Ultra-dense Networks Research Articles

Deployment of Ultra Dense Network Devices in Space. Part 2. Search Using Geometric Factor

This work is a continuation of the study devoted to mathematical and simulation modeling of methods for searching for locations of gNB base station devices in space according to the criterion of the geometric factor of reducing the positioning accuracy of the user device UE. The task in this formulation is relevant and in demand for ultra-dense networks of the millimeter (30–300 GHz) and submillimeter or terahertz band (0.3–3 THz) of the fifth and subsequent generations, respectively. As a criterion for the spatial placement of the infrastructure of fixed network devices acting as a base station, access point and / or repeater / router, the geometric factor of reducing the positioning accuracy of the DOP (Dilution of Precision) of the user device is used. Based on the algorithmic, methodological and software, formalized in the previous work for searching for gNB placements in space according to the criterion of geometric factors for reducing positioning accuracy in the horizontal plane HDOP (Horizontal DOP), the vertical plane VDOP (Vertical DOP) and by location in the PDOP (Position DOP) space, this study removes the restriction on the fixed placement of UEs in space, and also provides for an increase in the number of gNBs more than five, for which placements are searched. Subject of study. The article is devoted to mathematical and simulation modeling of methods for searching for locations of base station devices in space according to the criterion of a geometric factor for reducing the positioning accuracy of a user device. Method. Iterative enumeration of all possible locations of stationary base stations and mobile user devices with a given step in space to minimize the geometric factor of reducing positioning accuracy in the selected service area. Main results. The result of this work is the formalization of algorithmic, methodological and software to obtain the final configuration of locations for a given number of stationary base stations in the selected service area. Practical significance. The results obtained make it possible to search for and justify the locations of base stations and/or routers in scenarios of ultra-dense placement of user devices in a space saturated with biomass.

Comparison of Different Frequency Channel Allocation Methods in ultra-dense networks

The impact of information transmission on production and life under dense networking is self-evident. Dense networking application scenarios include airports, dense residential buildings, dense business districts and blocks, campuses, large gatherings, stadiums, subways, etc. However, with the increase of 5G frequency band, the 5G macro base station signal attenuates more when it penetrates the wall than 4G, which makes it difficult to cover indoor signals. The outdoor 5G macro base station signal can only provide shallow indoor coverage after penetrating obstacles such as brick walls, glass and cement, which cannot guarantee the good experience required for indoor deep coverage. This paper compares and discusses various channel allocation algorithms based on user scenarios in ultra-dense networks. By adjusting some parameters of 19 units, the integer frequency reuse (IFR) method (N=1,3), fractional frequency reuse (FFR) method, simultaneous water injection (SWF) method, and transmission interference temperature limit (TX-ITL) method are compared. In order to increase the capacity, a combination of several algorithms will also be used.

3 Dimension Multilayer Heterogenous Ultra Dense Networks

The development of communication networks poses new challenges in the field of scientific research. At the same time, one of the main directions of development is the creation of highdensity and ultra-dense networks. Ultra-dense networks already belong to the technologies of communication networks of the sixth generation and the requirements for them are formed in the conditions of their deployment in three-dimensional space. Starting with the construction of fifth generation communication networks, communication networks are considered as heterogeneous, in which various technologies can be used together in the process of providing network services, for example, the Internet of Things, unmanned aerial vehicles, vehicular ad hoc networks, etc. This leads to the need to define and study three-dimensional multi-layer heterogeneous ultra-dense networks, which is the subject of this article.

Deployment of Ultra Dense Network Devices in Space. Part 1. Models and Methods of Geometric Factor

