Cellular Access Networks Research Articles

Mitigating self-echo in public cellular access networks using counter-propagating light wave interferometry

Mitigating self-echo in public cellular access networks using counter-propagating light wave interferometry

Exploit Vulnerabilities in 4G and 5G Cellular Access Network

Portable gadgets back different specialized highlights and administrations for up-and-coming 2G, 3G, 4G and 5G systems. For illustration, these determinations contain physical layer rate sorts, radio convention data, security calculations, carrier conglomeration groups, and benefit sorts such as GSM-R, Voice over LTE, Within the setting of portable security standardization, these organize determinations and administrations are alluded to as gadget capabilities and are traded with the organize amid the gadget enrolment stage. In this article, we investigate data around indicated gadget capabilities for 4G and 5G gadgets and their part in building up a secure interface between the gadget and the arrange. Our inquire about comes about appear the plausibility of the gadget being traded with the carrier some time recently the confirmation step without any ensures and not affirmed by the carrier. Therefore, we display three unused sorts of assaults that misuse data approximately the capabilities of unprotected gadgets in future 5G systems: Character assaults, sending assaults, and battery deplete assaults against versatile gadgets. Conduct verification of concept assaults utilizing reasonable equipment and computer program arrangements to survey their effect on commercial 4G gadgets and systems. We have detailed the distinguished vulnerabilities to pertinent benchmarks bodies and given countermeasures to relieve assaults against gadget capabilities in up-and-coming 5G systems.

Joint spectrum and power allocation scheme based on value decomposition networks in D2D communication networks

Device-to-device (D2D) communications allow short-range communication devices to multiplex cellular-licensed spectrum to directly establish local connections for ultra-high number of terminal connections and greater system throughput. However, spectrum sharing also brings serious interference to the network. Therefore, a reliable and efficient resource allocation strategy is important to mitigate the interference and improve the system spectral efficiency. In this paper, we investigated spectrum access and power allocation in D2D communications underlay cellular networks based on deep reinforcement learning with the aim of finding a feasible resource allocation strategy to maximize data rate and system fairness. We proposed a value decomposition network-based resource allocation scheme for D2D communication networks. Our proposed scheme avoids frequent information exchanges among D2D users by centralized training, while allowing D2D users to make distributed joint resource allocation decisions. Simulation results show that the proposed scheme has stable convergence and good scalability, and can effectively improve the system capacity.

Non-orthogonal resource scheduling with enhanced preamble detection method for cellular random access networks

In this article, we propose a non-orthogonal resource scheduling (NORS) scheme with an enhanced preamble detection (PD) method for cellular random access (RA) systems. The enhanced PD method is designed based on a deep learning technique that can classify three PD statuses: idle, collision-free, and collision. Because of this collision resolution capability, the base station (BS) can exploit radio resources non-orthogonally in contention-based RA protocols. In addition, we mathematically analyze the performance of our proposed NORS scheme in terms of RA success probability and resource utilization efficiency and conduct a comparison with the baseline orthogonal resource scheduling (ORS) scheme. Through simulations, we verify the validity of our mathematical analysis. As a result, our results will be a useful guideline for resource scheduling and provide support for massive connectivity.

Coverage Probability Analysis for Uplink/Downlink Decoupled Access in Heterogeneous Cellular Networks

Coverage Probability Analysis for Uplink/Downlink Decoupled Access in Heterogeneous Cellular Networks

Decoupled Association With Rate Splitting Multiple Access in UAV-Assisted Cellular Networks Using Multi-Agent Deep Reinforcement Learning

