Compressive sensing based random access for machine type communications considering tradeoff between link performance and latency

Machine type communications (MTC) in the next generation of mobile communication systems require a new random access scheme to handle massive access with low signaling overhead and latency. The recently developed compressive sensing multi-user detection (CS-MUD) supports joint activity and data detection by exploiting the sparsity of device activity. In this paper, we adopt the CS-based random access scheme by assigning the unique identification sequences to distinguish the different sensor nodes and employ group orthogonal matching pursuit least square (GOMP-LS) and weighted iteration (WI) GOMP algorithms based on the conventional GOMP with respect to high-reliability and low-latency applications. In addition, to further reduce computational complexity and latency, we introduce a low complexity WIGOMP with inverse Cholesky factorization (WIGOMP-ICF). Based on the simulation results and analysis, we can observe that the proposed three algorithms are promising to support different services requirements for MTC by considering high reliability, low latency, and computational complexity.

[1]  An-Yeu Wu,et al.  Scalable compressive sensing-based multi-user detection scheme for Internet-of-Things applications , 2015, 2015 IEEE Workshop on Signal Processing Systems (SiPS).

[2]  Ki-Dong Lee,et al.  Throughput comparison of random access methods for M2M service over LTE networks , 2011, 2011 IEEE GLOBECOM Workshops (GC Wkshps).

[3]  Dong In Kim,et al.  Performance Modeling and Analysis of Heterogeneous Machine Type Communications , 2014, IEEE Transactions on Wireless Communications.

[4]  Carsten Bockelmann,et al.  Coping with CDMA Asynchronicity in Compressive Sensing Multi-User Detection , 2013, 2013 IEEE 77th Vehicular Technology Conference (VTC Spring).

[5]  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.

[6]  A. Majumdar,et al.  Fast group sparse classification , 2009, Canadian Journal of Electrical and Computer Engineering.

[7]  Mani B. Srivastava,et al.  Applications and Challenges in Securing Time , 2019, CSET @ USENIX Security Symposium.

[8]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[9]  Carsten Bockelmann,et al.  Compressive sensing for MTC in new LTE uplink multi-user random access channel , 2015, AFRICON 2015.

[10]  Alireza Bayesteh,et al.  Uplink contention based SCMA for 5G radio access , 2014, 2014 IEEE Globecom Workshops (GC Wkshps).

[11]  Chung Gu Kang,et al.  Compressive Sensing-Based Random Access with Multiple-Sequence Spreading for MTC , 2015, 2015 IEEE Globecom Workshops (GC Wkshps).

[12]  Zhifeng Zhao,et al.  On the application of compressed sensing in communication networks , 2010, 2010 5th International ICST Conference on Communications and Networking in China.

[13]  Gerhard P. Fettweis,et al.  The Tactile Internet: Applications and Challenges , 2014, IEEE Vehicular Technology Magazine.

[14]  Kwang-Cheng Chen,et al.  Toward ubiquitous massive accesses in 3GPP machine-to-machine communications , 2011, IEEE Communications Magazine.

[15]  Armin Dekorsy,et al.  Compressive Sensing Multi-User Detection with Block-Wise Orthogonal Least Squares , 2012, 2012 IEEE 75th Vehicular Technology Conference (VTC Spring).

[16]  Dusit Niyato,et al.  Random access for machine-to-machine communication in LTE-advanced networks: issues and approaches , 2013, IEEE Communications Magazine.

[17]  Carsten Bockelmann,et al.  Compressive Sensing Multi-User Detection for Multicarrier Systems in Sporadic Machine Type Communication , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[18]  R. Matthews,et al.  Issues and Approaches , 2003 .

[19]  Babak Hassibi,et al.  Summary based structures with improved sublinear recovery for compressed sensing , 2011, 2011 IEEE International Symposium on Information Theory Proceedings.

[20]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[21]  Armin Dekorsy,et al.  Sparse Multi-User Detection for CDMA transmission using greedy algorithms , 2011, 2011 8th International Symposium on Wireless Communication Systems.

[22]  Wen Chen,et al.  Efficient Implementations of Orthogonal Matching Pursuit Based on Inverse Cholesky Factorization , 2013, 2013 IEEE 78th Vehicular Technology Conference (VTC Fall).

[23]  Chung Gu Kang,et al.  Iterative Order Recursive Least Square Estimation for Exploiting Frame-Wise Sparsity in Compressive Sensing-Based MTC , 2016, IEEE Communications Letters.

[24]  C. Bockelmann,et al.  Compressed Sensing Based Multi-User Detection with Modified Sphere Detection in Machine-to-Machine Communications , 2014 .

[25]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[26]  Georgios B. Giannakis,et al.  Exploiting Sparse User Activity in Multiuser Detection , 2011 .

[27]  Carsten Bockelmann,et al.  Efficient Detectors for Joint Compressed Sensing Detection and Channel Decoding , 2015, IEEE Transactions on Communications.

[28]  Bin Li,et al.  An Improved Square-Root Algorithm for V-BLAST Based on Efficient Inverse Cholesky Factorization , 2020, IEEE Transactions on Wireless Communications.

[29]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[30]  Carsten Bockelmann,et al.  Compressive sensing based multi‐user detection for machine‐to‐machine communication , 2013, Trans. Emerg. Telecommun. Technol..