Backhaul Capacity Research Articles

Delay-Aware Satellite-Terrestrial Backhauling for Heterogeneous Small Cell Networks

This paper investigates a satellite-terrestrial backhaul framework to enhance efficient data offloading for heterogeneous terminals, including delay-sensitive and delay-tolerant users. In the considered architecture, ground terminals in satellite-terrestrial small cells can access different services via the satellite-terrestrial station (STS) in each cell. The satellite offloads the requested services to corresponding STSs, and each STS provides services to terrestrial terminals via an OFDM-based downlink system. We aim to maximize the sum throughput of all small cells while integrating joint satellite backhaul power allocation and STS downlink resource allocation. The problem is firstly decomposed into two types of subproblems by decoupling the optimization of satellite backhaul capacity and downlink capacity in small cells. Then, to satisfy users' delay requirements, the downlink STS throughput is maximized over multiple slots, and we propose a two-step algorithm to schedule users during these slots. By taking advantage of a delay-violation parameter, the algorithm iteratively approaches the optimal power and subchannel solution, while guaranteeing the delay requirements. Moreover, to reduce the computational complexity, we propose a greedy-based sub-optimal scheduling algorithm where delay requirements are guaranteed by users' self-search for favorable resources, aiming at sacrificing the minimum throughput in exchange for the delay performance. Simulation results show our algorithms effectively improve the throughput performance while ensuring the delay constraints, maintaining a well-performed balance between throughput and delay performance.

Contextual Multi-Armed Bandit for Cache-Aware Decoupled Multiple Association in UDNs: A Deep Learning Approach

The new trends in network densification and heterogeneity introduce new challenges for user association. Currently, user association is typically coupled, with which a user is constrained to associate with the same base station in both uplink (UL) and downlink (DL). In homogeneous networks, this is simple and effective. However, it could restrict the performance in heterogeneous ultra dense networks (UDNs), wherein BSs are densely deployed with highly variable transmit powers and topologies. Besides, the backhaul capacity could be the bottleneck in UDNs, and caching popular contents at BSs becomes an effective method for alleviating the backhauling traffic. In this paper, we propose a novel cache-aware decoupled multiple association mechanism for full-duplex UDNs, which allows a user to associate with multiple BSs in UL and DL, in a decoupled manner. Considering that users can form a self-learning system, a contextual multi-armed bandit (CMAB) problem is formulated where network states are unknown random variables. For obtaining the optimal strategy, an SINR-based linear upper confidence bound algorithm is developed. And deep learning is adopted when considering a large-scale network with expansion of the dimension in state space. The convergence of the algorithm is proven. Simulation results validate the feasibility and superiority of the proposed approach by comparisons.

QoE-Driven Transmission-Aware Cache Placement and Cooperative Beamforming Design in Cloud-RANs

Pre-caching popular videos at base stations (BSs) is a cost-effective way to significantly alleviate the backhaul pressure. With the video caching, the cache placement and the transmission strategy are intertwined with each other and jointly affect the system performance. Furthermore, the cache placement is updated in a much longer timescale than the transmission strategy. In this paper, the long-term transmission-aware cache placement and the short-term transmission strategy are designed to enhance the quality of experience (QoE) for the video streaming in cloud radio access networks (cloud-RANs). Specifically, consider a cache-enabled cloud-RAN, video contents are cached at BSs, and user requests are cooperatively satisfied by multiple BSs via the cooperative beamforming. To improve the weighted sum of users’ QoE, the long-term transmission-aware caching problem in the caching stage and the short-term transmission problem in the delivery stage are respectively formulated, taking into account the backhaul capacity constraint, the transmission power constraint, and the storage size constraint. For the caching problem, the sample average approach is first used to approximate the long-term average QoE value. Then, cache placement strategies are devised in both centralized and distributed manner. For the transmission problem, the full-cooperative beamforming scheme is studied with the optimized cache placement, and an iterative algorithm is proposed. Simulation results show that our proposed transmission-aware cache placement and transmission strategies can achieve higher QoE performance than other cache placement and transmission strategies.

