Tracking Angles of Departure and Arrival in a Mobile Millimeter Wave Channel (1512.06383v1)

Abstract: Millimeter wave provides a very promising approach for meeting the ever-growing traffic demand in next generation wireless networks. To utilize this band, it is crucial to obtain the channel state information in order to perform beamforming and combining to compensate for severe path loss. In contrast to lower frequencies, a typical millimeter wave channel consists of a few dominant paths. Thus it is generally sufficient to estimate the path gains, angles of departure (AoDs), and angles of arrival (AoAs) of those paths. Proposed in this paper is a dual timescale model to characterize abrupt channel changes (e.g., blockage) and slow variations of AoDs and AoAs. This work focuses on tracking the slow variations and detecting abrupt changes. A Kalman filter based tracking algorithm and an abrupt change detection method are proposed. The tracking algorithm is compared with the adaptive algorithm due to Alkhateeb, Ayach, Leus and Heath (2014) in the case with single radio frequency chain. Simulation results show that to achieve the same tracking performance, the proposed algorithm requires much lower signal-to-noise-ratio (SNR) and much fewer pilots than the other algorithm. Moreover, the change detection method can always detect abrupt changes with moderate number of pilots and SNR.

Insights into Tracking Angles of Departure and Arrival in Mobile Millimeter Wave Channels

The paper "Tracking Angles of Departure and Arrival in a Mobile Millimeter Wave Channel" offers an advanced analysis of channel estimation in millimeter wave communications, which are essential for next-generation wireless networks. The focus is on devising efficient methods for tracking channel parameters, specifically the angles of departure (AoDs) and arrival (AoAs), amidst the challenges posed by high path loss and limited scattering in millimeter wave bands.

Overview and Methodology

Recent advancements underline the need for precise channel state information (CSI) in millimeter wave bands, typically hosted by a sparse set of dominant paths due to the substantial attenuation loss. The paper addresses the unique challenges of millimeter wave communication, notably the limited use of analog beamforming and combining due to the high cost and power demands of RF chains at such high frequencies.

A dual timescale model is adopted to differentiate between abrupt changes, such as path blockages, and slow variations in AoDs and AoAs, contributing to the dynamics of mobile environments. The paper introduces a Kalman filter-based algorithm for tracking these slow variations, alongside an abrupt change detection method. The proposed tracking algorithm showcases superior performance by requiring lower signal-to-noise-ratio (SNR) and reduced pilot signals compared to prior adaptive algorithms by Alkhateeb et al.

Numerical Results and Implications

The comparative simulations reveal that the Kalman filter-based tracking algorithm significantly enhances channel estimation accuracy, outperforming existing methods even amidst acquisition errors. Notably, it shows substantial robustness to variations in SNR and quantization levels. The numerical results emphasize the algorithm's proficiency under rapid angle variation scenarios, highlighting potential applications in dynamic mobile environments.

The abrupt change detection method exhibits reliable identification of channel variations with minimal pilot overhead. This characteristic is vital for maintaining stable and efficient communication links in high-frequency bands susceptible to rapid environmental changes.

Impact on Future Research and Applications

The implications of this research are multifaceted. Practically, it aids in the reliable deployment of millimeter wave systems in urban landscapes where obstruction and dynamic change is prevalent. Theoretically, it stimulates further exploration into adaptive algorithms for dynamic beamforming and channel estimation.

The research also opens avenues for future work that could integrate machine learning approaches to further enhance estimation accuracy and speed for rapidly evolving channel conditions. Moreover, the robustness to acquisition errors suggests its potential utility in developing cost-effective communication systems with fewer RF chains, thereby reducing operational cost without compromising on performance.

Overall, this work contributes significantly to the domain of wireless communication by addressing critical challenges in millimeter wave channel estimation, offering insights that hold promise for advancing both theoretical research and practical communications applications.