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1. Space-Time Coded Massive MIMO for Next Generation Wireless Systems

   Ice, J.J.; Abdolee, R.; Vakilian, V. (July 2017, International Conference on Wireless Networks)

   This paper serves as an evaluation of an experimental wireless communications technique called space-time coded massive (STCM) multiple-input multiple-output (MIMO). The STCM-MIMO system utilizes two massive MIMO antenna arrays which transmit data over two channel vectors to a user with one receive antenna. This configuration permits the system to use the asymptotic orthogonal qualities of massive MIMO pre-coding to eliminate the interference from other users’ channel vectors and signals. The system also maintains the diversity of space-time codes to recover lost data through treating each transmitting massive MIMO array similarly to how a 2×1 Alamouti spacetime code would treat each transmitting antenna. Our results show that a wireless system with the proposed STCM-MIMO technology can significantly outperform those with space-time coding techniques. 

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2. Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO

   Chen, An; Xu, Wenbo; Lu, Liyang; Wang, Yue (December 2023, 2023 IEEE Global Communications Conference)

   Massive multiple-input multiple-output (MIMO) enjoys great advantage in 5G wireless communication systems owing to its spectrum and energy efficiency. However, hundreds of antennas require large volumes of pilot overhead to guarantee reliable channel estimation in FDD massive MIMO system. Compressive sensing (CS) has been applied for channel estimation by exploiting the inherent sparse structure of massive MIMO channel but suffer from high complexity. To overcome this challenge, this paper develops a hybrid channel estimation scheme by integrating the model-driven CS and data-driven deep unrolling technique. The proposed scheme consists of a coarse estimation part and a fine correction part to respectively exploit the inter- and intraframe sparsities of channels to greatly reduce the pilot overhead. Theoretical result is provided to indicate the convergence of the fine correction and coarse estimation net. Simulation results are provided to verify that our scheme can estimate MIMO channels with low pilot overhead while guaranteeing estimation accuracy with relatively low complexity. 

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3. Sparsity-Adaptive Beamspace Channel Estimation for 1-Bit mmWave Massive MIMO Systems

   https://doi.org/10.1109/SPAWC48557.2020.9154213  

   Gallyas-Sanhueza, Alexandra; Mirfarshbafan, Seyed Hadi; Ghods, Ramina; Studer, Christoph (May 2020, IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))

   null (Ed.)

   We propose sparsity-adaptive beamspace channel estimation algorithms that improve accuracy for 1-bit data converters in all-digital millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) basestations. Our algorithms include a tuning stage based on Stein's unbiased risk estimate (SURE) that automatically selects optimal denoising parameters depending on the instantaneous channel conditions. Simulation results with line-of-sight (LoS) and non-LoS mmWave massive MIMO channel models show that our algorithms improve channel estimation accuracy with 1-bit measurements in a computationally-efficient manner. 

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4. Massive MIMO Channel Estimation with 1-Bit Spatial Sigma-delta ADCs

   https://doi.org/10.1109/ICASSP.2019.8682842  

   Rao, Shilpa; Swindlehurst, A. Lee; Pirzadeh, Hessam (May 2019, 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   We consider channel estimation for an uplink massive multiple input multiple output (MIMO) system where the base station (BS) uses a first-order spatial Sigma-Delta (Σ△) analog-to-digital converter (ADC) array. The Σ△ array consists of closely spaced sensors which oversample the received signal and provide a coarsely quantized (1-bit) output. We develop a linear minimum mean squared error (LMMSE) estimator based on the Bussgang decomposition that reformulates the nonlinear quantizer model using an equivalent linear model plus quantization noise. The performance of the proposed Σ△ LMMSE estimator is compared via simulation to channel estimation using standard 1-bit quantization and also infinite resolution ADCs. 

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5. Minimizing Pilot Overhead in Cell-Free Massive MIMO Systems via Joint Estimation and Detection

   https://doi.org/10.1109/SPAWC48557.2020.9154310  

   Song, Haochuan; You, Xiaohu; Zhang, Chuan; Tirkkonen, Olav; Studer, Christoph (May 2020, IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))

   null (Ed.)

   We propose a joint channel estimation and data detection (JED) algorithm for cell-free massive multi-user (MU) multiple-input multiple-output (MIMO) systems. Our algorithm yields improved reliability and reduced latency while minimizing the pilot overhead of coherent uplink transmission. The proposed JED method builds upon a novel non-convex optimization problem that we solve approximately and efficiently using forward- backward splitting. We use simulation results to demonstrate that our algorithm achieves robust data transmission with more than 3x reduced pilot overhead compared to orthogonal training in a 128 antenna cell-free massive MU-MIMO system in which 128 users transmit data over 128 time slots. 

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