2 research outputs found
Adaptive Beam-Frequency Allocation Algorithm with Position Uncertainty for Millimeter-Wave MIMO Systems
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).Comment: 5 pages, 6 figures, 1 table, 1 algorith
Power and Beam Optimization for Uplink Millimeter-Wave Hotspot Communication Systems
We propose an effective interference management and beamforming mechanism for
uplink communication systems that yields fair allocation of rates. In
particular, we consider a hotspot area of a millimeter-wave (mmWave) access
network consisting of multiple user equipment (UE) in the uplink and multiple
access points (APs) with directional antennas and adjustable beam widths and
directions (beam configurations). This network suffers tremendously from
multi-beam multi-user interference, and, to improve the uplink transmission
performance, we propose a centralized scheme that optimizes the power, the beam
width, the beam direction of the APs, and the UE - AP assignments. This problem
involves both continuous and discrete variables, and it has the following
structure. If we fix all discrete variables, except for those related to the
UE-AP assignment, the resulting optimization problem can be solved optimally.
This property enables us to propose a heuristic based on simulated annealing
(SA) to address the intractable joint optimization problem with all discrete
variables. In more detail, for a fixed configuration of beams, we formulate a
weighted rate allocation problem where each user gets the same portion of its
maximum achievable rate that it would have under non-interfered conditions. We
solve this problem with an iterative fixed point algorithm that optimizes the
power of UEs and the UE - AP assignment in the uplink. This fixed point
algorithm is combined with SA to improve the beam configurations. Theoretical
and numerical results show that the proposed method improves both the UE rates
in the lower percentiles and the overall fairness in the network