Backhaul Links Research Articles

Coverage Performance Analysis of a Cache-Enabled UAV Base Station Assisted Cellular Network

Unmanned Aerial Vehicle base stations (UBSs) can be used to assist a ground cellular network to enhance its network services for cellular users. This paper studies the coverage performance analysis of a cache-enabled UBS-assisted cellular network. Analytical models are derived for investigating the overall coverage probability of the network and the average achievable rate of a cellular user. In deriving the analytical models, an air-to-ground (A2G) channel model with both a line-of-sight (LoS) link and a non-line-of-sight (NLoS) link, a BS association strategy based on the strongest average received power, and a cache model with a probabilistic caching strategy are considered. The cache hit probability of UBSs based on the cache model is also taken into consideration. Moreover, the association probabilities with the association strategy are derived for different types of base stations. The derived analytical models are validated through simulation results and the impacts of system parameters on the coverage performance of the network are investigated through numerical results. Compared with existing relevant work, the novelty of this work is that the effects of both an access link and a backhaul link are taken into account in the coverage performance analysis. The obtained results can provide theoretical guidance for the deployment of UBSs in a cache-enabled UBS-assisted cellular network.

UAV Edge Caching Content Recommendation Algorithm Based on Graph Neural Network

When responding to emergencies such as sudden natural disasters, communication networks face challenges such as network traffic surge and complex geographic environments. Aiming at the problems of high transmission delay and insensitivity to user's preference in the current UAV edge caching strategy, this paper proposes a UAV caching content recommendation algorithm based on graph neural network. Firstly, the location of UAV is determined by clustering algorithm; secondly, the interest preferences of user nodes in the cluster are predicted by GCLRSAN model, and the UAV cache content is designed according to the result; finally, simulation experiments show that the model and algorithm proposed in this paper can effectively reduce the backhaul link overhead and outperform the comparison algorithms in the indexes such as accuracy rate, recall rate, cache hit rate, and transmission delay.

Cluster Content Caching: A Deep Reinforcement Learning Approach to Improve Energy Efficiency in Cell-Free Massive Multiple-Input Multiple-Output Networks.

With the explosive growth of micro-video applications, the transmission burden of fronthaul and backhaul links is increasing, and meanwhile, a lot of energy consumption is also generated. For reducing energy consumption and transmission delay burden, we propose a cell-free massive multiple-input multiple-output (CF-mMIMO) system in which the cache on the access point (AP) is used to reduce the load on the link. In this paper, a total energy efficiency (EE) model of a cache-assisted CF-mMIMO system is established. When optimizing EE, forming the co-operation cluster is critical. Therefore, we propose an energy-efficient joint design of content caching, AP clustering, and low-resolution digital-to-analog converter (DAC) in a cache-assisted CF-mMIMO network based on deep reinforcement learning. This scheme can effectively cache content in APs and select the appropriate DAC resolution. Then, taking into account the channel state information and user equipment (UE)'s content request preference, a deep deterministic policy gradient algorithm is used to jointly optimize the cache strategy, AP clustering, and DAC resolution decisions. Simulation results show that the energy efficiency of the proposed scheme is 4% higher than that of other schemes without the resolution optimization and is much higher than that of the only AP clustering without the joint design of content caching and channel quality.

Analysis and Optimization of Cache-Enabled mmWave HetNets With Integrated Access and Backhaul

In millimeter wave heterogeneous networks with integrated access and backhaul (mABHetNets), a considerable part of spectrum resources are occupied by the backhaul link, which limits the performance of the access link. In order to overcome such backhaul “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spectrum occupancy</i> ”, we introduce cache into mABHetNets. Caching popular files at small base stations (SBSs) can offload the backhaul traffic and transfer spectrum from the backhaul link to the access link. To approach the optimal performance of cache-enabled mABHetNets, we first analyze the signal-to-interference-plus-noise ratio distribution and derive the average potential throughput (APT) expression by stochastic geometric tools. Then, based on our analytical work, we formulate a joint optimization problem of cache decision and spectrum partition to maximize the APT. Inspired by the block coordinate descent (BCD) method, we propose a joint cache decision, spectrum partition and power allocation (JCSPA) algorithm to approach the optimal solution. Numerical results show the effectiveness and enhancement of the proposed algorithm. Besides, we verify the APT under different parameters and find that the introduction of cache facilitates the transfer of backhaul spectrum to access link. Jointly deploying appropriate caching capacity at SBSs and performing specified spectrum partition can bring up about 90% APT gain in mABHetNets.

