Physics-inspired heuristics for soft MIMO detection in 5G new radio and beyond

Overcoming the conventional trade-off between throughput and bit error rate (BER) performance, versus computational complexity is a long-term challenge for uplink Multiple-Input Multiple-Output (MIMO) detection in base station design for the cellular 5G New Radio roadmap, as well as in next generation wireless local area networks. In this work, we present ParaMax, a MIMO detector architecture that for the first time brings to bear physics-inspired parallel tempering algorithmic techniques [28, 50, 67] on this class of problems. ParaMax can achieve near optimal maximum-likelihood (ML) throughput performance in the Large MIMO regime, Massive MIMO systems where the base station has additional RF chains, to approach the number of base station antennas, in order to support even more parallel spatial streams. ParaMax is able to achieve a near ML-BER performance up to 160 × 160 and 80 × 80 Large MIMO for low-order modulations such as BPSK and QPSK, respectively, only requiring less than tens of processing elements. With respect to Massive MIMO systems, in 12 × 24 MIMO with 16-QAM at SNR 16 dB, ParaMax achieves 330 Mbits/s near-optimal system throughput with 4--8 processing elements per subcarrier, which is approximately 1.4× throughput than linear detector-based Massive MIMO systems.

[1]  Lee-Fang Wei,et al.  Coded modulation with unequal error protection , 1993, IEEE Trans. Commun..

[2]  I. M. Boyarinov,et al.  Linear unequal error protection codes , 1981, IEEE Trans. Inf. Theory.

[3]  John S. Thompson,et al.  Fixing the Complexity of the Sphere Decoder for MIMO Detection , 2008, IEEE Transactions on Wireless Communications.

[4]  Anuj Kalia,et al.  Agora: Real-time massive MIMO baseband processing in software , 2020, CoNEXT.

[5]  E. Ising.  Beitrag zur Theorie des Ferromagnetismus , 1925 .

[6]  Emil Björnson,et al.  Massive MIMO for Maximal Spectral Efficiency: How Many Users and Pilots Should Be Allocated? , 2014, IEEE Transactions on Wireless Communications.

[7]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[8]  Toshiyuki Miyazawa,et al.  Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer , 2018, Front. Phys..

[9]  Jie Cheng,et al.  CUDA by Example: An Introduction to General-Purpose GPU Programming , 2010, Scalable Comput. Pract. Exp..

[10]  Reinaldo A. Valenzuela,et al.  V-BLAST: an architecture for realizing very high data rates over the rich-scattering wireless channel , 1998, 1998 URSI International Symposium on Signals, Systems, and Electronics. Conference Proceedings (Cat. No.98EX167).

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

[12]  Tharmalingam Ratnarajah,et al.  A low-complexity soft-MIMO detector based on the fixed-complexity sphere decoder , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[13]  Georgios Georgis,et al.  FlexCore: Massively Parallel and Flexible Processing for Large MIMO Access Points , 2017, NSDI.

[14]  Emanuele Viterbo,et al.  A universal lattice code decoder for fading channels , 1999, IEEE Trans. Inf. Theory.

[15]  Kyle Jamieson,et al.  Towards quantum belief propagation for LDPC decoding in wireless networks , 2020, MobiCom.

[16]  Björn E. Ottersten,et al.  The Error Probability of the Fixed-Complexity Sphere Decoder , 2009, IEEE Transactions on Signal Processing.

[17]  Ken-ichi Kawarabayashi,et al.  Experimental investigation of performance differences between coherent Ising machines and a quantum annealer , 2018, Science Advances.

[18]  Georgios B. Giannakis,et al.  Approaching MIMO channel capacity with reduced-complexity soft sphere decoding , 2004, 2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733).

[19]  Rahim Tafazolli,et al.  Massively Parallel Tree Search for High-Dimensional Sphere Decoders , 2019, IEEE Transactions on Parallel and Distributed Systems.

[20]  Sing-Wai Cheung,et al.  Simplified ordering for fixed-complexity sphere decoder , 2010, IWCMC.

[21]  U. Fincke,et al.  Improved methods for calculating vectors of short length in a lattice , 1985 .

[22]  Wang,et al.  Replica Monte Carlo simulation of spin glasses. , 1986, Physical review letters.

[23]  Imrich Chlamtac,et al.  Internet of things: Vision, applications and research challenges , 2012, Ad Hoc Networks.

[24]  Ahmed M. Eltawil,et al.  Design and Implementation of a Sort-Free K-Best Sphere Decoder , 2010, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[25]  Andrew Lucas,et al.  Ising formulations of many NP problems , 2013, Front. Physics.

[26]  Antonio M. Vidal,et al.  MIMOPack: a high-performance computing library for MIMO communication systems , 2014, The Journal of Supercomputing.

[27]  Ali Abedi,et al.  A millimeter wave network for billions of things , 2019, SIGCOMM.

[28]  Zhan Guo,et al.  Algorithm and implementation of the K-best sphere decoding for MIMO detection , 2006, IEEE Journal on Selected Areas in Communications.

[29]  Erik G. Larsson,et al.  Fixed-Complexity Soft MIMO Detection via Partial Marginalization , 2008, IEEE Transactions on Signal Processing.

[30]  Lajos Hanzo,et al.  Quantum Search Algorithms, Quantum Wireless, and a Low-Complexity Maximum Likelihood Iterative Quantum Multi-User Detector Design , 2013, IEEE Access.

[31]  Qingwei Li,et al.  Reduced Complexity K-Best Sphere Decoder Design for MIMO Systems , 2008 .

[32]  Alejandro Perdomo-Ortiz,et al.  Strengths and weaknesses of weak-strong cluster problems: A detailed overview of state-of-the-art classical heuristics versus quantum approaches , 2016, 1604.01746.