Resource Allocation and Power Control in Cooperative Small Cell Networks With Backhaul Constraint

A joint resource allocation (RA), user association (UA), and power control (PC) problem is addressed for proportional fairness (PF) maximization in cooperative multiuser downlink small cell networks with limited backhaul capacity, based on orthogonal frequency division multiplexing. Previous studies have relaxed the per-resource-block (RB) UA and RA problem to a continuous optimization problem based on long-term signal-to-noise ratio, because the original problem is known as a combinatorial NP-hard problem. We tackle the original per-RB UA and RA problem to obtain a near-optimal solution with feasible complexity. Motivated by the fact that the condition for obtaining the near-global solution with the dual problem approach is rarely satisfied for increasing number of users, we derive explicit first order optimality conditions to obtain a 2-distance ring solution of the primal UA and RA problem, and propose a sequential optimization method. In addition, we propose a PC algorithm based on the first order KKT optimality conditions, in which transmission power of each RB is iteratively updated. The overall proposed scheme can be implemented with feasible complexity even with a large variable dimension. Numerical results show that the proposed scheme achieves the PF close to its outer-bound. Though there have been extensive studies on UA, RA, and PC in multicell networks, the proposed scheme is first to closely achieve the optimal PF performance in frequency-selective fading channels with feasible computational complexity.

Content Placement and User Association for Delay Minimization in Small Cell Networks

In small cell networks, content placement can reduce the content download delay at backhaul links while imposing challenges on user association problems. Specifically, a user may be associated with a small base station (SBS) that has the desired contents but is far away, increasing the delay over radio access links. To this end, we investigate the joint content placement and user association problem to reduce the average download delay over backhaul and radio access links for content delivery. Considering both dynamic user population and natural tandem systems, we model content delivery in each SBS as a tandem queue and thus derive the average sojourn time (the average time contents stay in the tandem queue), i.e., the average download delay. On this basis, we formulate a delay minimization problem, encompassing user characteristics (traffic arrival rates, requested content lengths and content preferences) and SBS constraints (backhaul capacity and storage size). Then, this problem is decomposed into a user association subproblem and a content placement subproblem by exploiting the biconvexity. Finally, a distributed content placement and user association scheme is proposed by exploring the optimal match with the first derivative length method. Extensive simulation results reveal that the delay under the proposed content placement policy may be reduced as user characteristics become heterogeneous, which is opposite to the case under user association policies.

Optimization of Multi-UAV-Aided Wireless Networking Over a Ray-Tracing Channel Model

Recently, unmanned aerial vehicles (UAVs) have been utilized to extend the coverage and capacity of terrestrial wireless networks. In such a UAV-aided wireless network , we can exploit the more favorable line-of-sight (LOS) propagation in the UAV-user wireless link to achieve better coverage and higher capacity. Furthermore, the flexible deployment of UAVs makes them ideal for catering to ad hoc demand. Due to the lack of fixed-line backhaul, UAVs act like relays with wireless backhaul links in UAV-aided wireless networks. Unlike the conventional relays in terrestrial wireless networks, the positions of UAV-Rs can be optimized to maximize the system performance. Although the optimal UAV positioning problem has been studied under simple LOS/probabilistic channel models, the joint optimization of UAV positions, user association, and wireless backhaul capacity allocation remains unsolved under realistic channel models, which will be addressed in this paper. The joint optimization problem is a challenging mixed non-convex and combinatorial problem. Combining the block coordinate descent and successive convex approximation (SCA) methods, we propose a sparse parallel block SCA algorithm, which can find a near-optimal solution by exploiting the structures of the propagation model. The simulations verify the significant gain of the proposed solution over existing solutions.

A Multi-Layer Multi-Timescale Network Utility Maximization Framework for the SDN-Based LayBack Architecture Enabling Wireless Backhaul Resource Sharing

With the emergence of small cell networks and fifth-generation (5G) wireless networks, the backhaul becomes increasingly complex. This study addresses the problem of how a central SDN orchestrator can flexibly share the total backhaul capacity of the various wireless operators among their gateways and radio nodes (e.g., LTE enhanced Node Bs or Wi-Fi access points). In order to address this backhaul resource allocation problem, we introduce a novel backhaul optimization methodology in the context of the recently proposed LayBack SDN backhaul architecture. In particular, we explore the decomposition of the central optimization problem into a layered dual decomposition model that matches the architectural layers of the LayBack backhaul architecture. In order to promote scalability and responsiveness, we employ different timescales, i.e., fast timescales at the radio nodes and slower timescales in the higher LayBack layers that are closer to the central SDN orchestrator. We numerically evaluate the scalable layered optimization for a specific case of the LayBack backhaul architecture with four layers, namely a radio node (eNB) layer, a gateway layer, an operator layer, and central coordination in an SDN orchestrator layer. The coordinated sharing of the total backhaul capacity among multiple operators lowers the queue lengths compared to the conventional backhaul without sharing among operators.

