Backhaul Load Research Articles

Optimal Content Caching and Recommendation With Age of Information

Content caching at the network edge is an effective way of mitigating backhaul load and improving user experience. Caching efficiency can be enhanced by content recommendation and by keeping the information fresh. By content recommendation, a requested content that is not in the cache can be alternatively satisfied by a related cached content recommended by the system. Information freshness can be quantified by age of information (AoI). This paper has the following contributions. First, we address optimal scheduling of cache updates for a time-slotted system accounting for content recommendation and AoI, and to the best of our knowledge, there is no work that has jointly taken into account these aspects. Next, we rigorously prove the problem's NP-hardness. Then, we derive an integer linear formulation, by which the optimal solution can be obtained for small-scale scenarios. On the algorithmic side, our contributions include the development of an effective algorithm based on Lagrangian decomposition, and efficient algorithms for solving the resulting subproblems. Our algorithm computes a bound that can be used to evaluate the performance of any suboptimal solution. We conduct simulations to show the effectiveness of our algorithm.

Lightening Backhaul Load for Aerial Multi-Hop Integrated Access and Backhaul Networks

Lightening Backhaul Load for Aerial Multi-Hop Integrated Access and Backhaul Networks

Analysis and optimization on partition-based caching and delivery in satellite-terrestrial edge computing networks

As a viable component of 6G wireless communication architecture, satellite-terrestrial networks support efficient file delivery by leveraging the innate broadcast ability of satellite and the enhanced powerful file transmission approaches of multi-tier terrestrial networks. In the paper, we introduce edge computing technology into the satellite-terrestrial network and propose a partition-based cache and delivery strategy to make full use of the integrated resources and reducing the backhaul load. Focusing on the interference effect from varied nodes in different geographical distances, we derive the file successful transmission probability of the typical user and by utilizing the tool of stochastic geometry. Considering the constraint of nodes cache space and file sets parameters, we propose a near-optimal partition-based cache and delivery strategy by optimizing the asymptotic successful transmission probability of the typical user. The complex nonlinear programming problem is settled by jointly utilizing standard particle-based swarm optimization (PSO) method and greedy based multiple knapsack choice problem (MKCP) optimization method. Numerical results show that compared with the terrestrial only cache strategy, Ground Popular Strategy, Satellite Popular Strategy, and Independent and identically distributed popularity strategy, the performance of the proposed scheme improve by 30.5%, 9.3%, 12.5% and 13.7%.

An Efficient Content Popularity Prediction of Privacy Preserving Based on Federated Learning and Wasserstein GAN

To relieve the high backhaul load and long transmission time caused by the huge mobile data traffic, caching devices are deployed at the edge of mobile networks. The key to efficient caching is to predict the content popularity accurately while touching the users’ privacy as little as possible. Recently, many studies have applied federated learning in content caching to improve data security. However, they still give away some privacy of participants, especially ignoring the private data leakage in the trained model. To solve this problem and further improve the cache hit ratio, we propose an efficient content popularity prediction of privacy-preserving (CPPPP) scheme based on federated learning and Wasserstein generative adversarial network (WGAN), which achieves a high cache hit ratio. Benefited by the Federated-WGAN and the generated fake samples, the private data, the content preferences of individual users, and so on are well protected. In particular, gradient clipping and model parameter limitation are introduced in the model training, and the security of the modified model is greatly improved compared with the original model. Results show that the proposed scheme has a higher cache hit ratio than the existing federated learning-based methods while limiting the privacy leakage caused by the trained model to a quite low level.

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.

Adaptive Federated Deep Reinforcement Learning for Proactive Content Caching in Edge Computing

