Backhaul Capacity Research Articles

Efficient UAV Hovering, Resource Allocation, and Trajectory Design for ISAC With Limited Backhaul Capacity

Efficient UAV Hovering, Resource Allocation, and Trajectory Design for ISAC With Limited Backhaul Capacity

Optimizing cell association in 5G and beyond networks: a modified load-aware biased technique

Cellular networks are moving towards increasing heterogeneity by deploying more small cells into macro base station (MBS) to meet rapidly growing traffic demands. To leverage the advantages of these small cells, mobile users should be offloaded onto small base stations (BSs), which will typically be lightly populated and can give a higher data rate by presenting the mobile users with many more channels than the MBS. Likewise, a more balanced cell association will lessen the pressure on the MBS, allowing it to serve its remaining users more effectively. This paper addresses the cell association challenge for Quality of Service (QoS) provisioning in terms of throughput and load-balancing for 5G and future generation networks. This problem is quite challenging because BSs have varying backhaul capacities and users have varying QoS needs. Most of the previous studies are based on reference signal received power (RSRP), signal to interference and noise ratio (SINR) or its variants and most importantly majority of them are not load-aware. Therefore, a modified load-aware biased cell association scheme based on distance is proposed to attain better QoS provisioning in terms of throughput and load-balancing. Simulation results depict that the proposed load-aware-based method outperforms conventional cell association schemes based on RSRP and its variants, and in terms of throughput and load-balancing. Furthermore, the algorithm’s complexity has been assessed through a comparison and analysis of computational time, demonstrating better performance compared to state-of-the-art techniques.

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.

Decoupled Association With Rate Splitting Multiple Access in UAV-Assisted Cellular Networks Using Multi-Agent Deep Reinforcement Learning

In unmanned aerial vehicles (UAVs) assisted cellular networks, user association plays an important role in interference control and spectrum efficiency. In this paper, we study the performance of uplink-downlink decoupled (UDDe) user association in a multi-UAV assisted network in which each user can associate with different UAVs or the macro base station (MBS) for uplink (UL) and downlink (DL) transmissions. Since some popular data may be requested by multiple users, grouping these users and applying multicasting can significantly improve spectral efficiency. Unlike traditional linear precoding that treats interference entirely as noise, we propose a rate-splitting multiple access (RSMA) policy that employs rate splitting at the transmitter and successive interference cancellation (SIC) at the receiver. To be specific, the transmitted signal is split into a common part and a private part, and the interference is partially decoded and partially treated as noise. In this context, we formulate a joint optimization problem of UL-DL association and beamforming for maximizing the sum-rate of users in UL and that of multicast groups in DL under the constraints of UAV backhaul capacity and power budget. Since the formulated problem is non-convex with intricate states and an individual UAV may not know the rewards of other UAVs, we convert it into a robust partially observable Markov decision process (POMDP). Then we resort to multi-agent deep reinforcement learning (MADRL) that enables each UAV to learn and optimize its policy in a distributed manner. To achieve an optimal policy, we further propose an improved clip and count-based proximal policy optimization (PPO) algorithm to train actor and critic networks. Simulation results demonstrate the superiority of the proposed decoupled association strategy with RSMA and the MADRL learning algorithm.

Enhancing Small-Cell Capacity with Wireless Backhaul

Recently, hyperdense small cells have been proposed to meet the challenge of the tremendous increment in cellular data service requirements. To reduce the deployment cost, as well as operated cost, these small cells are usually connected to limited backhauls, in which case the backhaul capacity may become a bottleneck in busy hours. In this paper, we propose an optimal scheme for the small cells to utilize the macrocell links as its wireless backhaul. Based on stochastic geometry, the analytical expressions of network capacity with in-band and out-band wireless backhaul are derived and validated using simulation results. The optimized results show that our proposed scheme can significantly improve the network performance in scenarios with a high traffic load.

