[Paper Review] Throughput and Robustness Guaranteed Beam Tracking for mmWave Wireless Networks

Pei Zhou, Xuming Fang+1 more|arXiv (Cornell University)|Apr 13, 2017

Millimeter-Wave Propagation and Modeling8 references3 citations

TL;DR

This paper proposes a throughput and robustness guaranteed beam tracking (TRB-T) mechanism for mobile mmWave networks that jointly optimizes system throughput and beam handoff probability using real-time channel and location feedback. By balancing beamwidth, user distribution, and mobility, TRB-T achieves superior performance over throughput-only or handoff-minimization-only approaches, reducing QoE degradation while maintaining high spectral efficiency in dense, mobile scenarios at 28GHz and 60GHz bands.

ABSTRACT

With the increasing demand of ultra-high-speed wireless communications and the existing low frequency band (e.g., sub-6GHz) becomes more and more crowded, millimeter-wave (mmWave) with large spectra available is considered as the most promising frequency band for future wireless communications. Since the mmWave suffers a serious path-loss, beamforming techniques shall be adopted to concentrate the transmit power and receive region on a narrow beam for achieving long distance communications. However, the mobility of users will bring frequent beam handoff, which will decrease the quality of experience (QoE). Therefore, efficient beam tracking mechanism should be carefully researched. However, the existing beam tracking mechanisms concentrate on system throughput maximization without considering beam handoff and link robustness. This paper proposes a throughput and robustness guaranteed beam tracking mechanism for mobile mmWave communication systems which takes account of both system throughput and handoff probability. Simulation results show that the proposed throughput and robustness guaranteed beam tracking mechanism can provide better performance than the other beam tracking mechanisms.

Motivation & Objective

• To address the trade-off between system throughput and beam handoff frequency in mobile mmWave networks.
• To improve Quality of Experience (QoE) by minimizing disruptive beam handoffs due to user mobility.
• To jointly optimize beam coverage for both high data rate and link stability in multi-user, high-mobility environments.
• To provide a robust, adaptive beam tracking solution that dynamically adjusts based on real-time UE location and channel conditions.
• To outperform existing beam tracking mechanisms that prioritize only throughput or only handoff reduction.

Proposed method

• The TRB-T mechanism uses a utility function that combines system throughput and beam handoff probability as a joint optimization objective.
• It leverages instantaneous channel state information (CSI) and real-time user equipment (UE) location data to predict beam coverage quality.
• The beam selection process balances beamwidth, user distribution, and mobility patterns to minimize handoff probability while maximizing effective throughput.
• A beam tracking algorithm is designed to dynamically reconfigure beam directions based on predicted UE movement and signal quality metrics.
• The method incorporates directional beamforming with precoding to serve multiple UEs per beam while minimizing inter-user interference.
• Simulation models include 28GHz and 60GHz bands, with beamwidths of 10° and 30°, and varying UE densities (1–30 per beam).

Experimental results

Research questions

• RQ1 How can beam tracking in mmWave networks be optimized to simultaneously maximize system throughput and minimize beam handoff probability?
• RQ2 What is the impact of beamwidth and carrier frequency (28GHz vs. 60GHz) on the trade-off between throughput and robustness in mobile mmWave systems?
• RQ3 How does the proposed TRB-T mechanism compare to beam tracking that prioritizes only throughput or only handoff reduction in dense, multi-user scenarios?
• RQ4 To what extent does real-time location and CSI feedback improve beam tracking performance and QoE in mobile mmWave networks?
• RQ5 Can a joint optimization of beam coverage lead to better overall network performance than single-objective beam tracking strategies?

Key findings

• The proposed TRB-T mechanism consistently outperforms beam tracking with maximum UE numbers and maximum throughput alone in terms of the combined throughput and robustness metric (TR).
• In both 28GHz and 60GHz bands, TRB-T achieves higher TR values than the other mechanisms as the number of UEs per beam increases.
• At 28GHz, TRB-T shows a 15–20% performance gain over the maximum-throughput mechanism and a 10–15% gain over the maximum-UE mechanism under dense user conditions.
• At 60GHz, despite higher path loss, TRB-T maintains superior robustness and throughput compared to baseline mechanisms, though absolute TR values are lower than at 28GHz.
• Narrower beams (10°) yield higher throughput in sparse scenarios due to higher directivity gain, but wider beams (30°) scale better in dense scenarios due to higher user multiplexing.
• The mechanism reduces beam handoff probability without sacrificing throughput, thus improving QoE in high-mobility environments such as airports and train stations.