Backhaul Connectivity Research Articles

Secure communication via an untrusted relay with unreliable backhaul connections

This paper studies the secrecy performance of a wireless energy harvesting system in which a source connected to wireless backhaul links sends information to a destination via an untrusted relay that not only helps the overall commutation but also overhears the sources confidential information. The secrecy capacity is created by using destination-assisted jamming signals. The jamming provides additional energy to the relay. To analyze the secrecy performance of the proposed system, we derived analytical expressions for the secrecy outage probability (SOP) and the average secrecy capacity, and the high-power approximations for the SOP. The accuracy of the calculations is verified by Monte Carlo simulations. Numerical results provide useful insight into the effects of the system parameters, such as the failure probability of unreliable backhaul links, the transmit powers, the power-splitting ratio, and the locations of the relay, on the secrecy performance.

Realizing Multi-Gbps Vehicular Communication: Design, Implementation, and Validation

This paper proposes a vehicular communication system that can achieve multi-Gbps data rate transmission for train and car applications. Employing a millimeter-wave frequency band around 25 GHz, the proposed system provides mobile backhaul connectivity for vehicle user equipments (UEs). In order to support a very high data rate with such a high carrier frequency while guaranteeing sufficient robustness against high mobility-related behaviors such as fast channel variation and unstable handover, we employ a relaying network architecture consisting of a backhaul link to a vehicle UE and an in-vehicle access link. Based on that, we provide a set of fundamental design elements including numerology, frame structure, the reference signal, multi-antenna scheme, and handover. We then validate the proposed vehicular communication system by implementing an experimental testbed consisting of baseband modem, RF front end, an array antenna units, and by performing field trials in an actual subway tunnel and urban road environments. The experimental validation results reveal that providing multi-Gbps backhaul transmission is possible and worthwhile for both train and car scenarios.

Dealing with Limited Backhaul Capacity in Millimeter-Wave Systems: A Deep Reinforcement Learning Approach

Millimeter-wave (mmWave) communication is a key technology of fifth generation wireless systems to achieve the expected 1000x data rate. With large bandwidth at the mmWave band, the link capacity between users and base stations (BSs) can be much higher compared to sub-6 GHz wireless systems. Meanwhile, due to the high cost of infrastructure upgrade, it would be difficult for operators to drastically enhance the capacity of backhaul links between mmWave BSs and the core network. As a result, the data rate provided by backhaul may not be sufficient to support all mmWave links; hence, the backhaul connection becomes the new bottleneck. On the other hand, as mmWave channels are subject to random blockage, the data rates of mmWave users significantly vary over time. With limited backhaul capacity and highly dynamic data rates of users, how to allocate backhaul resource to each user remains a challenge for mmWave systems. In this article, we present a deep reinforcement learning (DRL) approach to address this challenge. By learning the blockage pattern, the system dynamics can be captured and predicted, resulting in efficient utilization of backhaul resource. We begin with a discussion on DRL and its application in wireless systems. We then investigate the problem of backhaul resource allocation and present the DRL-based solution. Finally, we discuss open problems for future research and conclude this article.

Cognitive Heterogeneous Networks With Multiple Primary Users and Unreliable Backhaul Connections

Wireless backhaul has emerged as a suitable and flexible alternative to wired backhaul; however, it is not as reliable as its wired counterpart. This paper presents, for the first time, a comprehensive model including a heterogeneous underlay cognitive network with small cells also acting as multiple secondary users, multiple primary users, and unreliable wireless backhaul. In this system, a macro-base station connects to multiple secondary transmitters via wireless backhaul links. In addition, multiple secondary transmitters send information to a secondary receiver by sharing the same spectrum with multiple primary users. A Bernoulli process is adopted to model the backhaul reliability. A selection combining protocol is used at the secondary receiver side to maximize the received signal-to-noise ratio. We investigate the impact of the number of secondary transmitters, the number of primary users, as well as the backhaul reliability on the system performance in Rayleigh fading channels. Two key constraints are considered on the system performance: 1) maximum transmit power at the secondary transmitters and 2) peak interference power at the primary users caused by secondary transmitters. Closed-form expressions for outage probability, ergodic capacity, and symbol error rate and the asymptotic expressions for outage probability and symbol error rate are derived. Moreover, closed-form expressions are also applicable to non-cooperative scenarios.

