lower bound coverage probability. Furthermore, the paper assumes that the Coordinated Multi-Point (CoMP) technique is only employed for the WCU to enhance its downlink signal and avoid the explosion of Intercell Interference (ICI). Through the analysis and simulation, the paper states that to improve the WCU performance under bad wireless environments, an increase in transmission power can be a possible solution. However, in good environments, the deployment of advanced techniques such as Joint Transmission (JT), Joint Scheduling (JS), and reinforcement learning is an suitable solution.

Coded Caching Schemes for Two-Dimensional Caching-Aided Ultra-Dense Networks

Coded Caching Schemes for Two-Dimensional Caching-Aided Ultra-Dense Networks

Frequency allocation algorithm

Wireless communication technology's quick advancement has facilitated the ongoing formation of numerous new communication business scenarios and the thorough connectivity of man-machine-object-space. The conflict between few channel resources and high demand is becoming more and more obvious, which adds to the stress on the entire network. In order to enhance communication quality with constrained frequency resources, more optimal channel allocation techniques are urgently required. This paper discusses the increasing demand for frequency resources due to the exponential growth of data traffic, which has necessitated more efficient channel allocation methods to optimize communication quality with limited frequency resources. The evolution of mobile wireless cellular communication systems from the first generation to the current fifth generation, and the imminent emergence of the sixth generation, are also discussed. The paper highlights the importance of ultra-dense networks and device-to-device communication in meeting the capacity and coverage requirements of 5G networks. The advantages and disadvantages of different frequency reuse methods, specifically Fractional Frequency Reuse (FFR) and water injection algorithm, are analyzed. The paper summarizes these methods' principles and application effects in specific situations, emphasizing the need for appropriate frequency reuse methods to achieve optimal channel allocation and communication quality.

Investigation on the Critical Densification Levels for Coupled and Decoupled User Association in Ultra-dense Networks

Investigation on the Critical Densification Levels for Coupled and Decoupled User Association in Ultra-dense Networks

Location Aware Beamforming in Millimeter-Wave Band Ultra-Dense Radio Access Networks. Part 1. Model of Two Links

The evolution of 1G to 4G radio access networks (RANs) over the past 40 years has shown that beamforming (BF) capabilities add an additional spatial dimension to traditional device multiplexing methods. When base stations (gNodeB - gNB) and user equipment (UE) form narrow antenna radiation patterns (APPs), in addition to frequency, time and code division of channels, an additional spatial dimension appears that implements spatial multiplexing. This concept has been known for quite a long time, but the full implementation of its capabilities in practice is expected with the widespread adoption of millimeter wave (mmWave) ultra-dense networks (UDN) of the fifth (5G) and subsequent (B5G) generations. To control APP, the approach of preliminary analysis of training sequences about the current situation in the radio channel - CSI (Channel State Information) - can be used, but its overhead costs become unacceptably high in conditions of ultra-dense distribution of devices. An alternative approach is positioning-based BF. The validity, relevance and prospects of this approach are determined by the fact that for 5G networks, unlike previous generations, for the first time the requirements for UE positioning accuracy up to one meter are formalized. Initial research in the field of location-aware BF has already been carried out over the past years, however, mainly for particular scenarios of one or more radio links between gNBs and fixed UEs. In this work, for the first time, a scientifically based methodology for controlling the beam pattern of a stationary gNB based on the positioning of a mobile UE for a two-radio link scenario is formalized and implemented in software. The problem of practical implementation of BF is the difficulties to predict level of interference due to the mutual influence of radio links with mobile UEs. When estimating the instantaneous signal-to-interference ratio in a two-radio link scenario between two fixed gNBs that perform BF based on the current location of mobile UEs as they move, it is necessary to take into account the mutual influence of each other's radio links on each other. In such a scenario, a transmitter on one radio link acts both as a source of a wanted signal for one UE and as a source of an interfering signal for another UE. The task of assessing interference for such a scenario is complicated by the nonlinearity of the transmitter and/or receiver ARPs. The model developed and implemented in software in this work uses the functions of the Phased Array System Toolbox Matlab extension package. The simulation results show a significant scatter (tens of dB) of the instantaneous signal-to-interference ratio depending on the territorial separation of devices and can be used to justify scenarios for the construction and operation of 5G/B5G UDN.

Performance Analysis of Cellular Ultradense IoT Networks With Wireless Backhauls

The rising era of smart living requires unprecedented advancements in cellular networks to support the communications of the Internet of Things (IoT), referred to as Machine-Type Communication (MTC). Hence, we consider an Ultra-Dense Network (UDN) environment supported by wireless backhaul links and investigate the achievable performance gains for MTC. By doing so, we avoid the complexity, cost, and/or infeasibility of providing fiber backhaul links for the massive number of Small Cells (SCs) found in UDNs. We utilize the Millimeter Wave (mmWave) band to support the communications between the IoT Devices (IoTDs) and their serving SCs. By doing so, the excess available bandwidth can be used to support a massive number of IoTDs while the propagation characteristics of the mmWave signals can be exploited to mitigate the severe Inter-Cell Interference (ICI) found in UDNs. In this regard, we formulate a mathematical framework using tools from stochastic geometry to derive analytical expressions for the density of supported IoTDs and the average capacities per SC on both the Access Link (AL) and the Backhaul Link (BH). In addition, we obtain a tight lower bound of the average capacity per SC under the considered wireless limited-capacity backhaul links. The obtained results show the existence of an optimal active SC density that maximizes SC utilization.

Load distribution of base stations in user-centric heterogeneous UDN

In ultra-dense networks (UDN), multiple association can be regarded as a user-centric pattern in which a user can be served by multiple base stations (BSs). The data rate and quality of service can be improved. However, BSs in user-centric paradigm are required to serve more users due to this multiple association scheme. The improvement of system performance may be limited by the improving load of BSs. In this letter, we develope an analytical framework for the load distribution of BSs in heterogeneous user-centric UDN. Based on open loop power control (OLPC), a user-centric scheme is considered in which the clustered serving BSs can provide given signal to interference plus noise ratio (SINR) for any typical user. As for any BS in different tiers, by leveraging stochastic geometry, we derive the Probability Mass Function (PMF) of the number of the served users, the Cumulative Distribution Function (CDF) of total power consumption, and the CDF bounds of downlink sum data rate. The accuracy of the theoretical analysis is validated by numerical simulations, and the effect the system parameters on the load of BSs is also presented.

BeSleep: Blockchain‐enabled distributed sleeping strategies of small base stations in ultra dense networks

SummarySmall cell networks can fulfill the increasing demandfor the high data rate of wireless applications. Energy efficiency is an important design parameter of the ultra dense small cell network (UDSCN). The sleeping strategy of small base stations (s‐BSs) is used to enhance the network's energy efficiency. An efficient sleeping strategy of s‐BSs is required while preserving users' quality of service (QoS). The idle s‐BSs can be switched to sleep mode. This paper proposes a blockchain‐enabled solution for the sleeping strategy of s‐BSs. Here, a blockchain‐enabled small cell network is created between the s‐BSs. The network is decentralized, which eliminates the workload of the macro base station (MBS). The proposed network architecture is enabled as a decentralized network through blockchain. The blockchain provides distributed control over the s‐BS operations through a smart contract. Here, smart contracts act as distributed self organizing network features to handle self‐transactions among small cells for switching off s‐BSs in the network. All the software logic required to perform s‐BS operations is written in a smart contract using Ethereum. The proposed solution improves energy efficiency and enables the ultra dense small cell network to be decentralized.