Fifth-generation small cell backhaul capacity enhancement and large-scale parameter effect

&lt;span lang="EN-US"&gt;The proliferation of handheld devices has continued to push the demand for higher data rates. Network providers will use small cells as an overlay to macrocell in fifth-generation (5G) for network capacity enhancement. The current cellular wireless backhauls suffer from the problem of insufficient backhaul capacity to cater to the new small cell deployment scenarios. Using the 3D digital map of Lagos Island in the Wireless InSite, small cells are deployed on a street canyon and in high-rise scenarios to simulate the backhaul links to the small cells at 28 GHz center frequency and 100 MHz bandwidth. Using a user-defined signal to interference plus noise ratio-throughput (SINR-throughput) table based on an adaptive modulation and coding scheme (MCS), the throughput values were generated based on the equation specified by 3GPP TS 38.306 V15.2.0 0, which estimates the peak data rate based on the modulation order and coding rate for each data stream calculated by the propagation model. Finding shows achieved channel capacity is comparable with gigabit passive optical networks (GPON) used in fiber to the ‘X’ (FTTX) for backhauling small cells. The effect of channel parameters such as root mean squared (RMS) delay spread and RMS angular spread on channel capacity are also investigated and explained.&lt;/span&gt;

E-band backhaul antenna for fine beam alignment

Abstract An E-band Cassegrain switched beam high-gain antenna concept for 5G backhauling systems is proposed in this article. The antenna requirements target the compensation of small misalignments (within ±1 degrees in both azimuth and elevation planes) in E-band backhauling links due to adverse weather conditions or thermal deformations. The intended antenna is able to realize beam-switching operations by using a feed-array architecture based on a 7 × 7 array of cavity-backed magnetoelectric (ME) dipoles, where every element is capable of providing a steering of ±0.33 degrees in both the elevation and the azimuth planes. The ME dipole illuminators combined with a Cassegrain reflector provide a gain of 52 dBi within the whole scanning range. Besides, they can be easily integrable with the front-end modules, thus being an easily implementable and low-cost alternative to other more complex solutions based on horns or lens antennas.

Resource Allocation of Multiple Base Stations for Throughput Enhancement in UAV Relay Networks

An unmanned aerial vehicle (UAV), with the advantages of mobility and easy deployment, serves as a relay node in wireless networks, which are known as UAV relay networks (URNs), to support user equipment that is out of service range (Uo) or does not have a direct communication link from/to the base station (BS) due to severe blockage. Furthermore, URNs have become crucial for delivering temporary communication services in emergency states or in disaster areas where the infrastructure is destroyed. The literature has explored single transmissions from one BS to a UAV to establish a wireless backhaul link in the URN; however, there exists a possibility of Uo outages due to severe interference from an adjacent BS, causing an overall throughput degradation of user equipment (UE) in the URN. In this paper, to improve the signal-to-interference-plus-noise ratio (SINR) of a wireless backhaul link, avoid an outage of Uo, and guarantee a reliable relay transmission, simultaneous transmissions from multiple BSs (e.g., macrocell BSs (mBSs) and small cell BSs (sBSs)) is considered. An outage probability is analyzed, and an optimal transmit time allocation algorithm is proposed to maximize the throughput of the UE and guarantee a reliable relay transmission. Simulation results demonstrate that simultaneous transmissions from multiple BSs in the URN leads to higher throughput and reliable transmission without an Uo outage compared to a single transmission in the URN from a single BS (e.g., mBS or sBS), and the optimization of transmit time allocation is essential in the URN.

Framework for fast and low-complexity deployment of UAVs-assisted communication

Unmanned Aerial Vehicles (UAVs) can work as aerial base stations along with the ground base stations to enhance capacity and provide ubiquitous coverage in 5G and beyond communication systems. UAVs have numerous features that distinguish them from the traditional fixed ground network infrastructure. These features include more flexibility, lower costs, faster integration, and better channel conditions with users owing to the UAVs maneuverability and adaptability to varying network conditions. This paper addresses UAVs positioning and radio resource management, which are two critical design issues that greatly impact the system performance and user experience in UAVs-assisted communication scenarios. Our objective is maximizing the achieved sum rate of a system consisting of a ground base station and a number of UAVs operating as aerial base stations under constraints on the minimum rate requirements of users, the capacity of wireless backhaul links between the UAVs and the ground base station, and the power budgets of the base stations. In doing so, we optimize the UAVs locations, the users-to-base stations association, the access link channels assignment, and the power allocation. The problem is a mixed-integer non-convex optimization, which is NP-hard to solve, and consequently we propose a fast low-complexity algorithm that achieves high performance compared to the common block coordinate descent-based solution approach and at much less execution time.

