Asymptotic Signal Detection Rates with 1-bit Array Measurements

This work considers detecting the presence of a band-limited random radio source using an antenna array featuring a low-complexity digitization process with single-bit output resolution. In contrast to high-resolution analog-to-digital conversion, such a direct transformation of the analog radio measurements to a binary representation can be implemented hardware and energy-efficient. However, the probabilistic model of the binary receive data becomes challenging. Therefore, we first consider the Neyman-Pearson test within generic exponential families and derive the associated analytic detection rate expressions. Then we use a specific replacement model for the binary likelihood and study the achievable detection performance with 1- bit radio array measurements. As an application, we explore the capability of a low-complexity GPS spectrum monitoring system with different numbers of antennas and different observation intervals. Results show that with a moderate amount of binary sensors it is possible to reliably perform the monitoring task

Channel Estimation and Uplink Achievable Rates in One-Bit Massive MIMO Systems

This paper considers channel estimation and achievable rates for the uplink of a massive multiple-input multiple-output (MIMO) system where the base station is equipped with one-bit analog-to-digital converters (ADCs). By rewriting the nonlinear one-bit quantization using a linear expression, we first derive a simple and insightful expression for the linear minimum mean-square-error (LMMSE) channel estimator. Then employing this channel estimator, we derive a closed-form expression for the lower bound of the achievable rate for the maximum ratio combiner (MRC) receiver. Numerical results are presented to verify our analysis and show that our proposed LMMSE channel estimator outperforms the near maximum likelihood (nML) estimator proposed previously.Comment: 5 pages, 2 figures, the Ninth IEEE Sensor Array and Multichannel Signal Processing Worksho

DSP Linearization for Millimeter-Wave All-Digital Receiver Array with Low-Resolution ADCs

Millimeter-wave (mmWave) communications and cell densification are the key techniques for the future evolution of cellular systems beyond 5G. Although the current mmWave radio designs are focused on hybrid digital and analog receiver array architectures, the fully digital architecture is an appealing option due to its flexibility and support for multi-user multiple-input multiple-output (MIMO). In order to achieve reasonable power consumption and hardware cost, the specifications of analog circuits are expected to be compromised, including the resolution of analog-to-digital converter (ADC) and the linearity of radio-frequency (RF) front end. Although the state-of-the-art studies focus on the ADC, the nonlinearity can also lead to severe system performance degradation when strong input signals introduce inter-modulation distortion (IMD). The impact of RF nonlinearity becomes more severe with densely deployed mmWave cells since signal sources closer to the receiver array are more likely to occur. In this work, we design and analyze the digital IMD compensation algorithm, and study the relaxation of the required linearity in the RF-chain. We propose novel algorithms that jointly process digitized samples to recover amplifier saturation, and relies on beam space operation which reduces the computational complexity as compared to per-antenna IMD compensation.Comment: 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC

Robust massive MIMO Equilization for mmWave systems with low resolution ADCs

Leveraging the available millimeter wave spectrum will be important for 5G. In this work, we investigate the performance of digital beamforming with low resolution ADCs based on link level simulations including channel estimation, MIMO equalization and channel decoding. We consider the recently agreed 3GPP NR type 1 OFDM reference signals. The comparison shows sequential DCD outperforms MMSE-based MIMO equalization both in terms of detection performance and complexity. We also show that the DCD based algorithm is more robust to channel estimation errors. In contrast to the common believe we also show that the complexity of MMSE equalization for a massive MIMO system is not dominated by the matrix inversion but by the computation of the Gram matrix.Comment: submitted to WCNC 2018 Workshop