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With the increasing scarcity of spectrum resources and the continuous exploration of high-frequency spectrum resources, millimeter wave (mmWave) has been introduced to 5G deployment scenarios, one of which is the high-speed railway wireless networks. Because of the characteristics of mmWave, it is suitable for Enhanced Mobile Broadband (eMBB) service in subway tunnel scenarios, in which there are increasing data rate requirement of passengers to be satisfied. To this end, we propose a new scheme of Integrated Access and Backhaul (IAB) plus small stations in the carriage, in which the backhaul link is 5G Non-Standalone (NSA) networking with 4G for master node and 5G mmWave for secondary node, so as to overcome the handover burden and penetration loss of mmWave, that is more severe in the high-frequency range. Based on the existing old 4G equipment, mmWave is applied for 5G user-plane connection in NSA dual connection, while the control plane connection still relies on 4G commercial frequency band, in order that even if the millimeter-wave link quality is seriously degraded, the IAB-node can still maintain the connection with the master node and re-establish the mmWave link through the exchange of control information on the 4G low-frequency band. Unlike previous attempts, the proposed approach applies dual link to the backhaul link and 5G commercial frequency band to the in-vehicle access link of small station with Radio Access Network (RAN) sharing technology, so that the bandwidth shared by the operators can be aggregated in one small station and fully used for user access to match the mmWave large bandwidth of the backhaul link. Field trials showed that although the data rates were somewhat unstable at the handover points, which still need to be improved in the future work, the average end-to-end uplink and downlink data rates were greatly improved, compared to the previous average data rates when only outdoor signal penetrating indoors was used in subway tunnel. In addition, the proposed scheme can reduce the operator’s construction and maintenance costs by making full use of the existing 4G equipment, which provides a reference for upgrading 4G to 5G signal coverage for all tunnel scenarios.

Summary: The ability of high splitting gain of dense small cells contributed to the rapid establishment of ultradense networks (UDNs). Its higher efficiency to deal with high traffic data demand made UDN a most‐promising technology for the future 5G environment. However, the UDN creates concern about user association, which causes more complexities in providing a high data transmission rate and low latency rate. To tackle these complexities, in this paper, the ambient intelligence exploration multi‐delay deep deterministic policy gradient‐based artificial rabbits optimization (AEMDPG‐ARO) algorithm is proposed for resolving data rate and the issues of latency in the small base station (SBS) and macro base station (MBS) of the wireless sensor network. The complexity in attaining lower latency and higher data rate is achieved through a novel technique AEMDPG‐ARO. The ambient intelligence exploration multi‐delay (AIEM) is combined with deep deterministic policy gradient (DDPG) for overcoming the local optimum and diversity issues of DDPG. The data sample for this study is obtained through the WINNER channel model. The proposed AEMDPG‐ARO algorithm's efficiency is compared to varied state of art methods. The performance evaluation is carried out with regard to network lifetime, end‐to‐end delay, packet delivery ratio, sum rate overall energy consumption, latency, and minimum rate and maximum rate of the network. The proposed AEMDPG‐ARO algorithm gives better performance with reduced time complexity and better metrics rate in the result analysis. The minimum latency achieved by the proposed AEMDPG‐ARO algorithm is about 0.1 s. Key findings: The AEMDPG‐ARO approach is proposed to solve user association problems in UDN.Data sample for this study is obtained through the WINNER channel model.AEMDPG‐ARO approach achieves high data transmission and less latency rate.It offers better performance with reduced time complexity and better metrics rate. [ABSTRACT FROM AUTHOR]

The transmission bandwidth of a vehicle–ground connection is low when an EMU (electric multiple unit) is running in a high-speed scenario. To this end, this paper focuses on the need to solve the problem of the poor bandwidth of the vehicle–ground integration information network, and proposes a vehicle–ground integration information network scheme for EMUs based on small base stations. Based on the existing wi-fi system of the EMU, in order to realize the coverage of the 5G signal in the carriage, this paper—through the deployment of the technical characteristics of 5G—sinks the customized UPF (user plane function) and MEC (mobile edge computing) to the train carriage, and expands the internet channels of the train carriage. Relying on the technologies of MEC and CDN (content delivery network) for high-speed railways, network extension service products can satisfy passengers' needs around network rate and delay. On the one hand, this can relieve the pressure of the network backhaul and save the bandwidth resources of the vehicle–ground integration information network. On the other hand, it can help operators to reduce the investment of network construction, operation, and maintenance. As a result, the proposed scheme can inspire the products that match the extended service needs of the passenger, realize the technical and innovation value of the 5G mobile network, and achieve business model innovation in high-speed mobile scenarios. [ABSTRACT FROM AUTHOR]

