Limited Backhaul Research Articles

Joint downlink user association and interference avoidance with a load balancing approach in backhaul-constrained HetNets.

In heterogeneous networks (HetNets), different lower-power base stations are added in a typically unplanned manner to the well-planned macro-only network, bringing new challenges to the network functions. Small cells experience limited backhaul capacity since cost-effective backhaul is not easily accessible to them. This study focuses on the issue of user association in backhaul-constrained HetNets. It shows that it is necessary to associate users with cells using a load balancing approach in order to fully leverage the addition of small cells. The cell association needs to be done jointly with an interference management technique that protects offloaded users and those prone to harmful interference. After modeling the system and describing the interference model, the problem of cell and subband allocation is formulated. We first examine the problem in a time-sharing mode and present a centralized heuristic solution to the cell and subband allocation problem. This is accomplished by solving the convex problem using the gradual removal method. The importance of providing distributed algorithms for HetNets leads to the development of a new algorithm through the application of the dual decomposition method to a reformulated problem and the use of an admission control mechanism. In the achieved algorithm, all computations are performed locally, with each user and base station relying only on local information. This algorithm obtains near-optimal answers, as confirmed by the simulation results. Compared with conventional cell allocation methods, our distributed algorithm prevents intensive interference for all users and achieves better load balance between network tiers, resulting in improved network utility.

Joint Learning and Optimization-Based Resource Management in Hybrid Network of Cooperative and Non-Cooperative Massive MIMO Systems

Multi-cell multiple-input multiple-output (MIMO) systems have the potential to significantly improve wireless network throughput. However, the cooperative MIMO network experiences the limited backhaul capacity, while the non-cooperative MIMO network requires high fronthaul bandwidth. To overcome these issues, we consider the hybrid MIMO networks to take advantages of both non-cooperative and cooperative MIMO network. In such hybrid system, the decision of whether cells operate in non-cooperative or cooperative mode, as well as the allocated bandwidth for cells are challenging due to the unavailability of channel state information (CSI). Furthermore, cells operating in the cooperative mode should optimize the quantized level selection and power allocation when the backhaul capacity is limited. To address this problem, we propose a joint learning-based and optimization-based framework. Particularly, we employ both large-scale and small-scale fading models to formulate the optimization problems, and then use the obtained solutions of these problems to determine fixed-size features in dynamic networks for the learner. Our numerical results show when the placement decision and the bandwidth assignment for cells are determined, the proposed algorithm can compute the quantized level and allocate power to significantly improve the system performance. Moreover, the proposed hybrid network model controlled by our algorithms outperforms both the non-cooperative and the cooperative MIMO systems in term of net throughput.

Robust Beamforming Design for RIS-Aided Cell-Free Systems With CSI Uncertainties and Capacity-Limited Backhaul

In this paper, we consider the robust beamforming design in a reconfigurable intelligent surface (RIS)-aided cell-free (CF) system considering the channel state information (CSI) uncertainties of both the direct channels and cascaded channels at the transmitter with capacity-limited backhaul. We jointly optimize the precoding at the access points (APs) and the phase shifts at multiple RISs to maximize the worst-case sum rate of the CF system subject to the constraints of maximum transmit power of APs, unit-modulus phase shifts, limited backhaul capacity, and bounded CSI errors. By applying a series of transformations, the non-smoothness and semi-infinite constraints are tackled in a low-complexity manner that facilitates the design of an alternating optimization (AO)-based iterative algorithm. The proposed algorithm divides the considered problem into two subproblems. For the RIS phase shifts optimization subproblem, we exploit the penalty convex-concave procedure (P-CCP) to obtain a stationary solution and achieve effective initialization. For precoding optimization subproblem, successive convex approximation (SCA) is adopted with a convergence guarantee to a Karush-Kuhn-Tucker (KKT) solution. Numerical results demonstrate the effectiveness of the proposed robust beamforming design, which achieves superior performance with low complexity. Moreover, the importance of RIS phase shift optimization for robustness and the advantages of distributed RISs in the CF system are further highlighted.

