Wireless Backhaul Research Articles

HD video transmission of multi-rotor Unmanned Aerial Vehicle based on 5G cellular communication network

In UAV (Unmanned Aerial Vehicle) mission scenarios for aerial reconnaissance and aerial photography, wireless video backhaul is one of the essential functions. In recent years, with the rapid development of UAV in the military and civilian fields, mobile high-quality video backhaul has become a trend. This paper analyzes the hardware structure and software architecture of the UAV HD video wireless transmission model. The overall model is described from three levels of hardware, software, and link interface, and its software architecture is analyzed. Aiming at the high-frequency and large-bandwidth transmission system under 5G small cells, the influence of new channel characteristics on physical layer parameters is analyzed, and the frame structure design parameters such as subcarrier spacing and cyclic prefix are optimized. Considering the low throughput rate in multi-hop wireless networks, this paper innovatively proposes a slide mode based on frame rate adjustment. The simulation results show that the revised throughput prediction value has higher accuracy than the traditional method, and the bit rate adjustment is timely and can maintain a certain stability. Combining the advantages of the high and low bit rate schemes, the continuous video transmission is maintained while providing higher quality.

Wireless backhaul network’s capacity optimization using time series forecasting approach

The extensive technological participation in our daily life and business world has transformed the technology model predominantly from last decade and it has created many directions for further research and development. In previous generations of technology, the major development was hardware side but now it is shifting to the smart and intelligent solutions based on AI. The new challenges have provoked and complexity is increasing for resource orchestration because of the nonstop increasing network and its applications. In cellular architecture, the backhaul network (intermediate network) is used to connect the radio and core network. The key technology is the point to point fixed links that is used in the wireless backhaul network. More than 60% radio base station are connected using this technology. Currently, the static approach is used for planning and optimization of the network which is now becoming very difficult as the network is increasing continuously and becoming more complex. The dynamic resource allocation method is proposed for the future capacity forecasting system which is founded on the factual employment of point to point links. Capacity is a crucial factor in wireless backhaul network so best capacity optimization can lead to a good frequency reuse and optimal use of other network resources. The development is based on three different models namely (1) Autoregressive (AR), (2) Seasonal Autoregressive Integrated Moving Average (SARIMA) and (3) Multi-Layer Perceptron (MLP) neural network. Root Means Squared Error (RMSE) and Means Absolute Percentage Error (MAPE) are performance criterion that are used to evaluate the models. When we compare the models' performance, the MLP results are most credible but it takes more time to converge than AR and SARIMA. By using the proposed estimation method, the static optimization will positively move to the dynamic (forecasted) optimization and the distribution of capacity utilization will be right skewed. Hence, the proposed system is efficient and has the ability to optimize the network according to the actual network’s capacity utilization. It will assist the network planner to perform more efficiently, resource distribution will be more balanced and the wastage of resources can be reduced.

Line-of-Sight MIMO for High Capacity Millimeter Wave Backhaul in FDD Systems

Wireless backhaul is considered to be the key part of the future wireless network with dense small cell traffic and high capacity demand. In this paper, we focus on the design of a high spectral efficiency line-of-sight (LoS) multiple-input multiple-output (MIMO) system for millimeter wave backhaul using dual-polarized frequency division duplex (FDD). High spectral efficiency is very challenging to achieve for the system due to various physical impairments such as phase noise (PHN), timing offset (TO) as well as the poor condition number of the LoS MIMO. In this paper, we propose a holistic solution containing TO compensation, PHN estimation, precoder/decorrelator optimization of the LoS MIMO for wireless backhaul, and the interleaving of each part. We show that the proposed solution has a robust performance with end-to-end spectral efficiency of 60 bit/s/Hz for 8 × 8 MIMO.

5G Key Technologies for Smart Railways

Railway communications has attracted significant attention from both academia and industries due to the booming development of railways, especially high-speed railways (HSRs). To be in line with the vision of future smart rail communications, the rail transport industry needs to develop innovative communication network architectures and key technologies that ensure high-quality transmissions for both passengers and railway operations and control systems. Under high mobility and with safety, eco-friendliness, comfort, transparency, predictability, and reliability. Fifth-generation (5G) technologies could be a promising solution to dealing with the design challenges on high reliability and high throughput for HSR communications. Based on our in-depth analysis of smart rail traffic services and communication scenarios, we propose a network slicing architecture for a 5G-based HSR system. With a ray tracing-based analysis of radio wave propagation characteristics and channel models for millimeter wave (mmWave) bands in railway scenarios, we draw important conclusions with regard to appropriate operating frequency bands for HSRs. mymargin Specifically, we have identified significant 5G-based key technologies for HSRs, such as spatial modulation, fast channel estimation, cell-free massive multiple-input-multiple-output (MIMO), mmWave, efficient beamforming, wireless backhaul, ultrareliable low latency communications, and enhanced handover strategies. Based on these technologies, we have developed a complete framework of 5G technologies for smart railways and pointed out exciting future research directions.