NDN-Based Coded Caching Strategy for Satellite Networks

To solve the transmission correlation issue arising from traditional named data networking (NDN) caching during segmenting contents, where users must obtain data blocks related to the requested file blocks to recover the source data, in this paper, we propose an NDN-based coded caching strategy for low Earth orbit (LEO) satellite networks, using coding operations to remove the transmission correlation of data. To achieve efficient content distribution, the satellite node’s coded package is strategically placed with the objectives of minimizing the backhaul link load and content acquisition latency. The optimization problem is solved by using a multi-colony ant colony algorithm, enabling fast content retrieval. We designed a cluster cooperative distribution mechanism to simplify the satellite network’s management and reduce the load of satellite links for content distribution when covered by multiple satellite nodes. Finally, we compare the multi-objective coded caching (CCMO) introduced in this paper with the most popular (MP) strategy and the random-based coded caching (CCR) strategy; the average cache hit ratio, backhaul link load, and content acquisition delay are compared and analyzed. The results show that CCMO performs better.

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.

Energy-Efficient Hierarchical Resource Allocation in Uplink–Downlink Decoupled NOMA HetNets

The dense deployment of small cell networks is a key feature of next-generation mobile networks aimed at providing the necessary capacity increase. It is noteworthy that small cell networks employ high-capacity backhaul links on millimeter-wave bands to develop multi-hop topologies in order to mitigate data transmission costs. The current static backhaul infrastructures cannot control severe fluctuating network traffic. To resolve this problem, this paper proposed a novel adaptive backhaul topology with the ability to adapt to different traffic patterns. Based on the graph theory, the adaptive system dynamically allows changes to the hybrid millimeter-wave backhaul architecture, and it also provides the possibility of effective channel allocation to each backhaul link to meet capacity and QoS demands. Also, regarding the importance of green networking in integrated-access-and-backhaul networks we proposed a dynamic optimization model which minimizes the overall energy consumption of UL/DL Decoupled NOMA heterogeneous networks in addition to providing the essential coverage and capacity. The proposed model optimizes user association/power utilization and presents an effective modular and scalable framework for analytical technology-oriented modeling of integrated multi-hop backhauls. The numerical results proved that the joint power optimization and hybrid backhaul architecture can increase the total network throughput by 18 percent compared to the current optimized static architectures. It can also reduce the energy consumption level by 30 percent, and enhance users’ quality satisfaction by 24.5 percent with respect to user distribution patterns.

Standalone-Intelligent Reflecting Surface Control Method Using Hierarchical Exploration by Beamwidth Expansion and Environment-Adaptive Codebook

Intelligent reflecting surfaces (IRS) have garnered attention for the high-capacity and reliable communication required by Beyond 5G. However, IRS control in existing studies assumes information sharing and control instructions from base stations (BSs), thus requiring a backhaul link to the core network and BSs. This requires the construction of a network system, ruining the advantages of IRS, such as its low cost and installation flexibility. Therefore, a standalone-IRS, which is completely independent of the core network and BSs, is expected to maximize the performance of the IRS. However, a standalone-IRS cannot obtain sufficient information for control decisions, such as channel state information, which is frequently used in existing studies; therefore, inefficient control methods, such as exploration, must be used. To this end, this study proposes a significant reduction in the overhead required for exploration by hierarchical exploration and a reduction in the exploration itself using codebooks to store the results of exploration. Furthermore, to prevent the degradation of communication quality due to the use of hierarchical exploration and codebooks, we propose an effective beamwidth expansion method for hierarchical exploration and an efficient codebook construction method based on user distribution as elemental methods. In this study, we evaluated the overhead reduction and communication quality of the standalone-IRS control by combining these methods through simulations.

