Photonic Computing to Accelerate Data Processing in Wireless Communications

Massive multiple-input multiple-output (MIMO) systems are considered as one of the leading technologies employed in the next generations of wireless communication networks (5G), which promise to provide higher spectral efficiency, lower latency, and more reliability. Due to the massive number of devices served by the base stations (BS) equipped with large antenna arrays, massive-MIMO systems need to perform high-dimensional signal processing in a considerably short amount of time. The computational complexity of such data processing, while satisfying the energy and latency requirements, is beyond the capabilities of the conventional widely-used digital electronics-based computing, i.e., Field-Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs). In this paper, the speed and lossless propagation of light is exploited to introduce a photonic computing approach that addresses the high computational complexity required by massive-MIMO systems. The proposed computing approach is based on photonic implementation of multiply and accumulate (MAC) operation achieved by broadcast-and-weight (B&W) architecture. The B&W protocol is limited to real and positive values to perform MAC operations. In this work, preprocessing steps are developed to enable the proposed photonic computing architecture to accept any arbitrary values as the input. This is a requirement for wireless communication systems that typically deal with complex values. Numerical analysis shows that the performance of the wireless communication system is not degraded by the proposed photonic computing architecture, while it provides significant improvements in time and energy efficiency for massive-MIMO systems as compared to the most powerful Graphics Processing Units (GPUs).

[1]  Fredrik Rusek,et al.  Hardware efficient approximative matrix inversion for linear pre-coding in massive MIMO , 2014, 2014 IEEE International Symposium on Circuits and Systems (ISCAS).

[2]  H. Rong,et al.  A 128 Gb/s PAM4 Silicon Microring Modulator With Integrated Thermo-Optic Resonance Tuning , 2019, Journal of Lightwave Technology.

[3]  Robert W. Heath,et al.  Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems , 2014, IEEE Journal of Selected Topics in Signal Processing.

[4]  Paul R. Prucnal,et al.  Broadcast and Weight: An Integrated Network For Scalable Photonic Spike Processing , 2014, Journal of Lightwave Technology.

[5]  J. Mora,et al.  Microwave Photonic Signal Processing , 2015, Journal of Lightwave Technology.

[6]  Walid Saad,et al.  A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems , 2019, IEEE Network.

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

[8]  Paul R. Prucnal,et al.  Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs) , 2019, IEEE Journal of Selected Topics in Quantum Electronics.

[9]  Emil Björnson,et al.  Massive MIMO: ten myths and one critical question , 2015, IEEE Communications Magazine.

[10]  Fredrik Rusek,et al.  Approximative matrix inverse computations for very-large MIMO and applications to linear pre-coding systems , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[11]  P. Dumon,et al.  Silicon microring resonators , 2012 .

[12]  Ursula Challita,et al.  Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial , 2017, IEEE Communications Surveys & Tutorials.

[13]  AKHIL GUPTA,et al.  A Survey of 5G Network: Architecture and Emerging Technologies , 2015, IEEE Access.

[14]  R. Hunger Floating Point Operations in Matrix-Vector Calculus , 2022 .

[15]  Chuan Tang,et al.  High Precision Low Complexity Matrix Inversion Based on Newton Iteration for Data Detection in the Massive MIMO , 2016, IEEE Communications Letters.

[16]  Ellen Zhou,et al.  Silicon Photonic Neural Networks , 2016 .

[17]  Mugen Peng,et al.  Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues , 2018, IEEE Communications Surveys & Tutorials.

[18]  Liang Liu,et al.  Algorithm and hardware aspects of pre-coding in massive MIMO systems , 2015, 2015 49th Asilomar Conference on Signals, Systems and Computers.

[19]  Paul R. Prucnal,et al.  Progress in neuromorphic photonics , 2017 .

[20]  Joseph R. Cavallaro,et al.  Large-Scale MIMO Detection for 3GPP LTE: Algorithms and FPGA Implementations , 2014, IEEE Journal of Selected Topics in Signal Processing.

[21]  Robert W. Heath,et al.  An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems , 2015, IEEE Journal of Selected Topics in Signal Processing.

[22]  Ali M. Niknejad,et al.  Design considerations for 60 GHz CMOS radios , 2004, IEEE Communications Magazine.

[23]  Sandra Sendra,et al.  A Survey on 5G Usage Scenarios and Traffic Models , 2020, IEEE Communications Surveys & Tutorials.

[24]  Boris Murmann,et al.  Power Dissipation Bounds for High-Speed Nyquist Analog-to-Digital Converters , 2009, IEEE Transactions on Circuits and Systems I: Regular Papers.

[25]  Xiaoke Yi,et al.  Microwave photonic signal processing , 2007, 2011 International Topical Meeting on Microwave Photonics jointly held with the 2011 Asia-Pacific Microwave Photonics Conference.

[26]  Stefan Parkvall,et al.  NR - The New 5G Radio-Access Technology , 2017, 2018 IEEE 87th Vehicular Technology Conference (VTC Spring).

[27]  Joseph R. Cavallaro,et al.  Decentralized Baseband Processing for Massive MU-MIMO Systems , 2017, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[28]  Erik G. Larsson,et al.  Efficient DSP and Circuit Architectures for Massive MIMO: State of the Art and Future Directions , 2018, IEEE Transactions on Signal Processing.

[29]  Wei Yu,et al.  Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays , 2016, IEEE Journal of Selected Topics in Signal Processing.

[30]  Bicky A. Marquez,et al.  Graphene-based photonic synapse for multi wavelength neural networks , 2020, MRS Advances.

[31]  Paul R. Prucnal,et al.  Silicon Photonic Modulator Neuron , 2018, Physical Review Applied.

[32]  T Pinguet,et al.  A Grating-Coupler-Enabled CMOS Photonics Platform , 2011, IEEE Journal of Selected Topics in Quantum Electronics.

[33]  Yurii A. Vlasov,et al.  Silicon CMOS-integrated nano-photonics for computer and data communications beyond 100G , 2012, IEEE Communications Magazine.

[34]  Lu Xu,et al.  Low Complexity Iterative MMSE-PIC Detection for Medium-Size Massive MIMO , 2016, IEEE Wireless Communications Letters.

[35]  Ning He,et al.  OFDM Numerology Design for 5G New Radio to Support IoT, eMBB, and MBSFN , 2018, IEEE Communications Standards Magazine.

[36]  Peng Zhang,et al.  Large-scale MIMO detection design and FPGA implementations using SOR method , 2016, 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN).

[37]  Wei Yu,et al.  Generalized Approximate Message Passing for Massive MIMO mmWave Channel Estimation With Laplacian Prior , 2019, IEEE Transactions on Communications.

[38]  R. Mongia RF and microwave coupled-line circuits , 1999 .

[39]  R. Uma,et al.  A Survey and Comparative Analysis of Multiply-Accumulate (MAC) Block for Digital Signal Processing Application on ASIC and FPGA , 2015 .

[40]  Joseph R. Cavallaro,et al.  High-Throughput Data Detection for Massive MU-MIMO-OFDM Using Coordinate Descent , 2016, IEEE Transactions on Circuits and Systems I: Regular Papers.