A novel uplink data transmission scheme for small packets in massive MIMO system

Intelligent terminals often produce a large number of data packets of small lengths. For these packets, it is inefficient to follow the conventional medium access control (MAC) protocols because they lead to poor utilization of service resources. We propose a novel multiple access scheme that targets massive multiple-input multiple-output (MIMO) systems based on compressive sensing (CS). We employ block precoding in the time domain to enable the simultaneous transmissions of many users, which could be even more than the number of receive antennas at the base station. We develop a block-sparse system model and adopt the block orthogonal matching pursuit (BOMP) algorithm to recover the transmitted signals. Conditions for data recovery guarantees are identified and numerical results demonstrate that our scheme is efficient for uplink small packet transmission.

[1]  Robert W. Heath,et al.  Improving throughput and fairness for MIMO ad hoc networks using antenna selection diversity , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[2]  W. Marsden.  I and J , 2012 .

[3]  Thomas L. Marzetta,et al.  Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas , 2010, IEEE Transactions on Wireless Communications.

[4]  John C. S. Lui,et al.  A Panoramic View of 3G Data/Control-Plane Traffic: Mobile Device Perspective , 2012, Networking.

[5]  Volkan Cevher,et al.  Model-Based Compressive Sensing , 2008, IEEE Transactions on Information Theory.

[6]  Lei Zhang,et al.  Neighbor discovery in wireless networks using compressed sensing with Reed-Muller codes , 2011, 2011 International Symposium of Modeling and Optimization of Mobile, Ad Hoc, and Wireless Networks.

[7]  Erik G. Larsson,et al.  Energy and Spectral Efficiency of Very Large Multiuser MIMO Systems , 2011, IEEE Transactions on Communications.

[8]  Yonina C. Eldar,et al.  Near-Oracle Performance of Greedy Block-Sparse Estimation Techniques From Noisy Measurements , 2010, IEEE Journal of Selected Topics in Signal Processing.

[9]  Waheed U. Bajwa,et al.  Multiuser detection in asynchronous on-off random access channels using lasso , 2010, 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[10]  Branka Vucetic,et al.  Distributed multiple-access for wireless communications: Compressed sensing with multiple antennas , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[11]  Zhen Xiao,et al.  Understanding Instant Messaging Traffic Characteristics , 2007, 27th International Conference on Distributed Computing Systems (ICDCS '07).

[12]  Husheng Li,et al.  Compressed Meter Reading for Delay-Sensitive and Secure Load Report in Smart Grid , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[13]  Leysia Palen,et al.  Instant messaging in teen life , 2002, CSCW '02.

[14]  Erik G. Larsson,et al.  Massive MIMO for next generation wireless systems , 2013, IEEE Communications Magazine.

[15]  Yonina C. Eldar,et al.  Structured Compressed Sensing: From Theory to Applications , 2011, IEEE Transactions on Signal Processing.

[16]  Erik G. Larsson,et al.  Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays , 2012, IEEE Signal Process. Mag..