Low Latency 5G IP Transmission Backhaul Network Architecture: A Techno-Economic Analysis

The steeply rising demand for mobile data drives the investigation of the transmission backhaul network architecture and cost for the fifth generation (5G) of mobile technologies. The proposed backhaul architecture will facilitate high throughput, low latency, scalability, low cost of ownership, and high capacity backhaul for 5G mobile technologies. This paper presents a transmission backhaul network architecture for 5G technology; the proposed internet protocol (IP) transmission backhauling architecture includes the data center, core network, distribution network, and access or IP random access network. A mathematical model for the data center IP core network, IP distributed network, and the IP access network for capital expenditure (Capex), operational expenditure (Opex), and the total cost of ownership (TCO) are presented, as well as a mathematical model for the entire backhauling architecture. The result shows that the increase in IP sites is positively proportional to the Capex and negatively proportional to the Opex. The selectivity analysis shows that the increase in bandwidth is directly proportional to the Capex, Opex, and TCO in the IP core network. The increase in data centers is directly proportional to the Capex, Opex, and TCO of the entire backhauling architecture.

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

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

Joint trajectory and CoMP clustering optimization in UAV-assisted cellular systems: a coalition formation game approach

In this paper, the flexibility of unmanned aerial vehicles (UAVs), as well as the benefits of coordinated multi-point (CoMP) transmission, are utilized for mitigating the interference in cellular networks. Specifically, the joint problem of CoMP clusters and UAVs’ trajectories is addressed for downlink transmission in a UAV-assisted cellular system. The problem is presented as a non-convex optimization problem that aims to maximize the sum rate of the ground users by taking into account the clustering, UAV mobility and backhaul capacity constraints. Since the formulated problem is known to be NP-hard, we partition it into two sub-problems. Particularly, by using coalitional game theory, the CoMP clusters are obtained with a given UAVs’ trajectories. Then, UAVs’ trajectories are optimized with given CoMP clusters using successive convex approximation technique. Based on the block coordinate descent method, the two sub-problems are solved alternatively until convergence. Numerical results are conducted and demonstrated the effectiveness of the proposed algorithm.

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.

Two-Timescale Learning-Based Task Offloading for Remote IoT in Integrated Satellite–Terrestrial Networks

In this paper, we propose an integrated satellite-terrestrial network (ISTN) architecture to support delay-sensitive task offloading for remote Internet of Things (IoT), in which satellite networks serve as a complement to terrestrial networks by providing additional communication resources, backhaul capacities, and seamless coverage. Under this architecture, we investigate how to jointly make offloading link selection and bandwidth allocation decisions for BSs and IoT users. Considering the differentiated decision-making time granularities, we formulate a two-timescale stochastic optimization problem to minimize the overall task offloading delay. To accommodate the two-timescale network dynamics and characterize state-action relations, we establish a hierarchical Markov decision process (H-MDP) framework with two separate agents tackling two-timescale network management decisions, and two evolved MDP-based subproblems are formulated accordingly. To efficiently solve the subproblems, we further develop a hybrid proximal policy optimization (H-PPO)-based algorithm. Specifically, a hybrid actor-critic architecture is designed to deal with the mixed discrete and continuous actions. In addition, an action mask layer and an action shaping function are designed to sample feasible task offloading decisions from the time-variant action set. Extensive simulation results have validated the superiority of the proposed ISTN architecture and the H-PPO-based algorithm, especially in scenarios with scarce spectrum resources and heavy traffic loads.

A Novel Feeder Link Handover Strategy for Backhaul in LEO Satellite Networks

Thanks to their wide coverage and relatively low latency compared to geosynchronous satellites, Low Earth Orbit (LEO) satellite networks have been regarded as one of the most promising solutions to provide global broadband backhaul for mobile users and IoT devices. In LEO satellite networks, the frequent feeder link handover invokes unacceptable communication interruptions and affects the backhaul quality. To overcome this challenge, we propose a maximum backhaul capacity handover strategy for feeder links in LEO satellite networks. To improve the backhaul capacity, we design an available backhaul capacity ratio to jointly consider feeder link quality and the inter-satellite network in handover decisions. In addition, we introduce a service time factor and handover control factor to reduce the handover frequency. Then, we propose the handover utility function based on the designed handover factors and propose a greedy-based handover strategy. Simulation results show that the proposed strategy outperforms conventional handover strategies in backhaul capacity with low handover frequency.

