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1. The Fading Gaussian Broadcast Channel with Channel State Information and Output Feedback

   https://doi.org/10.1109/ISIT44484.2020.9173934  

   Li, Siyao; Tuninetti, Daniela; Devroye, Natasha (June 2020, International Symposium on Information Theory)

   null (Ed.)

   The fading broadcast channel (BC) with additive white Gaussian noise (AWGN) channel, channel output feedback (COF) and channel state information (CSI) is considered. Perfect CSI is available at the receivers, and unit delayed CSI along with COF at the transmitter. Under the assumption of memoryless fading, a posterior matching scheme that incorporates the additional CSI feedback into the coding scheme is presented. With COF, the achievable rates depend on the joint distribution of the fading process. Numerical examples show that the capacity region of two-user fading AWGN-BC is enlarged by COF. The coding scheme is however suboptimal since some parts of the achievable rate region are outperformed by superposition coding without COF. 

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2. Causal (Progressive) Encoding over Binary Symmetric Channels with Noiseless Feedback

   https://doi.org/10.1109/ISIT45174.2021.9518287  

   Antonini, Amaael; Gimelshein, Rita; Wesel, Richard D. (July 2021, 2021 IEEE International Symposium on Information Theory (ISIT))

   Traditional communication systems transmit a codeword only after all message bits are available at the transmitter. This paper joins Guo & Kostina and Lalitha et al. in developing approaches for causal encoding, where the transmitter may begin transmitting codeword symbols as soon as the first message bit arrives. Building on the posterior matching encoders of Horstein, Shayevitz & Feder, and Naghshvar et al., this paper extends our computationally efficient systematic encoder to progressively encode using only the message bits that are causally available. Systematic codes work well with posterior matching on a channel with feedback, and they provide an immediate benefit when causal encoding is employed instead of traditional encoding. Our algorithm captures additional gains in the interesting region where the transmission rate μ is higher than the source rate λ at which message bits become available. In this region, we improve performance further through the transmission of additional, non- systematic symbols before a traditional encoder would have even begun transmission. 

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3. Adaptive Linear Secrecy Codes with Feedback

   Hunn, David; Harrison, Willie K. (October 2023, Proceedings International Telemetering Conference US)

   We identify a novel method of using feedback to provide enhanced information-theoretical security in the presence of an eavesdropper. This method begins with a fixed linear coset code providing both secrecy and error detection/correction, as has been described by several authors. The legitimate receiver then sends the syndrome information for the received codeword, and based on this feedback, the transmitter can provide further error correction information specifically tailored to assist only the legitimate receiver. We show that this method allows secure communication with the legitimate receiver even when the eavesdropper’s channel is superior to that of the legitimate receiver. 

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4. A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback

   Zhenyu Liu, Mason del (January 2021, IEEE transactions on wireless communications)

   null (Ed.)

   Channel state information (CSI) plays a vital role in scheduling and capacity-approaching transmission optimization of massive MIMO communication systems. In frequency division duplex (FDD) MIMO systems, forward link CSI reconstruction at transmitter relies on CSI feedback from receiving nodes and must carefully weigh the tradeoff between reconstruction accuracy and feedback bandwidth. Recent application of recurrent neural networks (RNN) has demonstrated promising results of massive MIMO CSI feedback compression. However, the cost of computation and memory associated with RNN deep learning remains high. In this work, we exploit channel temporal coherence to improve learning accuracy and feedback efficiency. Leveraging a Markovian model, we develop a deep convolutional neural network (CNN)-based framework called MarkovNet to efficiently encode CSI feedback to improve accuracy and efficiency. We explore important physical insights including spherical normalization of input data and deep learning network optimizations in feedback compression. We demonstrate that MarkovNet provides a substantial performance improvement and computational complexity reduction over the RNN-based work.We demonstrate MarkovNet’s performance under different MIMO configurations and for a range of feedback intervals and rates. CSI recovery with MarkovNet outperforms RNN-based CSI estimation with only a fraction of computational cost. 

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5. Interleaving Channel Estimation and Limited Feedback for Point-to-Point Systems with a Large Number of Transmit Antennas

   https://doi.org/10.1109/TWC.2018.2864204  

   Koyuncu, Erdem; Zou, Xun; Jafarkhani, Hamid (January 2019, IEEE Transactions on Wireless Communications)

   We introduce and investigate the opportunities of multi-antenna communication schemes whose training and feedback stages are interleaved and mutually interacting. Specifically, unlike the traditional schemes where the transmitter first trains all of its antennas at once and then receives a single feedback message, we consider a scenario where the transmitter instead trains its antennas one by one and receives feedback information immediately after training each one of its antennas. The feedback message may ask the transmitter to train another antenna; or, it may terminate the feedback/training phase and provide the quantized codeword (e.g., a beamforming vector) to be utilized for data transmission. As a specific application, we consider a multiple-input single-output system with t transmit antennas, a short-term power constraint P, and target data rate ρ. We show that for any t, the same outage probability as a system with perfect transmitter and receiver channel state information can be achieved with a feedback rate of R1 bits per channel state and via training R2 transmit antennas on average, where R1 and R2 are independent of t, and depend only on ρ and P. In addition, we design variable-rate quantizers for channel coefficients to further minimize the feedback rate of our scheme. 

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