Optimal power allocation for non‐orthogonal multiple access in wireless backhaul networks

In this study, the authors adopt the non-orthogonal multiple access (NOMA) technique to improve the spectrum efficiency in the wireless backhaul networks, whereby the downlink and uplink NOMA techniques are applied for the backhaul and access links, respectively. Due to the coupling between the backhaul and access transmission stages, the transmission power should be carefully allocated in both stages so that the overall throughput can be maximised. They start with the single user equipment (UE) case and consider different scenarios and analyse the tradeoff between the access and backhaul links, and the optimal power allocation solutions are obtained accordingly. They then extend the analysis to the multi-UE case and formulate the optimal power allocation problem, which is solved using the Lagrangian dual decomposition algorithm. Simulation results demonstrate that the proposed schemes are effective in improving the throughput and outperforms the conventional orthogonal multiple access technique under different network settings.

Integrated access and backhaul a New Type of Wireless Backhaul in 5G

The combination of millimeter wave (mmWave) spectrum - which is becoming available globally for 5G - with other spectrum assets below 6GHz results in high speeds and capacities. The mmWave radio resources can only provide limited coverage, though, which makes it reasonable to expect a fairly low level of utilization. As a result, there is an opportunity to use an innovative type of wireless backhaul in 5G - integrated access and backhaul (IAB) - to densify networks with multi-band radio sites at street level.

Multi-Hop Wireless Optical Backhauling for LiFi Attocell Networks: Bandwidth Scheduling and Power Control

The backhaul of hundreds of light fidelity (LiFi) base stations (BSs) constitutes a major challenge. Building on an indoor wireless optical backhauling approach, this paper presents the top-down design of a multi-hop wireless backhaul configuration for multi-tier optical attocell networks by proposing super cells. Such cells incorporate multiple clusters of attocells that are connected to the core network via a single gateway. Consequently, new challenges arise for managing the bandwidth and power of the bottleneck backhaul. By putting forward user-based bandwidth scheduling (UBS) and cell-based bandwidth scheduling (CBS) policies, the system-level modeling and analysis of the end-to-end multi-user sum rate is elaborated. In addition, optimal bandwidth scheduling under both UBS and CBS policies are formulated as constrained convex optimization problems, and solved by using the projected subgradient method. Furthermore, the transmission power of the backhaul system is opportunistically reduced using a fixed power control (FPC) strategy. The notion of backhaul bottleneck occurrence (BBO) is introduced. An accurate approximate expression of the probability of BBO is derived, and verified using Monte Carlo simulations. Several insights are provided by studying different aspects of the performance of super cells including the average sum rate, the BBO probability and the backhaul power efficiency (PE).

Reinforcement Learning Based Cooperative Coded Caching Under Dynamic Popularities in Ultra-Dense Networks

For ultra-dense networks with wireless backhaul, caching strategy at small base stations (SBSs), usually with limited storage, is critical to meet massive high data rate requests. Since the content popularity profile varies with time in an unknown way, we exploit reinforcement learning (RL) to design a cooperative caching strategy with maximum-distance separable (MDS) coding. We model the MDS coding based cooperative caching as a Markov decision process to capture the popularity dynamics and maximize the long-term expected cumulative traffic load served directly by the SBSs without accessing the macro base station. For the formulated problem, we first find the optimal solution for a small-scale system by embedding the cooperative MDS coding into Q-learning. To cope with the large-scale case, we approximate the state-action value function heuristically. The approximated function includes only a small number of learnable parameters and enables us to propose a fast and efficient action-selection approach, which dramatically reduces the complexity. Numerical results verify the optimality/near-optimality of the proposed RL based algorithms and show the superiority compared with the baseline schemes. They also exhibit good robustness to different environments.

