3 research outputs found
Distributed Pricing-Based User Association for Downlink Heterogeneous Cellular Networks
This paper considers the optimization of the user and base-station (BS)
association in a wireless downlink heterogeneous cellular network under the
proportional fairness criterion. We first consider the case where each BS has a
single antenna and transmits at fixed power, and propose a distributed price
update strategy for a pricing-based user association scheme, in which the users
are assigned to the BS based on the value of a utility function minus a price.
The proposed price update algorithm is based on a coordinate descent method for
solving the dual of the network utility maximization problem, and it has a
rigorous performance guarantee. The main advantage of the proposed algorithm as
compared to the existing subgradient method for price update is that the
proposed algorithm is independent of parameter choices and can be implemented
asynchronously. Further, this paper considers the joint user association and BS
power control problem, and proposes an iterative dual coordinate descent and
the power optimization algorithm that significantly outperforms existing
approaches. Finally, this paper considers the joint user association and BS
beamforming problem for the case where the BSs are equipped with multiple
antennas and spatially multiplex multiple users. We incorporate dual coordinate
descent with the weighted minimum mean-squared error (WMMSE) algorithm, and
show that it achieves nearly the same performance as a computationally more
complex benchmark algorithm (which applies the WMMSE algorithm on the entire
network for BS association), while avoiding excessive BS handover.Comment: IEEE Journal on Selected Areas in Communications, Special Issue on 5G
Communication Systems, June 201
Fairness and Sum-Rate Maximization via Joint Channel and Power Allocation in Uplink SCMA Networks
In this work, we consider a sparse code multiple access uplink system, where
users simultaneously transmit data over subcarriers, such that ,
with a constraint on the power transmitted by each user. To jointly optimize
the subcarrier assignment and the transmitted power per subcarrier, two new
iterative algorithms are proposed, the first one aims to maximize the sum-rate
(Max-SR) of the network, while the second aims to maximize the fairness
(Max-Min). In both cases, the optimization problem is of the mixed-integer
nonlinear programming (MINLP) type, with non-convex objective functions, which
are generally not tractable. We prove that both joint allocation problems are
NP-hard. To address these issues, we employ a variant of the block successive
upper-bound minimization (BSUM) \cite{razaviyayn.2013} framework, obtaining
polynomial-time approximation algorithms to the original problem. Moreover, we
evaluate the algorithms' robustness against outdated channel state information
(CSI), present an analysis of the convergence of the algorithms, and a
comparison of the sum-rate and Jain's fairness index of the novel algorithms
with three other algorithms proposed in the literature. The Max-SR algorithm
outperforms the others in the sum-rate sense, while the Max-Min outperforms
them in the fairness sense.Comment: This work has been submitted to the IEEE for possible publication.
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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