Manipulation With Domino Effect for Cache- and Buffer-Enabled Social IIoT: Preserving Stability in Tripartite Graphs

As a new Internet of Things (IoT) paradigm where smart devices work socially by exploiting social ties with adjacent devices, the Social IoT can effectively meet the real-time data sharing demands in Industrial IoT scenario, with the inter-device social relations being incentives. Besides, precaching on device level can potentially combat the backhaul capacity bottlenecks. Considering the limited cache memory, we may not use the whole capacity for caching, but leave a fraction for buffering data packets. In this article, we investigate how to maximize the quality of experience while minimizing the energy consumption. First, we design a proactive cache placement scheme for cost minimization. Next, we conceive the content sharing procedure with the framework of tripartite graph and propose a ternary stable matching algorithm to let devices self-organize the content sharing. Finally, we prove that inconspicuous manipulation with domino effect can further improve the system performance.

Energy Efficiency of the Cell-Free Massive MIMO Uplink With Optimal Uniform Quantization

A cell-free Massive multiple-input multiple-output (MIMO) uplink is considered, where the access points (APs) are connected to a central processing unit (CPU) through limited-capacity wireless microwave links. The quantized version of the weighted signals are available at the CPU, by exploiting the Bussgang decomposition to model the effect of quantization. A closed-form expression for spectral efficiency is derived taking into account the effects of channel estimation error and quantization distortion. The energy efficiency maximization problem is considered with per-user power, backhaul capacity and throughput requirement constraints. To solve this non-convex problem, we decouple the original problem into two sub-problems, namely, receiver filter coefficient design, and power allocation. The receiver filter coefficient design is formulated as a generalized eigenvalue problem whereas a successive convex approximation (SCA) and a heuristic sub-optimal scheme are exploited to convert the power allocation problem into a standard geometric programming (GP) problem. An iterative algorithm is proposed to alternately solve each sub-problem. Complexity analysis and convergence of the proposed schemes are investigated. Numerical results indicate the superiority of the proposed algorithms over the case of equal power allocation.

Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks.

Today’s wireless networks provide us reliable connectivity. However, if a disaster occurs, the whole network could be out of service and people cannot communicate. Using a fast deployable temporally network by mounting small cell base stations on unmanned aerial vehicles (UAVs) could solve the problem. Yet, this raises several challenges. We propose a capacity-deployment tool to design the backhaul network for UAV-aided networks and to evaluate the performance of the backhaul network in a realistic scenario in the city center of Ghent, Belgium. This tool assigns simultaneously resources to the ground users—access network—and to the backhaul network, taking into consideration backhaul capacity and power restrictions. We compare three types of backhaul scenarios using a 3.5 GHz link, 3.5 GHz with carrier aggregation (CA) and the 60 GHz band, considering three different types of drones. The results showed that an optimal UAV flight height (80 m) could satisfy both access and backhaul networks; however, full coverage was difficult to achieve. Finally, we discuss the influence of the flight height and the number of requesting users concerning the network performance and propose an optimal configuration and new mechanisms to improve the network capacity, based on realistic restrictions.

Caching mechanism for mobile edge computing in V2I networks

AbstractThe caching‐enabled mobile edge computing (MEC) system becomes increasingly crucial to the future long‐term evolution vehicle‐to‐infrastructure (LTE‐V2I) network, especially for fast delivery of popular content of delay‐sensitive applications in the backhaul capacity–limited vehicular networks. Most related studies mainly focus on computation offloading strategy design and coding caching, while the deployment strategy design of caching capacities for high‐speed vehicles has been overlooked. In this paper, we investigate the deployment problem in LTE‐V2I networks with particular consideration on high‐speed vehicles. First, in a one‐way scenario, a joint optimization framework is formulated to minimize the cache size of the MEC system, meanwhile maximizing the average downloading percentage, where the MEC system's backhaul capacities, vehicle speeds, content popularity distributions, and sides of the highway are taken into account. The impacts of LTE‐V2I system parameters, eg, vehicle speed, on the objective function are revealed through the one‐way optimization. Then, the two‐way optimization is formulated and solved by developing an iterative optimization approach based on the aforementioned one‐way scenario. Particularly, the optimal caching allocation between two ways is derived. Simulation results validate the effectiveness of the proposed approaches even with low backhaul capacities and high vehicle speed.