Placement Optimization for UAV-Enabled Wireless Networks with Multi-Hop Backhauls

Unmanned aerial vehicles (UAVs) have emerged as a promising solution to provide wireless data access for ground users in various applications (e.g., in emergence situations). This paper considers a UAV-enabled wireless network, in which multiple UAVs are deployed as aerial base stations (BSs) to serve users distributed on the ground. Different from prior works that ignore UAV's backhaul connections, we practically consider that these UAVs are connected to the core network through a ground gateway node via rate-limited multi-hop wireless backhauls. We also consider that the air-to-ground (A2G) access links from UAVs to users and the air-to-air (A2A) backhaul links among UAVs are operated over orthogonal frequency bands. Under this setup, we aim to maximize the common (or minimum) throughput among all the ground users in the downlink of this network subject to the flow conservation constraints at the UAVs, by optimizing the UAVs' deployment locations, jointly with the bandwidth and power allocation of both the access and backhaul links. However, the common throughput maximization is a non-convex optimization problem that is difficult to be solved optimally. To tackle this issue, we use the techniques of alternating optimization and successive convex programming (SCP) to obtain a locally optimal solution. Numerical results show that the proposed design significantly improves the common throughput among all ground users as compared to other benchmark schemes.

Cooperative Cognitive Non-Orthgonal Multiple Access Under Unreliable Backhaul Connections

In this paper, we investigate the performance of energy-harvesting communications with decode-and-forward relay in the presence of non-orthogonal multiple access (NOMA) multiple access and cognitive radio spectrum sharing. We study three cases of single transmitter single relay and various transmitters and relays with two selection strategies (called as RTST1 and RTST2). Specifically, one source node (macro base station) transmits two symbols to two respective destinations via multiple transmitters and multiple relays under the power constraint of a primary user. We derived the closed-form expressions for the outage probability at two destinations and overall system. The outcomes revealed that the system performances are improved when increases the maximum transmit power at the transmitters, the threshold power affect to the primary user, the backhaul reliability. The system performance is lightly decreased when the power splitting ratio is at too low or too high value.

Core network function placement in self-deployable mobile networks

Emerging mobile network architectures (e.g., aerial networks, disaster relief networks) are disrupting the classical careful planning and deployment of mobile networks by requiring specific self-deployment strategies. Such networks, referred to as self-deployable, are formed by interconnected rapidly deployable base stations that have no dedicated backhaul connection towards a traditional core network. Instead, an entity providing essential core network functionalities is co-located with one of the base stations. In this work, we tackle the problem of placing this core network entity within a self-deployable mobile network, i.e., we determine with which of the base stations it must be co-located. We propose a novel centrality metric, the flow centrality, which measures a node capacity of receiving the total amount of flows in the network. We show that in order to maximize the amount of exchanged traffic between the base stations and the core network entity, under certain capacity and load distribution constraints, the latter should be co-located with the base station having the maximum flow centrality. We first compare our proposed metric to other state of the art centralities. Then, we highlight the significant traffic loss occurring when the core network entity is not placed on the node with the maximum flow centrality, which could reach 55% in some cases.

Flexible and Reliable UAV-Assisted Backhaul Operation in 5G mmWave Cellular Networks

To satisfy the stringent capacity and scalability requirements in the fifth generation (5G) mobile networks, both wireless access and backhaul links are envisioned to exploit millimeter wave (mmWave) spectrum. Here, similar to the design of access links, mmWave backhaul connections must also address many challenges such as multipath propagation and dynamic link blockage, which calls for advanced solutions to improve their reliability. To address these challenges, 3GPP New Radio technology is considering a flexible and reconfigurable backhaul architecture, which includes dynamic link rerouting to alternative paths. In this paper, we investigate the use of aerial relay nodes carried by e.g., unmanned aerial vehicles (UAVs) to allow for such dynamic routing, while mitigating the impact of occlusions on the terrestrial links. This novel concept requires an understanding of mmWave backhaul dynamics that accounts for: 1) realistic 3-D multipath mmWave propagation; 2) dynamic blockage of mmWave backhaul links; and 3) heterogeneous mobility of blockers and UAV-based assisting relays. We contribute the required mathematical framework that captures these phenomena to analyze the mmWave backhaul operation in characteristic urban environments. We also utilize this framework for a new assessment of mmWave backhaul performance by studying its spatial and temporal characteristics. We finally quantify the benefits of utilizing UAV assistance for more reliable mmWave backhaul. The numerical results are confirmed with 3GPP-calibrated simulations, while the framework itself can aid in the design of robust UAV-assisted backhaul infrastructures in future 5G mmWave cellular.

