Energy Efficient Resource Allocation for M2M Devices in 5G

Resource allocation for machine-type communication (MTC) devices is one of the keys challenges in the 5G network as it affects the lifetime of battery powered devices and also the quality of service of the applications. MTC devices are battery restrained and cannot afford a lot of power consumption due to spectrum usage. In this paper, we propose a novel resource allocation algorithm termed threshold controlled access (TCA) protocol. We propose a novel technique of uplink resource allocation in which the devices make a decision of resource allocation blocks based on their battery status and related application's power profile that eventually leads to required quality of service (QoS) metric. The first phase of the TCA algorithm selects the number of carriers to be allocated to a certain device for the better lifetime of low power MTC devices. In the second phase, the efficient solution is implemented through inducing a threshold value. A certain value of the threshold is selected through a mapping based on a QoS metric. The threshold enhances the selection of subcarriers for less powered devices, such as small e-health sensors. The algorithm is simulated for the physical layer of the 5G network. Simulation results show that the proposed algorithm is less complex and achieves better performance when compared to existing solutions in the literature.

[1]  Rath Vannithamby,et al.  Towards 5G: Applications, Requirements and Candidate Technologies , 2016 .

[2]  Slawomir Stanczak,et al.  Fundamentals of Resource Allocation in Wireless Networks - Theory and Algorithms (2. ed.) , 2009, Foundations in Signal Processing, Communications and Networking.

[3]  Eduard A. Jorswieck,et al.  Energy‐efficient Resource Allocation in 5G with Application to D2D , 2016 .

[4]  Muhammad Omer Farooq,et al.  Technologies and challenges in developing Machine-to-Machine applications: A survey , 2017, J. Netw. Comput. Appl..

[5]  Ioannis Avgouleas IoT Networking Resource Allocation and Cooperation , 2017 .

[6]  Zhuo Wang,et al.  An efficient channel assignment algorithm for multicast wireless mesh networks , 2018 .

[7]  Carsten Bockelmann,et al.  Massive machine-type communications in 5g: physical and MAC-layer solutions , 2016, IEEE Communications Magazine.

[8]  Tarik Taleb,et al.  Machine-type communications: current status and future perspectives toward 5G systems , 2015, IEEE Communications Magazine.

[9]  Robert Seth Margolies Resource Allocation for the Internet of Everything: From Energy Harvesting Tags to Cellular Networks , 2015 .

[10]  Jen-Jee Chen,et al.  Energy-efficient uplink radio resource management in LTE-advanced relay networks for Internet of Things , 2014, 2014 International Wireless Communications and Mobile Computing Conference (IWCMC).

[11]  Chun-Wei Tsai,et al.  SEIRA: An effective algorithm for IoT resource allocation problem , 2017, Comput. Commun..

[12]  Abdorasoul Ghasemi,et al.  Two-Stage Resource Allocation for Random Access M2M Communications in LTE Network , 2016, IEEE Communications Letters.

[13]  Di Yuan,et al.  Allocation of Heterogeneous Resources of an IoT Device to Flexible Services , 2015, IEEE Internet of Things Journal.

[14]  Sher Ali,et al.  Resource allocation, interference management, and mode selection in device‐to‐device communication: A survey , 2017, Trans. Emerg. Telecommun. Technol..

[15]  Toktam Mahmoodi,et al.  Enabling the IoT Machine Age With 5G: Machine-Type Multicast Services for Innovative Real-Time Applications , 2016, IEEE Access.

[16]  Theodore S. Rappaport,et al.  Radio propagation path loss models for 5G cellular networks in the 28 GHZ and 38 GHZ millimeter-wave bands , 2014, IEEE Communications Magazine.

[17]  Raymond Knopp,et al.  Dynamic resource allocation for machine-type communications in LTE/LTE-A with contention-based access , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[18]  Fa-Long Luo,et al.  Signal processing for 5G : algorithms and implementations , 2016 .

[19]  Firooz B. Saghezchi,et al.  Towards 5G: Context Aware Resource Allocation for Energy Saving , 2016, J. Signal Process. Syst..

[20]  Xianwei Zhou,et al.  Resource Allocation in Wireless Powered IoT System: A Mean Field Stackelberg Game-Based Approach , 2018, Sensors.

[21]  Yanjing Sun,et al.  Energy-Efficient Resource Allocation for Industrial Cyber-Physical IoT Systems in 5G Era , 2018, IEEE Transactions on Industrial Informatics.

[22]  Jesus Alonso-Zarate,et al.  M2M Communications in 5G , 2017 .

[23]  Mukesh A. Zaveri,et al.  Graph-based Resource Allocation for Disaster Management in IoT Environment , 2017, AWICT 2017.

[24]  Jing Wang,et al.  Resource Allocation in a New Random Access for M2M Communications , 2015, IEEE Communications Letters.

[25]  Leila Musavian,et al.  Energy Efficient Resource Allocation in 5G Hybrid Heterogeneous Networks: A Game Theoretic Approach , 2016, 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall).

[26]  Xiaoli Chu,et al.  Energy-Efficient Uplink Resource Allocation in LTE Networks With M2M/H2H Co-Existence Under Statistical QoS Guarantees , 2014, IEEE Transactions on Communications.

[27]  Anum Ali,et al.  Energy efficient techniques for M2M communication: A survey , 2016, J. Netw. Comput. Appl..

[28]  Thorsten Wild,et al.  Waveform contenders for 5G — OFDM vs. FBMC vs. UFMC , 2014, 2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP).

[29]  Loutfi Nuaymi,et al.  Survey of radio resource management issues and proposals for energy-efficient cellular networks that will cover billions of machines , 2016, EURASIP Journal on Wireless Communications and Networking.