Caching at Base Stations With Multi-Cluster Multicast Wireless Backhaul via Accelerated First-Order Algorithms

Cloud radio access network (C-RAN) has been recognized as a promising architecture for next-generation wireless systems to support the rapidly increasing demand for higher data rate. However, the performance of C-RAN is limited by the backhaul capacities, especially for the wireless deployment. While C-RAN with fixed BS caching has been demonstrated to reduce backhaul consumption, it is more challenging to further optimize the cache allocation at BSs with multi-cluster multicast backhaul, where the inter-cluster interference induces additional non-convexity to the cache optimization problem. Despite the challenges, we propose an accelerated first-order algorithm, which achieves much higher content downloading sum-rate than a second-order algorithm running for the same amount of time. Simulation results demonstrate that, by simultaneously delivering the required contents to different multicast clusters, the proposed algorithm achieves significantly higher downloading sum-rate than those of time-division single-cluster transmission schemes. Moreover, it is found that the proposed algorithm allocates larger cache sizes to the farther BSs within the nearer clusters, which provides insight to the superiority of the proposed cache allocation.

Interference Management, Backhaul Routing & Switching off in 5G Hetnets with Mmwave Backhaul Links: A Relative Perspective

Today most of the next generation wireless communication systems(5G) faces the challenge of supporting the demands for higher data rates growing exponentially and ensuring to provide a stable quality of service (QoS) throughout the network. Over the next 2 years meeting up these requirements need to maximize the capacity of the network by a factor which will multiply it by thousands. Moreover, the most important societal as well as economical concerns include the power usage of data and communication technology industry and pollution mostly energy-related. On 5G two contrary requirements of providing capacity to support higher cellular networks along with consuming less energy needs to be focused upon. Due to the scarcity of spectral sources, a wide consensus can only be achieved by increasing significantly the number of antennas operating per unit area. Here a heterogeneous network named HetNet is analysed, comprising a macro base station (BS) along with several antennas and an overlaid dense tier possessing SCAs(small cell access points)through a wireless backhaul to get data traffic. The SCAs mostly associate with static as well as low mobility user equipment whereas macro BS serve the medium-to-high mobility. This work analyses the methods of the ultra-dense wireless 5G heterogeneous networks considering the interference management along the mm-Wave backhaul links to utilize the spectrum and network densification to operate mm-Wave 5G HetNet. The study reviews several literature works, their drawbacks and developing a joint model by combining base station switch-off technique with interface mitigation. This study further throws light on the scope of managing the backhaul-aware interference which in turn enhances potential capacity of the system and depending on the suitable backhaul the capacity is improved. Millimetre wave(mm-Wave) has proved to be a remarkable candidate to overcome the issue of ensuring a broad bandwidth having secure link transmission. The ultra-dense heterogeneous networks are discussed along with discussing the role interference management algorithms to minimize energy consumption. The importance of interference management is discussed along with discussing issues related. The research problem is formulated following a comparative analysis.

Trajectory optimization and resource allocation for UAV base stations under in-band backhaul constraint

The application of unmanned aerial vehicles (UAVs) to emerging communication systems has attracted a lot of research interests due to the advantages of UAVs, such as high mobility, flexible deployment, and cost-effectiveness. The UAV-carried base stations (UAV-BS) can provide on-demand service to users in temporary or emergency events. However, how to optimize the communication performance of a UAV-BS with a limited-bandwidth wireless backhaul is still a challenge. This paper focuses on improving the spectrum efficiency of a UAV-BS while guaranteeing user fairness under in-band backhaul constraint. We propose to maximize the minimum user rate among all the users served by the UAV-BS by jointly optimizing the allocation of bandwidth and transmit power, as well as the trajectory of the UAV-BS. As the formulated problem is non-convex, we propose an efficient algorithm to solve it suboptimally based on the alternating optimization and successive convex optimization methods. Computer simulation results show that the proposed algorithm achieves a significantly higher minimum user rate than the benchmark schemes.

Content Placement in Cache Networks Using Graph Coloring

Small cell densification is one of the effective ideas addressing the demand for higher capacity in cellular networks. The major problem in such networks is the limited capacity of wireless backhaul links. Caching the popular files in the memories of small cell base stations (SBSs) is an effective solution to this problem. One of the main challenges in caching is choosing the files to be stored in the memory of SBSs, especially when some SBSs have overlapped coverage. In this article, we propose a new caching scheme by exploiting the concept of graph coloring. To this end, we model the network scenario by a graph. This graph is divided into four subgraphs, i.e., placement, access, SBS, and delivery graphs. Through some modifications to the SBS graph, we convert it to a graph that can be colored. Then, we propose a graph coloring-based scheme to cache the popular files. We also derive a lower-bound for the hit rate improvement of our scheme compared to the conventional scheme which caches the most popular files anywhere. We evaluate the performance of our proposed scheme through simulations. Our results show that by employing the proposed scheme in a typical network, the load on the backhaul links is reduced significantly in average compared to the conventional scheme and some previously proposed schemes.