Impacts of Antenna Downtilt and Backhaul Connectivity on the UAV-Enabled Heterogeneous Networks

The performance of unmanned aerial vehicle (UAV)-enabled networks is generally bottlenecked by severe inter-cell interference and limited backhaul connectivity. Against this backdrop, we analyze the performance of the UAV-enabled heterogeneous networks, where antenna downtilt at each UAV-mounted BS is employed to mitigate inter-cell interference, and the tethered UAV (TUAV)-mounted BS is deployed to provide backhaul connectivity for multiple spatially randomly distributed untethered UAV-mounted BSs, while they are cooperatively serving the terrestrial users. Through leveraging stochastic geometry, the user association probability and conditional distance distributions of serving links are derived. Then, the compact expressions of coverage probability and ergodic rate of the UAV-enabled heterogeneous networks are derived, and the impacts of antenna downtilt and backhaul connectivity are investigated. Numerical results validate our analysis and show the effectiveness of antenna downtilt and the TUAV-enabled backhaul connectivity. Moreover, we find that a larger antenna downtilt angle is required for a higher deployment altitude. We show that the optimal size of the network area that maximizes the average coverage probability reduces, when the backhaul connectivity is considered. We also demonstrate that the average network performance can be significantly improved by judiciously selecting the number of untethered UAVs, which is closely related to the the size of the network area. Finally, the advantage of the proposed UAV-enabled heterogeneous network on the average ergodic rate has been validated by comparing it with two benchmarks under the 3GPP channel model.

Joint Optimization of Spectrum Resource Management and Position Placement for UAV Base Station Networks

The unmanned aerial vehicle (UAV) base station plays a significant role in enhancing the terrestrial network, when the ground base station (GBS) is destroyed in emergent cases or its load exceeds the capacity of the terrestrial network. Presently, many papers focus on optimizing the UAV position deployment and user access, while ignoring the optimization about the spectrum resource management. To solve this problem, we formulate a joint optimization problem of the spectrum resource management and the position placement for UAVs with the constraint of the limited backhaul capacity. Later, the joint optimization problem is modeled as a hierarchical game decision architecture comprised of a UAV position placement game and a spectrum resource management game. Further, we analyze the equilibrium property of the two games and propose two best response- (BR) based optimization algorithms to reach the Nash Equilibriums (NEs) of the two games, respectively. Specifically, the proposed algorithm about the UAV deployment considers the variable granularity local exploration and global random exploration. Simulation results show that the proposed UAV deployment algorithm can improve the total throughput by 7% and 20% at least in comparison with the K-means deployment algorithm and the fixed granularity exploration algorithm, respectively.

Joint Waveform and Clustering Design for Coordinated Multi-Point DFRC Systems

To improve both sensing and communication performances, this paper proposes a coordinated multi-point (CoMP) transmission design for a dual-functional radar-communication (DFRC) system. In the proposed CoMP-DFRC system, the central processor (CP) coordinates multiple base stations (BSs) to transmit both the communication signal and the dedicated probing signal. The communication performance and the sensing performance are both evaluated by the signal-to-interference-plus-noise ratio (SINR). Given the limited backhaul capacity, we study the waveform and clustering design from both the radar-centric perspective and the communication-centric perspective. Dinkelbach’s transform is adopted to handle the single-ratio fractional objective for the radar-centric problem. For the communication-centric problem, we adopt quadratic transform to convexitify the multi-ratio fractional objective. Then, the rank-one constraint of communication beamforming vector is relaxed by semidefinite relaxation (SDR), and the tightness of SDR is further proved to guarantee the optimal waveform design with fixed clustering. For dynamic clustering, equivalent continuous functions are used to represent the non-continuous clustering variables. Successive convex approximation (SCA) is further utilized to convexitify the equivalent functions. Simulation results are provided to verify the effectiveness of all proposed designs.

