Xinyu Gaol

Bio: Xinyu Gaol is an academic researcher from Tsinghua University. The author has contributed to research in topics: Algorithm design & Detection theory. The author has an hindex of 1, co-authored 1 publications receiving 33 citations.

Low-complexity signal detection using CG method for uplink large-scale MIMO systems

Abstract: Large-scale multiple-input multiple-output (LS-MIMO) is considered as a promising key technology for future 5G wireless communications due to its very high spectrum and energy efficiency. However, one challenging problem to achieve these benefits is a practical signal detection algorithm in the uplink. In this paper, we propose a low-complexity near-optimal signal detection algorithm using the conjugate gradient (CG) method for uplink multi-user large-scale MIMO systems, which can avoid the complicated matrix inversion required by linear minimum mean square error (MMSE) signal detection algorithm. We also provide the convergence proof of the proposed scheme to guarantee its usability in practice. The analysis indicates that the proposed scheme can reduce the computational complexity by about one order of magnitude. The simulation results of the bit error rate performance verify that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm, and achieves the near-optimal performance of the classical MMSE algorithm by using only a small number of iterations.

High-Throughput Data Detection for Massive MU-MIMO-OFDM using Coordinate Descent

Abstract: Data detection in massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems is among the most critical tasks due to the excessively high implementation complexity. In this paper, we propose a novel, equalization-based soft-output data-detection algorithm and corresponding reference FPGA designs for wideband massive MU-MIMO systems that use orthogonal frequency-division multiplexing (OFDM). Our data-detection algorithm performs approximate minimum mean-square error (MMSE) or box-constrained equalization using coordinate descent. We deploy a variety of algorithm-level optimizations that enable near-optimal error-rate performance at low implementation complexity, even for systems with hundreds of base-station (BS) antennas and thousands of subcarriers. We design a parallel VLSI architecture that uses pipeline interleaving and can be parametrized at design time to support various antenna configurations. We develop reference FPGA designs for massive MU-MIMO-OFDM systems and provide an extensive comparison to existing designs in terms of implementation complexity, throughput, and error-rate performance. For a 128 BS antenna, 8 user massive MU-MIMO-OFDM system, our FPGA design outperforms the next-best implementation by more than 2.6x in terms of throughput per FPGA look-up tables.

VLSI design of large-scale soft-output MIMO detection using conjugate gradients

Abstract: We propose an FPGA design for soft-output data detection in orthogonal frequency-division multiplexing (OFDM)-based large-scale (multi-user) MIMO systems. To reduce the high computational complexity of data detection, our design uses a modified version of the conjugate gradient least square (CGLS) algorithm. In contrast to existing linear detection algorithms for massive MIMO systems, our method avoids two of the most complex tasks, namely Gram-matrix computation and matrix inversion, while still being able to compute soft-outputs. Our architecture uses an array of reconfigurable processing elements to compute the CGLS algorithm in a hardware-efficient manner. Implementation results on Xilinx Virtex-7 FPGA for a 128 antenna, 8 user large-scale MIMO system show that our design only uses 70% of the area-delay product of the competitive method, while exhibiting superior error-rate performance.

High-Throughput Data Detection for Massive MU-MIMO-OFDM Using Coordinate Descent

Abstract: Data detection in massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems is among the most critical tasks due to the excessively high implementation complexity. In this paper, we propose a novel, equalization-based soft-output data-detection algorithm and corresponding reference FPGA designs for wideband massive MU-MIMO systems that use orthogonal frequency-division multiplexing (OFDM). Our data-detection algorithm performs approximate minimum mean-square error (MMSE) or box-constrained equalization using coordinate descent. We deploy a variety of algorithm-level optimizations that enable near-optimal error-rate performance at low implementation complexity, even for systems with hundreds of base-station (BS) antennas and thousands of subcarriers. We design a parallel VLSI architecture that uses pipeline interleaving and can be parametrized at design time to support various antenna configurations. We develop reference FPGA designs for massive MU-MIMO-OFDM systems and provide an extensive comparison to existing designs in terms of implementation complexity, throughput, and error-rate performance. For a 128 BS antenna, 8-user massive MU-MIMO-OFDM system, our FPGA design outperforms the next-best implementation by more than $2.6 \times $ in terms of throughput per FPGA look-up tables.

Data Detection in Large Multi-Antenna Wireless Systems via Approximate Semidefinite Relaxation

Abstract: Practical data detectors for future wireless systems with hundreds of antennas at the base station must achieve high throughput and low error rate at low complexity. Since the complexity of maximum-likelihood (ML) data detection is prohibitive for such large wireless systems, approximate methods are necessary. In this paper, we propose a novel data detection algorithm referred to as Triangular Approximate SEmidefinite Relaxation (TASER), which is suitable for two application scenarios: i) coherent data detection in large multi-user multiple-input multiple-output (MU-MIMO) wireless systems and ii) joint channel estimation and data detection in large single-input multiple-output (SIMO) wireless systems. For both scenarios, we show that TASER achieves near-ML error-rate performance at low complexity by relaxing the associated ML-detection problems into a semidefinite program, which we solve approximately using a preconditioned forward-backward splitting procedure. Since the resulting problem is non-convex, we provide convergence guarantees for our algorithm. To demonstrate the efficacy of TASER in practice, we design a systolic architecture that enables our algorithm to achieve high throughput at low hardware complexity, and we develop reference field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) designs for various antenna configurations.

Learned Conjugate Gradient Descent Network for Massive MIMO Detection

Abstract: In this work, we consider the use of model-driven deep learning techniques for massive multiple-input multiple-output (MIMO) detection. Compared with conventional MIMO systems, massive MIMO promises improved spectral efficiency, coverage and range. Unfortunately, these benefits are at the expense of significantly increased computational complexity. To reduce the complexity of signal detection and guarantee the performance, we present a learned conjugate gradient descent network (LcgNet), which is constructed by unfolding the iterative conjugate gradient descent (CG) detector. In the proposed network, instead of calculating the exact values of the scalar step-sizes, we explicitly learn their universal values. Also, we can enhance the proposed network by augmenting the dimensions of these step-sizes. Furthermore, in order to reduce the memory costs, a novel quantized LcgNet is proposed, where a low-resolution nonuniform quantizer is used to quantize the learned parameters. The quantizer is based on a specially designed soft staircase function with learnable parameters to adjust its shape. Meanwhile, due to fact that the number of learnable parameters is limited, the proposed networks are relatively easy to train. Numerical results demonstrate that the proposed network can achieve promising performance with much lower complexity.