With the aggravation of data explosion and backhaul loads on 5 G edge network, it is difficult for traditional centralized cloud to meet the low latency requirements for content access. The federated learning ( <u>F</u> L)-based <u>p</u> roactive content <u>c</u> aching (FPC) can alleviate the matter by placing content in local cache to achieve fast and repetitive data access while protecting the users’ privacy. However, due to the non-independent and identically distributed (Non-IID) data across the clients and limited edge resources, it is unrealistic for FL to aggregate all participated devices in parallel for model update and adopt the fixed iteration frequency in local training process. To address this issue, we propose a distributed resources-efficient FPC policy to improve the content caching efficiency and reduce the resources consumption. Through theoretical analysis, we first formulate the FPC problem into a stacked autoencoders (SAE) model loss minimization problem while satisfying resources constraint. We then propose an adaptive FPC (AFPC) algorithm combined deep reinforcement learning (DRL) consisting of two mechanisms of client selection and local iterations number decision. Next, we show that when training data are Non-IID, aggregating the model parameters of all participated devices may be not an optimal strategy to improve the FL-based content caching efficiency, and it is more meaningful to adopt adaptive local iteration frequency when resources are limited. Finally, experimental results in three real datasets demonstrate that AFPC can effectively improve cache efficiency up to 38.4 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and 6.84 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , and save resources up to 47.4 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and 35.6 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , respectively, compared with traditional multi-armed bandit (MAB)-based and FL-based algorithms.

Multiagent Cooperative Caching Policy in Industrial Internet of Things

To handle the impact of the explosive growth of data traffic generated by smart industrial applications in the Industrial Internet of Things (IIoT) scenario, edge caching is commonly used in the IIoT to reduce the content access delay and to release the backhaul load. However, due to the mobility of IIoT devices and temporal dependence of popularity, traditional content caching policies, such as least frequently used (LFU) and least recently used (LRU), cannot cache the required contents accurately within the coverage of edge server. Therefore, by exploiting the moving trajectory of the IIoT devices and the temporal dependence of the content popularity, we propose a multiagent cooperative caching policy, in which each edge server acts as an agent to cooperatively learn the optimal caching decision, then each edge server caches the corresponding contents to reduce the content access delay. Especially, we first apply the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -order Markov chain to predict the moving trajectory of the IIoT devices to get the IIoT device set within the coverage of each edge server. Second, we use long short-term memory (LSTM) to get the prior knowledge of the content requests by using the moving trajectory prediction results. Finally, we obtain the optimal caching decision by deep reinforcement learning to improve the Quality of Service (QoS) of the IIoT applications. Experimental results demonstrate that the proposed caching policy can efficiently improve the cache-hit ratio and content access delay.

Enabling Edge Computing in 5G for Mobile Augmented Reality

Mobile Augmented Reality is scoring more reputation due to the progression in the clever smartphones that include characteristics and hardware needs for integrating AR. MAR offers an exclusive knowledge in which the physical world is enhanced by virtual remarks. It also incorporates computationally intensive techniques that might be divested to edge servers on 5G systems, improving MAR by reducing communication latency and ensuring more robust network links, resulting in unified mobile augmented reality user experiences. Edge computing is one of the probable 5G technologies that could deliver content and processing resources familiar to the users, lowering latency and backhaul load. We presented a MAR structure situated on a 5G edge computing and latency results. Offer higher bandwidth while edge servers completely scale down the network restrictions to reduce the bandwidth severity of the network by operating closer to the user. This paper aims to address the 5G edge computing arrangement to aid consistent MAR and finding protocol for enhanced quality of experience (QoE) for MAR applications.

Intelligent Edge-Based Service Provisioning Using Smart Cloudlets, Fog and Mobile Edges

The advancements of mobile devices and the rapid development of computing applications are encouraging a trend shift from traditional, centralized mobile cloud computing to decentralized mobile edge computing (e.g., cloudlet, fog, and multi-access edge computing) focused on the quality of service (QoS) of applications and quality of experience of user equipment (UEs). Such edge technologies enable service and resource provisioning in the vicinity of UEs, drastically reducing the network propagation delay and backhaul load. However, the emergence of computation-intensive functions, latency-sensitive tasks, and bandwidth-hungry activities make QoS-aware scheduling and resource allocation in the edge environment an open research issue. In this article, we discuss the edge-based service pro-visioning issues from the perspective of artificial intelligence (AI). We first summize the three main edge-enabling technologies. The core functioning and research challenges are presented in the context of a layered edge environment. A comparison is made among these technologies with the presentation of similarities and differences. The edge processing model is further elaborated for centralized architecture and distributed architecture, respectively. Then, we identify and categorize AI-based technologies for the UE layer, edge platform layer, and inter-edge layer regarding core edge-based service provisioning issues. Finally, we highlight open challenges and research directions, and we conclude that the number of potential issues in the edge can be properly solved by AI-based technologies.

