Smart Real-Time Recommendation of Mobile Services

In this paper, a new vision is presented for highly personalized, customized, and contextualized real-time recommendation of services to mobile users (consumers) by considering the current consumer-, network-, and service context. A smart service recommendation system is elaborated, which builds up and dynamically manages personal profiles of consumers, aiming to facilitate and optimize the service discovery and recommendation process, in support of consumers’ choices, thereby achieving the best quality of experience (QoE) as perceived by those consumers when utilizing different mobile services. The algorithm-driven recommended mobile services, accessible anytime-anywhere-anyhow through any kind of mobile devices via heterogeneous wireless access networks, range from typical telecommunication services (e.g., outgoing voice calls) to Internet services (e.g., multimedia streaming). These algorithms also may be further enriched by their being adapted and expanded to cover more sophisticated services such as helping the consumer’s health and security needs, an example being the finding (with subsequent dynamic changing, if required) of the most 'healthy' or 'secure' driving/biking/jogging/walking route to follow so as to avoid areas posing particular, consumer-specific, health or safety risk.

Resource Allocation Scheme for Fog-Enabled Wireless Access Networks under the QoS of Users

With the complexity of the network architecture, the diversity of network slicing, and the introduction of advanced techniques such as device to device (D2D), it is difficult for the next-generation (5G+ or 6G) networks to comprehensively consider the requirements of users from different slices and jointly allocate wireless resources to improve network energy efficiency. This paper studies the energy efficiency optimization problem for D2D-enabled fog radio access networks (FRANs). A resource allocation algorithm is proposed to maximize the network energy efficiency by jointly optimizing the beamforming vector, resource block allocation, and transmission power of the remote radio heads (RRHs), fog access point (FAP), and D2D users. The developed algorithm is based on nonlinear programming, convex optimization, and Lagrangian duality. Simulation results show that, by applying the proposed algorithm, the system throughput is significantly improved, and the network energy consumption is greatly reduced, which can ultimately improve the network energy efficiency obviously.

A Private Strategy for Workload Forecasting on Large-Scale Wireless Networks

The growing convergence of various services characterizes wireless access networks. Therefore, there is a high demand for provisioning the spectrum to serve simultaneous users demanding high throughput rates. The load prediction at each access point is mandatory to allocate resources and to assist sophisticated network designs. However, the load at each access point varies according to the number of connected devices and traffic characteristics. In this paper, we propose a load estimation strategy based on a Markov’s Chain to predict the number of devices connected to each access point on the wireless network, and we apply an unsupervised machine learning model to identify traffic profiles. The main goals are to determine traffic patterns and overload projections in the wireless network, efficiently scale the network, and provide a knowledge base for security tools. We evaluate the proposal in a large-scale university network, with 670 access points spread over a wide area. The collected data is de-identified, and data processing occurs in the cloud. The evaluation results show that the proposal predicts the number of connected devices with 90% accuracy and discriminates five different user-traffic profiles on the load of the wireless network.

Whole Scenario of 5G Communication Access in the Digital Transformation of Medium and Large Enterprises

With the evolution and upgrading of the fifth-generation mobile communication technology, the mobile network will support a larger mobile user group and more diverse business scenarios, greatly enhance the Internet service experience, and fully support IoT-aware applications. As the forefront of the Internet of Everything, the wireless access network plays an important role in the evolution of the network. The continuous innovation of a new generation of information technology, especially the application of technologies such as big data, cloud computing, artificial intelligence, and the Internet of Things, has triggered an upsurge in the digital transformation of enterprises. Digital transformation has had a profound impact on the production and operation activities of enterprises and has also changed the organization and management of enterprise innovation activities. From the perspective of the innovation ecosystem, the digital transformation of a single enterprise leads to changes in innovation activities, which will converge and emerge new characteristics at the system level. The purpose of this article is to study the full scenarios of 5G communication access in the digital transformation of medium and large enterprises. This article starts with the development trend of wireless access networks, analyzes network slicing technologies in detail, and points out that wireless access networks are moving towards 5G. Important challenges are faced in the development of the times. Based on the adaptive random access model proposed in this paper, a feedback adaptive optimization method is proposed. This method estimates the real access load through feedback and calculates the access threshold through the access load. Then, broadcast the threshold value to each user equipment through downlink broadcast. The results show that the normalized throughput rate of random access methods using AC-RACH and FC-RACH is higher than that of RA-RACH. Especially, when the arrival rate of user requests is getting higher and higher, the throughput rate of RA-RACH access will continue to decrease with the collision until it approaches zero. By comparing the key data of normalized throughput rate and access success rate, it is concluded that the feedback adaptive method has a significant performance improvement under heavy load conditions compared with the previous method.

