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1. 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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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. Design Trade-offs for Decentralized Baseband Processing in Massive MU-MIMO Systems

   https://doi.org/10.1109/IEEECONF44664.2019.9048727  

   Li, Kaipeng; McNaney, James; Tarver, Chance; Castaneda, Oscar; Jeon, Charles; Cavallaro, Joseph R.; Studer, Christoph (November 2019, 2019 IEEE 53rd Asilomar Conference on Signals, Systems and Computers)

   null (Ed.)

   Massive multi-user (MU) multiple-input multiple-output (MIMO) provides high spectral efficiency by means of spatial multiplexing and fine-grained beamforming. However, conventional base-station (BS) architectures for systems with hundreds of antennas that rely on centralized baseband processing inevitably suffer from (i) excessive interconnect data rates between radio-frequency circuitry and processing fabrics, and (ii) prohibitive complexity at the centralized baseband processor. Recently, decentralized baseband processing (DBP) architectures and algorithms have been proposed, which mitigate the interconnect bandwidth and complexity bottlenecks. This paper systematically explores the design trade-offs between error-rate performance, computational complexity, and data transfer latency of DBP architectures under different system configurations and channel conditions. Considering architecture, algorithm, and numerical precision aspects, we provide practical guidelines to select the DBP architecture and algorithm that are able to realize the full benefits of massive MU-MIMO in the uplink and downlink. 

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4. Physical Layer Performance of a Cooperative Amplify and Forward Scheme for MIMO WLANs

   https://doi.org/10.1109/TVT.2019.2916375  

   Qi, Yuepeng; Sichitiu, Mihail L. (May 2019, IEEE Transactions on Vehicular Technology)

   This paper proposes a novel cooperative amplify and forward (CAF) architecture for MIMO WLANs to improve their coverages and transmission rates, without requiring any modifications in the IEEE 802.11 standards at the clients. System models are used for analyzing physical layer of CAF networks, and compared with conventional amplify-and-forward (AF) and decode-and-forward (DF) networks. Numerical results demonstrate that CAF network clearly outperforms conventional 802.11 networks, AF and DF networks in terms of coverage and throughput. 

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5. Tight Approximation of Achievable Rates in RIS-Based Multi-User MIMO Systems Under Channel Estimation Constraints

   https://doi.org/10.1109/OJCOMS.2025.3539229  

   Farghaly, Samar I; Ismail, Mohamed I; Fouda, Mostafa M; Alwakeel, Ahmed S (February 2025, IEEE Open Journal of the Communications Society)

   The rapid and low-power configuration capabilities of Reconfigurable Intelligent Surfaces (RISs) have made them an attractive option for future wireless networks in terms of energy efficiency. They have the ability to greatly increase connection and facilitate low-latency communications. However, because RIS-based systems often have a large number of RIS unit elements and unique hardware constraints, accurate and low-overhead channel estimate remains a crucial challenge. In this study, we offer a channel estimation framework and concentrate on the uplink of a multi-user multiple-input multiple-output (MU-MIMO) communication system driven by RIS. Our primary goal is to enhance the achievable rate and system capacity. We derive a closed-form deterministic expression for the uplink achievable rate under practical scenarios where channel state information (CSI) is not directly known and must be estimated. In contrast to previous studies assuming perfect CSI, our approach incorporates the channel estimation process, leading to a more realistic performance assessment. Extensive simulations validate the tightness of our derived expression compared to the actual achievable rate across various system parameters (with discrepancies typically within 2-5%). The results highlight the significant impact of RIS on system performance enhancement, even with imperfect CSI. Our findings provide crucial insights into the deployment and optimization of RIS-assisted multi-user wireless networks, underscoring their potential for substantial improvements in rate and capacity. 

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