Impact of beam misalignment on THz wireless systems

This paper focuses on deriving expected values for the transmit (TX) and receive (RX) antenna gains in terahertz (THz) wireless fronthaul and backhaul links under stochastic beam misalignment, which is created by antenna movement coming from the building or antenna mast swaying. In particular, four different antenna movement models are considered: (i) Gaussian motion of a single antenna; (ii) Gaussian motion of both the TX and RX antennas; (iii) 2-dimensional (2D) Gaussian motion of a single antenna; and (iv) 2D Gaussian motion of the one antenna and one-dimensional Gaussian motion of the other. Models (i) and (iii) depict fronthaul scenarios, in which the access point is usually installed in high buildings or in road-sides. Models (ii) and (iv) may model backhaul applications. To verify the analysis and quantify the impact of beam misalignment, analytic and simulations results are provided that reveal that the antenna motion can cause a significant degradation on the expected value of the TX and RX antenna gains. Moreover, the derived models are used in a link budget assessment and insightful results for a number of realistic scenarios are given. These result clearly highlight the impact of beam misalignment in the received signal quality as well as the importance of taking into account the beam misalignment and using the correct antenna movement model, when evaluating its impact.

An Overlapping Coalition Formation Game Based Multicast Scheme in Backhaul-Limited Small Cell Networks

This paper studies the resource allocation for multicast transmission in wireless small cell networks, which allocates appropriate Modulation and Coding Scheme (MCS) on each physical Resource Block (RB). This problem is not trivial due to the impact of interference and the limited wireless backhaul. We first formulate our concerned problem with the objective of maximizing the network throughput. Afterwards, we utilize framed overlapping coalition formulation game theory to design a distributed resource allocation algorithm. We further analyze the stability and convergence of the proposed algorithm. Finally, we conduct extensive simulations to demonstrate that the limited wireless backhaul capacity is an important factor when identifying the optimal multicast resource allocation solution, and our proposed algorithm can significantly improve the multicast throughput.

Joint backhaul bandwidth and power allocation in heterogeneous cellular networks: A two‐level approach

SummaryWe investigate the problem of joint downlink wireless backhaul bandwidth (WBB) and power allocation in heterogeneous cellular networks (HCNs). A WBB partitioning scheme is considered, which allocates the whole bandwidth between the macrocell and small cells for data transmission and backhauling. We formulate an optimization problem to maximize the weighted sum logarithmic utility function by jointly optimizing WBB portion and fronthaul power allocation of each base station with consideration of the backhaul capacity limitation on each small cell. In order to solve this joint optimization problem, we propose a hierarchical two‐level approach and decompose the original problem into two independent subproblems: the WBB allocation at the macrocell base station (MBS) and the power allocation at both the MBS and small cell base stations (SBSs). Accordingly, the optimal WBB portion and power allocation solutions are obtained, respectively. Furthermore, we develop a distributed algorithm to implement the joint WBB and power allocation. Numerical results verify the effectiveness of the proposed approach and analyze the impact of the weighted coefficient and backhaul capacity limitation on the network performance. In addition, significant performance gains can be achieved by the proposed approach over the benchmark.

ORCHESTRA: Supercharging Wireless Backhaul Networks Through Multi-technology Management

Today’s and tomorrow’s networks are becoming increasingly complex and heterogeneous with a large diversity of devices and technologies. To meet growing demand, and support client mobility there is need for intelligent mechanisms like multi-technology load balancing and handovers. Current solutions, like Multipath Transmission Control Protocol (MPTCP), fail to provide a fine-grained, coordinated, and transparent answer to this heterogeneity, while the lower layers of the Open Systems Interconnection stack simply ignore it by providing full separation of layers. Therefore, we introduce ORCHESTRA, a data link layer framework for the management of multi-technology networks and devices, enabling packet-level dynamic handovers, load balancing, and duplication across network technologies. The framework is the first of its kind in providing fine-grained packet-level control across different technologies in a network-wide manner. Moreover, it works on top of existing standards without the need for hardware changes. This is achieved through a fully transparent virtual Medium Access Control layer and a Software-Defined Networking controller with global intelligence. The framework is implemented in a prototype running on off-the-shelf hardware and we demonstrate its features across different IEEE 802.11 technologies and 4G (Long Term Evolution). We demonstrate that ORCHESTRA outperforms MPTCP and allows for real-time inter-technology handovers and that overall throughput and reliability are improved in wireless networks.