Towards Enhanced Mobile Broadband Communications: A Tutorial on Enabling Technologies, Design Considerations, and Prospects of 5G and beyond Fixed Wireless Access Networks

There has been a growing interconnection across the world owing to various multimedia applications and services. Fixed wireless access (FWA) is an attractive wireless solution for delivering multimedia services to different homes. With the fifth-generation (5G) and beyond mobile networks, the FWA performance can be enhanced significantly. However, their implementation will present different challenges on the transport network due to the incessant increase in the number of required cell-sites and the subsequent increase in the per-site requirements. This paper presents a comprehensive tutorial on the enabling technologies, design considerations, requirements, and prospects of broadband schemes. Furthermore, the related technical challenges of FWA are reviewed, and we proffer potential solutions to address them. Besides, we review various transport network options that can be employed for FWA deployment. In this regard, we offer an in-depth discussion on their related requirements for different use cases. Moreover, we give an insight into the 3GPP RAN functional split implementations and implications on the 5G FWA transport network solutions. The concepts of virtualized RANs for attending flexibly to the dynamic nature of different use cases are also presented.

Deep Reinforcement Learning for Task Offloading and Power Allocation in UAV-assisted MEC System

Mobile edge computing (MEC) can provide computing services for mobile users (MUs) by offloading computing tasks to edge clouds through wireless access networks. Unmanned aerial vehicles (UAVs) are deployed as supplementary edge clouds to provide effective MEC services for MUs with poor wireless communication condition. In this paper, a joint task offloading and power allocation (TOPA) optimization problem is investigated in UAV-assisted MEC system. Since the joint TOPA problem has a strong non-convex characteristic, a method based on deep reinforcement learning is proposed. Specifically, the joint TOPA problem is modeled as Markov decision process. Then, considering the large state space and continuous action space, a twin delayed deep deterministic policy gradient algorithm is proposed. Simulation results show that the proposed scheme has lower smoothing training cost than other optimization methods.

Convergence of Wireless and Optical Network in Future Communication Network

The requirement of data increases many-fold in recent years to support the newest technologies in B5G and 6G. Wireless is the last mile solution as access with an optical network as the backbone in future communication systems. Over the years in every new generation, the distance between the base station and the user is decreasing and the optical node is coming closer to the user. There are several technologies like AR/VR, AI, holographic communication, holographic telepresence, etc. are the main candidates in B5G and 6G, which are required high-speed connection with low latency. To support these services, it is almost mandatory that transmit data across the network should be smooth and seamless to provide successful communication. Providing a successful and appropriate wireless link among the users simultaneously to achieve the requirements is becoming more complex, hence challenging. The optical backbone of all wireless access networks requires supporting these user’s requirements, needs to evolve continuously with wireless network evolution. This chapter will study the evolution of both networks to understand their cooperation, alignment, and support.

Integration of WiFi With WiMAX Technology in Vehicular Ad-hoc Networks

Wireless access networks based on IEEE 802.11 and IEEE 802.16 have become very popular in providing different data services. In this paper our first goal is to design and implement an integrated Wimax and Wi-Fi network and compare two of the most promising infrastructure-based wireless technologies such as IEEE 802.16e standard and upcoming IEEE 802.11p standard. We investigate, through simulation, the potential and limitations of both technologies as a communication media for vehicle-to-infrastructure (V2I) communications. The performance of the two systems is evaluated for delay, packet delivery ratio, and throughput. This research work is to integrated of WiFi with WiMAX technology in an Vehicular Ad-hoc and evaluate the performance using the NS2.31 simulator. To improve the packet delivery ratio, and End-to-End delay the proposed system is implemented using Wi-Fi with WiMAX (IEEE 802.16) routing technique. we conclude that, the comparsion results shows integration of WiFi with WiMAX will produce better result when compared the existing schemes.