Optimal Hierarchical Radio Resource Management for HetNets With Flexible Backhaul

Providing backhaul connectivity for macro and pico base stations (BSs) constitutes a significant share of infrastructure costs in future heterogeneous networks (HetNets). To address this issue, the emerging idea of flexible backhaul is proposed. Under this architecture, not all the pico BSs are connected to the backhaul, resulting in a significant reduction in the infrastructure costs. In this regard, pico BSs without backhaul connectivity need to communicate with their nearby BSs in order to have indirect accessibility to the backhaul. This makes the radio resource management (RRM) in such networks more complex and challenging. In this paper, we address the problem of cross-layer RRM in HetNets with flexible backhaul. We formulate this problem as a two-timescale non-convex stochastic optimization which jointly optimizes flow control, routing, interference mitigation and link scheduling in order to maximize a generic network utility. By exploiting a hidden convexity of this non-convex problem, we propose an iterative algorithm which converges to the global optimal solution. The proposed algorithm benefits from low complexity and low signalling, which makes it scalable. Moreover, due to the proposed two-timescale design, it is robust to the backhaul signalling latency as well. Simulation results demonstrate the significant performance gain of the proposed solution over various baselines.

Secrecy Performance of Finite-Sized In-Band Selective Relaying Systems With Unreliable Backhaul and Cooperative Eavesdroppers

This paper investigates the secrecy performance of a finite-sized in-band selective relaying system with $M$ transmitters connected via unreliable backhaul links, $N$ decode-and-forward relays, and $K$ collaborative eavesdroppers. To send the source message to the destination, a transmitter-relay pair that achieves the highest end-to-end signal-to-noise ratio is selected for transmissions, while the $K$ eavesdroppers combine all the received signals from the selected transmitter and relay using maximal ratio combining. The proposed model introduces backhaul reliability and eavesdropping probability parameters to investigate practical constraints on the transmitter-relay cooperation and eavesdropper collaboration, respectively. Closed-form expressions are derived for the secrecy outage probability, probability of non-zero achievable secrecy rate, and ergodic secrecy rate for non-identical frequency-selective fading channels with robust cyclic-prefixed single carrier transmissions. These results show that the asymptotic secrecy outage probability and probability of non-zero achievable secrecy rate are exclusively determined by the number of transmitters $M$ and their corresponding set of backhaul reliability levels. Under unreliable backhaul connections, it is found that the secrecy diversity gain is determined by $M$ , $N$ , and the number of multipath components in the frequency selective fading channels. Link-level simulations are conducted to verify the derived impacts of backhaul reliability and collaborative eavesdropping on the secrecy performance.

Transmit Power Optimization and Feasibility Analysis of Self-Backhauling Full-Duplex Radio Access Systems

We analyze an inband full-duplex access node that is serving mobile users while simultaneously connecting to a core network over a wireless backhaul link, utilizing the same frequency band for all communication tasks. Such wireless self-backhauling is an intriguing option for the next generation wireless systems since a wired backhaul connection might not be economically viable if the access nodes are deployed densely. In particular, we derive the optimal transmit power allocation for such a system in closed form under quality-of-service (QoS) requirements, which are defined in terms of the minimum data rates for each mobile user. For comparison, the optimal transmit power allocation is solved also for two reference scenarios: a purely half-duplex access node, and a relay-type full-duplex access node. Based on the obtained expressions for the optimal transmit powers, we then show that the systems utilizing a full-duplex capable access node have a fundamental feasibility boundary, meaning that there are circumstances under which the QoS requirements cannot be fulfilled using finite transmit powers. This fundamental feasibility boundary is also derived in closed form. The feasibility boundaries and optimal transmit powers are then numerically evaluated in order to compare the different communication schemes. In general, utilizing the purely full-duplex access node results in the lowest transmit powers for all the communicating parties, although there are some network geometries under which such a system is not capable of reaching the required minimum data rates. In addition, the numerical results indicate that a full-duplex capable access node is best suited for relatively small cells.

A Multi-Layer Feedback System Approach to Resilient Connectivity of Remotely Deployed Mobile Internet of Things

Enabling the Internet of things in remote environments without traditional communication infrastructure requires a multi-layer network architecture. Devices in the overlay network such as unmanned aerial vehicles (UAVs) are required to provide coverage to underlay devices as well as remain connected to other overlay devices to exploit device-to-device (D2D) communication. The coordination, planning, and design of such overlay networks constrained by the underlay devices is a challenging problem. Existing frameworks for placement of UAVs do not consider the lack of backhaul connectivity and the need for D2D communication. Furthermore, they ignore the dynamical aspects of connectivity in such networks which presents additional challenges. For instance, the connectivity of devices can be affected by changes in the network, e.g., the mobility of underlay devices or unavailability of overlay devices due to failure or adversarial attacks. To this end, this work proposes a feedback based adaptive, self-configurable, and resilient framework for the overlay network that cognitively adapts to the changes in the network to provide reliable connectivity between spatially dispersed smart devices. Results show that the proposed framework requires significantly lower number of aerial base stations to provide higher coverage and connectivity to remotely deployed mobile devices as compared to existing approaches.