In unmanned aerial vehicles (UAVs) assisted cellular networks, user association plays an important role in interference control and spectrum efficiency. In this paper, we study the performance of uplink-downlink decoupled (UDDe) user association in a multi-UAV assisted network in which each user can associate with different UAVs or the macro base station (MBS) for uplink (UL) and downlink (DL) transmissions. Since some popular data may be requested by multiple users, grouping these users and applying multicasting can significantly improve spectral efficiency. Unlike traditional linear precoding that treats interference entirely as noise, we propose a rate-splitting multiple access (RSMA) policy that employs rate splitting at the transmitter and successive interference cancellation (SIC) at the receiver. To be specific, the transmitted signal is split into a common part and a private part, and the interference is partially decoded and partially treated as noise. In this context, we formulate a joint optimization problem of UL-DL association and beamforming for maximizing the sum-rate of users in UL and that of multicast groups in DL under the constraints of UAV backhaul capacity and power budget. Since the formulated problem is non-convex with intricate states and an individual UAV may not know the rewards of other UAVs, we convert it into a robust partially observable Markov decision process (POMDP). Then we resort to multi-agent deep reinforcement learning (MADRL) that enables each UAV to learn and optimize its policy in a distributed manner. To achieve an optimal policy, we further propose an improved clip and count-based proximal policy optimization (PPO) algorithm to train actor and critic networks. Simulation results demonstrate the superiority of the proposed decoupled association strategy with RSMA and the MADRL learning algorithm.

Coverage and Data Rate Analysis for a Novel Cell-Sweeping-Based RAN Deployment

Adequate and uniform network coverage provision is one of the main objectives of cellular service providers. Additionally, the densification of cells exacerbates coverage and service provision challenges, particularly at the cell-edges. In this paper, we present a new approach of cell-sweeping-based Base Stations (BSs) deployments in cellular Radio Access Networks (RANs) where the coverage is improved by enhancing the cell-edge performance. In essence, the concept of cell-sweeping rotates/sweeps the sectors of a site in azimuth continuously/discretely resulting in near-uniform distribution of the signal-to-interference-plus-noise ratio (SINR) around the sweeping site. This paper investigates the proposed concept analytically by deriving expressions for the PDF/CDF of SINR and achievable rate; and with the help of system-level simulations, it shows that the proposed concept can provide throughput gains of up to 125% at the cell-edge. Then, using a link-budget analysis, it is shown that the maximum allowable path loss (MAPL) increases by 2.1 dB to 4.1 dB corresponding to the gains in wideband SINR and post-equalized SINR, respectively. This increase in MAPL can be translated to cell-radius/area with the help of the Okumura-Hata propagation model and results in cell-coverage area enhancement by 30% to 66% in a Typical Urban cell deployment scenario.

Crowd‐sourced and incentive driven UAV system to assist with network slices

AbstractOwing to their flexibility, fast deployment, increasing affordability, and numerous application opportunities, unmanned aerial vehicles (UAVs) became an undisputed target of studies and research to discover and exploit the immense opportunities they provide. Moreover, network slicing is a critical enabler for 5G and beyond. Network slices guarantee certain network user expectations such as data rate and latency thresh‐holds. There is no doubt that crowd‐sourced and incentive driven solutions are on the rise. Crowd‐sourcing allowed companies and organizations both small and large to gather data and perform other large‐scale tasks with minimum expenses and maintenance. Indeed, the major concerns for 5G and network slicing are guaranteeing high throughput, reliability, and latency. With data rates being the most important attribute for common regular users, that is, mobile devices, video streaming, navigation, and content consumption. As a result, the focus of this work is to utilize crowd‐sourced UAVs in an incentive driven network slice assistance system to extend coverage and provide mobile users with the high throughput they require. This work presents some basic 5G cellular radio access technologies and network slicing background and proposes a novel system, namely UAV slice assistance (UAVSA), and its hybrid alternative version: UAV hybrid slice assistance (UAVHSA). The hybrid mode combines benefits from the proposed UAVSA system and the traditional base station (BS) system. The proposed algorithms address the issue of coverage and guaranteeing high data rate slices for users. The proposed systems maximize satisfaction rate of network slice users. Moreover, all systems are extensively tested and compared in various scenarios to determine their performance and provide a coherent study. Finally, future works and opportunities are highlighted in conclusion.