The use of the millimeter band (30 GHz–300 GHz) in fifth generation networks and the submillimeter or terahertz band (0.3–3 THz) in sixth generation networks, due to the physical characteristics of radio wave propagation, inevitably leads to a reduction in the radio communication range and the use of predominantly lineof-sight radio links. From the point of view of the network architecture, this circumstance entails an increase in the density of the spatial arrangement of devices, which, in turn, leads to the emergence of new tasks for the network organization at the physical, channel and network levels of the open systems inter-action model. One of the approaches to the spatial arrangement of the infrastructure of stationary network devices acting as a base station, access point and / or repeater / router can be the geometric factor of DOP (Dilution of Precision) in positioning accuracy known in classical radar and radio navigation. To date, from the analysis of open foreign sources, it can be argued that this approach has already become widespread in the tasks of finding and justifying the locations of base stations of fifth generation communication networks. This is due to the new role of positioning not only as a service of communication net-works, but also as an auxiliary means of solving the problems of net-work organization at the three lower levels of the data transmission medium. In connection with the ongoing convergence of infocommunication and positioning services provided by the infrastructure of communication networks of the fifth and subsequent generations, there is every reason to believe that the approach of placing network devices based on DOP deserves attention and can be used for communication networks of the sixth-generation of terahertz band, where the density of transceivers will already exceed 1 device per square meter. Subject of study. The article is devoted to mathematical and simulation modeling of methods for searching for locations of base station devices in space according to the criterion of the geometric factor of the dilution of precision for positioning accuracy of the user device. Method. Calculation of geometric factors for the dilution of precision of positioning accuracy in the horizontal plane HDOP (Horizontal DOP), in the vertical plane VDOP (Vertical DOP) and by location in space PDOP (Position DOP). Main results. The result of this work is the formalization of algorithmic, methodological and software for DOP calculations and search for locations of base station devices in space based on geometric factors of the DOP for the positioning accuracy of user devices. Practical significance. The results obtained make it possible to search for the locations of the base stations of an ultra-dense network in space under the condition of a fixed location of the user device, as well as to perform a quantitative and qualitative assessment of the obtained placements according to the DOP criteria.

Problem Statement for the Developing a New Training Course «5G Smart Antennas Technologies»

Interest in smart antenna technologies is due to the development of ultra-dense networks of the fifth 5G and subsequent B5G (Beyond 5G) generations of the millimeter-wave band (MMW, millimeter-wave – mmWave), which is confirmed by the high publication activity of recent years both in the native, and in foreign scientific community. Together with scientific publications in foreign literature, textbooks in this area are published and republished. At the same time, in the native scientific and methodological literature, there is currently no textbook that reveals new approaches to studying the issues of adaptive beamforming using smart antennas in relation to ultra-dense 5G and B5G networks. In this paper, an analysis of the state of the problem in the field of intelligent antennas is carried out and a task is formulated for the development of an appropriate training course. Subject of study. The article is devoted to a contemporary review of the state of the problem in the field of smart antennas based on the analysis of Russian sources. Method. Analysis of the evolution of the development of methods and means of smart antenna technologies. Main results. The result of this work is the setting of the task for the development of a new training course "Technologies of smart antennas 5G". Practical significance. The analysis carried out serves as a rationale for the relevance and necessity of developing a new training course "Technologies of smart antennas 5G".

Ultra-Dense Networks: Taxonomy and Key Performance Indicators

One major influence on the future deployment of cellular networks will be a continuous increase in traffic inside mobile broadband systems. Moreover, traditional macrocell-based mobile communication networks will struggle to keep up with the enormous expansion in the demand for communications services in the future. Densification of networks is required if we are to meet the comprehensive needs for end terminals for a wide range of applications. One of the leading concepts in this competitive environment is the Ultra-Dense Network (UDN) where the access nodes and/or the communication links per unit area are densified, with the aim of improving overall network performance. The location of the UDN nodes meets the criteria for symmetry with a high probability. Ultra-dense cell deployment aims to reduce the physical distance between the transmitter and receiver in order to boost system performance and generally optimize the values of a wide variety of key performance indicators (KPIs). This paper aims to provide a taxonomy of UDNs and specifically of UDN-related KPIs. Initially, we address the complex questions “What is the current understanding of what ultra-dense networks are and what they should be, and how can we measure their performance?” by shedding light on the fundamental characteristics of UDNs.

