Abstract-This paper deals with linear equalization in massive multi-user multiple-input multiple-output (MU-MIMO) wireless systems. We first provide simple conditions on the antenna configuration for which the well-known linear minimum meansquare error (L-MMSE) equalizer provides near-optimal spectral efficiency, and we analyze its performance in the presence of parameter mismatches in the signal and/or noise powers. We then propose a novel, optimally-tuned NOnParametric Equalizer (NOPE) for massive MU-MIMO systems, which avoids knowledge of the transmit signal and noise powers altogether. We show that NOPE achieves the same performance as that of the L-MMSE equalizer in the large-antenna limit, and we demonstrate its efficacy in realistic, finite-dimensional systems. From a practical perspective, NOPE is computationally efficient and avoids dedicated training that is typically required for parameter estimation. I. INTRODUCTIONMassive (or large-scale) multi-user multiple-input multipleoutput (MU-MIMO) will be among the key technologies for fifth-generation (5G) wireless systems as it provides (often significantly) higher spectral efficiency than traditional, smallscale MIMO [1], [2]. Data detection at the infrastructure base-stations (BSs) in such massive MU-MIMO systems is among the most critical components from a spectral efficiency and computational complexity perspective. In particular, since optimal data detection is known to be an NP-hard problem [3], a naïve exhaustive search over all possible transmit signals would result in prohibitive computational complexity for such largedimensional systems. Hence, alternative algorithms that achieve high spectral efficiency at low complexity must be deployed in practice. In addition, practical massive MU-MIMO systems suffer-as do traditional MIMO systems-from real-world hardware impairments and model mismatches (for example amplifier nonlinearities, phase noise, quantization artifacts, channel-estimation errors, etc.). Such system nonidealities are known to substantially reduce the performance of optimal data-detection algorithms unless one explicitly models these impairments and estimates the associated parameters [4].

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Linear minimum mean-square error (L-MMSE) equalization is among the most popular methods for data detection in massive multi-user multiple-input multiple-output (MU-MIMO) wireless systems. While L-MMSE equalization enables near-optimal spectral efficiency, accurate knowledge of the signal and noise powers is necessary. Furthermore, corresponding VLSI designs must solve linear systems of equations, which requires high arithmetic precision, exhibits stringent data dependencies, and results in high circuit complexity. This paper proposes the first VLSI design of the NOnParametric Equalizer (NOPE), which avoids knowledge of the transmit signal and noise powers, provably delivers the performance of L-MMSE equalization for massive MU-MIMO systems, and is resilient to numerous system and hardware impairments due to its parameter-free nature. Moreover, NOPE avoids computation of a matrix inverse and only requires hardware-friendly matrix-vector multiplications. To showcase the practical advantages of NOPE, we propose a parallel VLSI architecture and provide synthesis results in 28 nm CMOS. We demonstrate that NOPE performs on par with existing data detectors for massive MU-MIMO that require accurate knowledge of the signal and noise powers.

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