3 research outputs found
Distributed Uplink Beamforming in Cell-Free Networks Using Deep Reinforcement Learning
The emergence of new wireless technologies together with the requirement of
massive connectivity results in several technical issues such as excessive
interference, high computational demand for signal processing, and lengthy
processing delays. In this work, we propose several beamforming techniques for
an uplink cell-free network with centralized, semi-distributed, and fully
distributed processing, all based on deep reinforcement learning (DRL). First,
we propose a fully centralized beamforming method that uses the deep
deterministic policy gradient algorithm (DDPG) with continuous space. We then
enhance this method by enabling distributed experience at access points (AP).
Indeed, we develop a beamforming scheme that uses the distributed
distributional deterministic policy gradients algorithm (D4PG) with the APs
representing the distributed agents. Finally, to decrease the computational
complexity, we propose a fully distributed beamforming scheme that divides the
beamforming computations among APs. The results show that the D4PG scheme with
distributed experience achieves the best performance irrespective of the
network size. Furthermore, the proposed distributed beamforming technique
performs better than the DDPG algorithm with centralized learning only for
small-scale networks. The performance superiority of the DDPG model becomes
more evident as the number of APs and/or users increases. Moreover, during the
operation stage, all DRL models demonstrate a significantly shorter processing
time than that of the conventional gradient descent (GD) solution
Deep Learning Based Frequency-Selective Channel Estimation for Hybrid mmWave MIMO Systems
Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO)
systems typically employ hybrid mixed signal processing to avoid expensive
hardware and high training overheads. {However, the lack of fully digital
beamforming at mmWave bands imposes additional challenges in channel
estimation. Prior art on hybrid architectures has mainly focused on greedy
optimization algorithms to estimate frequency-flat narrowband mmWave channels,
despite the fact that in practice, the large bandwidth associated with mmWave
channels results in frequency-selective channels. In this paper, we consider a
frequency-selective wideband mmWave system and propose two deep learning (DL)
compressive sensing (CS) based algorithms for channel estimation.} The proposed
algorithms learn critical apriori information from training data to provide
highly accurate channel estimates with low training overhead. In the first
approach, a DL-CS based algorithm simultaneously estimates the channel supports
in the frequency domain, which are then used for channel reconstruction. The
second approach exploits the estimated supports to apply a low-complexity
multi-resolution fine-tuning method to further enhance the estimation
performance. Simulation results demonstrate that the proposed DL-based schemes
significantly outperform conventional orthogonal matching pursuit (OMP)
techniques in terms of the normalized mean-squared error (NMSE), computational
complexity, and spectral efficiency, particularly in the low signal-to-noise
ratio regime. When compared to OMP approaches that achieve an NMSE gap of
\$\unit[\{4-10\}]{dB}\$ with respect to the Cramer Rao Lower Bound (CRLB), the
proposed algorithms reduce the CRLB gap to only \$\unit[\{1-1.5\}]{dB}\$, while
significantly reducing complexity by two orders of magnitude.Comment: 16 pages, 8 figures, submitted to IEEE transactions on wireless
communications. arXiv admin note: text overlap with arXiv:1704.08572 by other
author
Efficient Angle-Domain Processing for FDD-based Cell-free Massive MIMO Systems
Cell-free massive MIMO communications is an emerging network technology for
5G wireless communications wherein distributed multi-antenna access points
(APs) serve many users simultaneously. Most prior work on cell-free massive
MIMO systems assume time-division duplexing mode, although frequency-division
duplexing (FDD) systems dominate current wireless standards. The key challenges
in FDD massive MIMO systems are channel-state information (CSI) acquisition and
feedback overhead. To address these challenges, we exploit the so-called angle
reciprocity of multipath components in the uplink and downlink, so that the
required CSI acquisition overhead scales only with the number of served users,
and not the number of AP antennas nor APs. We propose a low complexity
multipath component estimation technique and present linear angle-of-arrival
(AoA)-based beamforming/combining schemes for FDD-based cell-free massive MIMO
systems. We analyze the performance of these schemes by deriving closed-form
expressions for the mean-square-error of the estimated multipath components, as
well as expressions for the uplink and downlink spectral efficiency. Using
semi-definite programming, we solve a max-min power allocation problem that
maximizes the minimum user rate under per-user power constraints. Furthermore,
we present a user-centric (UC) AP selection scheme in which each user chooses a
subset of APs to improve the overall energy efficiency of the system.
Simulation results demonstrate that the proposed multipath component estimation
technique outperforms conventional subspace-based and gradient-descent based
techniques. We also show that the proposed beamforming and combining techniques
along with the proposed power control scheme substantially enhance the spectral
and energy efficiencies with an adequate number of antennas at the APs