Improved spectral efficiency in massive MIMO ultra-dense networks through optimal pilot-based vector perturbation precoding

In multi-user massive MIMO (mMIMO) Ultra Dense Networks (UDN), the acquisition of Channel State Information (CSI) is crucial at the transmitter to maximize the estimation, but it suffers from increased overhead and complexity. The processing of CSI from imperfect channels further increases the complexity of precoders. Because of the complexity, it is hard to find the optimal perturbing vector, which increases the transmit power with reduction in spectral efficiency. This research improves the spectral efficiency of mMIMO UDN using optimal pilot-based vector perturbation (OPVP) precoding. The optimal pilot design senses the CSI intelligently for feedback into the transmitter, and the perturbing signal, for transmission and efficient reception, is selected optimally using Evolutionary Chaotic Behavior (ECB). This integrated OPVP precoding uses compressive sensing to select the low-dimensional CSI, based on which the EEA allows the generation of perturbing signals within its constellation bounds to get efficiently detected with reduced transmit power, computational complexity, and overhead in comparison with the conventional CSI estimation schemes. The simulation is carried out in Matlab to evaluate if the OPVP scheme makes the transmission in MU-MIMO UDN a spectrally efficient one. Compared to state-of-the-art precoding schemes, the integrated model reduces the feedback overhead and avoids high-dimensional CSI recovery.

Power Allocation in Ultra-Dense Networks Through Deep Deterministic Policy Gradient

The existing reinforcement learning-based downlink power allocation (PA) schemes mostly consider the power optimization space as a discrete value space, however, their results will deviate from the optimal result in ultra-dense networks, and the deviation grows as the network size increases. This letter proposed a PA model based on deep deterministic policy gradient (DDPG), where policy-based power selection assisted with value-based evaluation is leveraged to explore the optimal result from a continuous power space. Specifically, this model uses two CNNs named actors to formulate continuous deterministic PA strategy function instead of discrete power distribution sampling, and designs another two CNNs named critics for PA strategy evaluation and actor CNNs’ update supervision. Additionally, to reduce the interference, a tunable service base station set is designed for each user and is considered for the model training. Experiments demonstrate the proposed DDPG-based PA model respectively reaches 116.2% and 95.9% sum-rate relative to the iterative algorithm in small and large-scale networks.

Optimal power allocation for NOMA-based Internet of things over OFDM sub bands

As a result of continued expansion of 5G technology, the density of IoT devices has increased dramatically.Increasing the throughput of 5G systems is now extremely important. Non-orthogonal multiple access technologiesand Ultra-dense networks have lately attracted a lot of attention in the context of Internet of Things networksbecause to their capacity to multiplex from the space domain and power domain. In order to boost systemthroughput, this article integrates non-orthogonal multiple access technology with ultra-dense network technology,taking into consideration orthogonal frequency division multiplexing non-orthogonal multiple access-based ultradensenetworks with several base stations. The network model and the channel model were created first. As aresult, under the condition of total power, the downlink transmission rate maximization problem is formulated.Then, the problem is divided into two subproblems to solve: device grouping and sub-band power distributionand built the best power allocation strategies by using convex optimization theory to these subproblems. Finally,numerical simulations are undertaken to validate the efficiency of proposed optimal downlink power distributionapproach and the total throughput of the system has substantially enhanced as compared to orthogonal Multipleaccess.

Joint cell zooming and sleeping strategy in ultra dense heterogeneous networks

To optimize the ultra dense distribution of base stations (BSs) which consist of micro BS and macro BS, balancing the tradeoff between user communication quality and energy saving is essential. In this paper, a joint cell zooming and sleeping strategy is proposed for the downlink two-tier heterogeneous network under the Poisson Point Process (PPP) model, in order to balance the user benefits and communication system operating cost. First, the cell zooming scheme is presented based on game theory, where the association probability of each base station is achieved by adjusting the cell zooming factor (CZF). With the improved BS association probability, more users can benefit with better network experience by connecting to micro BS instead of macro BS. Then a two-step sleeping strategy is put forward, by applying the cell zooming. The first step is to ascertain which BSs are kept based on the analysis of the energy consumption of candidates. The second step is used to measure the overall overlapping degree of each BS and provide different sleep decisions according to the radius of the area covered by MBS. The proposed sleeping scheme is able to stimulate the BS with a high overlapping degree to be slept to achieve more energy saving. Through the proposed joint schemes, the service efficiency of communication system is improved, when the users connect to micro BS instead of macro BS and some micro BSs with low utility are turned off. Simulation results indicate the improved performance in terms of the user experiences and energy saving.

