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1. Low-Complexity Blind Parameter Estimation in Wireless Systems with Noisy Sparse Signals

   https://doi.org/10.1109/TWC.2023.3247887  

   Gallyas-Sanhueza, Alexandra; Studer, Christoph (October 2023, IEEE Transactions on Wireless Communications)

   Baseband processing algorithms often require knowledge of the noise power, signal power, or signal-to-noise ratio (SNR). In practice, these parameters are typically unknown and must be estimated. Furthermore, the mean-square error (MSE) is a desirable metric to be minimized in a variety of estimation and signal recovery algorithms. However, the MSE cannot directly be used as it depends on the true signal that is generally unknown to the estimator. In this paper, we propose novel blind estimators for the average noise power, average receive signal power, SNR, and MSE. The proposed estimators can be computed at low complexity and solely rely on the large-dimensional and sparse nature of the processed data. Our estimators can be used (i) to quickly track some of the key system parameters while avoiding additional pilot overhead, (ii) to design low-complexity nonparametric algorithms that require such quantities, and (iii) to accelerate more sophisticated estimation or recovery algorithms. We conduct a theoretical analysis of the proposed estimators for a Bernoulli complex Gaussian (BCG) prior, and we demonstrate their efficacy via synthetic experiments. We also provide three application examples that deviate from the BCG prior in millimeter-wave multi-antenna and cell-free wireless systems for which we develop nonparametric denoising algorithms that improve channel-estimation accuracy with a performance comparable to denoisers that assume perfect knowledge of the system parameters. 

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2. Optimally-tuned nonparametric linear equalization for massive MU-MIMO systems

   https://doi.org/10.1109/ISIT.2017.8006903  

   Ghods, Ramina; Jeon, Charles; Mirza, Gulnar; Maleki, Arian; Studer, Christoph (June 2017, IEEE International Symposium on Information Theory (ISIT))

   This paper deals with linear equalization in massive multi-user multiple-input multiple-output (MU-MIMO) wireless systems. We first provide simple conditions on the antenna configuration for which the well-known linear minimum mean-square error (L-MMSE) equalizer provides near-optimal spectral efficiency, and we analyze its performance in the presence of parameter mismatches in the signal and/or noise powers. We then propose a novel, optimally-tuned NOnParametric Equalizer (NOPE) for massive MU-MIMO systems, which avoids knowledge of the transmit signal and noise powers altogether. We show that NOPE achieves the same performance as that of the L-MMSE equalizer in the large-antenna limit, and we demonstrate its efficacy in realistic, finite-dimensional systems. From a practical perspective, NOPE is computationally efficient and avoids dedicated training that is typically required for parameter estimation. 

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3. Resolution-Adaptive All-Digital Spatial Equalization for mmWave Massive MU-MIMO

   https://doi.org/10.1109/SPAWC51858.2021.9593110  

   Castaneda, Oscar; Mirfarshbafan, Seyed Hadi; Ghajari, Shahaboddin; Molnar, Alyosha; Jacobsson, Sven; Durisi, Giuseppe; Studer, Christoph (September 2021, IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))

   All-digital basestation (BS) architectures for millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO), which equip each radio-frequency chain with dedicated data converters, have advantages in spectral efficiency, flexibility, and baseband-processing simplicity over hybrid analog-digital solutions. For all-digital architectures to be competitive with hybrid solutions in terms of power consumption, novel signal-processing methods and baseband architectures are necessary. In this paper, we demonstrate that adapting the resolution of the analog-to-digital converters (ADCs) and spatial equalizer of an all-digital system to the communication scenario (e.g., the number of users, modulation scheme, and propagation conditions) enables orders-of-magnitude power savings for realistic mmWave channels. For example, for a 256-BS-antenna 16-user system supporting 1 GHz bandwidth, a traditional baseline architecture designed for a 64-user worst-case scenario would consume 23 W in 28 nm CMOS for the ADC array and the spatial equalizer, whereas a resolution-adaptive architecture is able to reduce the power consumption by 6.7×. 

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4. A Comparison of Hybrid Beamforming and Digital Beamforming with Low-Resolution ADCs for Multiple Users and Imperfect CSI

   https://doi.org/10.1109/JSTSP.2018.2813973  

   Roth, Kilian; Pirzadeh, Hessam; Swindlehurst, A. Lee; Nossek, Josef A. (March 2018, IEEE Journal of Selected Topics in Signal Processing)

   For 5G it will be important to leverage the available millimeter wave spectrum. To achieve an approximately omni- directional coverage with a similar effective antenna aperture compared to state-of-the-art cellular systems, an antenna array is required at both the mobile and basestation. Due to the large bandwidth and inefficient amplifiers available in CMOS for mmWave, the analog front-end of the receiver with a large number of antennas becomes especially power hungry. Two main solutions exist to reduce the power consumption: hybrid beam forming and digital beam forming with low resolution Analog to Digital Converters (ADCs). In this work we compare the spectral and energy efficiency of both systems under practical system constraints. We consider the effects of channel estimation, transmitter impairments and multiple simultaneous users for a wideband multipath model. Our power consumption model considers components reported in literature at 60 GHz. In contrast to many other works we also consider the correlation of the quantization error, and generalize the modeling of it to non- uniform quantizers and different quantizers at each antenna. The result shows that as the Signal to Noise Ratio (SNR) gets larger the ADC resolution achieving the optimal energy efficiency gets also larger. The energy efficiency peaks for 5 bit resolution at high SNR, since due to other limiting factors the achievable rate almost saturates at this resolution. We also show that in the multi- user scenario digital beamforming is in any case more energy efficient than hybrid beamforming. In addition we show that if mixed ADC resolutions are used we can achieve any desired trade-off between power consumption and rate close to those achieved with only one ADC resolution. 

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5. Comparative Performance Evaluation of mmWave 5G NR and LTE in a Campus Scenario

   Tehrani-Moayyed, M.; Restuccia, F.; Basagni, S. (October 2020, Proceedings of IEEE VTC 2020 Fall)

   The extremely high data rates provided by communications in the millimeter-length (mmWave) frequency bands can help address the unprecedented demands of next-generation wireless communications. However, atmospheric attenuation and high propagation loss severely limit the coverage of mmWave networks. To overcome these challenges, multi-input-multi-output (MIMO) provides beamforming capabilities and high-gain steer- able antennas to expand communication coverage at mmWave frequencies. The main contribution of this paper is the per- formance evaluation of mmWave communications on top of the recently released NR standard for 5G cellular networks. Furthermore, we compare the performance of NR with the 4G long-term evolution (LTE) standard on a highly realistic campus environment. We consider physical layer constraints such as transmit power, ambient noise, receiver noise figure, and practical antenna gain in both cases, and examine bitrate and area coverage as the criteria to benchmark the performance. We also show the impact of MIMO technology to improve the performance of the 5G NR cellular network. Our evaluation demonstrates that 5G NR provides on average 6.7 times bitrate improvement without remarkable coverage degradation. 

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