On the Performance of Clustered Fog Radio Access Networks With Data Compression

The fog-radio-access-network (F-RAN) has been proposed to address the strict latency requirements, which offloads computation tasks generated in the user equipment (UE) to the edge to reduce the processing latency. However, it incorporates the task transmission latency, which may become the bottleneck of latency requirements. Data compression (DC) has been considered as one of the promising techniques to reduce the transmission latency. By compressing the computation tasks before transmitting, the transmission delay is reduced due to the shrink transmitted data size, and the original computing task can be retrieved by employing data decompressing (DD) at the edge nodes or the centre cloud. Nevertheless, the DC and DD incorporate extra processing latency. For the F-RAN system, the latency performance has not been investigated considering the DC and DD processes. Therefore, in this work, the successful data compression probability (SDCP), i.e., the probability of the task execution latency being smaller than a target latency and the signal to interference ratio (SIR) of the received signal being higher than a threshold, is defined to analyse the latency performance of the DC-enabled F-RAN. Moreover, to analyse the impact of compression offloading ratio (COR), which determines the proportion of tasks being compressed at the edge, on the SDCP of the F-RAN, a novel hybrid compression mode is proposed based on the queueing theory. Based on this, the closed-form result of SDCP in the large-scale DC-enabled F-RAN is derived by combining the Matern cluster process and M/G/1 queueing model, and validated by the Monte-Carlo simulation. Based on the derived SDCP results, the effects of COR on the SDCP is analysed numerically. The results show that the SDCP with the optimal COR can be enhanced with a maximum value of 0.3 and 0.55 as compared with the cases of compressing all computing tasks at the edge and at the UE, respectively. Moreover, for the system requiring the minimal latency, the proposed hybrid compression mode can alleviate the requirement on the backhaul capacity.

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.

Exploiting Collaborative Computing to Improve Downlink Sum Rate in Satellite Integrated Terrestrial Networks

In this paper, we target at improving the downlink user sum rate in satellite integrated terrestrial network (SITN). Wherein, the densely deployed low earth orbit (LEO) satellites provide backhaul to terrestrial base stations (TBSs) and TBSs serve the ground users (GUs) over different bands. Note that the user sum rate is dependent on the user association and backhaul capacity. Moreover, the backhaul capacity is determined by multi-connectivity feature between the TBSs and LEO satellites. However, the densification and high dynamics of SITN aggravate the inter-satellite interference, which degrades the backhaul capacity. To this end, we propose a joint interference management and user association (JIMUA) scheme, which exploits collaborative computing to enhance the backhaul capacity and satisfy the user demands. The service demands of GUs and dwell duration of LEO satellites can be captured by introducing a collaborative computing coefficient. With this aid, the TBSs can predict the highly dynamic trajectory of dense deployed LEO satellites. Accordingly, the searching space of satellites can be reduced, the interference can be mitigated and backhaul capacity can be improved with high time efficiency. Moreover, the user association is achieved by dividing GUs into different groups, while the required user demands can be satisfied. Considering the dense deployment of LEO satellites, the user sum rate of SITN could be enhanced by JIMUA compared with the benchmark. With the well tuned collaborative computing coefficient, the backhaul capacity can be increased over 12%. Consequently, the user sum rate can be improved over 7%. Moreover, with the increasing user density, the gap between the user sum rate of JIMUA and the optimal solution can be reduced to less than 4%.

Handover Analysis in Ultra-Dense LEO Satellite Networks With Beamforming Methods

With the ever-growing demands of communication quality and rate, the combination of terrestrial networks and space networks is becoming the next recognized solution. The space-ground integrated network (SGIN) is a massive Internet of Things (IoT) network that accommodates large amounts of mobile terminals. The interactivity of information between those terminals will request higher communication rates and lead to more frequent handovers, which will increase the network burden and degrade the user's experience, especially in ultra-dense Low-Earth Orbit (LEO) satellite networks. In this article, a modified matching algorithm is proposed to get the optimal association matrix between users and base stations. Based on the results, the sum rate of users and the handover rate are calculated. A modified particle swarm optimization (MPSO) method is also proposed to generate the special-shaped beams on the satellites to deal with the complex optimization problems. By combining the MPSO-based beamforming algorithm and the modified matching algorithm, the non-convex sum-rate optimization problem is decomposed into two optimal subproblems, which are solved separately. The simulation results demonstrate that the SGIN significantly outperforms the non-integrated ones in terms of the sum data rate and backhaul capacity. The handover performance under the user mobility model is also discussed.

