IEEE Transactions on Mobile Computing

IEEE Transactions on Mobile Computing (TMC) is a scholarly archival journal published monthly that focuses on the key technical issues related to Mobile Computing. It is the intent of TMC to publish mature works of research, typically those that have appeared in part in conferences. Furthermore, it is the intent of TMC to focus on issues at the link-layer and above in wireless communications, and to focus only on topics explicitly or plausibly related to mobile systems. Read the full scope of TMC.

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From the October 2018 issue

Sophisticated Online Learning Scheme for Green Resource Allocation in 5G Heterogeneous Cloud Radio Access Networks

By Ismail Alqerm and Basem Shihada

featured article thumbnail 5G is the upcoming evolution for the current cellular networks that aims at satisfying the future demand for data services. Heterogeneous cloud radio access networks (H-CRANs) are envisioned as a new trend of 5G that exploits the advantages of heterogeneous and cloud radio access networks to enhance spectral and energy efficiency. Remote radio heads (RRHs) are small cells utilized to provide high data rates for users with high quality of service (QoS) requirements, while high power macro base station (BS) is deployed for coverage maintenance and low QoS users service. Inter-tier interference between macro BSs and RRHs and energy efficiency are critical challenges that accompany resource allocation in H-CRANs. Therefore, we propose an efficient resource allocation scheme using online learning, which mitigates interference and maximizes energy efficiency while maintaining QoS requirements for all users. The resource allocation includes resource blocks (RBs) and power. The proposed scheme is implemented using two approaches: centralized, where the resource allocation is processed at a controller integrated with the baseband processing unit and decentralized, where macro BSs cooperate to achieve optimal resource allocation strategy. To foster the performance of such sophisticated scheme with a model free learning, we consider users’ priority in RB allocation and compact state representation learning methodology to improve the speed of convergence and account for the curse of dimensionality during the learning process. The proposed scheme including both approaches is implemented using software defined radios testbed. The obtained results and simulation results confirm that the proposed resource allocation solution in H-CRANs increases the energy efficiency significantly and maintains users’ QoS.

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Editorials and Announcements


  • We are pleased to announce that Eylem Ekici, Professor of Electrical and Computer Engineering at Ohio State University, has been named an Associate Editor-in-Chief of the IEEE Transactions on Mobile Computing starting April 2018.
  • TMC now offers authors access to Code Ocean. Code Ocean is a cloud-based executable research platform that allows authors to share their algorithms in an effort to make the world’s scientific code more open and reproducible. Learn more or sign up for free.
  • We are pleased to announce that Marwan Krunz, the Kenneth VonBehren Endowed Professor in the Department of Electrical and Computer Engineering at the University of Arizona, USA, has been named the new Editor-in-Chief of the IEEE Transactions on Mobile Computing starting in 2017.
  • We are pleased to announce that Kevin Almeroth, Professor of Computer Science at the University of California in Santa Barbara, has been named the new Associate Editor-in-Chief of the IEEE Transactions on Mobile Computing starting in 2017.
  • According to Thomson Reuters' 2016 Journal Citation Report, TMC has an impact factor of 3.822.


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TMC is a joint publication of the:

IEEE Computer SocietyIEEE Circuits and Systems Society IEEE Comunications Society IEEE Signal Processing Society


TMC is published in cooperation with: IEEE Electromagnetic Compatibility Society, IEEE Engineering in Medicine and Biology Society, IEEE Technology Management Council, IEEE Information Theory Society, IEEE Instrumentation and Measurement Society, IEEE Power & Energy Society, IEEE Robotics and Automation Society, and IEEE Systems, Man, and Cybernetics Society

TMC is indexed in ISI

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