1,593 research outputs found
Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance
The pervasive nature of wireless telecommunication has made it the foundation
for mainstream technologies like automation, smart vehicles, virtual reality,
and unmanned aerial vehicles. As these technologies experience widespread
adoption in our daily lives, ensuring the reliable performance of cellular
networks in mobile scenarios has become a paramount challenge. Beamforming, an
integral component of modern mobile networks, enables spatial selectivity and
improves network quality. However, many beamforming techniques are iterative,
introducing unwanted latency to the system. In recent times, there has been a
growing interest in leveraging mobile users' location information to expedite
beamforming processes. This paper explores the concept of contextual
beamforming, discussing its advantages, disadvantages and implications.
Notably, the study presents an impressive 53% improvement in signal-to-noise
ratio (SNR) by implementing the adaptive beamforming (MRT) algorithm compared
to scenarios without beamforming. It further elucidates how MRT contributes to
contextual beamforming. The importance of localization in implementing
contextual beamforming is also examined. Additionally, the paper delves into
the use of artificial intelligence schemes, including machine learning and deep
learning, in implementing contextual beamforming techniques that leverage user
location information. Based on the comprehensive review, the results suggest
that the combination of MRT and Zero forcing (ZF) techniques, alongside deep
neural networks (DNN) employing Bayesian Optimization (BO), represents the most
promising approach for contextual beamforming. Furthermore, the study discusses
the future potential of programmable switches, such as Tofino, in enabling
location-aware beamforming
A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks
The large number of antennas in massive MIMO systems allows the base station
to communicate with multiple users at the same time and frequency resource with
multi-user beamforming. However, highly correlated user channels could
drastically impede the spectral efficiency that multi-user beamforming can
achieve. As such, it is critical for the base station to schedule a suitable
group of users in each time and frequency resource block to achieve maximum
spectral efficiency while adhering to fairness constraints among the users. In
this paper, we consider the resource scheduling problem for massive MIMO
systems with its optimal solution known to be NP-hard. Inspired by recent
achievements in deep reinforcement learning (DRL) to solve problems with large
action sets, we propose \name{}, a dynamic scheduler for massive MIMO based on
the state-of-the-art Soft Actor-Critic (SAC) DRL model and the K-Nearest
Neighbors (KNN) algorithm. Through comprehensive simulations using realistic
massive MIMO channel models as well as real-world datasets from channel
measurement experiments, we demonstrate the effectiveness of our proposed model
in various channel conditions. Our results show that our proposed model
performs very close to the optimal proportionally fair (Opt-PF) scheduler in
terms of spectral efficiency and fairness with more than one order of magnitude
lower computational complexity in medium network sizes where Opt-PF is
computationally feasible. Our results also show the feasibility and high
performance of our proposed scheduler in networks with a large number of users
and resource blocks.Comment: IEEE Transactions on Machine Learning in Communications and
Networking (TMLCN) 202
Multi-Agent Double Deep Q-Learning for Beamforming in mmWave MIMO Networks
Beamforming is one of the key techniques in millimeter wave (mmWave)
multi-input multi-output (MIMO) communications. Designing appropriate
beamforming not only improves the quality and strength of the received signal,
but also can help reduce the interference, consequently enhancing the data
rate. In this paper, we propose a distributed multi-agent double deep
Q-learning algorithm for beamforming in mmWave MIMO networks, where multiple
base stations (BSs) can automatically and dynamically adjust their beams to
serve multiple highly-mobile user equipments (UEs). In the analysis, largest
received power association criterion is considered for UEs, and a realistic
channel model is taken into account. Simulation results demonstrate that the
proposed learning-based algorithm can achieve comparable performance with
respect to exhaustive search while operating at much lower complexity.Comment: To be published in IEEE International Symposium on Personal, Indoor
and Mobile Radio Communications (PIMRC) 202
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