Compressive channel estimation and tracking for large arrays in mm wave picocells (1506.05367v1)

Abstract: We propose and investigate a compressive architecture for estimation and tracking of sparse spatial channels in millimeter (mm) wave picocellular networks. The base stations are equipped with antenna arrays with a large number of elements (which can fit within compact form factors because of the small carrier wavelength) and employ radio frequency (RF) beamforming, so that standard least squares adaptation techniques (which require access to individual antenna elements) are not applicable. We focus on the downlink, and show that "compressive beacons," transmitted using pseudorandom phase settings at the base station array, and compressively processed using pseudorandom phase settings at the mobile array, provide information sufficient for accurate estimation of the two-dimensional (2D) spatial frequencies associated with the directions of departure of the dominant rays from the base station, and the associated complex gains. This compressive approach is compatible with coarse phase-only control, and is based on a near-optimal sequential algorithm for frequency estimation which can exploit the geometric continuity of the channel across successive beaconing intervals to reduce the overhead to less than 1% even for very large (32 x 32) arrays. Compressive beaconing is essentially omnidirectional, and hence does not enjoy the SNR and spatial reuse benefits of beamforming obtained during data transmission. We therefore discuss system level design considerations for ensuring that the beacon SNR is sufficient for accurate channel estimation, and that inter-cell beacon interference is controlled by an appropriate reuse scheme.

• The paper introduces a compressive sensing approach that uses pseudorandom phase beacons to estimate sparse mmWave channels.
• It reduces measurement overhead to below 1% while enabling accurate tracking across large antenna arrays in picocells.
• Simulation results confirm low estimation error and near-ideal beamforming performance, demonstrating the method’s scalability and efficiency.

Compressive Channel Estimation and Tracking for Large Arrays in mm Wave Picocells

The paper "Compressive channel estimation and tracking for large arrays in mm wave picocells" presents a comprehensive architecture to address spatial channel estimation in millimeter-wave (mmWave) picocellular networks, leveraging the potential of large antenna arrays. This work is particularly focused on downlink communication and emphasizes the role of "compressive beacons" for estimation and tracking of sparse spatial channels.

In mmWave communications, the small physical wavelength allows for compact, large-element antenna arrays facilitating enhanced spatial reuse and considerable improvements in network capacity. However, channel estimation with such large arrays presents unique challenges. Traditional least squares adaptation techniques are not feasible due to their requirement for access to individual antenna elements, which is impractical in such scenarios.

Compressive Estimation Architecture

The proposed architecture leverages compressive sensing principles to enable efficient channel estimation. The central concept is the use of compressive beacons projected from the base station using pseudorandom phase settings. This approach, optimized for sparsity, requires considerably fewer measurements for accurate channel estimation compared to traditional methods. The algorithm procedures include:

• Transmission of Compressive Beacons: The base station sends out a predefined number of beacons, each with distinct pseudorandom phase settings. Correspondingly, the mobile station computes the channel's virtual matrix using its array settings.
• Sparsity Exploitation: The sparse nature of the mmWave channel is exploited, as mmWave communications typically have a smaller set of dominant paths due to their directional nature.
• Algorithmic Processing: A near-optimal sequential algorithm is used for estimating spatial frequencies and gains, significantly reducing the overhead and maintaining high fidelity in the estimation.

System Design Implications

Several design considerations are necessary for the practical deployment of the proposed system:

1. Overhead and Interference Management: The system design ensures that the overhead for channel estimation remains below 1%, even with large antenna arrays. The quasi-omnidirectional nature of compressive beacons implies they do not benefit substantially from beamforming gains; system-level strategies like appropriate reuse schemes mitigate inter-cell beacon interference.
2. Enhanced Spatial Resolution: The proposed system outperforms traditional architectures by utilizing fewer RF chains and allows coarse phase control. This extends the feasibility of deploying large-scale antenna arrays in dense urban settings.
3. Broad Applicability: While the system is exemplified with 60 GHz bands within urban picocells, the methods are generalizable across various frequency bands and deployment scenarios, especially where large arrays are feasible.

Numerical Results and Validation

The paper provides strong empirical validation for the proposed methodology. Simulation results show that the estimation error is consistently controlled across different antenna array sizes (e.g., $8 \times 8$ and $32 \times 32$ configurations). The beamforming performance aligns closely with ideal scenarios, demonstrating the efficacy of coarse phase control techniques bi-directionally.

Future Prospects and Challenges

This research opens several avenues for further exploration:

• Experimental Validation: While the theoretical and simulation results validate the approach, field trials and real-world usage would provide more insights.
• Adaptive Interference Management: Developing adaptive strategies for interference management, especially in non-static environments, will enhance system robustness.
• Integration with Advanced MIMO Techniques: Exploring how compressive sensing techniques could be integrated with multi-user MIMO systems or hybrid analog-digital beamforming configurations could further enhance the scalability and efficiency of mmWave networks.

The paper establishes a substantive foundation for how sparse channel estimation techniques can be adapted for large-scale, bandwidth-limited networks, pointing towards more efficient and practical deployment of next-generation wireless networks.