A Markov Decision Process Solution for Energy-Saving Network Selection and Computation Offloading in Vehicular Networks

Vehicular Edge Computing (VEC) enables the integration of edge computing facilities in vehicular networks (VNs), allowing data-intensive and latency-critical applications and services to end-users. Though VEC brings several benefits in terms of reduced task computation time, energy consumption, backhaul link congestion, and data security risks, VEC servers are often resource-constrained. Therefore, the selection of proper edge nodes and the amount of data to be offloaded becomes important for having VEC process benefits. However, with the involvement of high mobility vehicles and dynamically changing vehicular environments, proper VEC node selection and data offloading can be challenging. In this work, we consider a joint network selection and computation offloading problem over a VEC environment for minimizing the overall latency and energy consumption during vehicular task processing, considering both user and infrastructure side energy-saving mechanisms. We have modeled the problem as a sequential decision-making problem and incorporated it in a Markov Decision Process (MDP). Numerous vehicular scenarios are considered based upon the users' positions, the states of the surrounding environment, and the available resources for creating a better environment model for the MDP analysis. We use a value iteration algorithm for finding an optimal policy of the MDPs over an uncertain vehicular environment. Simulation results show that the proposed approaches improve the network performance in terms of latency and consumed energy.

LEO satellite assisted UAV distribution using combinatorial bandit with fairness and budget constraints.

In this paper, an integration between a low earth orbit satellite (LEO-Sat) and unmanned aerial vehicle (UAV) is proposed to assist users in post-disaster areas. In this scenario, multiple UAVs will be distributed to fully cover the victims and provide rescue services, while LEO-Sat provides backhaul links for UAVs to the ground base station (GBS). In this regard, we consider the problem of efficient UAVs distribution to maximize the total sum rate of the victims while assuring fairness in their coverage within the limited resources of UAVs batteries and LEO-Sat bandwidth. In this paper, UAV distribution problem is considered as a combinatorial multi-armed bandit (MAB) with arms' fairness and limited UAVs battery budget (CMAB-FB) constraints. Additionally, the utilization of LEO-Sat bandwidth resources is optimized based on the average traffic demands of the LEO-UAV links by means of gradient decent algorithm. The results of numerical analysis indicate that the proposed approach outperforms other naïve ben chmarks.

Management and Orchestration of Edge Computing for IoT: A Comprehensive Survey

With the development of telecommunication technologies and the proliferation of network applications in the past decades, the traditional cloud network architecture becomes unable to accommodate such demands due to the heavy burden on the backhaul links and long latency. Therefore, edge computing, which brings network functions close to end-users by providing caching, computing and communication resources at network edges, turns into a promising paradigm. Benefit from its nature, edge computing enables emerging scenarios and use cases like Augmented Reality (AR) and Internet of Things (IowT). However, it also creates complexities to efficiently orchestrate heterogeneous services and manage distributed resources in the edge network. In this survey, we make a comprehensive review of the research efforts on service orchestration and resource management for edge computing. We first give an overview of edge computing, including architectures, advantages, enabling technologies and standardization. Next, a comprehensive survey of state-of-the-art techniques in the management and orchestration of edge computing is presented. Subsequently, the state-of-the-art research on the infrastructure of edge computing is discussed in various aspects. Finally, open research challenges and future directions are presented as well.

UAV-Assisted Caching Strategy Based on Content Cache Pricing in Vehicular Networks

A UAV-assisted caching strategy considering content cache pricing in vehicular networks is proposed to address the problem of high communication load and high backhaul link overhead in vehicular networks. Consider a traffic scenario consisting of a content provider (CP), a network operator (NO), and multiple mobile users, where the NO has a set of cache-enabled roadside units (RSUs) and an unmanned aerial vehicle (UAV). The CP leases some popular contents to the NO for its benefit and the NO places this leased content in its RSU’s local cache to save expensive backhaul transmission overhead and latency. However, both NO and CP are selfish and their interests conflict with each other because they have opposing expectations for content pricing. In order to take into account the interests of both, this paper defines the utilities of CP and MNO and uses the Stackelberg game framework to model the competition between the two entities, where CP acts as a leader and sets the rental price of the content and NO acts as a follower responding to CP’s actions. An iteration-based dynamic programming algorithm is also designed to find the Stackelberg equilibrium. Meanwhile, a caching-capable UAV is introduced into the vehicular network and, based on this, a Dijkstra-based path planning algorithm is designed to further increase the total utility of NO by optimizing the trajectory of the UAV. The simulation results show that the strategy in this paper can reasonably allocate the benefits of CP and NO, reduce the average request delay, and increase the utility of NO; for example, we reduced the request latency for vehicle users by 27% and increased the total utility of NO by 13%.

