3 research outputs found
Multiuser Precoding for Sum-Rate Maximization in Relay-Aided mmWave Communications
Relay-aided transmission is envisioned as a key strategy to combat severe
path loss and link blockages emerging as unique challenges in millimeter-wave
(mmWave) communications. This work considers a relay-aided multiuser mmWave
communications scenario aiming at maximizing the sum rate through optimal
transmit and relay precoder design. We propose a novel joint precoder design
strategy, which exploits weighted minimum mean-square error (WMMSE) using its
equivalency to sum-rate maximization. We obtain closed form expressions of
transmit and relay precoders, and propose to compute them through
alternating-optimization iterations without having to resort to complicated
numerical optimization techniques. Numerical results verify the superiority of
the proposed precoding strategy as compared to conventional precoding schemes
Learning Based Hybrid Beamforming Design for Full-Duplex Millimeter Wave Systems
Millimeter Wave (mmWave) communications with full-duplex (FD) have the
potential of increasing the spectral efficiency, relative to those with
half-duplex. However, the residual self-interference (SI) from FD and high
pathloss inherent to mmWave signals may degrade the system performance.
Meanwhile, hybrid beamforming (HBF) is an efficient technology to enhance the
channel gain and mitigate interference with reasonable complexity. However,
conventional HBF approaches for FD mmWave systems are based on optimization
processes, which are either too complex or strongly rely on the quality of
channel state information (CSI). We propose two learning schemes to design HBF
for FD mmWave systems, i.e., extreme learning machine based HBF (ELM-HBF) and
convolutional neural networks based HBF (CNN-HBF). Specifically, we first
propose an alternating direction method of multipliers (ADMM) based algorithm
to achieve SI cancellation beamforming, and then use a
majorization-minimization (MM) based algorithm for joint transmitting and
receiving HBF optimization. To train the learning networks, we simulate noisy
channels as input, and select the hybrid beamformers calculated by proposed
algorithms as targets. Results show that both learning based schemes can
provide more robust HBF performance and achieve at least 22.1% higher spectral
efficiency compared to orthogonal matching pursuit (OMP) algorithms. Besides,
the online prediction time of proposed learning based schemes is almost 20
times faster than the OMP scheme. Furthermore, the training time of ELM-HBF is
about 600 times faster than that of CNN-HBF with 64 transmitting and receiving
antennas.Comment: 13 pages, 8 figures, 1 tabl
Millimeter-Wave Full-Duplex UAV Relay: Joint Positioning, Beamforming, and Power Control
In this paper, a full-duplex unmanned aerial vehicle (FD-UAV) relay is
employed to increase the communication capacity of millimeter-wave (mmWave)
networks. Large antenna arrays are equipped at the source node (SN),
destination node (DN), and FD-UAV relay to overcome the high path loss of
mmWave channels and to help mitigate the self-interference at the FD-UAV relay.
Specifically, we formulate a problem for maximization of the achievable rate
from the SN to the DN, where the UAV position, analog beamforming, and power
control are jointly optimized. Since the problem is highly non-convex and
involves high-dimensional, highly coupled variable vectors, we first obtain the
conditional optimal position of the FD-UAV relay for maximization of an
approximate upper bound on the achievable rate in closed form, under the
assumption of a line-of-sight (LoS) environment and ideal beamforming. Then,
the UAV is deployed to the position which is closest to the conditional optimal
position and yields LoS paths for both air-to-ground links. Subsequently, we
propose an alternating interference suppression (AIS) algorithm for the joint
design of the beamforming vectors and the power control variables. In each
iteration, the beamforming vectors are optimized for maximization of the
beamforming gains of the target signals and the successive reduction of the
interference, where the optimal power control variables are obtained in closed
form. Our simulation results confirm the superiority of the proposed
positioning, beamforming, and power control method compared to three benchmark
schemes. Furthermore, our results show that the proposed solution closely
approaches a performance upper bound for mmWave FD-UAV systems.Comment: This paper has been accepted by IEEE Journal on Selected Areas in
Communications, special issue on Multiple Antenna Technologies for Beyond 5