Efficient Online Learning Based Cross-Tier Uplink Scheduling in HetNets

Heterogeneous cellular networks (HetNets), where low-power low-complexity base stations (Pico-BSs) are deployed inside the coverage of macro base stations (Macro-BSs), can significantly improve the spectrum efficiency by Pico- and Macro base station collaboration. Due to cross-tier interference, joint detection of uplink signals is widely adopted so that Pico-BS can either detect the uplink signals locally or forward them to Macro-BS for processing. The latter can achieve increased throughput at the cost of additional backhaul transmission. In this paper, we study the delay-optimal uplink scheduling problem in HetNets with limited backhaul capacity. Local signal detection or joint signal detection is scheduled in a unified delay-optimal framework. Specifically, we first prove that the problem is NP-hard and then formulate it as a Markov Decision Process. We propose an efficient algorithm, called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OLIUS</monospace> , that can deal with the exponentially growing state and action space. Furthermore, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OLIUS</monospace> is online learning-based which does not require any prior knowledge on user behavior or channel characteristics. We prove the convergence of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OLIUS</monospace> and derive an upper bound on its approximation error. Extensive experiments in various scenarios show our algorithm outperforms existing methods in reducing delay and power consumption.

Intelligent Caching for Mobile Video Streaming in Vehicular Networks with Deep Reinforcement Learning

Caching-enabled multi-access edge computing (MEC) has attracted wide attention to support future intelligent vehicular networks, especially for delivering high-definition videos in the internet of vehicles with limited backhaul capacity. However, factors such as the constrained storage capacity of MEC servers and the mobility of vehicles pose challenges to caching reliability, particularly for supporting multiple bitrate video streaming caching while achieving considerable quality of experience (QoE). Motivated by the above challenges, in this paper, we propose an intelligent caching strategy that takes into account vehicle mobility, time-varying content popularity, and backhaul capability to improve the QoE of vehicle users effectively. First, based on the mobile video mean opinion score (MV-MOS), we designed an average download percentage (ADP) weighted QoE evaluation model. Then, the video content caching problem is formulated as a Markov decision process (MDP) to maximize the ADP weighted MV-MOS. Owing to the prior knowledge of video content popularity and channel state information that may not be available at the road side unit in practical scenarios, we propose a deep reinforcement learning (DRL)-based caching strategy to solve the problem while achieving a maximum ADP weighted MV-MOS. To accelerate its convergence speed, we further integrate the prioritized experience replay, dueling, and double deep Q-network technologies, which improve the performance of DRL algorithm. Numerical results demonstrate that the proposed DRL-based caching strategy significantly improves QoE, and achieves better video delivery reliability compared to existing non-learning approaches.

Enabling efficient caching in high mobility UAV communications network under limited backhaul

Due to flexible deployment, unmanned aerial vehicle (UAV) mounted aerial access points are capable of expanding the coverage capabilities of existing terrestrial base stations (TBSs). Different from TBSs, however, UAV access points (UAPs) are of high mobility in horizontal and vertical dimensions, which may deteriorate the coverage performance. Worsestill, the mobility of UAPs would as well increase the pressure of wireless backhaul. In this light, we investigate the performance of the cache-enabled UAV communications network (CUCN) in terms of network spatial throughput (ST) by analyzing the line of sight (LoS) connections and non-line of sight (NLoS) connections. It is found that the network ST is exponentially decreased with the square of UAP altitude. Furthermore, contrary to intuition, a large cache size may deteriorate the network ST when UAPs are over-deployed. The reason is that a large cache size increases the hit probability, which may increase the activation of UAPs and consequently result in complicated interference. Aiming to maximize the network ST, we optimize the cache strategy under limited backhaul. Remarkably, the results show that network ST could be substantially improved by the optimized cache strategy and the performance degeneration brought by UAP high mobility could be even eliminated especially when the UAP altitude is high.

5G/B5G mmWave Cellular Networks with MEC Prefetching Based on User Context Information.

