[Paper Review] Cooperative Beamforming with Predictive Relay Selection for Urban mmWave Communications

Anastasios Dimas, Dionysios S. Kalogerias+1 more|arXiv (Cornell University)|Jul 29, 2019

Millimeter-Wave Propagation and ModelingEngineering48 references23 citations

TL;DR

This paper proposes a predictive, resource-efficient relay selection scheme for mmWave networks in urban environments, leveraging spatiotemporal correlations in Received Signal Strength (RSS) to enable distributed, low-overhead relay selection. The method achieves near-ideal performance (within 2.3 dB of the ideal scheme) while significantly reducing channel estimation overhead, especially in dense topologies.

ABSTRACT

While millimeter wave (mmWave) communications promise high data rates, their sensitivity to blockage and severe signal attenuation presents challenges in their deployment in urban settings. To overcome these effects, we consider a distributed cooperative beamforming system, which relies on static relays deployed in clusters with similar channel characteristics, and where, at every time instance, only one relay from each cluster is selected to participate in beamforming to the destination. To meet the quality-of-service guarantees of the network, a key prerequisite for beamforming is relay selection. However, as the channels change with time, relay selection becomes a resource demanding task. Indeed, estimation of channel state information for all candidate relays, essential for relay selection, is a process that takes up bandwidth, wastes power and introduces latency and interference in the network. We instead propose a unique, predictive scheme for resource efficient relay selection, which exploits the special propagation patterns of the mmWave medium, and can be executed distributively across clusters, and in parallel to optimal beamforming-based communication. The proposed predictive scheme efficiently exploits spatiotemporal channel correlations with current and past networkwide Received Signal Strength (RSS), the latter being invariant to relay cluster size, measured sequentially during the operation of the system. Our numerical results confirm that our proposed relay selection strategy outperforms any randomized selection policy that does not exploit channel correlations, whereas, at the same time, it performs very close to an ideal scheme that uses complete, cluster size dependent RSS, and offers significant savings in terms of channel estimation overhead, providing substantially better network utilization, especially in dense topologies, typical in mmWave networks.

Motivation & Objective

• To address the high resource cost of traditional CSI-based relay selection in mmWave networks.
• To reduce channel estimation overhead and latency in dynamic urban mmWave environments.
• To enable distributed, real-time relay selection that maintains high Quality-of-Service (QoS) without requiring full CSI feedback.
• To exploit spatiotemporal correlations in RSS to predict optimal relay selection one time slot ahead.
• To improve network utilization in dense mmWave deployments with minimal signaling and power overhead.

Proposed method

• Proposes a predictive relay selection scheme that uses current and past networkwide RSS measurements, which are invariant to relay cluster size.
• Employs a sampling-based adaptive algorithm (SAA) to estimate one-step-ahead SINR and select the optimal relay per cluster.
• Executes relay selection distributively across clusters in parallel with beamforming, minimizing coordination overhead.
• Leverages spatiotemporal channel correlations in mmWave propagation, particularly in line-of-sight (LoS) and street-canyon environments.
• Uses magnitude-only CSI (RSS) instead of full CSI, reducing feedback requirements and latency.
• Integrates with 2-hop Amplify-and-Forward (AF) beamforming to enhance signal strength and reliability.

Experimental results

Research questions

• RQ1 Can predictive relay selection based on RSS correlations outperform randomized selection in mmWave networks?
• RQ2 To what extent can RSS-based prediction reduce the need for full CSI feedback in dynamic mmWave environments?
• RQ3 How does the performance of predictive relay selection compare to an ideal scheme with full CSI?
• RQ4 What is the impact of relay cluster placement on system performance and QoS?
• RQ5 Can distributed, low-complexity relay selection achieve near-ideal performance with minimal overhead?

Key findings

• The proposed SAA-based relay selection achieves 7.7 dB higher SINR than randomized selection, demonstrating significant performance gains.
• The SAA policy performs only 2.3 dB worse than the ideal CSI-based scheme, indicating near-optimal performance with drastically reduced feedback overhead.
• The ideal and SAA policies both favor selecting relays near cluster endpoints, with over 99% of selections targeting the two end relays in the SAA case.
• System performance improves by approximately 3 dB when increasing from 4 to 6 relay clusters, and degrades by ~5 dB with only 2 clusters, highlighting the importance of cluster density.
• The method reduces channel estimation overhead substantially, especially in dense urban mmWave networks, leading to better network utilization.
• The system is sensitive to spatial cluster placement, with performance varying significantly based on relay distribution, indicating that optimal placement is critical for high QoS.