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1. Channel Feedback Based on AoD-Adaptive Subspace Codebook in FDD Massive MIMO Systems

   https://doi.org/10.1109/TCOMM.2018.2849755  

   Shen, Wenqian; Dai, Linglong; Shim, Byonghyo; Wang, Zhaocheng; Heath, Robert W. (January 2018, IEEE Transactions on Communications)

   Channel feedback is essential in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. Unfortunately, prior work on multiuser MIMO has shown that the feedback overhead scales linearly with the number of base station (BS) antennas, which is large in massive MIMO systems. To reduce the feedback overhead, we propose an angle-of-departure (AoD) adaptive subspace codebook for channel feedback in FDD massive MIMO systems. Our key insight is to leverage the observation that path AoDs vary more slowly than the path gains. Within the angle coherence time, by utilizing the constant AoD information, the proposed AoDadaptive subspace codebook is able to quantize the channel vector in a more accurate way. From the performance analysis, we show that the feedback overhead of the proposed codebook only scales linearly with a small number of dominant (path) AoDs instead of the large number of BS antennas. Moreover, we compare the proposed quantized feedback technique using the AoD-adaptive subspace codebook with a comparable analog feedback method. Extensive simulations show that the proposed AoD-adaptive subspace codebook achieves good channel feedback quality, while requiring low overhead. 

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2. Analog Compression and Communication for Federated Learning over Wireless MAC

   https://doi.org/10.1109/SPAWC48557.2020.9154309  

   Abdi, Afshin; Saidutta, Yashas Malur; Fekri, Faramarz (August 2020, IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))

   null (Ed.)

   In this paper, we consider federated learning in wireless edge networks. Transmitting stochastic gradients (SG) or deep model's parameters over a limited-bandwidth wireless channel can incur large training latency and excessive power consumption. Hence, data compressing is often used to reduce the communication overhead. However, efficient communication requires the compression algorithm to satisfy the constraints imposed by the communication medium and take advantage of its characteristics, such as over-the-air computations inherent in wireless multiple-access channels (MAC), unreliable transmission and idle nodes in the edge network, limited transmission power, and preserving the privacy of data. To achieve these goals, we propose a novel framework based on Random Linear Coding (RLC) and develop efficient power management and channel usage techniques to manage the trade-offs between power consumption, communication bit-rate and convergence rate of federated learning over wireless MAC. We show that the proposed encoding/decoding results in an unbiased compression of SG, hence guaranteeing the convergence of the training algorithm without requiring error-feedback. Finally, through simulations, we show the superior performance of the proposed method over other existing techniques. 

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3. ML-Based Feedback-Free Adaptive MCS Selection for Massive Multi-User MIMO

   An, Qing; Zafari, Mehdi; Dick, Chris; Segarra, Santiago; Sabharwal, Ashutosh; Doost-Mohammady, Rahman (April 2024, Conference record)

   Abstract—As wireless communication systems strive to improve spectral efficiency, there has been a growing interest in employing machine learning (ML)-based approaches for adaptive modulation and coding scheme (MCS) selection. In this paper, we introduce a new adaptive MCS selection framework for massive MIMO systems that operates without any feedback from users by solely relying on instantaneous uplink channel estimates. Our proposed method can effectively operate in multi-user scenarios where user feedback imposes excessive delay and bandwidth overhead. To learn the mapping between the user channel matrices and the optimal MCS level of each user, we develop a Convolutional Neural Network (CNN)-Long Short-Term Memory Network (LSTM)-based model and compare the performance with the state-of-the-art methods. Finally, we validate the effectiveness of our algorithm by evaluating it experimentally using real-world datasets collected from the RENEW massive MIMO platform. Index Terms—Adaptive MCS Selection, Machine Learning, Convolutional Neural Network, Long Short-Term Memory Network, Channel State Information, Feedback Delay. 

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4. Blind SNR Estimation and Nonparametric Channel Denoising in Multi-Antenna mmWave Systems

   https://doi.org/10.1109/ICC42927.2021.9500387  

   Gallyas-Sanhueza, Alexandra; Studer, Christoph (June 2021, IEEE International Conference on Communications)

   We propose blind estimators for the average noise power, receive signal power, signal-to-noise ratio (SNR), and mean-square error (MSE), suitable for multi-antenna millimeter wave (mmWave) wireless systems. The proposed estimators can be computed at low complexity and solely rely on beamspace sparsity, i.e., the fact that only a small number of dominant propagation paths exist in typical mmWave channels. Our estimators can be used (i) to quickly track some of the key quantities in multi-antenna mmWave systems while avoiding additional pilot overhead and (ii) to design efficient nonparametric algorithms that require such quantities. We provide a theoretical analysis of the proposed estimators, and we demonstrate their efficacy via synthetic experiments and using a nonparametric channel-vector denoising task with realistic multi-antenna mmWave channels. 

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5. Joint Band Assignment and Beam Management Using Hierarchical Reinforcement Learning for Multi-Band Communication

   https://doi.org/10.1109/TVT.2024.3397615  

   Kim, Dohyun; Castellanos, Miguel R; Heath, Robert W (January 2024, IEEE Transactions on Vehicular Technology)

   Multi-band operation in wireless networks can improve data rates by leveraging the benefits of propagation in different frequency ranges. Distinctive beam management procedures in different bands complicate band assignment because they require considering not only the channel quality but also the associated beam management overhead. Reinforcement learning (RL) is a promising approach for multi-band operation as it enables the system to learn and adjust its behavior through environmental feedback. In this paper, we formulate a sequential decision problem to jointly perform band assignment and beam management. We propose a method based on hierarchical RL (HRL) to handle the complexity of the problem by separating the policies for band selection and beam management. We evaluate the proposed HRL-based algorithm on a realistic channel generated based on ray-tracing simulators. Our results show that the proposed approach outperforms traditional RL approaches in terms of reduced beam training overhead and increased data rates under a realistic vehicular channel. 

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