To deal with recent increasing mobile traffic, ultra-broadband communication with millimeter-wave (mmWave) has been regarded as a key technology for 5G cellular networks. In a previous study, a mmWave heterogeneous network was composed of several mmWave small cells overlaid on the coverage of a macro cell. However, as seen from the optical fiber penetration rate worldwide, it is difficult to say that backhaul with Gbps order is available everywhere. In the case of using mmWave access under a limited backhaul capacity, it becomes a bottleneck at the backhaul; thus, mmWave access cannot fully demonstrate its potential. On the other hand, the concept of multi-access edge computing (MEC) has been proposed to decrease the response latency compared to cloud computing by deploying storage and computation resources to the user side of mobile networks. This paper introduces MEC into mmWave heterogeneous networks and proposes a content prefetching algorithm to resolve such backhaul issues. Context information, such as the destination, mobility, and traffic tendency, is shared through the macro cell to the prefetch application and data that the users request. Prefetched data is stored in the MEC and then transmitted via mmWave without a backhaul bottleneck. The effectiveness is verified through computer simulations where we implement realistic user mobility as well as traffic and backhauling models. The results show that the proposed framework achieved 95% system capacity even under the constraint of a 1 Gbps backhaul link.

Resource Allocation for IRS-Aided JP-CoMP Downlink Cellular Networks With Underlaying D2D Communications

This paper investigates resource allocation design for intelligent reflecting surface (IRS)-aided joint processing coordinated multipoint (JP-CoMP) downlink cellular networks with underlaying device-to-device (D2D) communications. In particular, an IRS is employed to establish favorable communication channel conditions and to mitigate the malignant interference caused by D2D devices. We aim to maximize the system sum-rate by jointly designing the cellular user (CU) association, the active beamforming at the base stations (BSs), the passive beamforming at the IRS, and the transmit power of each D2D transmitter (DT). The resource allocation design is formulated as a non-convex optimization problem while taking into account the quality of service (QoS) requirement of CUs, the power allocations for both CUs and D2D pairs, and the limited backhaul capacity. To handle the non-convex optimization problem, we propose a computationally efficient iterative algorithm exploiting the big-M formulation, the penalty method, and the successive convex approximation, which is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) solution. Simulation results demonstrate that the proposed scheme can increase the system sum-rate by 70% and 20% compared with the schemes with no IRS and random phase shifts, respectively, when the minimum required SINR of CUs is 5 dB. Additionally, our results confirm the superiority of introducing IRS for harnessing interference in wireless communication systems.

A New Framework for Multi-Hop ABS-Assisted 5G-Networks With Users’ Mobility Prediction

Unmanned Aerial Vehicles (UAVs) have gained momentum as one of the potential solutions in 5 G networks. Indeed, using UAVs as Aerial Base Stations (ABS) to assist the traditional cellular infrastructure and/or extend its coverage represents a high-potential flexible and cost-effective approach to provide on-demand communications in these new generations of networks. Yet, their effective deployment brings out many challenges, including their optimal 3D placement, coverage optimization, backhaul constraints, etc. Hence, we tackle these challenges by proposing a new framework for 3D positioning of an ABS swarm in order to extend the coverage of ground base stations (GBS). Unlike existing work that ignore ABS&#x2019;s limited backhaul capacity and how it restraints the number of users that can be served, we consider a multi-hop backhaul scheme that allows ABSs to remain connected to the core network through the GBSs directly, or other deployed ABSs. Under this setup, we aim to maximize the number of served UEs, by optimizing the ABS&#x2019;s placement, jointly with their association with UEs and backhauling base stations. Besides, our framework, named ASF for Adaptive Social Spider Optimization based framework, uses machine learning techniques to predict the users&#x2019; mobility and enable ABSs to adjust their position according to their spatial distribution. Simulations show that ASF framework significantly improves the cellular network&#x2019;s coverage and provides access to 92&#x0025; of mobile users, as compared to other benchmark schemes that only serve 79&#x0025; and 58&#x0025; of users.

Backhaul-Aware Storage Allocation and Pricing Mechanism for RSU-Based Caching Networks