Enhancing Downlink Transmission in MIMO HetNet With Wireless Backhaul

We propose a transmission scheme that exploits the spatial degrees of freedom from large antenna arrays to maximize the downlink sum-rate of user equipment (UE) in a heterogeneous network. Our scheme incorporates the design of 1) a new precoder for the millimeter wave (mmWave) wireless backhaul; and 2) an enhanced small cell range expansion method for data link transmission. Aside from the advantage of lower implementation and computational complexity, the wireless backhaul precoder can simultaneously support multiple small access points with multi-streams. Under the constraint of wireless backhaul capacity, a downlink cooperation precoding algorithm is proposed to suppress the interference caused by small cell range expansion. Simulation results show that the proposed precoder in backhaul link can use a small number of radio frequency (RF) chains to achieve the sum-rate performance that approaches the one under traditional block diagonalization precoder, which requires the number of the RF chains to be comparable with that of antenna elements. Moreover, we give a lower bound for the overall UE sum-rate of the downlink cooperation precoding algorithm.

Cooperative Caching for Scalable Video Transmissions Over Heterogeneous Networks

We investigate a cooperative video caching problem in heterogeneous networks (HetNet). In cellular networks with limited backhaul capacity, by equipping the small-cell base stations (SBSs) with caches, more data can be offloaded. The backhaul data is reduced and user equipment (UE) can enjoy a low latency. We formulate the total transmission delay (TTD) minimization problem as a nonlinear integer programming. To solve the NP-hard problem, we relax the constraints and propose a greedy algorithm for a feasible solution. We prove that the greedy algorithm is asymptotically optimal. Simulation results demonstrate that the proposed cooperative caching algorithm can significantly reduce the TTD.

Utility-Based Opportunistic Scheduling Under Multi-Connectivity With Limited Backhaul Capacity

Multi-connectivity is a 5G key enabler to fulfill multi-service quality-of-service (QoS) requirements. This letter examines an opportunistic resource allocation problem under multi-connectivity and limited backhaul capacity in evolved LTE using two utility functions: 1) proportional fairness (PF) and 2) utility proportional fairness (UPF). We then propose an efficient algorithm to deal with the formulated NP-hard problem. Such algorithm guarantees to produce a solution with an explicit bound on the optimal solution. Finally, simulation results justify that UPF utility function with opportunistic scheduling in multi-connectivity can better satisfy QoS requirements.

MmWave Backhaul Testbed Configurability Using Software-Defined Networking

Future mobile data traffic predictions expect a significant increase in user data traffic, requiring new forms of mobile network infrastructures. Fifth generation (5G) communication standards propose the densification of small cell access base stations (BSs) in order to provide multigigabit and low latency connectivity. This densification requires a high capacity backhaul network. Using optical links to connect all the small cells is economically not feasible for large scale radio access networks where multiple BSs are deployed. A wireless backhaul formed by a mesh of millimeter-wave (mmWave) links is an attractive mobile backhaul solution, as flexible wireless (multihop) paths can be formed to interconnect all the access BSs. Moreover, a wireless backhaul allows the dynamic reconfiguration of the backhaul topology to match varying traffic demands or adaptively power on/off small cells for green backhaul operation. However, conducting and precisely controlling reconfiguration experiments over real mmWave multihop networks is a challenging task. In this paper, we develop a Software-Defined Networking (SDN) based approach to enable such a dynamic backhaul reconfiguration and use real-world mmWave equipment to setup a SDN-enabled mmWave testbed to conduct various reconfiguration experiments. In our approach, the SDN control plane is not only responsible for configuring the forwarding plane but also for the link configuration, antenna alignment, and adaptive mesh node power on/off operations. We implement the SDN-based reconfiguration operations in a testbed with four nodes, each equipped with multiple mmWave interfaces that can be mechanically steered to connect to different neighbors. We evaluate the impact of various reconfiguration operations on existing user traffic using a set of extensive testbed measurements. Moreover, we measure the impact of the channel assignment on existing traffic, showing that a setup with an optimal channel assignment between the mesh links can result in a 44% throughput increase, when compared to a suboptimal configuration.

Latency-Optimal Virtual Network Functions Resource Allocation for 5G Backhaul Transport Network Slicing

The concept of network slicing (NS) has been proposed for flexible resource provisioning where a physical resource is partitioned into logically independent networks on demand. The NS resource allocation implies the definition of a feasible path in the infrastructure network with adequate resource availability. However, due to complex structural characteristics of the backhaul transport network, a number of issues arise when fast deploying the end-to-end (E2E) slices onto network infrastructures. In this paper, a pair-decision resource allocation model is firstly formulated to construct the mapping relationship between logical networks and substrate networks in a coordinated way. In order to improve extreme quality of service (QoS) and user experiment, latency-optimal virtual resource allocation problem is defined, subject to the backhaul capacity and bandwidth constraints. The problem is formulated as an integer linear programming (ILP) and solved with the branch-and-bound scheme, whose resolution yields an optimal virtual network function (VNF) placement and traffic routing policy. Numerical results reveal that the proposed scheme can enable the transport network latency optimization with a reduction of up to 30% and 41.6% compared to the Network Slice Design Problem (NSDP) and Random Fit Placement Algorithm (RFPA) schemes respectively. In the meanwhile, the network load balance and serviceability have been improved efficiently with better resource utilization as well.