User-centric base station clustering and resource allocation for cell-edge users in 6G ultra-dense networks

Ultra-Dense Networks (UDNs) have been proposed to meet the ultra-high system capacity and ultra-high user experience rate requirements of sixth generation (6G) mobile networks. However, ultra-dense base stations (BSs) deployment poses challenges for cell-edge users such as BS selection, severe interference, resource allocation, and inter-cell handover. To tackle these obstacles, this paper formulates a joint BS clustering and resource allocation problem for cell-edge users in a 6G heterogeneous UDN system. For efficient resolution, this problem is decoupled into two sub-problems to be solved independently. We first propose a user-centric BS clustering algorithm based on many-to-many matching for the BS clustering problem in the heterogeneous UDN system; This algorithm considers the constraint of the system capacity. A many-to-many stable matching between users and small cell BSs is constructed under the optimization objective of maximizing the achievable user rate. Furthermore, we analyze the effectiveness, stability, convergence, and complexity of the BS clustering algorithm. Given established small cell BS clusters, we also propose a user-centric resource allocation algorithm based on network partitioning. This algorithm accounts for the interference of all users in the heterogeneous UDN system and maximizes intra-sub-network interference while minimizing inter-sub-network interference via a spectral clustering-based sub-network partitioning algorithm. Next, orthogonal allocation of resource blocks (RBs) is implemented within each sub-network, and RBs are spatially multiplexed between sub-networks. Numerical results confirm the benefits of the proposed methods: our algorithms outperform benchmark solutions in terms of the sum achievable system rate, average achievable user rate, and user spectral efficiency.

CEAR: A cooperation based energy aware reward scheme for next generation green cognitive radio networks

Cognitive Radio (CR) networks are envisioned as a key empowering technology of the fifth-generation (5G) wireless communication networks, which solves the major issues of 5G, like high-speed data transmission, seamless connectivity, and increased demand for mobile data. Another significant characteristic of the 5G network is green communications, as energy consumption from the communication field is predicted to rise remarkably by the year 2030. In this work, we are concerned about energy-related issues and propose a cooperation-based energy-aware reward scheme (CEAR) for next-generation green CR networks. The proposed CEAR scheme is based on the antenna and temporal diversity of the primary users (PUs). For providing the service to the PUs, the users of another network called cognitive users (CUs) work as a cooperative relay node, and, in return, they get more spectrum access opportunities as a reward from the primary network. The CUs with delay-tolerant data packets take a cooperative decision by recognizing the availability and traffic load of PUs, channel state information, and data transmission requirements. We utilize the optimal stopping protocol for solving the decision-making problem and use the backward induction method to obtain the optimal cooperative solution. The simulation results reveal notable enhancements in energy efficiency (EE) of CUs compared with other cooperative schemes. The proposed CEAR scheme is more energy-efficient for ultra-dense network deployment because results show that the CU’s EE, spectral efficiency (SE), and throughput improved with the increase of PUs.

An Interference-Oriented 5G Radio Resource Allocation Framework for Ultradense Networks

To cope with the explosive growth in demands of wireless network, ultradense network (UDN) technology is widely adopted, which could increase the capacity of wireless network, but also bring severe intercell interference (ICI). However, existing solutions cannot work well in such complex scenarios, due to the limits of their mechanisms. To solve the problem, in this article, an interference-oriented radio resource allocation framework is proposed with multiple usages, including supplying precise, stable, and timely performance feedbacks, near perfect offline training, and high compatibility. As the use of the framework is derived from precise interference identification, a practical regression-based interference modeling algorithm is proposed to support the framework. With in-depth analysis of the mechanism of interference, the proposed algorithm could efficiently and accurately model interference between users using only data collected from operating wireless networks. Compared with the baseline algorithm, the proposed algorithm could reach the same accuracy with training time of two orders of magnitude shorter. To further show the advantages of the framework, a high-performance double-deep- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network-based resource allocation algorithm is also proposed. By integrating into the proposed framework, the proposed algorithm could coordinate ICI better, with 40% to 101% higher energy efficiency compared with baseline algorithms.

