Multipair Massive MIMO Relaying Systems with One-Bit ADCs and DACs

This paper considers a multipair amplify-and-forward massive MIMO relaying system with one-bit ADCs and one-bit DACs at the relay. The channel state information is estimated via pilot training, and then utilized by the relay to perform simple maximum-ratio combining/maximum-ratio transmission processing. Leveraging on the Bussgang decomposition, an exact achievable rate is derived for the system with correlated quantization noise. Based on this, a closed-form asymptotic approximation for the achievable rate is presented, thereby enabling efficient evaluation of the impact of key parameters on the system performance. Furthermore, power scaling laws are characterized to study the potential energy efficiency associated with deploying massive one-bit antenna arrays at the relay. In addition, a power allocation strategy is designed to compensate for the rate degradation caused by the coarse quantization. Our results suggest that the quality of the channel estimates depends on the specific orthogonal pilot sequences that are used, contrary to unquantized systems where any set of orthogonal pilot sequences gives the same result. Moreover, the sum rate gap between the double-quantized relay system and an ideal non-quantized system is a moderate factor of $4/\pi^2$ in the low power regime.Comment: 14 pages, 10 figures, submitted to IEEE Trans. Signal Processin

Signal Recovery From 1-Bit Quantized Noisy Samples via Adaptive Thresholding

In this paper, we consider the problem of signal recovery from 1-bit noisy measurements. We present an efficient method to obtain an estimation of the signal of interest when the measurements are corrupted by white or colored noise. To the best of our knowledge, the proposed framework is the pioneer effort in the area of 1-bit sampling and signal recovery in providing a unified framework to deal with the presence of noise with an arbitrary covariance matrix including that of the colored noise. The proposed method is based on a constrained quadratic program (CQP) formulation utilizing an adaptive quantization thresholding approach, that further enables us to accurately recover the signal of interest from its 1-bit noisy measurements. In addition, due to the adaptive nature of the proposed method, it can recover both fixed and time-varying parameters from their quantized 1-bit samples.Comment: This is a pre-print version of the original conference paper that has been accepted at the 2018 IEEE Asilomar Conference on Signals, Systems, and Computer

Massive MIMO and Full-duplex Relaying Systems

In this thesis, we study how massive multiple-input and multiple-output (MIMO) can be employed to mitigate loop-interference (LI), multi-user interference and noise in a full-duplex (FD) relaying system. For a FD relaying system with massive MIMO deployed at both source and destination, we investigate three FD relaying schemes: co-located, distributed cooperative, and distributed non-cooperative relaying. Asymptotic analysis shows that the three schemes can completely cancel multi-user interference and LI when the number of antennas at the source and destination grows without bound, in the case where the relay has a finite number of antennas. For the system with massive MIMO deployed at the FD relay, we propose a pilot protocol for LI channel minimum-mean-square-error estimation by exploiting the channel coherence time difference between static and moving transceivers. To maximize the end-to-end achievable rate, we design a novel power allocation scheme to adjust the transmit power of each link at the relay in order to equalize the achievable rate of the source-to-relay and relay-to-destination links. The analytical and numerical results show that the proposed pilot protocol and power allocation scheme jointly improve both spectral and energy efficiency significantly. To enable the use of low resolution analog-to-digital converters (ADCs) at relays for energy saving, we propose a novel iterative power allocation scheme to mitigate the resulting quantization noise via reducing the received LI power and numerically identify the optimum resolutions of ADCs for maximizing throughput and energy efficiency. For massive MIMO receivers employing one-bit ADCs, we propose three carrier frequency (CFO) offset estimation schemes for dual-pilot and multiple-pilot cases. The three schemes are developed under different scenarios: large but finite number of antennas at the receiver, infinite number of antennas at the receiver, and very small CFO, respectively

DOA Estimation for Hybrid Massive MIMO Systems using Mixed-ADCs: Performance Loss and Energy Efficiency

Due to the power consumption and high circuit cost in antenna arrays, the practical application of massive multipleinput multiple-output (MIMO) in the sixth generation (6G) and future wireless networks is still challenging. Employing lowresolution analog-to-digital converters (ADCs) and hybrid analog and digital (HAD) structure is two low-cost choice with acceptable performance loss. In this paper, the combination of the mixedADC architecture and HAD structure employed at receiver is proposed for direction of arrival (DOA) estimation, which will be applied to the beamforming tracking and alignment in 6G. By adopting the additive quantization noise model, the exact closedform expression of the Cramer-Rao lower bound (CRLB) for the HAD architecture with mixed-ADCs is derived. Moreover, the closed-form expression of the performance loss factor is derived as a benchmark. In addition, to take power consumption into account, energy efficiency is also investigated in our paper. The numerical results reveal that the HAD structure with mixedADCs can significantly reduce the power consumption and hardware cost. Furthermore, that architecture is able to achieve a better trade-off between the performance loss and the power consumption. Finally, adopting 2-4 bits of resolution may be a good choice in practical massive MIMO systems.Comment: 11 pages, 7 figure