Explicit Modelling and Performance Analysis of Cell Group Selection With Backhaul-Aware Biasing

In existing works on performance analysis, cell selection is distance-based, and the relation of backhaul capacity variance and cell selection has not been explicitly modelled. Using stochastic geometry, this letter analyzes the performance of backhaul-aware cell selection by deriving the analytical expression for the outage probability, where high-backhaul-capacity small base stations (SBSs) have larger association biases to offload users from low-backhaul-capacity SBSs. Specifically, the general case of cell group (CG) selection is considered instead of single cell selection, and a CG consists of multiple SBSs with the largest biased reference signal received power, where the bias value is the SBS's backhaul capacity. In addition, the serving SBS changes adaptively which is the one with the largest transmission rate (minimum of access and backhaul rate) inside CG, thus exploiting the diversity of SBSs within CG. The results show that the outage probability is decreased by 30.6% when the CG size equals 5, compared with the distance-based CG formation strategy.

A Wireless Optical Backhaul Solution for Optical Attocell Networks

The problem of backhauling for optical attocell networks has been approached by a number of wired solutions such as in-building power line communication (PLC), Ethernet, and optical fiber. In this paper, an alternative solution is proposed based on the wireless optical communication in visible light and infrared (IR) bands. A thorough analysis of signal-to-noise-plus-interference ratio (SINR) is elaborated for a multi-user optical attocell network based on the direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) and decode-and-forward (DF) relaying, taking into account the effects of inter-backhaul and backhaul-to-access interferences. Inspired by concepts developed for radio frequency (RF) cellular networks, full-reuse visible light (FR-VL) and in-band visible light (IB-VL) bandwidth allocation policies are proposed to realize backhauling in the visible light band. The transmission power is opportunistically minimized to enhance the backhaul power efficiency. For a two-tier FR-VL network, there is a technological challenge due to the limited capacity of the bottleneck backhaul link. The IR band is employed to add an extra degree of freedom for the backhaul capacity. For the IR backhaul system, a power-bandwidth trade-off formulation is presented. Closed form analytical expressions are derived for the corresponding power control coefficients. Finally, the network sum rate performance is studied using extensive Monte Carlo simulations.

Converse Results for the Downlink Multicell Processing With Finite Backhaul Capacity

In this paper, we study outer bounds on the capacity region of the downlink multicell processing model with finite backhaul capacity for the simple case of two base stations and two mobile users. It is modeled as a two-user multiple access diamond channel. It consists of a first hop from the central processor to the base stations via orthogonal links of finite capacity and the second hop from the base stations to the mobile users via a Gaussian interference channel. The outer bound is derived using the converse tools of the multiple access diamond channel and that of the Gaussian MIMO broadcast channel. Through numerical results, it is shown that our outer bound improves upon the existing outer bounds greatly in the medium backhaul capacity range, and as a result, the gap between the outer bounds and the rate of the time-sharing of the known achievable schemes is significantly reduced.

Dual-Mode User-Centric Open-Loop Cooperative Caching for Backhaul-Limited Small-Cell Wireless Networks

The spectral efficiency of small-cell wireless networks is limited by the backhaul capacity of the base stations (BSs) as well as the severe interference from the neighboring BSs. One promising approach to improve the spectral efficiency of small-cell wireless networks is cache-aided cooperative transmission, where caching at the BSs can alleviate the high-speed backhaul capacity requirement and cooperative transmission can enhance the signal-to-interference-plus-noise ratio. A key issue is that the cached content may not be located at the nearest BS, which means that to access such content, a user needs to overcome strong interference from the nearby BSs. We propose an interference-aware dual-mode caching and user-centric open-loop cooperative transmission scheme that embraces spatial caching diversity and user-centric open-loop cooperative transmission, and alleviate the interference issue in the system. Based on the proposed scheme, we derive a tractable expression of the average successful transmission probability (STP) in terms of key system parameters. We then design a low-complexity algorithm to optimize the average STP with respect to the bandwidth and the cache storage capacity allocation. Simulations show that the proposed scheme achieves a higher average STP than the existing caching schemes.