Adaptive Beam-Frequency Allocation Algorithm with Position Uncertainty for Millimeter-Wave MIMO Systems

Abstract

Envisioned for fifth generation (5G) systems, millimeter-wave (mmWave) communications are under very active research worldwide. Although pencil beams with accurate beamtracking may boost the throughput of mmWave systems, this poses great challenges in the design of radio resource allocation for highly mobile users. In this paper, we propose a joint adaptive beam-frequency allocation algorithm that takes into account the position uncertainty inherent to high mobility and/or unstable users as, e.g., Unmanned Aerial Vehicles (UAV), for whom this is a major problem. Our proposed method provides an optimized beamwidth selection under quality of service (QoS) requirements for maximizing system proportional fairness, under user position uncertainty. The rationale of our scheme is to adapt the beamwidth such that the best trade-off among system performance (narrower beam) and robustness to uncertainty (wider beam) is achieved. Simulation results show that the proposed method largely enhances the system performance compared to reference algorithms, by an appropriate adaptation of the mmWave beamwidths, even under severe uncertainties and imperfect channel state information (CSIs). 1

Joint Vehicle-Beam Allocation for Reliability and Coverage in Vehicular Communication Systems

“…In this system, V2I transmission is carried out at f 1 and V2V at f 2 . We adopt the position uncertainty model based on estimated and actual user positions based on the Manhattan mobility model in [15]. In a conventional switchedbeam system, the number of users is generally more than the number of beams i.e.…”

Machine Learning-Based Handovers for Sub-6 GHz and mmWave Integrated Vehicular Networks

“…For the proposed scheme, the simulation parameters are set to σ = 4 and η = 2 −18 , under which the average estimated errors of the x-dimension and y-dimension are 0.62m and 0.17m, respectively. Besides, in Fig.4(a), the Kalman filter based positioning scheme is employed to compare the positioning performance between different methods, in which we set both the measurement error variances of x-dimension and y-dimension to 1m [37]. The result shows that after the Kalman filter process, the final estimation errors of the x-dimension and y-dimension are reduced to 0.63m and 0.75m, respectively.…”

“…More than that, in the Kalman filter scheme, vehicles need to periodically feed back movement related information to help the network to figure out more accurate trajectories. Suppose the position measurements of vehicles are obtained through the mostly used Global Positioning System (GPS) whose information update rate r GP S is typically 1∼10Hz, i.e., updating positioning results every 0.1∼1s [37]. By denoting the volume of quantized position information as ο GP S bits, the feedback overhead of the Kalman filter based positioning scheme can be expressed as r GP S × ο GP S bits/s.…”

Beamwidth Optimization for Millimeter-Wave V2V Communication Between Neighbor Vehicles in Highway Scenarios

“…In [25] and [26], similar overhead-free beam training mechanisms are exploited with the estimated vehicular position and motion information in V2I communication. In [27], taking into account the position uncertainty of high mobility users, a joint adaptive beam-frequency allocation algorithm is developed to balance the tradeoff between system performance and robustness to uncertainty. The study in [28] derives a close-form expression of the optimal receive beamwidth in V2I downlink transmissions.…”