Frequency Domain Backoff for Continuous Beamforming Space Division Multiple Access on Massive MIMO Wireless Backhaul Systems

This paper newly proposes a frequency domain backoff scheme dedicated to continuous beamforming space division multiple access (CB-SDMA) on massive antenna systems for wireless entrance (MAS-WE). The entrance base station (EBS) has individual base band signal processing units for respective relay stations (RSs) to be accommodated. EBS then continuously applies beamforming weight to transmission/reception signals. CB-SDMA yields virtual point-to-point backhaul link where radio resource control messages and complicated multiuser scheduling are not required. This simplified structure allows RSs to work in a distributed manner. However, one issue remains to be resolved; overloaded multiple access resulting in collision due to its random access nature. The frequency domain backoff mechanism is introduced instead of the time domain one. It can flexibly avoid co-channel interference caused by excessive spatial multiplexing. Computer simulation verifies its superiority in terms of system throughput and packet delay.

Integrated Access and Backhaul in 5G mmWave Networks: Potential and Challenges

IAB is being considered as a means to reduce the deployment costs of ultra-dense 5G mmWave networks, using wireless backhaul links to relay the access traffic. In this work we describe the most recent standardization activities on IAB, and compare architectures with and without IAB in mmWave deployments. While it is well understood that IAB networks reduce deployment costs by obviating the need to provide wired backhaul to each cellular base station, it is still necessary to validate the IAB performance in realistic scenarios. In this article we demonstrate the cell edge throughput advantage offered by IAB using end-to-end system-level simulations. We also highlight some research challenges for IAB that will require further investigations.

UAV-Aided Edge/Fog Computing in Smart IoT Community for Social Augmented Reality

In 5G and beyond, the adequate interaction between densely deployed Internet-of-Things (IoT) devices and cellular users will generate a massive cyber-physical information stream in a real-time manner. How to capture insights underneath these data in a smart city context is gaining great attention nowadays. In this article, we introduce a highly function-differentiated metropolitan scenario, which is covered by multiple unmanned aerial vehicles (UAVs) serving as cache-enabled edge computing nodes. With the help of wireless backhaul technology, coverage capability of UAV can be dynamically configured through smart 3-D placement, where trajectories of two types of UAVs are optimized. A social augmented reality (AR)-based use case is proposed and discussed in the proposed scenario, from which we derive the fundamental mechanisms and strategies to maintain a green and sustainable edge/fog computing framework. We sequentially establish two nonconvex programming problems and optimize delay and energy performance during AR data acquisition and AR content downloading, respectively. Two convex approximation skills are applied to transform the original problems into tractable form. The experimental results show that our proposed edge computing framework can help provide energy-efficient AR service to cellular users, catering to pretty tight delay constraints.

Near‐infrared reflectance spectroscopy based online moisture measurement in copra

AbstractMoisture in raw Copra (dried mature coconut) is an important quality parameter in the process of extraction of Coconut oil. The online and instantaneous measurement of moisture in copra chips with Near‐Infrared Reflectance Spectroscopy (NIRS) has been explored in this work. To do so, a NIRS moisture sensor with in‐house designed wireless backhaul network was commissioned at an industrial‐scale oil mill in Southern India. The accuracy of the results was validated with laboratory‐tested values. Furthermore, a chemometric analysis of NIRS signals captured from copra samples was carried out to accurately measure the moisture content. The moisture sensor was interfaced with a Supervisory Control and Data Acquisition (SCADA) system through a wireless communication network designed and developed in‐house. The system was trialed for 13 months. The system enabled for monitoring the process parameters of the oil extraction process from a safe and convenient distance away from critical instruments. The moisture content values predicted by the online NIRS sensors closely matched laboratory values (over 70% of the data was within the standard allowable tolerances). The system enabled for smarter operation, aided in improving process capability, reduced manpower, and energy requirements.Practical ApplicationsThe moisture content of the copra plays a crucial role in the oil extraction process and drives the market price of copra. Typically, moisture content of <7% is acceptable to the industry. High moisture content can make the copra vulnerable to fungal and pestilential attack. The in‐situ and online measurement of moisture can help improve the process of extraction of oil by considering optimum moisture and temperature. Through this process, the oil extraction efficiency and yield can be increased. By adaptively adjusting the temperature of the cooker, the energy requirements of running the plant are also optimized.