Secure 5G Positioning With Truth Discovery, Attack Detection, and Tracing

The fifth-generation (5G) cellular network is expected to provide submeter positioning accuracy without draining the battery of user equipment (UE). As a solution, ultradense network (UDN) deployment and network-based positioning were proposed. However, the openness of UDN and the vulnerability of network devices [e.g., access nodes (ANs)] make it easy for attackers to poison such a positioning system. However, no existing work explores how to overcome this issue. This article concentrates on jamming and collusion attacks in the network-based positioning system. Specifically, we design a novel scheme that contains three functional modules to erase the influence of these attacks. A truth discovery module applies a clustering-based method aiming to generate the most approximate position value and find out suspicious signals. Based on neural network models, we further develop an attack detection module and an attack tracing module to perceive attacked UE and locate malicious or attacked ANs. Through simulation, we conduct extensive experiments to illustrate the effectiveness of our scheme. The result shows high detection and tracing accuracy with very simple neural network models, which also implies the potential of our proposed scheme in practical deployment.

Privacy information protection algorithm of ultra dense network nodes based on edge computing

In order to solve the problems of high node loss rate, high time overhead and high risk of privacy disclosure in network node privacy information protection, an ultra dense network node privacy information protection algorithm based on edge computing is proposed. The weight update algorithm is used to detect the security vulnerabilities of ultra dense network nodes. According to the detection results, the characteristics of node vulnerabilities are obtained, the sensitive label information of node vulnerabilities is protected through the weighted graph, and the k-anonymity technology is used to anonymize the privacy information of ultra dense network nodes; Finally, edge computing is used to protect the privacy information of nodes. The experimental results show that the node loss rate of the proposed method is always less than 2%, the time overhead is small, and the risk coefficient of privacy disclosure is small.

Non-orthogonal transmission for user and control plane split architecture in 5G systems and beyond: Performance analysis and design insights

In next generation networks, mobility management will be a critical issue due to dense base station (BS) deployment, for which user and control plane split architecture provides a promising solution. Jointly designing such architecture with nonorthogonal transmission brings in more flexibility to further improve system efficiency. This paper proposes a non-orthogonal transmission design for user and control plane split architecture. In this design, user equipments (UEs) will select the BS providing the strongest received signal to associate its data channel, but constantly connect its control channel to the nearest macro-cell BS (MBS). Upon non-orthogonal transmission, an MBS can multiplex data traffics and control signals on the same resource. Stochastic geometry based analysis is carried out to investigate outage probability, which extends its regular definition by jointly considering data and control channels, and then mobility-aware outage rate. Numerical results show that: 1) The proposed split architecture alleviates the increase in handover rate for ultra dense networking, compared with conventional architecture. 2) Non-orthogonal transmission outperforms traditional orthogonal transmission in the split architecture, because it is capable of accommodating more control channels. 3) By carefully adjusting power levels, minimum outage probabilities can be reached for macro-cell UEs in the proposed design.

Learn to Communicate With Neural Calibration: Scalability and Generalization

The conventional design of wireless communication systems typically relies on established mathematical models that capture the characteristics of different communication modules. Unfortunately, such design cannot be easily and directly applied to future wireless networks, which will be characterized by large-scale ultra-dense networks whose design complexity scales exponentially with the network size. Furthermore, such networks will vary dynamically in a significant way, which makes it intractable to develop comprehensive analytical models. Recently, deep learning-based approaches have emerged as potential alternatives for designing complex and dynamic wireless systems. However, existing learning-based methods have limited capabilities to scale with the problem size and to generalize with varying network settings. In this paper, we propose a scalable and generalizable neural calibration framework for future wireless system design, where a neural network is adopted to calibrate the input of conventional model-based algorithms. Specifically, the backbone of a traditional time-efficient algorithm is integrated with deep neural networks to achieve a high computational efficiency, while enjoying enhanced performance. The permutation equivariance property, carried out by the topological structure of wireless systems, is furthermore utilized to develop a generalizable neural network architecture. The proposed neural calibration framework is applied to solve challenging resource management problems in massive multiple-input multiple-output (MIMO) systems. Simulation results will show that the proposed neural calibration approach enjoys significantly improved scalability and generalization compared with the existing learning-based methods.