Mean-Field Game Theory Based Optimal Caching Control in Mobile Edge Computing

Mobile edge computing (MEC) can use wireless access network (RAN) to provide users with nearby information technology (IT) services and cloud computing functions, which creates a high-performance and low latency service environment. By caching the popular content at small base station (SBS) can reduce the heavy backhaul load and the content retransmission. However, the time-varying and dynamic of the content requests may lead to the base station to cache the useless contents. In this paper, we study a distributed caching optimization problem in edge networks (ENs) with the spatio-temporal requirements. In the considered ENs, the cache control is described as a stochastic differential game (SDG) in which each SBS defines a caching strategy to reduce the total cost in terms of the service delay and backhaul link load. To reduce the computational complexity, the original optimization problem is transformed into a mean field game (MFG). We propose a distributed caching iterative control algorithm that decouples the information interaction between the general SBS and others through the mean field distribution. In addition, we obtain the optimal edge caching control strategy, while the existence and uniqueness of the mean field equilibrium (MFE) can also be guaranteed. Simulation results demonstrate that our proposed caching control algorithm can average reduce 27.12&#x0025; storage cost and achieve better performance than other existing schemes.

Centralized and Distributed Algorithms for Energy and Spectrum Efficient User Association in Small Cell Networks

Macro base stations are overlaid by small cells to satisfy the demands of user equipment in heterogeneous networks. To provide wide coverage, some small cells are not directly connected to macro base stations and thus backhaul connections are required to connect small cells to macro base stations. Millimeter wave backhauls which have high bandwidths are preferred for small cell backhaul communication, since they can increase the capacity of network considerably. In this context, association of user equipment to base stations becomes challenging due to the backhaul architecture. Considering environmental concerns, energy efficiency is a vital criterion in designing user association algorithms. In this paper, we study the user association problem aiming at the maximization of energy efficiency given a specific spectral efficiency target. We develop centralized and distributed user association algorithms based on sequentially minimizing the power consumption. Finally, we evaluate the performance of the proposed algorithms under two scenarios and show that they achieve higher energy efficiency compared to the existing algorithms in the literature, while maintaining high spectral efficiency and backhaul load balancing.

Stochastic Game Based Cooperative Alternating Q-Learning Caching in Dynamic D2D Networks

Edge caching has become an effective solution to cope with the challenges brought by the massive content delivery in cellular networks. In device-to-device (D2D) enabled caching cellular networks with time-varying content popularity distribution and user terminal (UT) location, we model these dynamic networks as a stochastic game to design a cooperative cache placement policy. The cache placement reward of each UT is defined as the caching incentive minus the transmission power cost for content caching and sharing. We consider the long-term cache placement reward of all UTs in this stochastic game. In an effort to solve the stochastic game problem, we propose a multi-agent cooperative alternating Q-learning (CAQL) based cache placement algorithm. The caching control unit is defined to execute the proposed CAQL, in which, the cache placement policy of each UT is alternatively updated according to the stable policy of other UTs during the learning process, until the stable cache placement policy of all the UTs in the cell is obtained. We discuss the convergence and complexity of CAQL, which obtains the stable cache placement policy with low space complexity. Simulation results show that the proposed algorithm can effectively reduce the backhaul load and the average content access delay in dynamic networks.

Collaborative Data Caching and Computation Offloading for Multi-Service Mobile Edge Computing

Mobile edge computing (MEC) can use wireless access network (RAN) to provide the services required by user's information technology (IT) and cloud computing functions nearby, which can create a high-performance and low latency service environment. Performing task offloading and data caching at access points (APs) in a cooperative manner can reduce the heavy backhaul load and the retransmission of content downloading. However, in edge networks (ENs), how to maximize storage utilization while reducing service latency and energy consumption is still a key issue, because the heterogeneity of ENs and the uneven distribution of users make it difficult to determine which MEC server and what data should be cached. In this paper, we study a two-tier MEC system, which enables data caching and computing offloading policy to minimize the network cost at the user equipment (UE) side, while satisfying the constraints of task offloading deadline, the cache capacity at APs and the computing capability of MEC servers. The optimization problem is formulated as a mixed integer nonlinear program (MINLP) problem. In order to solve the problem, we transform it into an equivalent task offloading convex optimization problem by fixing an optimization variable. Furthermore, we solve a cache placement problem by dynamic programming (DP) algorithm. Then we propose a distributed collaborative data caching and computing offloading (CDCCO) iterative algorithm. Simulation results demonstrate that our proposed CDCCO algorithm can significantly reduce the network cost and achieve better performance than other existing schemes.