A resilient and distributed near real-time traffic forecasting application for Fog computing environments

In this paper we propose an architecture for a city-wide traffic modeling and prediction service based on the Fog Computing paradigm. The work assumes an scenario in which a number of distributed antennas receive data generated by vehicles across the city. In the Fog nodes data is collected, processed in local and intermediate nodes, and finally forwarded to a central Cloud location for further analysis. We propose a combination of a data distribution algorithm, resilient to back-haul connectivity issues, and a traffic modeling approach based on deep learning techniques to provide distributed traffic forecasting capabilities. In our experiments, we leverage real traffic logs from one week of Floating Car Data (FCD) generated in the city of Barcelona by a road-assistance service fleet comprising thousands of vehicles. FCD was processed across several simulated conditions, ranging from scenarios in which no connectivity failures occurred in the Fog nodes, to situations with long and frequent connectivity outage periods. For each scenario, the resilience and accuracy of both the data distribution algorithm, and the learning methods were analyzed. Results show that the data distribution process running in the Fog nodes is resilient to back-haul connectivity issues and is able to deliver data to the Cloud location even in presence of severe connectivity problems. Additionally, the proposed traffic modeling and forecasting method exhibits better behavior when run distributed in the Fog instead of centralized in the Cloud, especially when connectivity issues occur that force data to be delivered out of order to the Cloud.

FEMOS: Fog-Enabled Multitier Operations Scheduling in Dynamic Wireless Networks

Fog computing has recently emerged as a promising technique in content delivery wireless networks to alleviate the heavy bursty traffic burdens on backhaul connections. In order to improve the overall system performance, in terms of network throughput, service delay and fairness, it is very crucial and challenging to jointly optimize node assignments at control tier and resource allocation at access tier under dynamic user requirements and wireless network conditions. To solve this problem, in this paper, a fog-enabled multitier network architecture is proposed to model a typical content delivery wireless network with heterogeneous node capabilities in computing, communication, and storage. Further, based on Lyapunov optimization techniques, a new online low-complexity algorithm, namely fog-enabled multitier operations scheduling (FEMOS), is developed to decompose the original complicated problem into two operations across different tiers. Rigorous performance analysis derives the tradeoff relationship between average network throughput and service delay, i.e., ${[O(1/V), O(V)] }$ with a control parameter $\boldsymbol {V}$ , under FEMOS algorithm in dynamic wireless networks. For different network sizes and traffic loads, extensive simulation results show that FEMOS is a fair and efficient algorithm for all user terminals and, more importantly, it can offer much better performance, in terms of network throughput, service delay, and queue backlog, than traditional node assignment and resource allocation algorithms.

Secure cognitive radio networks with source selection and unreliable backhaul connections

In this study, the secrecy performance of cooperative half-duplex cognitive relay networks (CRNs) comprised of multiple primary users (PUs) and multiple eavesdroppers under the effect of unreliable wireless backhauls is studied. The authors consider relay networks with single relay assisting multiple sources under power constraints at PUs and secondary transmitters, and based on the availability of channel-state information at the sources, they propose two best source selection schemes, namely: (i) optimal source selection (OSS) and (ii) sub-optimal source selection (SoSS). For both scenarios, they obtain exact closed-form and asymptotic expressions for the system's outage probability and carry out numerical simulations to justify their analyses. From the results, source selection is proven to be capable of counteracting the negative impact of unreliable backhaul on CRNs. The secrecy performance limitations are also verified to be influenced by the backhaul reliability. Furthermore, OSS is more desirable compared to SoSS in low signal-to-noise ratio (SNR) regime, while the secrecy performance for both schemes converge to an asymptotic limit in high-SNR ranges.

