ASIC implementation of an interpolation-based wideband massive MIMO detector

File:IIP LMMSE systolic array.pdf

Short Description

Millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) are key technologies in 5G and beyond 5G wireless communication systems. While mmWave provides vast unused spectrum to increase the communication bandwidth, massive MIMO improves the power and spectral efficiencies via high array gain and spatial multiplexing. To deal with the inter-symbol interference caused by the frequency-selectivity of wideband wireless channels, orthogonal frequency division multiplexing (OFDM) is employed in the 5G standard [1], like many other standards such as LTE and IEEE 802.11 (WiFi).

The implementation of a wideband mmWave massive MIMO-OFDM detector is challenging due to the following reasons:

1. Large number of basestation (BS) antennas in massive MIMO results in large data dimensions to be processed, which makes the detection task complicated. Usually, linear receivers are employed in massive MIMO, to tame the detection complexity.

2. In the wideband communications enabled by mmWave, the baseband sampling rates are high (in the order of Giga samples per second), which results in excessive amount of data to be processed.

3. An OFDM detector needs to detect the data in the frequency domain for N ~ 1000 subcarriers. In a straightforward implementation, this requires N preprocessing operations for a linear receiver, resulting in prohibitive complexity.

One approach to reduce the preprocessing complexity associated with a linear massive MIMO-OFDM receiver is an interpolation scheme in which the preprocessing matrices are explicitly computed only for a subset of subcarriers and for the remaining subcarriers, the preprocessing matrices are obtained using interpolation [2]. In this project, the goal is to extend this idea as follows:

1. Explore the complexity-performance trade-offs associated with interpolating the preprocessing matrices at different levels (e.g. channel matrix, gram matrix, equalization matrix). In the first step, we measure the complexity by counting the number of real-valued multiplications and the amount of required storage (for the channel matrices of different subcarriers).

2. Explore different interpolation methods and evaluate their complexity-performance trade-offs.

3. Compare interpolation in the antenna domain with the beamspace domain [5], and explore the possibility of using only a subset of beams to reduce the complexity or power [3]. The beamspace sparsity can also be exploited in the equalization step to reduce power consumption or increase throughput [4].

4. ASIC Implementation of a full massive MIMO-OFDM detector chip that supports a certain number of subcarriers using an interpolation scheme decided considering the trade-off.

[1] European Telecommunications Standards Institute, “5G NR Base Station (BS) radio transmission and reception,” Apr. 2020, 3GPP TS 38.104 version 16.4.0 Release 16

[2] C. Jeon, Z. Li and C. Studer, "Approximate Gram-Matrix Interpolation for Wideband Massive MU-MIMO Systems," in IEEE Transactions on Vehicular Technology, May 2020

[3] S. H. Mirfarshbafan and C. Studer, "Sparse Beamspace Equalization for Massive MU-MIMO MMWave Systems," ICASSP, Apr. 2020

[4] S. H. Mirfarshbafan and C. Studer, "SPADE: Sparsity-Adaptive Equalization for MMwave Massive MU-MIMO," IEEE Statistical Signal Processing Workshop (SSP), Aug. 2021

[5] M. Mahdavi, O. Edfors, V. Öwall and L. Liu, "Angular-Domain Massive MIMO Detection: Algorithm, Implementation, and Design Tradeoffs," in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 67, no. 6, pp. 1948-1961, June 2020

Status: Available

master project

Contact: Seyed Hadi Mirfarshbafan

Prerequisites

Matlab

Verilog or VHDL

VLSI I and II

Character

70% VLSI implementation

30% MATLAB simulation

Professor

Christoph Studer

Links

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