Data caching has emerged as a promising technique to handle growing data traffic and backhaul congestion of wireless networks. However, there is a concern regarding how and where to place contents to optimize data access by the users. Data caching can be exploited close to users by deploying cache entities at Small Base Stations (SBSs). In this approach, SBSs cache contents through the core network during off-peak traffic hours. Then, SBSs provide cached contents to content-demanding users during peak traffic hours with low latency. In this paper, we exploit the potential of data caching at the SBS level to minimize data access delay. We propose an intelligence-based data caching mechanism inspired by an artificial intelligence approach known as Reinforcement Learning (RL). Our proposed RL-based data caching mechanism is adaptive to dynamic learning and tracks network states to capture users' diverse and varying data demands. Our proposed approach optimizes data caching at the SBS level by observing users' data demands and locations to efficiently utilize the limited cache resources of SBS. Extensive simulations are performed to evaluate the performance of proposed caching mechanism based on various factors such as caching capacity, data library size, etc. The obtained results demonstrate that our proposed caching mechanism achieves 4% performance gain in terms of delay vs. contents, 3.5% performance gain in terms of delay vs. users, 2.6% performance gain in terms of delay vs. cache capacity, 18% performance gain in terms of percentage traffic offloading vs. popularity skewness (γ), and 6% performance gain in terms of backhaul saving vs. cache capacity. [ABSTRACT FROM AUTHOR]

Data caching has emerged as a promising technique to handle growing data traffic and backhaul congestion of wireless networks. However, there is a concern regarding how and where to place contents to optimize data access by the users. Data caching can be exploited close to users by deploying cache entities at Small Base Stations (SBSs). In this approach, SBSs cache contents through the core network during off-peak traffic hours. Then, SBSs provide cached contents to content-demanding users during peak traffic hours with low latency. In this paper, we exploit the potential of data caching at the SBS level to minimize data access delay. We propose an intelligence-based data caching mechanism inspired by an artificial intelligence approach known as Reinforcement Learning (RL). Our proposed RL-based data caching mechanism is adaptive to dynamic learning and tracks network states to capture users’ diverse and varying data demands. Our proposed approach optimizes data caching at the SBS level by observing users’ data demands and locations to efficiently utilize the limited cache resources of SBS. Extensive simulations are performed to evaluate the performance of proposed caching mechanism based on various factors such as caching capacity, data library size, etc. The obtained results demonstrate that our proposed caching mechanism achieves 4% performance gain in terms of delay vs. contents, 3.5% performance gain in terms of delay vs. users, 2.6% performance gain in terms of delay vs. cache capacity, 18% performance gain in terms of percentage traffic offloading vs. popularity skewness (γ), and 6% performance gain in terms of backhaul saving vs. cache capacity.

Wireless content caching in small cell networks has recently been considered as a promising way to alleviate the congestion of the backhaul in emerging heterogenous cellular network. However, how to select files which are cached in SBSs and how to make SBSs work together is an important issue for cooperative cache research for the propose of reducing file download time. In this paper, a Cooperative-Greedy strategy (CGS) among cache-enabled small base stations (SBSs) in small cell network is proposed, in order to minimize the download time of files. This problem is formulated as a Chinese restaurant game. Using this game model, we can configure file caching schemes based on file popularity and the spectrum resources allocated to several adjacent SBSs. Both the existence and uniquencess of a Nash equilibrium are proved. In the theoretical analysis section, SBSs cooperate with each other in order to cache popular files as many as possible near UEs. Simulation results show that the CGS scheme outperforms other schemes in terms of the file-download time. [ABSTRACT FROM AUTHOR]

The strategy and suggestions about the scene of 4G small base station based on network architecture of TD-LTE were described.Some problems at the engineering applications were combed,items were analyzed and the solving methods were introduced.

Next-generation 5G networks are envisioned to support ultra low latency, leveraging the benefits of caching at radio access network (RAN). While the low latency is proliferated by proactive caching, cache efficiency depends on caching the right data. Social behaviour of mobile users and highly evolved learning algorithms are the new trends to support the network evolution for improved quality of experience to the users. In this paper, we explore a proactive caching scheme that takes the advent of social ties of mobile users accessing the cached contents. By exploring users behaviour and cached content properties, the proposed system not only increases cache efficiency but also improves 5G network capabilities. Simulation with real-world observations shows 14% − −28% improvements in the cache hit ratio with reduced cache replacement rate. Application of the proposed caching policy with hierarchical video caching in 5G network improves delay by 10%. [ABSTRACT FROM AUTHOR]

This paper considers co-operation between primary and secondary users in shared spectrum radio networks via caching. We first consider a network with one channel shared between a single macro (primary) base station and multiple micro (secondary) base stations. Secondary base stations can cache some primary files and thereby satisfy content requests generated from nearby primary users. For this co-operative scenario, we develop two caching and scheduling policies under which the set of primary and secondary request generation rates that can be supported increases from the case without cooperation. The first of these algorithms, fixed primary caching policy (FPCP), provides more gain in the set of supportable request generation rates. However under this algorithm primary packet transmissions from secondary base stations have the same priority of access as secondary packets and thus might suffer in terms of delay. In the second algorithm, variable primary caching policy (VPCP), primary packet transmissions from the secondary base stations have higher priority of access than that of secondary packets. We find that the set of request generation rate vectors for which all queues in the network are stable under each of these algorithms is greater than that under any non-cooperative algorithm. We conduct extensive simulations to compare the performance of both algorithms against that of an optimal nonco-operative algorithm. Finally, we extend the analysis to a network with multiple channels. [ABSTRACT FROM PUBLISHER]