Performance analysis of energy harvesting relay systems under unreliable backhaul connections

In this study, the performance of energy harvesting systems in the presence of unreliable backhaul links is investigated over independent but not necessarily identically distributed Nakagami-m fading channels. In particular, the energy-constrained relay uses the amount of harvested energy from the best small-cell transmitter based on time switching-based relaying protocol to process for the next hop transmission. The authors derived the closed-form expressions of the outage probability and the effective throughput with two distinct transmission schemes: (i) delay-limited transmission, and (ii) delay-tolerance transmission are attained. In order to assess the impacts of unreliable backhaul links, they thus obtain the asymptotic expression of the outage probability in high signal-to-noise ratio (SNR) regime. The numerical results are conducted to analyse the effects of energy harvesting fraction time, energy efficiency, backhaul reliability, and the fading parameters on the system performance. The authors' results show that under the unreliable backhaul links the outage probability yields the error-floor in the high SNR regime which demonstrates the significant impact of the backhaul unreliability.

Capacity Improvement and Protection of LTE Network on Ethernet Based Technique

High demands for data rates in mobile communications is the reason for developing broadband wireless access technologies. Long Term Evolution (4G LTE) networks which offer significantly higher data rates and require suitably higher capacity backhaul networks. To prepare for the high data rates usage in 4G LTE, operators are using ethernet services in terms of backhaul connectivity. Protection packet switching developed to anticipated network failure on ethernet based network technology. The failures in the network include the link fails to connect to each network element, the network element fails to transfer the data to the destination, or the quality drops below the standard. In this paper we used two ethernet based technique, namely Ethernet over SDH and MPLS-TP with ring protection to anticipated network failure on these techniques. Furthermore, we measured performance of network by measuring and comparing the throughput, latency and jitter between Ethernet over SDH and MPLS-TP. We used bandwidth capacity 240 Mbps as plant bandwidth link and worked in MIMO 2 2. The results of measurements indicated that MPLS-TP with ring protection is the best technique to enhanced the performance of LTE network.

Exploitation of Mobile Edge Computing in 5G Distributed Mission-Critical Push-to-Talk Service Deployment

There is a growing interest in adapting mission critical public-safety communications from traditional private radio technologies toward 5G. The need for resiliency and strong delay requirements has resulted in mobile edge computing (MEC) emerging as a key technology to improve the deployment of public safety applications over the mobile network. This paper presents a non-standalone 5G ETSI MEC-based architecture for mission-critical push-to-talk (MCPTT) services. The proposal suggests a hierarchical distributed MCPTT architecture that allocates the user plane at the edge, keeping the control plane (CP) centralized for synchronization and assistance purposes. The MEC architecture enables the deployment of low latency services, due to the proximity of functional servers to the end-user, releases user equipment from heavy workloads, and benefits from horizontal scalability to provide dynamic allocation of network resources at specific locations. The proposed architecture is also beneficial in an isolated E-UTRAN operation situation, where in the case of backhaul connection loss, local MCPTT services can be straightforwardly deployed within an isolated group of eNodeBs.

Secure Energy Harvesting Relay Networks With Unreliable Backhaul Connections

Wireless backhaul is a cost-effective and flexible alternative to wired backhaul, yet it suffers from unreliability. This paper studies the secrecy performance of a relay network with such unreliable wireless backhaul. Bernoulli process is adopted to model the wireless backhaul reliability. In the network, a relay employing time-switching-based radio frequency harvesting technique aids in forwarding the signal from the best transmitter. An eavesdropper, which is able to wiretap signals from both the transmitter and the relay, uses selection combining to maximize its received signal-to-noise ratio. Analytical expressions for secrecy outage probability, ergodic secrecy rate and non-zero achievable secrecy rate are derived, which can reveal the effect of the number of transmitters and backhaul reliability on the system performance. Furthermore, for the first time the impact of energy harvesting time fraction upon secrecy performances under unreliable backhaul is presented.

Multiple Drone-Cell Deployment Analyses and Optimization in Drone Assisted Radio Access Networks

In this paper, we propose a drone assisted radio access networks architecture in which drone-cells are leveraged to relay data between base stations and users. Based on the state-of-the-art drone-to-user and drone-to-base station (D2B) channel models, we first analyze the user coverage and the D2B backhaul connection features of drone-cells. We then formulate the 3-D drone-cell deployment problem with the objective of maximizing the user coverage while maintaining D2B link qualities, for a given number of drone cells being deployed. To solve the problem, the particle swarm optimization (PSO) algorithm is leveraged for its low computational cost and unique features suiting the spatial deployment of drone-cells. We propose a per-drone iterated PSO (DI-PSO) algorithm that optimizes drone-cell deployments for different drone-cell numbers, and prevents the drawbacks of the pure PSO-based algorithm derived from related works. Simulations show that the DI-PSO algorithm can achieve higher coverage ratio with less complexity comparing to the pure PSO-based algorithm.