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.

Backhaul Capacity-Intent Interference Easing for Sum Rate Improving in 6G Cellular Internet of Things

A mmWave-enabled integrated access backhaul (IAB) network for the 6G cellular Internet of Things (IoT) is necessary to handle the escalating wideband communications needs. A decreased sum rate and low successful transmission probability were the results of the mmWave-enabled IAB network's significant interference problems between access links and differentiated small-cell base stations' backhaul capacities. This article suggests the Q-learn JUP algorithm, which stands for joint user equipment association and power allocation. In order to establish the UA and PA problem, we first investigate how the SBSs-UE association and transmit power are related. Second, using the interference and backhaul burden as the state, UA and PA as the action, and the overall sum rate increment as the reward function of Q-learning, we determine the best joint optimization framework through off-line training. In order to lessen interference and backhaul load, a new backhaul capacity and cautious matching approach utility function has also been designed. Simulation results demonstrate that, in contrast to existing algorithms, the proposed algorithm may vastly enhance the network sum rate and successful transmission probability.

Backhaul-Aware User Association and Throughput Maximization in UAV-Aided Hybrid FSO/RF Network

Free-space optical (FSO) communication is expected to play an indispensable role with high data rates and low system complexity in beyond fifth-generation (B5G) networks. However, infrequent adverse weather conditions can incapacitate its performance. The combination of FSO and radio frequency (RF) has emerged as an effective alternative for meeting the growing need for high data rates in wireless communication networks. Unmanned aerial vehicles (UAVs) are also anticipated to play an instrumental role in B5G networks due to their flexible movement and deployment. In this paper, a UAV-aided hybrid FSO/RF backhauling system using a matching game theory (GT) and reinforcement learning (RL) framework is investigated. We deploy a UAV to provide a user offloading service to an already existing ground base station (GBS), which is facing a reduced backhaul capacity due to weather attenuation (e.g., fog). It is considered that the GBS has a pre-installed FSO backhaul connection to a macro-base station (MBS). However, during adverse weather conditions, the FSO backhaul is severely affected, compromising the reliability of the FSO link. With the reduced FSO backhaul capacity, the GBS needs an additional backhaul link to support its backhaul data transmission to the destination MBS. As a result, instead of building an expensive permanent parallel RF link for the rare foggy situation, a UAV can be hired to serve a portion of the users, thereby reducing the GBS load. The users perform a matching game-based procedure to select the base station (BS) of their choice to maximize their utility. The UAV is deployed at an optimal altitude, and the bandwidth partition between the GBS and the UAV is optimized to maximize the system throughput using RL. Real weather data from the cities of Edinburgh and London in the U.K. are used to evaluate the performance of the system. The numerical results show the superiority and effectiveness of the proposed scheme compared to conventional methods.

MIMO Unmanned Surface Vessels Enabled Maritime Wireless Network Coexisting With Satellite Network: Beamforming and Trajectory Design

Due to the flexible deployment, unmanned surface vessels (USVs) have attracted much interest recently. To solve the resource scarcity problem at sea, USV needs to leverage existing terrestrial and satellite systems for efficient backhaul and spectrum sharing. In this case, the multiple input multiple output (MIMO) technology can be applied for diversity gain improvement and interference coordination. However, how to adopt MIMO technology into maritime networks with a sparse scattering environment is still an open issue. In this paper, we employ a multi-antenna USV to support on-demand communications. Utilizing the two-ray channel, we aim to maximize the sum throughput over all USV intended users, by jointly optimizing the cooperative beamforming and trajectory, subject to several practical constraints, including the USV kinetics, quality of service requirement and backhaul capacity. Different from existing whole period designs, we decompose the problem into sequential one-slot problems. Within each slot, the non-convex problem is solved iteratively by using problem decomposition and successive convex optimization methods. Then, channel estimation errors are considered to investigate a robust beamforming scheme. Numerical simulations validate that the USV coexists well with the satellite network and show that the beamforming scheme and trajectory design complement each other for performance improvement.