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1. A 28-GHz Massive MIMO Receiver Deploying One-Bit Direct Detection with Wireless Synchronization

   https://doi.org/10.1109/ISCAS45731.2020.9180889  

   Zhao, Hang; Green, Michael (October 2020, 2020 IEEE International Symposium on Circuits and Systems (ISCAS))

   null (Ed.)

   A 28-GHz one-bit direct-detection based MIMO receiver with wireless LO distribution is presented. Unlike a conventional MIMO structure, in this work the antenna receives both RF and the broadcast single-ended LO, and directly converts them to an IF signal by using a simple low-power square-law detector without the need for a conventional mixer or LO buffers. An LNA with a notch filter is designed to help reduce the non-idealities that appear when the input LO power is lower than that of the RF. A 1-GS/s symbol rate with a high error-vector magnitude is achieved with a power consumption of 33 mW by using a 0.18um SiGe BiCMOS process. 

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2. Detection Through Deep Neural Networks: A Reservoir Computing Approach for MIMO-OFDM Symbol Detection

   Kangjun Bai, Lingjia Liu (November 2020, IEEE International Conference on Computer-Aided Design)

   null (Ed.)

   The Reservoir Computing, a neural computing framework suited for temporal information processing, utilizes a dynamic reservoir layer for high-dimensional encoding, enhancing the separability of the network. In this paper, we exploit a Deep Learning (DL)-based detection strategy for Multiple-input, Multiple-output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) symbol detection. To be specific, we introduce a Deep Echo State Network (DESN), a unique hierarchical processing structure with multiple time intervals, to enhance the memory capacity and accelerate the detection efficiency. The resulting hardware prototype with the hybrid memristor-CMOS co-design provides in-memory computing and parallel processing capabilities, significantly reducing the hardware and power overhead. With the standard 180nm CMOS process and memristive synapses, the introduced DESN consumes merely 105mW of power consumption, exhibiting 16.7% power reduction compared to shallow ESN designs even with more dynamic layers and associated neurons. Furthermore, numerical evaluations demonstrate the advantages of the DESN over state-of-the-art detection techniques in the literate for MIMO-OFDM systems even with a very limited training set, yielding a 47.8% improvement against conventional symbol detection techniques. 

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3. 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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4. Reservoir Computing Meets Wi-Fi in Software Radios: Neural Network-based Symbol Detection using Training Sequences and Pilots

   https://doi.org/10.1109/WOCC48579.2020.9114937  

   Li, Lianjun; Liu, Lingjia; Zhang, Jianzhong Charlie; Ashdown, Jonathan D.; Yi, Yang (May 2020, 2020 29th Wireless and Optical Communications Conference (WOCC))

   In this paper, we introduce a neural network (NN)-based symbol detection scheme for Wi-Fi systems and its associated hardware implementation in software radios. To be specific, reservoir computing (RC), a special type of recurrent neural network (RNN), is adopted to conduct the task of symbol detection for Wi-Fi receivers. Instead of introducing extra training overhead/set to facilitate the RC-based symbol detection, a new training framework is introduced to take advantage of the signal structure in existing Wi-Fi protocols (e.g., IEEE 802.11 standards), that is, the introduced RC-based symbol detector will utilize the inherent long/short training sequences and structured pilots sent by the Wi-Fi transmitter to conduct online learning of the transmit symbols. In other words, our introduced NN-based symbol detector does not require any additional training sets compared to existing Wi-Fi systems. The introduced RC-based Wi-Fi symbol detector is implemented on the software-defined radio (SDR) platform to further provide realistic and meaningful performance comparisons against the traditional Wi-Fi receiver. Over the air, experiment results show that the introduced RC based Wi-Fi symbol detector outperforms conventional Wi-Fi symbol detection methods in various environments indicating the significance and the relevance of our work. 

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5. Mixed-Carrier Communication for Technology Division Multiplexing

   https://doi.org/10.3390/electronics10182248  

   Hussein, Ahmed F.; Saha, Dola; Elgala, Hany (September 2021, Electronics)

   null (Ed.)

   Recently, research on sixth-generation (6G) networks has gained significant interest. 6G is expected to enable a wide-range of applications that fifth-generation (5G) networks will not be able to serve reliably, such as tactile Internet. Additionally, 6G is expected to offer Terabits per second (Tbps) data rates, 10 times lower latency, and near 100% coverage, compared to 5G. Thus, 6G is expected to expand across all available spectrums including terahertz (THz) and optical frequency bands. In this manuscript, mixed-carrier communication (MCC) is investigated as a novel physical layer (PHY) design for 6G networks. The proposed MCC version in this study is based on visible light communication (VLC). MCC enables a unified transmission PHY design to connect devices with different complexities, simultaneously. The design trade-offs and the required signal-to-noise ratio (SNR) per individual modulation schemes embedded within MCC are investigated. The complexity analysis shows that a conventional optical OFDM receiver can capture the high-speed bit-stream embedded within MCC. For a forward error correction (FEC) bit-error-rate (BER) threshold of 3.8×10−3, MCC is optimized to maximize the spectral efficiency by embedding 2-beacon phase-shift keying (2-BnPSK) within an MCC envelope on top of 12 bits per beacon position modulation (BPM) symbol. 

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