Remarkable prevalence of in-car entertainment systems empowers vehicular users to download multimedia-enabled contents in transit and creates a new business opportunity for content providers (CPs). However, the timely delivery of requested content is a major concern for CPs to improve the quality of service (QoS) of their subscribe users. Roadside unit (RSU)-based caching appears as a promising solution for CPs wherein CPs proactively store their content at the RSU to reduce the content delivery time. Since the RSUs are enabled with limited storage capacity, the competition among multiple CPs for storage space is unavoidable. Further, the CPs are connected with RSUs using capacity limited backhaul links. Hence the allocation of RSUs’ storage among CPs becomes a fundamental issue in RSU-based caching networks. In this paper, we design a market scenario in which the set of CPs competes for the storage space of RSUs. In the unavailability of utility and cost functions of the CPs and the RSUs, we introduce a market maker to manage the marketplace. Further, we employ iteration-based double-sided auction mechanism to compute the optimal storage allocation and corresponding payment transfer for CPs which maximizes the social welfare of the networks. The simulation results demonstrate the proposed auction mechanism improves the social welfare of the network by at least 29.3% compared to the benchmark schemes. Further, with the help of both analytical and numerical analysis, we show that the proposed auction mechanism also holds vital economical properties.

Energy Efficiency Maximization in RIS-Aided Cell-Free Network With Limited Backhaul

Integrating the reconfigurable intelligent surface in a cell-free (RIS-CF) network is an effective solution to improve the capacity and coverage of future wireless systems with low cost and power consumption. The reflecting coefficients of RISs can be programmed to enhance signals received at users. This letter addresses a joint design of transmit beamformers at access points and reflecting coefficients at RISs to maximize the energy efficiency (EE) of RIS-CF networks, taking into account the limited backhaul capacity constraints. Due to a very computationally challenging nonconvex problem, we develop a simple yet efficient alternating algorithm for its solution. Numerical results verify that the EE of RIS-CF networks is greatly improved, showing the benefit of using RISs.

Linear Discriminant Analysis-Based Motion Classification Using Distributed Micro-Doppler Radars with Limited Backhaul.

In this paper, we propose a cooperative linear discriminant analysis (LDA)-based motion classification algorithm for distributed micro-Doppler (MD) radars which are connected to a data fusion center through the limited backhaul. Due to the limited backhaul, each radar cannot report the high-dimensional data of a multi-aspect angle MD signature to the fusion center. Instead, at each radar, the dimensionality of the MD signature is reduced by using the LDA algorithm and the dimensionally-reduced MD signature can be collected at the data fusion center. To further reduce the burden of backhaul, we also propose the softmax processing method in which the distances of the sensed MD signatures from the centers of clusters for all motion candidates are computed at each radar. The output of the softmax process at each radar is quantized through the pyramid vector quantization with a finite number of bits and is reported to the data fusion center. To improve the classification performance at the fusion center, the channel resources of the backhaul are adaptively allocated based on the classification separability at each radar. The proposed classification performance was assessed with synthetic simulation data as well as experimental data measured through the USRP-based MD radar.

Distributed Resource Allocation for Green HetNets with Renewable Energy Resources

In fifth generation (5G) systems, green heterogeneous network (HetNet) is capable of achieving energy efficiency by densely deploying renewable-powered small cells. However, the small cells may suffer performance degradation due to the limited backhaul from macro base station (BS) and renewable intermittency. In this paper, we introduce a distributed HetNet architecture in which the renewable-powered small cell BSs collaboratively exchange information and allocate the spectrum and power resources by themselves. Considering the uncertainty of the available spectrum, renewable energy supply and traffic loads, a stochastic optimization problem is formulated to maximize the energy efficiency for distributed small cell BSs. A distributed resource allocation algorithm is proposed to obtain the optimal spectrum and power allocating strategies for each small cell. Finally, the numerical results demonstrate the effectiveness of the proposed algorithm.

On the Performance of Cache-Enabled Hybrid Wireless Networks

To alleviate the backhaul congestion in future hybrid heterogenous networks, this paper investigates the potential benefits of implementing storages in small base stations (SBSs) operating at frequency range 2 (FR2) bands that co-exist with a tier of massive multiple input multiple output (MIMO) macro BSs (MBSs) operating at frequency range 1 (FR1) bands. We develop a unified analytical framework and derive theoretical bounds for such a cache-enabled FR1-FR2 hybrid network under limited backhaul scenario to analyze the exact and approximate latency, average success probability of file delivery, and average data rate considering two open-access user association policies: i) location-based and ii) content-based association. Numerical results demonstrate that wireless edge caching (WEC) can improve the performance of hybrid wireless networks, albeit certain trade-offs, e.g., increasing cache-enabled SBSs cannot always improve the network performance and there exists an optimal SBS density that provides the best latency and throughput performance. Furthermore, we compare the performance of the network with respect to other key network design parameters such as cache size, content popularity, backhaul capacity, and blockages for both the user associations. Our results show that latency under content-based user association is less than that of location-based user association, and although the difference in the average rates under the two user associations is not obvious, content-based association can extricate more backhaul capacity and thus reduce installation cost significantly.

