An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios

This paper demonstrates a route selection mechanism on a testbed with heterogeneous device-to-device (D2D) wireless communication for a 5G network scenario. The source node receives information about the primary users’ (PUs’) (or licensed users’) activities and available routes from the macrocell base station (or a central controller) and makes a decision to select a multihop route to the destination node. The source node from small cells can either choose: (a) a route with direct communication with the macrocell base station to improve the route performance; or (b) a route with D2D communication among nodes in the small cells to offload traffic from the macrocell to improve spectrum efficiency. The selected D2D route has the least PUs’ activities. The route selection mechanism is investigated on our testbed that helps to improve the accuracy of network performance measurement. In traditional testbeds, each node (e.g., Universal Software Radio Peripheral (USRP) that serves as the front-end communication block) is connected to a single processing unit (e.g., a personal computer) via a switch using cables. In our testbed, each USRP node is connected to a separate processing unit, i.e., raspberry Pi3 B+ (or RP3), which offers three main advantages: (a) control messages and data packets are exchanged via the wireless medium; (b) separate processing units make decisions in a distributed and heterogeneous manner; and (c) the nodes are placed further apart from one another. Therefore, in the investigation of our route selection scheme, the response delay of control message exchange and the packet loss caused by the operating environment (e.g., ambient noise) are implied in our end-to-end delay and packet delivery ratio measurement. Our results show an increase of end-to-end delay and a decrease of packet delivery ratio due to the transmission of control messages and data packets in the wireless medium in the presence of the dynamic PUs’ activities. Furthermore, D2D communication can offload 25% to 75% traffic from macrocell base station to small cells.

[1]  Sayed Abdulhayan,et al.  Direct Device-to-Device communication in 5G Networks , 2016, 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS).

[2]  Yuan Hu,et al.  Emergency route selection for D2D cellular communications during an urban terrorist attack , 2014, 2014 IEEE International Conference on Communications Workshops (ICC).

[3]  Kok-Lim Alvin Yau,et al.  Route Selection for Multi-Hop Cognitive Radio Networks Using Reinforcement Learning: An Experimental Study , 2016, IEEE Access.

[4]  Muhammad Imran,et al.  Packet-Drop Design in URLLC for Real-Time Wireless Control Systems , 2019, IEEE Access.

[5]  Wei Song,et al.  Evolving to 5G: A fast and near-optimal request routing protocol for mobile core networks , 2014, 2014 IEEE Global Communications Conference.

[6]  Zexian Li,et al.  Delay analysis of network architectures for machine-to-machine communications in LTE system , 2014, 2014 21st International Conference on Telecommunications (ICT).

[7]  Martin Braun,et al.  The Universal Software Radio Peripheral (USRP) Family of Low‐Cost SDRs , 2015 .

[8]  Anna Umbert,et al.  Implementation of Cognitive Radio Networks to evaluate spectrum management strategies in real-time , 2016, Comput. Commun..

[9]  Young-Joo Suh,et al.  Latency Analysis in GNU Radio/USRP-Based Software Radio Platforms , 2013, MILCOM 2013 - 2013 IEEE Military Communications Conference.

[10]  Suleiman Zubair,et al.  Assessing Routing Strategies for Cognitive Radio Sensor Networks , 2013, Sensors.

[11]  Andreas Kassler,et al.  Optimal user association, backhaul routing and switching off in 5G heterogeneous networks with mesh millimeter wave backhaul links , 2018, Ad Hoc Networks.

[12]  Hadi Larijani,et al.  A Survey on Centralised and Distributed Clustering Routing Algorithms for WSNs , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[13]  Amith Khandakar,et al.  Understanding Probabilistic Cognitive Relaying Communication with Experimental Implementation and Performance Analysis , 2019, Sensors.

[14]  Juan Carlos Martínez-Quintero,et al.  Raspberry PI 3 RF signal generation system , 2019 .

[15]  Gilberto Berardinelli,et al.  Distributed Synchronization of a testbed network with USRP N200 radio boards , 2014, 2014 48th Asilomar Conference on Signals, Systems and Computers.

[16]  Dominique Noguet,et al.  Cognitive Radio Oriented Wireless Networks , 2016, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

[17]  Wen-Tzu Chen,et al.  Spectrum monitoring for wireless TV and FM broadcast using software-defined radio , 2015, Multimedia Tools and Applications.

[18]  Cheng-Xiang Wang,et al.  5G Ultra-Dense Cellular Networks , 2015, IEEE Wireless Communications.

[19]  Atílio Gameiro,et al.  Selective reporting - a half signalling load algorithm for distributed sensing , 2013, EURASIP J. Wirel. Commun. Netw..

