Reliable Beamspace Channel Estimation for Millimeter-Wave Massive MIMO Systems with Lens Antenna Array (1707.07624v2)

Abstract: Millimeter-wave massive MIMO with lens antenna array can considerably reduce the number of required radio-frequency (RF) chains by beam selection. However, beam selection requires the base station to acquire the accurate information of beamspace channel. This is a challenging task, as the size of beamspace channel is large while the number of RF chains is limited. In this paper, we investigate the beamspace channel estimation problem in mmWave massive MIMO systems with lens antenna array. Specifically, we first design an adaptive selecting network for mmWave massive MIMO systems with lens antenna array, and based on this network, we further formulate the beamspace channel estimation problem as a sparse signal recovery problem. Then, by fully utilizing the structural characteristics of mmWave beamspace channel, we propose a support detection (SD)-based channel estimation scheme with reliable performance and low pilot overhead. Finally, the performance and complexity analyses are provided to prove that the proposed SD-based channel estimation scheme can estimate the support of sparse beamspace channel with comparable or higher accuracy than conventional schemes. Simulation results verify that the proposed SD-based channel estimation scheme outperforms conventional schemes and enjoys satisfying accuracy, even in the low SNR region as the structural characteristics of beamspace channel can be exploited.

• The paper proposes a novel support detection (SD)-based scheme for reliable beamspace channel estimation in mmWave massive MIMO systems with lens arrays.
• Simulation results show the proposed SD-based scheme achieves superior accuracy compared to classical methods, requiring significantly fewer pilot symbols.
• The method reduces hardware/energy constraints for practical mmWave massive MIMO deployment and shows promise for optimizing future 5G systems.

Reliable Beamspace Channel Estimation for Millimeter-Wave Massive MIMO Systems with Lens Antenna Array

The paper discusses a novel approach to estimate beamspace channels in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems utilizing lens antenna arrays. The central challenge addressed is the efficient estimation of beamspace channel information to facilitate beam selection, thereby significantly reducing the number of required radio-frequency (RF) chains in the base station (BS).

Key Contributions

The authors propose a support detection (SD)-based channel estimation scheme. They design an adaptive selecting network embracing 1-bit phase shifters, which can functionally replace conventional selecting networks in mmWave MIMO systems with lens antenna arrays. This network significantly alters channel estimation into a sparse signal recovery problem, enabling effective estimation by exploiting the inherent sparse nature of mmWave channels.

The scheme decomposes the channel estimation into sub-problems, each focusing on a sparse channel component. Numerical support is detected for each channel component by leveraging structural characteristics unique to mmWave beamspace channels. Simulation results demonstrate that the SD-based scheme can achieve higher accuracy compared to classical compressive sensing (CS) algorithms, especially in low signal-to-noise ratio (SNR) conditions.

Numerical Results

Notably, simulation results corroborate the efficacy of the SD-based channel estimation scheme. It exhibits superior normalized mean square error (NMSE) performance in comparison to conventional schemes, maintaining accuracy even with reduced pilot overhead. For instance, the proposed scheme with 96 pilot symbols performed closely to methods employing 256 symbols, underscoring its efficiency in minimizing resource utilization.

Implications and Future Directions

This research presents significant implications for the practical deployment of mmWave massive MIMO systems. By efficiently estimating channels with low pilot overhead, the proposed method mitigates constraints related to hardware and energy consumption, which are particularly acute at higher frequencies associated with mmWave communications.

Theoretically, the paper advances the understanding of sparse recovery methods in wireless communications, highlighting the substantial role of lens antenna arrays in beamspace channel estimation. Practically, the reduced complexity and improved estimation accuracy hold promise for optimizing future 5G systems.

Future work may entail extending the SD-based approach to scenarios where user equipment incorporates multiple antennas. This would potentially enhance the applicability of the method across diverse mmWave MIMO configurations, further refining the efficiency of spatial multiplexing in massive MIMO systems.