269 research outputs found
Selective Fair Scheduling over Fading Channels
Imposing fairness in resource allocation incurs a loss of system throughput,
known as the Price of Fairness (). In wireless scheduling, increases
when serving users with very poor channel quality because the scheduler wastes
resources trying to be fair. This paper proposes a novel resource allocation
framework to rigorously address this issue. We introduce selective fairness:
being fair only to selected users, and improving by momentarily blocking
the rest. We study the associated admission control problem of finding the user
selection that minimizes subject to selective fairness, and show that
this combinatorial problem can be solved efficiently if the feasibility set
satisfies a condition; in our model it suffices that the wireless channels are
stochastically dominated. Exploiting selective fairness, we design a stochastic
framework where we minimize subject to an SLA, which ensures that an
ergodic subscriber is served frequently enough. In this context, we propose an
online policy that combines the drift-plus-penalty technique with
Gradient-Based Scheduling experts, and we prove it achieves the optimal .
Simulations show that our intelligent blocking outperforms by 40 in
throughput previous approaches which satisfy the SLA by blocking low-SNR users
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
Millimeter-wave Evolution for 5G Cellular Networks
Triggered by the explosion of mobile traffic, 5G (5th Generation) cellular
network requires evolution to increase the system rate 1000 times higher than
the current systems in 10 years. Motivated by this common problem, there are
several studies to integrate mm-wave access into current cellular networks as
multi-band heterogeneous networks to exploit the ultra-wideband aspect of the
mm-wave band. The authors of this paper have proposed comprehensive
architecture of cellular networks with mm-wave access, where mm-wave small cell
basestations and a conventional macro basestation are connected to
Centralized-RAN (C-RAN) to effectively operate the system by enabling power
efficient seamless handover as well as centralized resource control including
dynamic cell structuring to match the limited coverage of mm-wave access with
high traffic user locations via user-plane/control-plane splitting. In this
paper, to prove the effectiveness of the proposed 5G cellular networks with
mm-wave access, system level simulation is conducted by introducing an expected
future traffic model, a measurement based mm-wave propagation model, and a
centralized cell association algorithm by exploiting the C-RAN architecture.
The numerical results show the effectiveness of the proposed network to realize
1000 times higher system rate than the current network in 10 years which is not
achieved by the small cells using commonly considered 3.5 GHz band.
Furthermore, the paper also gives latest status of mm-wave devices and
regulations to show the feasibility of using mm-wave in the 5G systems.Comment: 17 pages, 12 figures, accepted to be published in IEICE Transactions
on Communications. (Mar. 2015
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