MSE minimized joint transmission in coordinated multipoint systems with sparse feedback and constrained backhaul requirements

In a joint transmission coordinated multipoint (JT-CoMP) system, a shared spectrum is utilized by all neighbor cells. In the downlink, a group of base stations (BSs) coordinately transmit the users’ data to avoid serious interference at the users in the boundary of the cells, thus substantially improving area fairness. However, this comes at the cost of high feedback and backhaul load; In a frequency division duplex system, all users at the cell boundaries have to collect and send feedback of the downlink channel state information (CSI). In centralized JT-CoMP, although with capabilities for perfect coordination, a central coordination node have to send the computed precoding weights and corresponding data to all cells which can overwhelm the backhaul resources. In this paper, we design a JT-CoMP scheme, by which the sum of the mean square error (MSE) at the boundary users is minimized, while feedback and backhaul loads are constrained and the load is balanced between BSs. Our design is based on the singular value decomposition of CSI matrix and optimization of a binary link selection matrix to provide sparse feedback—constrained backhaul link. For comparison, we adopt the previously presented schemes for feedback and backhaul reduction in the physical layer. Extensive numerical evaluations show that the proposed scheme can reduce the MSE with at least 25%, compared to the adopted and existing schemes.

Radio &amp; BH Load-Aware Multi-Objective Clustering in Multi-Cell MIMO Cooperative Networks

Coordinated Multipoint (CoMP) is one of the key technologies identified for future wireless networks to mitigate inter-cell interference, especially in a dense deployment scenario. However, CoMP can’t be realized for the whole network due to its computational complexity, synchronization between coordinating base stations (BSs) and high backhaul (BH) capacity requirement. BSs need to be clustered into smaller groups and CoMP can be activated within these smaller clusters. In this paper, we develop a multi-objective, dynamic clustering model for multi-user, joint-transmission CoMP to jointly optimize spectral efficiency (SE), radio access network (RAN) load and BH load. We formulate our load-aware model as two coalitional sub-games for small cell and user equipment clustering, respectively. Merge/split/transfer actions for each sub-game are defined and a complexity and stability analysis is provided. Extensive simulation results show that our model provides as good SE in low load when compared to a greedy model, and significantly better load balancing with a reduced number of unsatisfied users and increased throughput in high load scenario. On average 49% increase in the overall system throughput is observed in our simulations when compared to the greedy model.

Deployment and Migration of Virtualized Services with Joint Optimization of Backhaul Bandwidth and Load Balancing in Mobile Edge-Cloud Environments

Mobile edge-cloud computing environments appear as a novel computing paradigm to offer effective processing and storage solutions for delay sensitive applications. Besides, the container based virtualization technology becomes solicited due to its natural lightweight and portability as well as its small migration overhead that leads to seamless service migration and load balancing. However, with the mobility property, the users’ demands in terms of the backhaul bandwidth is a critical parameter that influences the delay constraints of the running applications. Accordingly, a Binary Integer Programming (BIP) optimization problem is formulated. It minimizes the users’ perceived backhaul delays and enhances the load-balancing degree in order to offer more chance to accept new requests along the network. Also, by introducing bandwidth constraints, the available user backhaul bandwidth after the placement are enhanced. Then, the adopted methodology to design two heuristic algorithms based on Ant Colony System (ACS) and Simulated Annealing (SA) is presented. The proposed schemes are compared using different metrics,and the benefits of the ACS-based solution compared to the SA-based as well as a genetic algorithm (GA) based solutions are demonstrated. Indeed, the normalized cost and the total backhaul costs are given by more optimal values using the ACS algorithm compared to the other solutions.

