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Spectral and Energy Efficient Low-Overhead Uplink and Downlink Channel Estimation for 5G Massive MIMO Systems
Imran Khan1, Mohammad Haseeb Zafar1, Mohammad Tariq Jan2
1Department of Electrical Engineering, University of Engineering and Technology, Peshawar 814, Pakistan.
Entropy (Basel, Switzerland)
|December 3, 2020
Summary
This study introduces Compressed-Sensing (CS) algorithms to reduce channel estimation overhead in massive MIMO systems. These methods significantly cut feedback and pilot requirements, enhancing system efficiency and energy efficiency.
Area of Science:
- Wireless communication systems
- Signal processing
- Information theory
Background:
- Massive MIMO systems face challenges with high channel estimation overhead due to large channel matrices.
- Traditional codebook schemes lead to significant bandwidth consumption and reduced system efficiency.
Purpose of the Study:
- To decrease channel estimation overhead in massive MIMO systems.
- To optimize the energy efficiency (EE) of these systems.
- To leverage sparse attributes for improved performance.
Main Methods:
- Utilizing Compressed-Sensing (CS) with techniques like Block Iterative-Support-Detection (Block-ISD), Angle-of-Departure (AoD), and Structured Compressive Sampling Matching Pursuit (S-CoSaMP).
- Employing CS to exploit temporal-correlation for Differential-Channel Impulse Response (DCIR) generation.
- Leveraging spatial-correlation for block-sparsity with Block-ISD.
- Quantizing channels based on Angle-of-Departure (AoD) variations.
- Deploying structured-sparsity with S-CoSaMP for reliable Channel-State-Information (CSI).
Main Results:
- CS-based algorithms significantly reduce feedback and pilot overhead compared to traditional methods.
- MATLAB simulations demonstrate improved system capacity.
- Energy efficiency increases with higher Base Station (BS) and User Equipment (UE) density, and reduced hardware impairments.
Conclusions:
- Proposed CS-based algorithms effectively reduce channel estimation overhead in massive MIMO.
- These methods enhance system capacity and energy efficiency.
- The findings offer a pathway to more efficient wireless communication systems.
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