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1. Discrete Phase Shift Design for Practical Large Intelligent Surface Communication

   https://doi.org/10.1109/PACRIM47961.2019.8985103  

   Xu, Jindan; Xu, Wei; Swindlehurst, A. Lee (August 2019, Proc. IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM))

   In this paper, we investigate a downlink channel of a large intelligent surface (LIS) communication system. The LIS is equipped with B-bit discrete phase shifts while base station (BS) exploits low-resolution digital-to-analog converters (DACs). Without the knowledge of channel state information (CSI) related to the LIS, we propose a practical phase shift design method, whose computational complexity increases by 2 B independent of the number of reflecting elements N. A tight lower bound for the asymptotic rate of the user is obtained in closed form. As N increases, we observe that the asymptotic rate becomes saturated because both the received signal power and the DAC quantization noise increase. Compared to the optimal continuous phase shift design with perfect CSI, our proposed method asymptotically approaches the ideal benchmark performance for moderate to high values of B. The derived results and observations are verified by simulation results. 

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2. Multiuser Massive Mimo Downlink Precoding Using Second-Order Spatial Sigma-Delta Modulation

   https://doi.org/10.1109/ICASSP40776.2020.9053267  

   Shao, Mingjie; Ma, Wing-Kin; Swindlehurst, Lee (May 2020, International Conference on Acoustics, Speech and Signal Processing)

   Massive MIMO using low-resolution digital-to-analog converters (DACs) at the base station (BS) is an attractive downlink approach for reducing hardware overhead and for reducing power consumption, but managing the large quantization noise effect is a challenge. Spatial Sigma-Delta modulation is a recently emerged technique for tackling the aforementioned effect. Assuming a uniform linear array at the BS, it works by shaping the quantization noise as high spatial-frequency, or angle, noise. By restricting the user-serving region to be within a smaller angular region, the quantization noise incurred by the users can be effectively reduced. We previously showed that, under the one-bit DAC case, the quantization noise can be satisfactorily contained using a simple first-order Sigma-Delta modulation scheme. In this work we study the potential of spatial Sigma-Delta modulation in the two-bit DAC case and under second-order modulation. Our empirical results indicate that second-order spatial Sigma-Delta modulation provides better quantization noise suppression. 

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3. Doubly 1-Bit Quantized Massive MIMO

   https://doi.org/10.1109/IEEECONF59524.2023.10476782  

   Atzeni, Italo; Tölli, Antti; Nguyen, Duy_H N; Swindlehurst, A Lee (October 2023, IEEE)

   Enabling communications in the (sub-)THz band will call for massive multiple-input multiple-output (MIMO) arrays at either the transmit- or receive-side, or at both. To scale down the complexity and power consumption when operating across massive frequency and antenna dimensions, a sacrifice in the resolution of the digital-to-analog/analog-to-digital converters (DACs/ADCs) will be inevitable. In this paper, we analyze the extreme scenario where both the transmit- and receive-side are equipped with fully digital massive MIMO arrays and 1-bit DACs/ADCs, which leads to a system with minimum radio-frequency complexity, cost, and power consumption. Building upon the Bussgang decomposition, we derive a tractable approximation of the mean squared error (MSE) between the transmitted data symbols and their soft estimates. Numerical results show that, despite its simplicity, a doubly 1-bit quantized massive MIMO system with very large antenna arrays can deliver an impressive performance in terms of MSE and symbol error rate. 

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4. 10.1109/MCAS.2019.2909447

   https://doi.org/10.1109/MCAS.2019.2909447  

   Yan, Han; Ramesh, Sridhar; Gallagher, Timothy; Ling, Curtis; Cabric, Danijela (April 2019, IEEE circuits and systems magazine)

   Millimeter wave (mmW) communications is viewed as the key enabler of 5G cellular networks due to vast spectrum availability that could boost peak rate and capacity. Due to increased propagation loss in mmW band, transceivers with massive antenna array are required to meet a link budget, but their power consumption and cost become limiting factors for commercial systems. Radio designs based on hybrid digital and analog array architectures and the usage of radio frequency (RF) signal processing via phase shifters have emerged as potential solutions to improve radio energy efficiency and deliver performances close to the conventional digital antenna arrays. In this paper, we provide an overview of the state-of-the-art mmW massive antenna array designs and comparison among three array architectures, namely digital array, partially-connected hybrid array (sub-array), and fully-connected hybrid array. The comparison of performance, power, and area for these three architectures is performed for three representative 5G downlink use cases, which cover a range of pre-beamforming signal-to-noise-ratios (SNR) and multiplexing regimes. This is the first study to comprehensively model and quantitatively analyze all design aspects and criteria including: 1) optimal linear precoder, 2) impact of quantization error in digital-to-analog converter (DAC) and phase shifters, 3) RF signal distribution network, 4) power and area estimation based on state-of-the-art mmW circuits including baseband digital precoding, digital signal distribution network, high-speed DACs, oscillators, mixers, phase shifters, RF signal distribution network, and power amplifiers. Our simulation results show that the fully-digital array architecture is the most power and area efficient compared against optimized designs for sub-array and hybrid array architectures. Our analysis shows that digital array architecture benefits greatly from multi-user multiplexing. The analysis also reveals that sub-array architecture performance is limited by reduced beamforming gain due to array partitioning, while the system bottleneck of the fully-connected hybrid architecture is the excessively complicated and power hungry RF signal distribution network. 

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5. Linearization for High-Speed Current-Steering DACs Using Neural Networks

   Beauchamp, Daniel; Chugg, Keith M (February 2021, IEEE 12th Latin America Symposium on Circuits and Systems)

   null (Ed.)

   This paper proposes a novel foreground lineariza- tion scheme for a high-speed current-steering (CS) digital-to- analog converter (DAC). The technique leverages neural networks (NNs) to derive a lookup-table (LUT) that maps the inverse of the DAC transfer characteristic onto the input codes. The algorithm is shown to improve conventional methods by at least 6dB in terms of intermodulation (IM) performance for frequencies up to 9GHz on a state-of-the-art 10-bit CS-DAC operating at 40.96GS/s (gigasamples-per-second) in 14nm CMOS. 

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