2 research outputs found
Centralized & Distributed Deep Reinforcement Learning Methods for Downlink Sum-Rate Optimization
For a multi-cell, multi-user, cellular network downlink sum-rate maximization
through power allocation is a nonconvex and NP-hard optimization problem. In
this paper, we present an effective approach to solving this problem through
single- and multi-agent actor-critic deep reinforcement learning (DRL).
Specifically, we use finite-horizon trust region optimization. Through
extensive simulations, we show that we can simultaneously achieve higher
spectral efficiency than state-of-the-art optimization algorithms like weighted
minimum mean-squared error (WMMSE) and fractional programming (FP), while
offering execution times more than two orders of magnitude faster than these
approaches. Additionally, the proposed trust region methods demonstrate
superior performance and convergence properties than the Advantage Actor-Critic
(A2C) DRL algorithm. In contrast to prior approaches, the proposed
decentralized DRL approaches allow for distributed optimization with limited
CSI and controllable information exchange between BSs while offering
competitive performance and reduced training times.Comment: Accepted for publication in IEEE Transactions on Wireless
Communication
Optimizing Downlink Resource Allocation in Multiuser MIMO Networks via Fractional Programming and the Hungarian Algorithm
Optimizing the sum-log-utility for the downlink of multi-frequency band,
multiuser, multiantenna networks requires joint solutions to the associated
beamforming and user scheduling problems through the use of cloud radio access
network (CRAN) architecture; optimizing such a network is, however, non-convex
and NP-hard. In this paper, we present a novel iterative beamforming and
scheduling strategy based on fractional programming and the Hungarian
algorithm. The beamforming strategy allows us to iteratively maximize the
chosen objective function in a fashion similar to block coordinate ascent.
Furthermore, based on the crucial insight that, in the downlink, the
interference pattern remains fixed for a given set of beamforming weights, we
use the Hungarian algorithm as an efficient approach to optimally schedule
users for the given set of beamforming weights. Specifically, this approach
allows us to select the best subset of users (amongst the larger set of all
available users). Our simulation results show that, in terms of average
sum-log-utility, as well as sum-rate, the proposed scheme substantially
outperforms both the state-of-the-art multicell weighted minimum mean-squared
error (WMMSE) and greedy proportionally fair WMMSE schemes, as well as standard
interior-point and sequential quadratic solvers. Importantly, our proposed
scheme is also far more computationally efficient than the multicell WMMSE
scheme.Comment: Accepted for publication in IEEE Transactions on Wireless
Communications (2020