Coverage Probability and Area Potential Spectral Efficiency Analysis of 3D Dense SCMA Cellular Networks

This paper investigates the dense multi-user sparse code multiple access (SCMA) cellular networks, where base stations (BSs) and users are randomly deployed in a 3D space by using stochastic geometry tools. First, a method increasing the dimension of the assignment matrix is presented to provide massive connectivity in each cell. The random pattern of arrangement of non-zero elements access (RPA) policy is utilized to allocate the available SCMA resources. Then the coverage probability is characterized under the nearest-BS (NBS) association mechanism by developing a multi-user connectivity model based on the RPA policy and average connectivity ratio (ACR). A compact closed-form expression of the coverage probability and its approximation are derived by utilizing the Toeplitz matrix and finite sum of power series, respectively. We then extract the area potential spectral efficiency (APSE) based on the coverage probability, and provide new insights into the 3D dense multi-cell SCMA networks. Simulation results confirm the precision of the theoretical results and demonstrate the advantage of the SCMA scheme.

Optimization of Channel Segregation-Based Fractional Frequency Reuse for Inter-Cell Interference Coordination in Cellular Ultra-Dense RAN

To cope with ever growing mobile data traffic, we recently proposed a concept of cellular ultra-dense radio access network (RAN). In the cellular ultra-dense RAN, a number of distributed antennas are deployed in the base station (BS) coverage area (cell) and user-clusters are formed to perform small-scale distributed multiuser multi-input multi-output (MU-MIMO) transmission and reception in each user-cluster in parallel using the same frequency resource. We also proposed a decentralized interference coordination (IC) framework to effectively mitigate both intra-cell and inter-cell interferences caused in the cellular ultra-dense RAN. The inter-cell IC adopted in this framework is the fractional frequency reuse (FFR), realized by applying the channel segregation (CS) algorithm, and is called CS-FFR in this paper. CS-FFR divides the available bandwidth into several sub-bands and allocates multiple sub-bands to different cells. In this paper, focusing on the optimization of the CS-FFR, we find by computer simulation the optimum bandwidth division number and the sub-band allocation ratio to maximize the link capacity. We also discuss the convergence speed of CS-FFR in a cellular ultra-dense RAN.

Performance Analysis of UL-DL Decoupled Access in Two-Tier Cellular Heterogeneous Networks

With the advent of the 5th generation mobile communication technology (5G) era, cellular networks are evolving towards highly heterogeneous and ultra-dense network structures, posing challenges for achieving high communication rates. Existing coupled access methods can no longer meet user demands. As a promising solution to this issue, decoupled uplink-downlink access has gained significant attention. This paper investigates the performance of decoupled uplink-downlink access in a two-tier cellular network model. We obtain the analytical expressions of association probabilities, distance distribution, and spectral efficiency of base stations (BSs) when femto-cell BSs are selected for uplink access and macro-cell BSs are selected for downlink access by a user device. Our findings provide insights into the selection of access patterns and decoupled methods for better efficiency and quality of service. Furthermore, our model and approach can guide the design and deployment of next-generation networks to enhance performance.

Energy harvesting in cellular radio access networks based on Q-learning

Energy harvesting in cellular radio access networks based on Q-learning

Non-Orthogonal Random Access (NORA) in Large Cellular Networks

Non-orthogonal random access (NORA) is promising to alleviate random access (RA) collision and increase RA throughout. However, the application of NORA in multi-cell scenarios is challenging due to uncertain spatial distribution of NORA UEs and complex inter-cell interference. In this letter, an analytical framework for NORA in multi-cell scenarios is proposed using stochastic geometry. In particular, the approximate spatial distributions of NORA UEs are given. The analytical model of NORA gain is derived taking into account inter-cell interference. Analytical and simulation results indicate the preferred power control scheme and demonstrate that the base station (BS) deployment scheme should be adjusted according to given wireless channel characteristics.

