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1. RIS-aided mmWave beam-forming for two-way communications of multiple pairs

   https://doi.org/10.52953/VBEX2484  

   Torkzaban, Nariman; Amir), Mohammad A.; Farajzadeh-Tehrani, Mohammad; Baras, John S. (January 2023, ITU Journal on Future and Evolving Technologies)

   Millimeter-wave (mmWave) communications is a key enabler towards realizing enhanced Mobile Broadband (eMBB) as a key promise of 5G and beyond, due to the abundance of bandwidth available at mmWave bands. An mmWave coverage map consists of blind spots due to shadowing and fading especially in dense urban environments. Beam-forming employing massive MIMO is primarily used to address high attenuation in the mmWave channel. Due to their ability in manipulating the impinging electromagnetic waves in an energy-efficient fashion, Reconfigurable Intelligent Surfaces (RISs) are considered a great match to complement the massive MIMO systems in realizing the beam-forming task and therefore effectively filling in the mmWave coverage gap. In this paper, we propose a novel RIS architecture, namely RIS-UPA where the RIS elements are arranged in a Uniform Planar Array (UPA). We show how RIS-UPA can be used in an RIS-aided MIMO system to fill the coverage gap in mmWave by forming beams of a custom footprint, with optimized main lobe gain, minimum leakage, and fairly sharp edges. Further, we propose a configuration for RIS-UPA that can support multiple two-way communication pairs, simultaneously. We theoretically obtain closed-form low-complexity solutions for our design and validate our theoretical findings by extensive numerical experiments. 

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2. Physics-inspired heuristics for soft MIMO detection in 5G new radio and beyond

   https://doi.org/10.1145/3447993.3448619  

   Kim, Minsung; Mandrà, Salvatore; Venturelli, Davide; Jamieson, Kyle (January 2021, Proceedings of the 27th Annual International Conference on Mobile Computing and Networking)

   null (Ed.)

   Overcoming the conventional trade-off between throughput and bit error rate (BER) performance, versus computational complexity is a long-term challenge for uplink Multiple-Input Multiple-Output (MIMO) detection in base station design for the cellular 5G New Radio roadmap, as well as in next generation wireless local area networks. In this work, we present ParaMax, a MIMO detector architecture that for the first time brings to bear physics-inspired parallel tempering algorithmic techniques [28, 50, 67] on this class of problems. ParaMax can achieve near optimal maximum-likelihood (ML) throughput performance in the Large MIMO regime, Massive MIMO systems where the base station has additional RF chains, to approach the number of base station antennas, in order to support even more parallel spatial streams. ParaMax is able to achieve a near ML-BER performance up to 160 × 160 and 80 × 80 Large MIMO for low-order modulations such as BPSK and QPSK, respectively, only requiring less than tens of processing elements. With respect to Massive MIMO systems, in 12 × 24 MIMO with 16-QAM at SNR 16 dB, ParaMax achieves 330 Mbits/s near-optimal system throughput with 4--8 processing elements per subcarrier, which is approximately 1.4× throughput than linear detector-based Massive MIMO systems. 

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3. An Evaluation Platform for Channel Estimation in MIMO Systems

   https://doi.org/10.1109/NAECON58068.2023.10365882  

   Mercado-Perez, Dalyana; Kumar, Venkataramani; Ye, Feng; Hu, Rose Qingyang; Qian, Yi (August 2023, NAECON 2023 - IEEE National Aerospace and Electronics Conference)

   Multiple-input multiple-outputs (MIMO) systems are integral to the implementation of the current fifth-generation (5G) and beyond wireless networks. Accurate channel state information (CSI) is imperative to a MIMO system for its optimal performance. In this work, we develop an end-to-end software evaluation platform for the channel estimation process in a MIMO system. With this platform, different channel estimation and reconstruction processes, as well as precoding methods can be implemented and evaluated. Channel reconstruction error and transmission bit error rate are chosen metrics in the current implementation. Direct channel estimation with the least square method, and CSI feedback methods with compressive sensing and deep-learning approaches are tested to demonstrate the evaluation platform 

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4. 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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5. Millimeter-Wave Extended NYUSIM Channel Model for Spatial Consistency

   https://doi.org/10.1109/GLOCOM.2018.8647188  

   Ju, Shihao; Rappaport, Theodore S. (December 2018, 2018 IEEE Global Communications Conference (GLOBECOM))

   Abstract: Commonly used drop-based channel models cannot satisfy the requirements of spatial consistency for millimeterwave (mmWave) channel modeling where transient motion or closely-spaced users need to be considered. A channel model having spatial consistency can capture the smooth variations of channels, when a user moves, or when multiple users are close to each other in a local area within, say, 10 m in an outdoor scenario. Spatial consistency is needed to support the testing of beamforming and beam tracking for massive multiple-input and multiple-output (MIMO) and multi-user MIMO in fifth-generation (5G) mmWave mobile networks. This paper presents a channel model extension and an associated implementation of spatial consistency in the NYUSIM channel simulation platform [1], [2]. Along with a mathematical model, we use measurements where the user moved along a street and turned at a corner over a path length of 75 m in order to derive realistic values of several key parameters such as correlation distance and the rate of cluster birth and death, that are shown to provide spatial consistency for NYUSIM in an urban microcell street canyon scenario. 

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