Detection of Cache Pollution Attack Based on Federated Learning in Ultra-Dense Network

Ultra-Dense Network (UDN) as one of the key technologies of 5G aims to increase the capacity per area of the current 4G network by 1,000-fold through the deployment of dense Small Base Stations (SBSs). To reduce the traffic on backhaul links, Content-Centric Networking (CCN) as a promising network architecture has been applied to UDN recently. However, its in-network caching mechanism is subject to Cache Pollution Attack (CPA), where attackers aim to occupy limited caching space with unpopular contents. Existing studies focusing on the static networks cannot be directly used in dynamic UDN scenarios. In this paper, we propose a detection and defense scheme of CPA based on federated learning, where CPA is determined by the cooperation of multiple SBSs. First, each SBS can build or join a cluster based on the metrics of distance similarity and load similarity. Then, each cluster head as the working node trains the local classifier to detect CPA based on the statistics within the cluster. The macro base station aggregates all the local models to generate an improved global classifier for all the SBSs. In our scheme, federated learning as a special case of distributed machine learning allows each cluster head to independently train the local data even when it is non-i.i.d. or unbalanced. Simulation results show that our scheme outperforms in terms of detection ratio, cache hit ratio, access delay, and false alarm ratio compared with other state-of-the-art schemes.

Learning-Based Resource Allocation in Heterogeneous Ultradense Network

Learning-based resource allocation (LRA) is envisioned as an integral element of 6G. This article proposes a novel learning-based paradigm to address resource allocation problems in heterogeneous ultradense networks (HUDNs). Our paradigm is a highly efficient realization for utilizing the inherence properties in HUDN, which comprise the local validity and correlation attenuation. Concretely, we formulate the HUDNs as a heterogeneous bipartite graph model and propose the corresponding heterogeneous bipartite graph neural network (HBGNN). The local sampling characteristic of HBGNN matches the inherence properties. Meanwhile, our paradigm combines data-driven and model-driven learnings and employs online and offline trainings. Hence, two of LRA’s obstacles: 1) the overreliance on the perfect data set and 2) the low calculation efficiency are mitigated, and the realizability of our paradigm is improved. Besides, entropy regularization is utilized to guarantee the effectiveness of exploration in the configuration space. We apply our approach to a representative resource allocation problem, the jointly user association (UA) and power allocation (JUAPA) problem. We formulate JUAPA as a combination classification and regression problem and adopt a dynamical hyperparameter output layer to address the discrete variable of UA. Simulation results demonstrate that the proposed method has better performance and higher computational efficiency than traditional optimization algorithms.

Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks

With the explosive increase in traffic volume in fifth-generation (5G) mobile wireless networks, an ultra-dense network (UDN) architecture, composed of highly concentrated millimeter-wave base stations within the fourth-generation (4G) system, has been developed. User equipment (UE) may encounter more frequent handover opportunities when moving in a UDN. Conventional handover schemes are too simple to adapt to the diverse handover scenarios encountered in 5G UDNs because they consider only UE signal strength. Unnecessary handovers aggravate the ping-pong effect and degrade the quality of service of cellular networks. Fuzzy logic (FL) is considered the best technique to unravel the handover problem in a high-density scenario of small cells for 4G/5G networks. In this paper, we propose an FL-based handover scheme to dynamically adjust the values of two handover parameters, namely handover margin (HOM) and time to trigger (TTT), with respect to each UE. The proposed scheme, abbreviated as FLDHDT, has dynamic adjustment of TTT in addition to HOM by using the signal to interference plus noise ratio and horizontal moving speed of the UE as inputs to the FL controller. To demonstrate the effectiveness and superiority of FLDHDT, we perform simulations using the well-known ns-3 simulator. The performance measures include the number of handovers, overall system throughput, and ping-pong ratio. The simulation results demonstrate that FLDHDT improves the handover performance of 5G UDNs in terms of the number of handovers, ping-pong ratio, and overall system throughput compared to a conventional handover scheme, namely Event A3, and an FL-based handover scheme with dynamic adjustment of only HOM.