Knowledge Transfer-Based Multiagent Q-Learning for Medium Access in Dense Cellular Networks

Inter-cell interference in dense cellular networks causes severe network performance degradation because the base stations (BSs) and devices cannot fully utilize all the network status information in practice. Reinforcement learning enables the wireless network to find an effective transmission strategy with limited information and the network entities to operate in a distributed manner. We herein propose a medium access algorithm based on multi-agent reinforcement learning by exchanging knowledge of transmissions from other cells. Specifically, a state mapping function between the device and the BS and a transfer value iteration are proposed. The BS transfers reward processes derived from inter-cell interference so that the partially observed medium of devices is expanded to monitor entire networks. Extensive simulation results show that simple knowledge transfer can help performance improvement in terms of network stability and throughput.

Digital compass for multi-user beam access in mmWave cellular networks

Beamforming in stand-alone Millimeter-Wave (mmWave) communications results in prolonged access times and latencies due to the increased number of measurements required to determine the optimal beam directions at the Mobile Station (MS) and Base Station (BS), returning the highest received signal level. Therefore, dynamic and fast access schemes that meet the Third-Generation Partnership Project (3GPP) specifications are required here. Therefore, in this paper, a novel initial access scheme is proposed for multiple MS users by leveraging for the first time a digital compass in the access procedure. Namely, when a new MS joins the footprint of a BS, it probes the channel for beacon signaling about the BS direction, i.e., broadcasted by a neighboring MS that completed beam association at previous time steps. Then, a digital compass is utilized to adjust the coordinates of the BS according to the location of the new MS. The proposed scheme is applied for a single and multi-user settings at various broadcasting approaches. This includes a single associated MS user that broadcasts information to a single incoming MS user, a single user that broadcasts signals to multiple incoming users, or all multiple associated users broadcast to multiple incoming users. Overall, the proposed schemes yield in notable efficiency in terms of the computational complexity, access times, power and energy consumption as compared to existing access schemes. Further, high success rates are achieved at the detriment of relatively higher cost.

A comprehensive multi-topology minimum set cover link-state routing approach for emerging random all-IP access network topologies

Wireless access networks are ever-changing their setup, topologies and provisions of coverage. Forthcoming deployment trends include densification of cells and convergence of the ‘native IP’ with cellular access networks with more than best-effort expectations. Greater meshing in networks due to practical deployment requirements is inevitable and needed. This paper proposes a routing protocol to address the “randomness” of topology interconnections, routing paths and sizes in emerging IP access networks. Termed as Minimum Set Cover (MSC) approach, it is a generalization of the NP and NP complete mathematical problem. The offline component of the MSC multiplies intra-domain routing installations, called Routing Planes (RPs) modelled as graphs, to suitably cover the whole routing topology prior to the traffic injections in the online component. We introduce novel offline optimization features using a dynamic cost function that plans availability of capacities and correlation of routing paths via the chosen set of RPs. Our simulations verify effective path diversity using MSC with its modest protocol overhead and a heuristic used for RPs selection. For randomly constructed access network topologies with 7, 18 and 33 routers and various meshing levels, our results show convincing suitability and key performances gains compared with rival routing solutions.

NOMA-Enabled Cellular Radio Access Networks Relying on Control- and User-Plane Separation

In a cellular radio access network relying on control- and user-plane separation (RAN-CUPS), control base stations (CBSs) make up the control plane (CP) taking charge of coverage, and traffic base stations (TBSs) in the user plane (UP) are responsible for transmitting high-speed data under the umbrella of CP. In order to increase the coverage probability of CP, non-orthogonal multiple access (NOMA) is introduced into CP while UP adopts orthogonal frequency division multiple access (OFDMA). Then, we develop a tractable analytical model for NOMA-enabled RAN-CUPS based on stochastic geometry. Simulation results show that compared with OFDMA, NOMA could improve coverage probability, spectral efficiency, and energy efficiency of CP, which can further improve the spectral efficiency of UP.

