Performance analysis of faster than symbol rate sampling in 1-bit massive MIMO systems

Low resolution analog-to-digital converters (ADC) attracted much attention lately for massive multiple-input multiple-output (MIMO) communication and systems with high bandwidth. Especially, 1-bit ADCs are suitable for such systems due to their low power consumption and cost. In this study, we illustrate the benefits of using faster than symbol rate (FTSR) sampling in an uplink massive MIMO system with 1-bit ADCs in terms of symbol error rate (SER). We show that FTSR sampling provides about 4 dB signal-to-noise ratio (SNR) advantage in terms of SER with a linear low complexity zero-forcing type receiver. We also obtain an analytical bound for the SER performance of uplink massive MIMO structures with 1-bit quantization for the FTSR sampling scenario for low, medium and high SNR regimes. The proposed analytical bound is also applicable to no FTSR sampling case and shown to yield more accurate results compared to some other analytical expressions in the literature. Our results establish a tradeoff between temporal oversampling and the number of receive antennas.

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

[2]  Heinrich Meyr,et al.  On sampling rate, analog prefiltering, and sufficient statistics for digital receivers , 1994, IEEE Trans. Commun..

[3]  Robert W. Heath,et al.  Capacity Analysis of One-Bit Quantized MIMO Systems With Transmitter Channel State Information , 2014, IEEE Transactions on Signal Processing.

[4]  Sven Jacobsson,et al.  One-bit massive MIMO: Channel estimation and high-order modulations , 2015, 2015 IEEE International Conference on Communication Workshop (ICCW).

[5]  Erik G. Larsson,et al.  Per-Antenna Constant Envelope Precoding for Large Multi-User MIMO Systems , 2012, IEEE Transactions on Communications.

[6]  Antonia Maria Tulino,et al.  Time-varying narrow-band interference rejection in asynchronous multiuser DS/CDMA systems over frequency-selective fading channels , 1999, IEEE Trans. Commun..

[7]  Josef A. Nossek,et al.  Challenges in Coding for Quantized MIMO Systems , 2006, 2006 IEEE International Symposium on Information Theory.

[8]  Robert W. Heath,et al.  Near Maximum-Likelihood Detector and Channel Estimator for Uplink Multiuser Massive MIMO Systems With One-Bit ADCs , 2015, IEEE Transactions on Communications.

[9]  A. C. Aitken,et al.  Random variables and probability distributions , 1938 .

[10]  Hien Quoc Ngo,et al.  Massive MIMO: Fundamentals and System Designs , 2015, 5G and Beyond.

[11]  R. D. Gitlin,et al.  Fractionally-spaced equalization: An improved digital transversal equalizer , 1981, The Bell System Technical Journal.

[12]  Jeffrey G. Andrews,et al.  What Will 5G Be? , 2014, IEEE Journal on Selected Areas in Communications.

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

[14]  Erik G. Larsson,et al.  Massive MIMO with 1-bit ADC , 2014, ArXiv.

[15]  J. Nossek,et al.  Capacity Lower Bound of MIMO Channels with Output Quantization and Correlated Noise , 2012 .

[16]  D. Rajan Probability, Random Variables, and Stochastic Processes , 2017 .

[17]  M. Lops,et al.  Simultaneous suppression of multiaccess and narrow-band interference in asynchronous CDMA networks , 2000, IEEE Trans. Veh. Technol..