Challenges on wireless heterogeneous networks for mobile cloud computing

Lei, Lei; Zhang, Zhong; Zheng, Kan; Chen, Jiadi; Meng, Hanlin

doi:10.1109/mwc.2013.6549281

Cited by 143 publications

(4 citation statements)

References 18 publications

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“…Simultaneously, real-time prediction and monitoring may bring up dormant energy savings cells in time for peak traffic. Simultaneously, dormant energy savings cells may be woken up in time for peak traffic through real-time prediction and monitoring, maximizing user experience while decreasing network resource/power usage (Lei et al, 2013).…”

Section: Energy Efficiencymentioning

confidence: 99%

Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G technology

Abd Elaziz,

Al‐qaness,

Dahou

et al. 2023

WIREs Data Min & Knowl

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The sixth generation (6G) represents the next evolution in wireless communication technology and is currently under research and development. It is expected to deliver faster speeds, reduced latency, and greater capacity compared to the current 5G wireless technology. 6G is envisioned as a technology capable of establishing a fully data‐driven network, proficient in analyzing and optimizing end‐to‐end behavior and handling massive volumes of real‐time data at rates of up to terabits per second (Tb/s). Moreover, 6G is designed to accommodate an average of 1000+ substantial connections per person over the course of a decade. The concept of a data‐driven network introduces a new service paradigm, which offers fresh opportunities for applications within 6G wireless communication and network design in the future. This paper aims to provide a survey of existing applications of 6G that are based on deep learning techniques. It also explores the potential, essential technologies, scenarios, challenges, and related topics associated with 6G. These aspects are crucial for meeting the requirements for the development of future intelligent networks. Furthermore, this work delves into various research gaps between deep learning and 6G that remain unexplored. Different potential deep learning applications for 6G networks, including privacy, security, environmentally friendly communication, sustainability, and various wireless applications, are discussed. Additionally, we shed light on the challenges and future trends in this field.This article is categorized under: Technologies > Computational Intelligence Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning

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Section: Energy Efficiencymentioning

confidence: 99%

Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G technology

Abd Elaziz,

Al‐qaness,

Dahou

et al. 2023

WIREs Data Min & Knowl

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“…However, the high dependence on large‐capacity fronthaul links has become a bottleneck for enhancing the system performance, which causes several challenges for 5G applications [6]. The heterogeneous cloud radio access network (H‐CRAN) has been proposed in [7] to overcome the shortcomings of C‐RANs. The user and control planes are separated in H‐CRANs, where a high‐power node (HPN) such as a macro base station is primarily used to provide seamless coverage and execute the functions of control plane, while RRHs are deployed to provide high data rates in the user plane [8].…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning‐based joint optimization of computation offloading and resource allocation in F‐RAN

Jo¹,

Kim²,

Kim³

et al. 2022

IET Communications

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The fog radio access network (F-RAN) has been regarded as a promising wireless access network architecture in the fifth generation (5G) and beyond systems to satisfy the increasing requirements for low-latency and high-throughput services by providing fog computing. However, because the cloud computing centre and fog computing-enabled access points (F-APs) in the F-RAN have different computation and communication capabilities, it is crucial to make an efficient computation offloading and resource allocation strategy that can fully exploit the potential of the F-RAN system. In this paper, the authors investigate a decentralized low-complexity deep reinforcement learning (DRL)based framework for joint computation task offloading and resource allocation in the F-RAN, which supports assistive computing-enabled tasks offloading between F-APs. Considering the constraints of task latency, wireless transmission rate, transmission power, and computational resource capacity, the authors formulate the system processing efficiency maximization problem by jointly optimizing offloading mode selection, channel allocation, power control, and computation resource allocation in the F-RAN. To solve this non-linear and non-convex problem, the authors propose a federated DRL-based computation offloading and resource allocation algorithm to improve the task processing efficiency and ensure privacy in the system, which can significantly reduce the computing complexity and signalling overhead of the training process compared with the centralized learning-based method. Specifically, each local F-AP agent consists of dueling deep Q-network (DDQN) and deep deterministic policy gradient (DDPG) networks to appropriately deal with discrete and continuous valuable action spaces, respectively. Finally, the simulation results show that the proposed federated DRL algorithm can achieve significant performance improvements in terms of system processing efficiency and task latency compared with other benchmarks.

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“…HetNet also has the potential to degrade load imbalance among different BSs due to disparate transmit powers and capabilities [14]. However, with HetNet environment, there are some critical challenges, such as user association , power control , and resource allocation that need to be addressed [15]. The traditional cell-selection strategy, max-SINR(signal-to-interference-plus-noise ratio) [16], used by user equipment (UEs) causes load imbalance in HetNet [17].…”

Section: Introductionmentioning

confidence: 99%

Online Distributed User Association for Heterogeneous Radio Access Network

Kumar

Sharma

2019

Sensors

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Future-generation radio access networks (RAN) are projected to fulfill the diverse requirements of user equipment (UE) by adopting a heterogeneous network (HetNet) environment. Necessary integration of different radio access technologies (RAT), such as 2G, 3G, 4G, wireless local area network (WLAN), and visible light communication (VLC) is inevitable. Moreover, UEs equipped with diverse requirements will be capable of accessing some or all the RATs. The complex HetNet environment with diverse requirements of UEs will present many challenges. The HetNet is likely to suffer severely from load imbalance among the base stations (BSs) from inheriting the traditional user association scheme such as max-SINR (signal-to-interference-plus-noise ratio)/max-RSSI (received signal strength indicator), unless some sophisticated schemes are invented. In this paper, a novel scheme is devised for a joint-user association for load balancing, where BSs are densely deployed and UEs typically have a certain degree of mobility. Unlike most of the present works, a dynamic network is considered where the position and channel condition of the UEs are not fixed. We develop two complex and distributed association schemes based on probability and d-choices, while carefully considering both loads of the BSs and SINR experienced by the UEs. Numerical results validate the efficiency of the proposed schemes by showing a received data-rate fairness among UEs and an improvement in the UE’s minimum received data rate.

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Challenges on wireless heterogeneous networks for mobile cloud computing

Cited by 143 publications

References 18 publications

Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G technology

Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G technology

Deep reinforcement learning‐based joint optimization of computation offloading and resource allocation in F‐RAN

Online Distributed User Association for Heterogeneous Radio Access Network

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