On the Energy Efficiency of OFDMA Cellular Networks

In this article, we study the energy efficiency (EE) of orthogonal frequency-division multiple access (OFDMA) cellular networks under the 5 G requirement of EE enhancement. We aim to present a power allocation scheme maximizing the EE of downlink cellular communications while avoiding numerical methods such as fractional programming. We focus on two EE forms; global EE and weighted sum EE. Therefore, we propose a novel explicit expression of the optimal power allocation related to each subcarrier. We also present the power control with limited power budget or/and minimal transmission rate constraint in both base station and subcarrier perspectives. As a result, we notice the occurrence of some transmission outage events depending on the constraints’ parameters. From another side, we extend our study to analyze the effect of the channel state information (CSI) unavailability on our proposed power scheme. In the numerical results, we show that our proposed power control improves the EE, especially at high power budget regime and low minimal rate regime. We also show that having more subcarriers enhances the OFDMA EE. Finally, we show that EE degradation due to CSI unavailability is very small showing the robustness of the proposed scheme against CSI imperfectness. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

Hyperbolic K-means for traffic-aware clustering in cloud and virtualized RANs

As the internet and connected objects gain more and more in popularity, serving the ever-increasing data traffic becomes a challenge for the mobile operators. The traditional cellular radio access network (RAN), where each base station is co-located with its own processing unit and is responsible for a specific geographic area, has evolved first with the so-called Cloud RAN (C-RAN), and is currently undergoing further architectural evolution under the virtualized RAN (vRAN), Software-Defined RAN (SD-RAN), and Open RAN (O-RAN) architectures. In all these versions, the data processing units can be dynamically centralized into a pool and shared between several base stations, enlarging the geographical view for scheduling and resource allocation algorithms. For instance, resource utilization is improved by avoiding resource idling during off-peak hours. C-RAN and vRAN gains depend strongly on the clustering scheme of radio units (RRHs and RUs). In this paper, we propose a novel radio clustering algorithm that takes into account both the traffic demand and the position of stations, by using the hyperbolic distance in 3-dimensions. We introduce a modified K-means clustering algorithm, called Hyperbolic K-means, and show that this generates geographically compact RU clusters with traffic charge equally shared among them. Application of our algorithm on real-world mobile data traffic, collected from the cities of Nantes and Lille in France, shows an increase in resource utilization by 25%, and a reduction in deployment cost by 15%, compared to the standard RAN. Furthermore, the performance of our Hyperbolic K-means algorithm is compared extensively against alternative C-RAN clustering proposals from the literature and is shown to outperform them, in resource utilization as well as in cost reduction.

A strong smartphone authentication model to control cellular network access using blockchain

In an era that requests facilitating access to most services by providing the online platforms, the need to own a small electronic device is increasing. The smartphone is the ideal choice for most people due to its multifunctionality. On the other hand, the rate of physical crimes on these devices raised, especially theft and counterfeiting. As a solution to mitigate these crimes, stolen or counterfeit devices are prevented from connecting to the cellular network, but is this solution effective in mitigating the problem? To answer this question, this paper conducts a comprehensive survey on the current solutions tackled the smartphone theft or counterfeit issues, and it provides a taxonomy classify the current solutions into three categories: manufacturers solutions, cellular service provider solutions, and third-party solutions. The proposed smartphone authentication model for cellular network authentication utilizes the permissioned Blockchain network and consists of three protocols, which are smartphone registration protocol, ownership transfer protocol, and cellular network authentication protocol. In order to overcome the two serious physical crimes (theft and counterfeiting), the paper identifies five main requirements that must be met in any cellular network authentication approach, namely ability to detect counterfeit IMEI, ability to identify the device owner, ability to block the service on the stolen/lost devices, less processing time, and acceptable performance. The comparisons with related solutions showed that the proposed work meets all the requirements to mitigate smartphone theft and counterfeiting crimes.