[20]  Sanjib Kumar Deka,et al.  Distributed TDMA based MAC protocol for data dissemination in ad-hoc Cognitive Radio networks , 2013, 2013 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS).

[21]  Lena Wosinska,et al.  Data Plane and Control Architectures for 5G Transport Networks , 2016, Journal of Lightwave Technology.

[22]  Lei Ding,et al.  Implementation of a Distributed Joint Routing and Dynamic Spectrum Allocation Algorithm on USRP2 Radios , 2010, 2010 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON).

[23]  Vimal Bhatia,et al.  Low Cost and Power Software Defined Radio Using Raspberry Pi for Disaster Effected Regions , 2015 .

[24]  Hussein M. ElAttar,et al.  Aggregated Throughput Prediction for Collated Massive Machine-Type Communications in 5G Wireless Networks , 2019, Sensors.

[25]  Pawel A. Dmochowski,et al.  Analysis and implementation of reinforcement learning on a GNU Radio cognitive radio platform , 2010, 2010 Proceedings of the Fifth International Conference on Cognitive Radio Oriented Wireless Networks and Communications.

[26]  Marian Verhelst,et al.  CLAWS: Cross-Layer Adaptable Wireless System enabling full cross-layer experimentation on real-time software-defined 802.15.4 , 2014, EURASIP J. Wirel. Commun. Netw..

[27]  Hsiao-Hwa Chen,et al.  Mode Selection, Radio Resource Allocation, and Power Coordination in D2D Communications , 2017, IEEE Wireless Communications.

[28]  Bruno B. Albert,et al.  Complete software defined RFID system using GNU radio , 2012, 2012 IEEE International Conference on RFID-Technologies and Applications (RFID-TA).

[29]  Zhigang Jin,et al.  A Q-Learning-Based Delay-Aware Routing Algorithm to Extend the Lifetime of Underwater Sensor Networks , 2017, Sensors.

[30]  Hideaki Tanaka,et al.  Distributed autonomous multi-hop vehicle-to-vehicle communications over TV white space , 2013, CCNC.

[31]  Omar Y. K. Alani,et al.  Cognitive Neural Network Delay Predictor for High Speed Mobility in 5G C-RAN Cellular Networks , 2018, 2018 IEEE 5G World Forum (5GWF).

[32]  Tuyen X. Tran,et al.  Cooperative Hierarchical Caching in 5G Cloud Radio Access Networks , 2016, IEEE Network.

[33]  Ning Zhang,et al.  ROSTER: Radio Context Attestation in Cognitive Radio Network , 2018, 2018 IEEE Conference on Communications and Network Security (CNS).

[34]  Sangman Moh,et al.  Robust Evolutionary-Game-Based Routing for Wireless Multimedia Sensor Networks , 2019, Sensors.

[35]  Qing Wang,et al.  A Survey on Device-to-Device Communication in Cellular Networks , 2013, IEEE Communications Surveys & Tutorials.

[36]  Yan Shi,et al.  Building UAV-Based Testbeds for Autonomous Mobility and Beamforming Experimentation , 2018, 2018 IEEE International Conference on Sensing, Communication and Networking (SECON Workshops).

[37]  Kah Phooi Seng,et al.  Classical and swarm intelligence based routing protocols for wireless sensor networks: A survey and comparison , 2012, J. Netw. Comput. Appl..

[38]  Joongheon Kim,et al.  Quality-aware millimeter-wave device-to-device multi-hop routing for 5G cellular networks , 2014, 2014 IEEE International Conference on Communications (ICC).

[39]  Juraj Gazda,et al.  Deep Learning Based Massive MIMO Beamforming for 5G Mobile Network , 2018, 2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS).

[40]  Byeong-Hee Roh,et al.  Implementation of Multi-Hop Cognitive Radio Testbed using Raspberry Pi and USRP , 2017, Int. J. Interdiscip. Telecommun. Netw..

[41]  Zhi Chen,et al.  Dynamic Wireless QoS Analysis for Real-Time Control in URLLC , 2018, 2018 IEEE Globecom Workshops (GC Wkshps).

[42]  Navrati Saxena,et al.  Next Generation 5G Wireless Networks: A Comprehensive Survey , 2016, IEEE Communications Surveys & Tutorials.

[43]  Rong Wang,et al.  Mode selection and resource allocation for device-to-device communications in 5G cellular networks , 2016, China Communications.

[44]  Fotis Foukalas,et al.  5G: The Convergence of Wireless Communications , 2015, Wirel. Pers. Commun..

[45]  Sang-Seon Byun,et al.  TCP over scarce transmission opportunity in cognitive radio networks , 2016, Comput. Networks.

[46]  Kok-Lim Alvin Yau,et al.  A Distributed Testbed for 5G Scenarios: An Experimental Study , 2020, Sensors.