Multitier Service Migration Framework Based on Mobility Prediction in Mobile Edge Computing

Mobile edge computing (MEC) pushes computing resources to the edge of the network and distributes them at the edge of the mobile network. Offloading computing tasks to the edge instead of the cloud can reduce computing latency and backhaul load simultaneously. However, new challenges incurred by user mobility and limited coverage of MEC server service arise. Services should be dynamically migrated between multiple MEC servers to maintain service performance due to user movement. Tackling this problem is nontrivial because it is arduous to predict user movement, and service migration will generate service interruptions and redundant network traffic. Service interruption time must be minimized, and redundant network traffic should be reduced to ensure service quality. In this paper, the container live migration technology based on prediction is studied, and an online prediction method based on map data that does not rely on prior knowledge such as user trajectories is proposed to address this challenge in terms of mobility prediction accuracy. A multitier framework and scheduling algorithm are designed to select MEC servers according to moving speeds of users and latency requirements of offloading tasks to reduce redundant network traffic. Based on the map of Beijing, extensive experiments are conducted using simulation platforms and real‐world data trace. Experimental results show that our online prediction methods perform better than the common strategy. Our system reduces network traffic by 65% while meeting task delay requirements. Moreover, it can flexibly respond to changes in the user’s moving speed and environment to ensure the stability of offload service.

Social-Aware Proactive Content Caching and Sharing in Multi-Access Edge Networks

The ever-increasing data explosion, particular mobile video traffic, increases the backhaul load and makes it difficult for the centralized cloud to meet the requirements of various services. Accordingly, proactive caching at mobile devices gains more attentions, which is envisioned as a promising technology to relieve the backhaul traffic and cater to diverse quality of service in multi-access edge networks. In this article, we study the problem of proactive caching and content sharing for mobile devices in multi-access edge networks. Since users’ mobility behaviors and social impacts on content preference distort the performance of content requests, a long-term strategy of proactive caching is proposed to minimize the sum of communication costs to get the requested contents. By taking the mutual interaction of devices into account, we design a local cooperative utility function that is used to derive a distributed proactive content caching algorithm using game theory. Theoretical analysis is provided to confirm the stability and optimality of the proposed algorithm. Furthermore, simulation results show that the proposed algorithm converges efficiently and, most importantly, it outperforms compared to no local cooperation caching scheme, non-social aware caching scheme and randomly caching scheme for various kinds of user behaviors and device caching capacity.

On recommendation-aware content caching for 6G: An artificial intelligence and optimization empowered paradigm

Recommendation-aware Content Caching (RCC) at the edge enables a significant reduction of the network latency and the backhaul load, thereby invigorating ubiquitous latency-sensitive innovative services. However, the effectiveness of RCC strategies is highly dependent on explicit information as regards subscribers’ content request patterns, the sophisticated caching placement policy, and the personalized recommendation tactics. In this article, we investigate how the potentials of Artificial Intelligence (AI) and optimization techniques can be harnessed to address those core issues and facilitate the full implementation of RCC for the upcoming intelligent 6G era. Towards this end, we first elaborate on the hierarchical RCC network architecture. Then, the devised AI and optimization empowered paradigm is introduced, whereas AI and optimization techniques are leveraged to predict the users’ content preferences in real-time situations with the assistance of their historical behavior data and determine the cache pushing and recommendation decision, respectively. Through extensive case studies, we validate the effectiveness of AI-based predictors in estimating users’ content preference and the superiority of optimized RCC policies over the conventional benchmarks. At last, we shed light on the opportunities and challenges in the future.

Content caching based on mobility prediction and joint user Prefetch in Mobile edge networks

With the development of 5G mobile networks, people’s demand for network response speed and services has increased to meet the needs of a large amount of data traffic, reduce the backhaul load caused by frequently requesting the same data (or content). The file is pre-stored in the base station by the edge device, and the user can directly obtain the requested data in the local cache without remotely. However, changes in popularity are difficult to capture, and data is updated more frequently through the backhaul. In order to reduce the number of backhauls and provide caching services for users with specific needs, we can provide proactive caching with users without affecting user activity. We propose a content caching strategy based on mobility prediction and joint user prefetching (MPJUP). The policy predicts the prefetching device data by predicting the user’s movement position with respect to time by the mobility of the user and then splits the partial cache space for prefetching data based on the user experience gain. Besides, we propose to reduce the backhaul load by reducing the number of content backhauls by cooperating prefetch data between the user and the edge cache device. Experimental analysis shows that our method further reduces the average delay and backhaul load, and the prefetch method is also suitable for more extensive networks.