Massive Machine Type Communication With Data Aggregation and Resource Scheduling

To enable massive machine type communication (mMTC), data aggregation is a promising approach to reduce the congestion caused by a massive number of machine type devices (MTDs). In this paper, we consider a two-phase cellular-based mMTC network, where MTDs transmit to aggregators (i.e., aggregation phase) and the aggregated data is then relayed to base stations (i.e., relaying phase). Due to the limited resources, the aggregators not only aggregate data, but also schedule resources among MTDs. We consider two scheduling schemes: random resource scheduling (RRS) and channel-aware resource scheduling (CRS). By leveraging the stochastic geometry, we present a tractable analytical framework to investigate the signal-to-interference ratio (SIR) for each phase, thereby computing the MTD success probability, the average number of successful MTDs and probability of successful channel utilization, which are the key metrics characterizing the overall mMTC performance. Our numerical results show that, although the CRS outperforms the RRS in terms of SIR at the aggregation phase, the simpler RRS has almost the same performance as the CRS for most of the cases with regards to the overall mMTC performance. Furthermore, the provision of more resources at the aggregation phase is not always beneficial to the mMTC performance.

[1]  Amin Azari.  Energy-efficient scheduling and grouping for machine-type communications over cellular networks , 2016, Ad Hoc Networks.

[2]  Martin Haenggi,et al.  Stochastic Geometry for Wireless Networks , 2012 .

[3]  Yacine Ghamri-Doudane,et al.  Radio Resource Sharing for MTC in LTE-A: An Interference-Aware Bipartite Graph Approach , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[4]  Sarah J. Johnson,et al.  Massive Multiple Access Based on Superposition Raptor Codes for Cellular M2M Communications , 2017, IEEE Transactions on Wireless Communications.

[5]  Marco Di Renzo,et al.  Stochastic Geometry Modeling of Coverage and Rate of Cellular Networks Using the Gil-Pelaez Inversion Theorem , 2014, IEEE Communications Letters.

[6]  Jeffrey G. Andrews,et al.  Joint Rate and SINR Coverage Analysis for Decoupled Uplink-Downlink Biased Cell Associations in HetNets , 2014, IEEE Transactions on Wireless Communications.

[7]  Chia-han Lee,et al.  PRADA: Prioritized Random Access With Dynamic Access Barring for MTC in 3GPP LTE-A Networks , 2014, IEEE Transactions on Vehicular Technology.

[8]  Jeffrey G. Andrews,et al.  Optimizing Data Aggregation for Uplink Machine-to-Machine Communication Networks , 2016, IEEE Transactions on Communications.

[9]  Taesoo Kwon,et al.  Random Deployment of Data Collectors for Serving Randomly-Located Sensors , 2013, IEEE Transactions on Wireless Communications.

[10]  Harish Viswanathan,et al.  Fundamentals of Throughput Maximization With Random Arrivals for M2M Communications , 2013, IEEE Transactions on Communications.

[11]  Z. Néda,et al.  On the size-distribution of Poisson Voronoi cells , 2004, cond-mat/0406116.

[12]  Ching-Yao Huang,et al.  Energy-Saving Massive Access Control and Resource Allocation Schemes for M2M Communications in OFDMA Cellular Networks , 2012, IEEE Wireless Communications Letters.

[13]  Wei Xiang,et al.  Radio resource allocation in LTE-advanced cellular networks with M2M communications , 2012, IEEE Communications Magazine.

[14]  Petar Popovski,et al.  Zero-Outage Cellular Downlink With Fixed-Rate D2D Underlay , 2014, IEEE Transactions on Wireless Communications.

[15]  Hung-Yun Hsieh,et al.  Not every bit counts: A resource allocation problem for data gathering in machine-to-machine communications , 2012, GLOBECOM.

[16]  Walid Saad,et al.  Toward Massive Machine Type Cellular Communications , 2017, IEEE Wireless Communications.

[17]  Romano Fantacci,et al.  Aggregation and trunking of M2M traffic via D2D connections , 2015, 2015 IEEE International Conference on Communications (ICC).

[18]  Jeffrey G. Andrews,et al.  A Tractable Approach to Coverage and Rate in Cellular Networks , 2010, IEEE Transactions on Communications.

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

[20]  Salman Durrani,et al.  Device-to-Device Communication Underlaying a Finite Cellular Network Region , 2015, IEEE Transactions on Wireless Communications.

[21]  J. Gil-Pelaez.  Note on the inversion theorem , 1951 .

[22]  Abd-Elhamid M. Taha,et al.  Uplink Scheduling in LTE and LTE-Advanced: Tutorial, Survey and Evaluation Framework , 2014, IEEE Communications Surveys & Tutorials.

[23]  Jesus Alonso-Zarate,et al.  Is the Random Access Channel of LTE and LTE-A Suitable for M2M Communications? A Survey of Alternatives , 2014, IEEE Communications Surveys & Tutorials.

[24]  Abraham O. Fapojuwo,et al.  Performance Analysis and Parameter Optimization of Random Access Backoff Algorithm in LTE , 2012, 2012 IEEE Vehicular Technology Conference (VTC Fall).

[25]  S. Gupta,et al.  Order Statistics from the Gamma Distribution , 1960 .

[26]  Athanasios S. Lioumpas,et al.  Evolution of packet scheduling for Machine-Type communications over LTE: Algorithmic design and performance analysis , 2012, 2012 IEEE Globecom Workshops.

[27]  Bartlomiej Blaszczyszyn,et al.  Clustering Comparison of Point Processes, with Applications to Random Geometric Models , 2012, 1212.5285.

[28]  Halim Yanikomeroglu,et al.  Spatial Clustering in Slotted ALOHA Two-Hop Random Access for Machine Type Communication , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

[29]  Yi Zhong,et al.  Multi-Channel Hybrid Access Femtocells: A Stochastic Geometric Analysis , 2013, IEEE Transactions on Communications.

[30]  Jeffrey G. Andrews,et al.  Analytical Modeling of Uplink Cellular Networks , 2012, IEEE Transactions on Wireless Communications.

[31]  Sarah J. Johnson,et al.  Massive Non-Orthogonal Multiple Access for Cellular IoT: Potentials and Limitations , 2016, IEEE Communications Magazine.

[32]  Herbert A. David,et al.  Order Statistics , 2011, International Encyclopedia of Statistical Science.

[33]  Hao Xu,et al.  Modeling and analysis of Uplink-Downlink relationship in Heterogeneous Cellular Network , 2014, 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC).

[34]  Akshay Kumar,et al.  A Delay Optimal MAC and Packet Scheduler for Heterogeneous M2M Uplink , 2016, ArXiv.

[35]  Chun-Hung Liu,et al.  Random cell association and void probability in poisson-distributed cellular networks , 2015, 2015 IEEE International Conference on Communications (ICC).

[36]  Mohamed-Slim Alouini,et al.  Spatiotemporal Stochastic Modeling of IoT Enabled Cellular Networks: Scalability and Stability Analysis , 2016, IEEE Transactions on Communications.

[37]  Guowang Miao,et al.  $E^{2}$ -MAC: Energy Efficient Medium Access for Massive M2M Communications , 2016, IEEE Transactions on Communications.