Design of Antenna Polarization Plane for Concurrent Uplink/Downlink Drone Networks

In recent years, drones have been used in a wide range of fields, such as agriculture, transportation of goods, and security. Drones equipped with communication facilities are expected to play an active role as base stations in areas where ground base stations are unavailable, such as disaster areas. In addition, asynchronous operation is being considered for local 5G, in order to support all kinds of use cases. In asynchronous operation, cross-link interference between base stations is an issue. This paper attempts to reduce the interference caused by the drone network by introducing circularly polarized antennas against the conventional system using linearly polarized antennas. Numerical analyses are conducted to validate the effectiveness of the proposed system, where Signal-to-Interference Ratios (SIRs) are shown to be improved significantly as the numerical evaluation results. Specifically, for the scenario of only access links, in the region where conventional antenna architecture can only achieve an SIR of less than 20 dB, our proposed system applying circularly polarized antennas can almost realize an SIR of more than 40 dB. Significant improvement can be also observed in the scenario with the existence of backhaul links, where the conventional system had difficulty achieving our system design goal SIR of 16.8 dB, while the proposed antenna architecture could easily attain this goal in most regions of our evaluation ranges.

Modeling and Analysis of Edge Caching for 6G mmWave Vehicular Networks

As one of the most promising solutions, edge caching is emerging to reduce the content retrieval latency and relieve the huge burden on the backhaul links in the 6th generation (6G) millimeter wave (mmWave) intelligent vehicular networks. In this paper, we first formulate the optimization problem in terms of request hit probability and investigate two crucial factors, which impact the effectiveness of cache placement in device-to-device (D2D) enabled mmWave vehicular networks. Then, the general mathematical expressions for optimization of the request hit probability with the constraints of caching capacity are derived. Moreover, a group caching scheme (GCS) is proposed to obtain the sub-optimal results by utilizing relaxation method and Karush-Kuhn-Tucker (KKT) conditions. Inspired from the Matern hard-core processes and by exploiting the spatially correlated characteristics of users distribution, hard-cored based caching algorithm (HCCA) is proposed to avoid the simultaneous caching for a particular file. In addition, a joint caching and scheduling strategy is investigated which maximizes the number of concurrent transmissions by D2D enabled vehicle pairs matching and antennas adjustment. Comprehensive simulations validate our theoretical analysis and demonstrate that the proposed scheme can achieve higher performance in terms of request hit probability and the number of concurrent transmissions compared to the existing methods. Consequently, the proposed caching scheme has great potential in future intelligent vehicular applications.

RIS-aided Multi-hop Backhauling for 5G/6G UAV-assisted Access Points

Drones are considered to be an important part of future 6G telecommunication systems. Thanks to their quick deployment potential, they provide additional connectivity options in the form of a flying hotspot. However, in such use cases, they typically require a wireless backhaul link to facilitate their proper operation, which might be a challenging task in dense urban environments. One of the potential methods that may be relied upon to connect such nodes is the integrated access and backhaul (IAB) approach, where part of the spectrum allocated to users accessing the base station is used for wireless backhauling. Thus, in this work, we consider the problem of establishing a multi-hop wireless backhaul link following the IAB concept, with the aid of drone relay stations (DRSs) and reconfigurable intelligent surfaces (RISs). We formulate the problem of coverage improvement with a fixed number of relays, assuming certain throughput requirements for the backhaul link. The simulations show that the use of RISs offers a coverage improvement in such a scenario or a reduction in the number of nodes involved in ensuring the required backhaul performance.

Performance Enhancement of Cooperative MIMO-NOMA Systems Over Sub-6 GHz and mmWave Bands

In this paper, two radio links with different frequency bands are considered for base stations (BS) serving users via decode-and-forward (DF) cooperative relays. Backhaul and access links are proposed with sub-6 GHz and millimeter wave (mmWave) bands, respectively. Non-orthogonal multiple access (NOMA) is employed in the backhaul link to simultaneously transmit a superposed signal in the power domain, using the same band. The superposed signals, containing two signals that differ in terms of power allocation factors (PAFs), are designed for two selected DF relays in the BS. The two relays are chosen from several relays to be serviced by the BS based on a pairing algorithm that depends on different users’ circumstances. The furthest DF relay detects the incoming NOMA signal directly, while the nearest one applies successive interference cancellation (SIC) before extracting its signal. Each DF relay forwards the detected signals toward their intended users over mmWave channels. Three performance metrics are utilized to evaluate the system’s performance: outage probability, achievable throughput, and bit error rate. Comparisons between two mmWave bands in the access link (28 and 73 GHz) are made to demonstrate the superiority of the 28 GHz band in terms of the three performance-related metrics.