This paper focuses on downlink channel state information (CSI) acquisition. A frequency division duplex (FDD) of massive MIMO system is considered. In such systems, the base station (BS) obtains the downlink CSI from the mobile users' feedback. A key consideration is to reduce the feedback overhead while ensuring that the BS accurately recovers the downlink CSI. Existing approaches often resort to dictionary-based or tensor/matrix decomposition techniques, which either exhibit unsatisfactory accuracy or induce heavy computational load at the mobile end. To circumvent these challenges, this work formulates the limited channel feedback problem as a quantized and compressed matrix recovery problem. The formulation presents a computationally challenging maximum likelihood estimation (MLE) problem. An ADMM algorithm leveraging existing harmonic retrieval tools is proposed to effectively tackle the optimization problem. Simulations show that the proposed method attains promising channel estimation accuracy, using a much smaller amount of feedback bits relative to existing methods.
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This content will become publicly available on June 4, 2024
Overlay Cognitive Radio Using Symbol Level Precoding With Quantized CSI
Overlay cognitive radio (CR) networks include a primary and cognitive base station (BS) sharing the same frequency band. This paper focuses on designing a robust symbol-level pre-coding (SLP) scheme where the primary BS shares data and quantized channel state information (CSI) with the cognitive BS. The proposed approach minimizes the cognitive BS transmission power under symbol-wise Safety Margin (SM) constraints for both the primary and cognitive systems. We apply the additive quantization noise model to describe the statistics of the quantized PBS CSI and employ a stochastic constraint to formulate the optimization problem, which is then converted to be deterministic. Simulation results show that the robust SLP protects the primary users from the effect of the imperfect CSI and simultaneously offers significantly improved energy efficiency compared to nonrobust methods.
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- NSF-PAR ID:
- 10465493
- Date Published:
- Journal Name:
- Proc. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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