[33]  Bernd Girod,et al.  Robust Internet video transmission based on scalable coding and unequal error protection , 1999, Signal Process. Image Commun..

[34]  A P Young,et al.  Absence of an Almeida-Thouless line in three-dimensional spin glasses. , 2004, Physical review letters.

[35]  Matthias Troyer,et al.  Feedback-optimized parallel tempering Monte Carlo , 2006, cond-mat/0602085.

[36]  Ramjee Prasad,et al.  OFDM for Wireless Multimedia Communications , 1999 .

[37]  John S. Thompson,et al.  Extending a Fixed-Complexity Sphere Decoder to Obtain Likelihood Information for Turbo-MIMO Systems , 2008, IEEE Transactions on Vehicular Technology.

[38]  Qing Yang,et al.  BigStation: enabling scalable real-time signal processingin large mu-mimo systems , 2013, SIGCOMM.

[39]  Yang Guangda,et al.  The Multi-user Detection for the MIMO-OFDM System Based on the Genetic Simulated Annealing Algorithm , 2009 .

[40]  Alexandros G. Dimakis,et al.  Near-Optimal Detection in MIMO Systems Using Gibbs Sampling , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[41]  Erik Dahlman,et al.  4G: LTE/LTE-Advanced for Mobile Broadband , 2011 .

[42]  Nicolai M. Josuttis.  The C++ Standard Library: A Tutorial and Reference , 2012 .

[43]  Kyle Jamieson,et al.  Leveraging quantum annealing for large MIMO processing in centralized radio access networks , 2019, SIGCOMM.

[44]  Hua Yu,et al.  The K-Best Sphere Decoding for Soft Detection of Generalized Spatial Modulation , 2017, IEEE Transactions on Communications.

[45]  Hartmut Neven,et al.  Optimizing Variational Quantum Algorithms using Pontryagin's Minimum Principle , 2016, ArXiv.

[46]  Helmut G. Katzgraber,et al.  A deceptive step towards quantum speedup detection , 2017, Quantum Science and Technology.

[47]  Babak Hassibi,et al.  On the sphere-decoding algorithm I. Expected complexity , 2005, IEEE Transactions on Signal Processing.

[48]  Dominic J. A. Welsh,et al.  The Computational Complexity of Some Classical Problems from Statistical Physics , 1990 .

[49]  Erik G. Larsson,et al.  On maximum-likelihood detection and decoding for space-time coding systems , 2002, IEEE Trans. Signal Process..

[50]  A. Burg,et al.  Soft-Output Sphere Decoding: Performance and Implementation Aspects , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[51]  Antonio M. Vidal,et al.  An efficient GPU implementation of fixed-complexity sphere decoders for MIMO wireless systems , 2012, Integr. Comput. Aided Eng..

[52]  Wolfgang Porod,et al.  Coupled oscillators for computing: A review and perspective , 2020 .

[53]  Fredrik Tufvesson,et al.  A flexible 100-antenna testbed for Massive MIMO , 2014, 2014 IEEE Globecom Workshops (GC Wkshps).

[54]  Rohit Chandra,et al.  Parallel programming in openMP , 2000 .

[55]  Gavin E Crooks,et al.  Measuring thermodynamic length. , 2007, Physical review letters.

[56]  Haidong Zhu,et al.  Markov chain Monte Carlo algorithms for CDMA and MIMO communication systems , 2006, IEEE Transactions on Signal Processing.

[57]  Erwin Riegler,et al.  Soft-Heuristic Detectors for Large MIMO Systems , 2013, IEEE Transactions on Signal Processing.

[58]  P. Atzberger.  The Monte-Carlo Method , 2006 .

[59]  Giuseppe Caire,et al.  On maximum-likelihood detection and the search for the closest lattice point , 2003, IEEE Trans. Inf. Theory.

[60]  Kyle Jamieson,et al.  Towards Hybrid Classical-Quantum Computation Structures in Wirelessly-Networked Systems , 2020, HotNets.

[61]  H. Katzgraber,et al.  Exponentially Biased Ground-State Sampling of Quantum Annealing Machines with Transverse-Field Driving Hamiltonians. , 2016, Physical review letters.

[62]  Clayton Shepard,et al.  Understanding real many-antenna MU-MIMO channels , 2016, 2016 50th Asilomar Conference on Signals, Systems and Computers.

[63]  Taufik Abrão,et al.  S/MIMO MC-CDMA Heuristic Multiuser Detectors Based on Single-Objective Optimization , 2010, Wirel. Pers. Commun..

[64]  Rupak Biswas,et al.  Quantum Approximate Optimization with Hard and Soft Constraints , 2017 .

[65]  Fredrik Tufvesson,et al.  The World’s First Real-Time Testbed for Massive MIMO: Design, Implementation, and Validation , 2016, IEEE Access.

[66]  Rong-Rong Chen,et al.  Achieving Near MAP Performance With an Excited Markov Chain Monte Carlo MIMO Detector , 2017, IEEE Transactions on Wireless Communications.

[67]  Thomas L. Marzetta,et al.  Argos: practical many-antenna base stations , 2012, Mobicom '12.

[68]  Alexander Vardy,et al.  Closest point search in lattices , 2002, IEEE Trans. Inf. Theory.

[69]  L. Dagum,et al.  OpenMP: an industry standard API for shared-memory programming , 1998 .

[70]  Gili Rosenberg,et al.  Boosting quantum annealer performance via sample persistence , 2016, Quantum Inf. Process..

[71]  M. Troyer,et al.  Optimized parallel tempering simulations of proteins. , 2006, The Journal of chemical physics.

[72]  Ethan L. Miller,et al.  Screaming fast Galois field arithmetic using intel SIMD instructions , 2013, FAST.