Channel Estimation for Hybrid Architecture Based Wideband Millimeter Wave Systems (1611.03046v2)

Abstract: Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the hybrid precoders and combiners, though, is usually based on knowledge of the channel. Prior work on mmWave channel estimation with hybrid architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for hybrid architectures based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing based solutions for the wideband mmWave channel estimation problem for hybrid architectures. First, we leverage the sparse structure of the frequency selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains. Our solutions are suitable for both SC-FDE and OFDM systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead. Leveraging the hybrid architecture at the transceivers gives further improvement in estimation error performance and achievable rates.

• The paper introduces a compressed sensing algorithm to estimate wideband mmWave channels in hybrid analog-digital architectures.
• It develops a novel sparsifying dictionary that captures angular and delay sparsity for both SC-FDE and OFDM systems.
• Simulations show reduced training overhead and improved estimation accuracy, approaching fully digital system performance.

Channel Estimation for Hybrid Architecture Based Wideband Millimeter Wave Systems

This paper addresses the channel estimation challenges in wideband mmWave systems that utilize hybrid analog and digital precoding architectures. Millimeter wave (mmWave) frequencies offer significant potential for high-data-rate communications due to their wide bandwidth availability. However, channel estimation at these frequencies is inherently challenging due to large channel matrix dimensions and low signal-to-noise ratio (SNR) conditions prior to beamforming. The paper specifically focuses on wideband mmWave channel estimation—a crucial aspect that has been less explored compared to narrowband solutions—bringing to light the importance of effectively estimating channels in frequency-selective environments.

Sparse Formulation and Compressed Sensing Approach

Central to this work is the formulation of the mmWave channel estimation as a sparse recovery problem, leveraging the sparse nature of mmWave channels in both angular and delay domains. The paper proposes a methodology that builds upon compressed sensing techniques, developing explicit channel estimation algorithms suitable for both single-carrier frequency domain equalization (SC-FDE) and orthogonal frequency-division multiplexing (OFDM) systems.

The proposed solution incorporates several key aspects:

1. Sparse Representation: A novel sparsifying dictionary is defined, considering both angular and delay sparsity. This enables the formulation of the channel estimation problem in a way that makes use of compressed sensing for efficient recovery of channel parameters.
2. Time and Frequency Domain Solutions: The paper explores channel estimation techniques within purely time or frequency domains and presents a combined approach that utilizes insights from both.
3. Hardware Considerations: The algorithms take into account the hybrid architecture's constraints at mmWave transceivers, ensuring practical applicability in real-world scenarios.

Numerical Results and Implications

Simulation results demonstrate the effectiveness of the proposed channel estimation algorithms in improving channel estimation quality while minimizing training overhead. Notably, the solutions achieve superior performance with multiple RF chains, which further reduce estimation errors and training requirements, suggesting that hybrid architectures can approach the performance of fully digital systems in terms of achievable rates. These findings have significant potential applications for facilitating multi-user MIMO communications in standards such as IEEE 802.11ad.

The implications of this research are manifold. Practically, the reduction in training overhead and improvement in estimation accuracy can lead to more efficient utilization of mmWave systems and advancement in wireless communications protocols. Theoretically, the successful application of compressed sensing to wideband mmWave channel estimation enriches the understanding of signal processing techniques applicable to large-scale MIMO systems and paves the way for further innovations in adaptive and random sensing strategies tailored specifically for hybrid precoding architectures.

Future Directions

Future research could expand upon this foundation by considering various channel models and environments, examining the trade-offs between computational complexity and estimation accuracy. Additionally, exploring more advanced dictionary learning techniques or adaptive grid quantization methods could alleviate the off-grid error and further refine the precision of channel estimations.

Overall, this paper makes a significant contribution to the field of mmWave communications, presenting a robust framework for wideband channel estimation that can facilitate the widespread adoption and optimization of hybrid architecture-based communication systems. As such, it sets the stage for future advancements in the efficient deployment of high-throughput wireless networks.