Performance Analysis of Backhaul‐Aware User Association in 5G Ultradense Heterogeneous Networks

In 5G ultradense heterogeneous networks, wireless backhaul, as one of the important base station (BS) resources that affect user services, has attracted more and more attention. However, a user would access to the BS which is the nearest for the user based on the conventional user association scheme, which constrains the network performance improvement due to the limited backhaul capacity. In this paper, using backhaul‐aware user association scheme, semiclosed expressions of network performance metrics are derived in ultradense heterogeneous networks, including coverage probability, rate coverage, and network delay. Specifically, all possible access and backhaul links within the user connectable range of BSs and anchor base stations (A‐BSs) are considered to minimize the analytical results of outage probability. The outage for the user occurs only when the access link or backhaul link which forms the link combination with the optimal performance is failure. Furthermore, the theoretical analysis and numerical results evaluate the impact of the fraction of A‐BSs and the BS‐to‐user density ratio on network performance metric to seek for a more reasonable deployment of BSs in the practical scenario. The simulation results show that the coverage probability of backhaul‐aware user association scheme is improved significantly by about 2× compared to that of the conventional user association scheme when backhaul is constrained.

Cluster‐Based Cooperative Cache Deployment and Coded Delivery Strategy in C‐V2X Networks

Cellular vehicle‐to‐everything‐ (C‐V2X‐) based communications can support various content‐oriented applications and have gained significant progress in recent years. However, the limited backhaul bandwidth and dynamic topology make it difficult to obtain the multimedia service with high‐reliability and low‐latency communication in C‐V2X networks, which may degrade the quality of experience (QoE). In this paper, we propose a novel cluster‐based cooperative cache deployment and coded delivery strategy for C‐V2X networks to improve the cache hit ratio and response time, reduce the request‐response delay, and improve the bandwidth efficiency. To begin with, we design an effective vehicle cluster method. Based on the constructed cluster, we propose a two‐level cooperative cache deployment approach to cache the frequently requested files on the edge nodes, LTE evolved NodeB (eNodeB) and cluster head (CH), to maximize the overall cache hit ratio. Furthermore, we propose an effective coded delivery strategy to minimize the network load and the ratio of redundant files. Simulation results demonstrate that our proposed method can effectively reduce the average response delay and network load and improve both the hit ratio and the ratio of redundant files.

Integrated VLC/RF Wireless Technologies for Reliable Content Caching System in Vehicular Networks

In a vehicular communications environment, the need for information sharing, entertainment, and multimedia will increase, leading to congestion of backhaul networks. The major challenge of this network is latency and resource limitations. Proactive caching can be obtained from local caches rather than from remote servers, which can avoid long delays resulting from limited backhaul capacity and resources. Therefore, proactive caching reduces latency and improves the quality of services. Determining which files should be cached in memory is a critical issue. The paper proposes various placement schemes for caching at the vehicle and mobile base station (MBS) layers. The optimal caching place represents better user demand and latency based on the demand and mobility model. Non-cooperative and cooperative vehicles with vehicle-to-vehicle communication (V2V) are included. In both models, the high data rate of visible light communication (VLC) in the vehicular networks tends to enhance the caching capability. We also illustrate a sub-optimal algorithm to solve optimization problems compared to the optimal brute force solver and the sub-optimal genetic algorithm. Numerical results show that proactive caching schemes have a significant gain in system performance. The analysis shows that VLC improves proactive caching performance when there are less than 25 vehicles. The joint proactive caching scheme works better as the number of vehicles increases. The V2V proactive caching is useful when vehicles per platoon are ≥ 10