102 research outputs found
Inter-tier Interference Suppression in Heterogeneous Cloud Radio Access Networks
Incorporating cloud computing into heterogeneous networks, the heterogeneous
cloud radio access network (H-CRAN) has been proposed as a promising paradigm
to enhance both spectral and energy efficiencies. Developing interference
suppression strategies is critical for suppressing the inter-tier interference
between remote radio heads (RRHs) and a macro base station (MBS) in H-CRANs. In
this paper, inter-tier interference suppression techniques are considered in
the contexts of collaborative processing and cooperative radio resource
allocation (CRRA). In particular, interference collaboration (IC) and
beamforming (BF) are proposed to suppress the inter-tier interference, and
their corresponding performance is evaluated. Closed-form expressions for the
overall outage probabilities, system capacities, and average bit error rates
under these two schemes are derived. Furthermore, IC and BF based CRRA
optimization models are presented to maximize the RRH-accessed users' sum rates
via power allocation, which is solved with convex optimization. Simulation
results demonstrate that the derived expressions for these performance metrics
for IC and BF are accurate; and the relative performance between IC and BF
schemes depends on system parameters, such as the number of antennas at the
MBS, the number of RRHs, and the target signal-to-interference-plus-noise ratio
threshold. Furthermore, it is seen that the sum rates of IC and BF schemes
increase almost linearly with the transmit power threshold under the proposed
CRRA optimization solution
Robust Monotonic Optimization Framework for Multicell MISO Systems
The performance of multiuser systems is both difficult to measure fairly and
to optimize. Most resource allocation problems are non-convex and NP-hard, even
under simplifying assumptions such as perfect channel knowledge, homogeneous
channel properties among users, and simple power constraints. We establish a
general optimization framework that systematically solves these problems to
global optimality. The proposed branch-reduce-and-bound (BRB) algorithm handles
general multicell downlink systems with single-antenna users, multiantenna
transmitters, arbitrary quadratic power constraints, and robustness to channel
uncertainty. A robust fairness-profile optimization (RFO) problem is solved at
each iteration, which is a quasi-convex problem and a novel generalization of
max-min fairness. The BRB algorithm is computationally costly, but it shows
better convergence than the previously proposed outer polyblock approximation
algorithm. Our framework is suitable for computing benchmarks in general
multicell systems with or without channel uncertainty. We illustrate this by
deriving and evaluating a zero-forcing solution to the general problem.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 9
figures, 2 table
Energy-Efficient Scheduling and Power Allocation in Downlink OFDMA Networks with Base Station Coordination
This paper addresses the problem of energy-efficient resource allocation in
the downlink of a cellular OFDMA system. Three definitions of the energy
efficiency are considered for system design, accounting for both the radiated
and the circuit power. User scheduling and power allocation are optimized
across a cluster of coordinated base stations with a constraint on the maximum
transmit power (either per subcarrier or per base station). The asymptotic
noise-limited regime is discussed as a special case. %The performance of both
an isolated and a non-isolated cluster of coordinated base stations is examined
in the numerical experiments. Results show that the maximization of the energy
efficiency is approximately equivalent to the maximization of the spectral
efficiency for small values of the maximum transmit power, while there is a
wide range of values of the maximum transmit power for which a moderate
reduction of the data rate provides a large saving in terms of dissipated
energy. Also, the performance gap among the considered resource allocation
strategies reduces as the out-of-cluster interference increases.Comment: to appear on IEEE Transactions on Wireless Communication
Energy-Efficient Coordinated Multi-Cell Multigroup Multicast Beamforming with Antenna Selection
This paper studies energy-efficient coordinated beamforming in multi-cell
multi-user multigroup multicast multiple-input single-output systems. We aim at
maximizing the network energy efficiency by taking into account the fact that
some of the radio frequency chains can be switched off in order to save power.
We consider the antenna specific maximum power constraints to avoid non-linear
distortion in power amplifiers and user-specific quality of service (QoS)
constraints to guarantee a certain QoS levels. We first introduce binary
antenna selection variables and use the perspective formulation to model the
relation between them and the beamformers. Subsequently, we propose a new
formulation which reduces the feasible set of the continuous relaxation,
resulting in better performance compared to the original perspective
formulation based problem. However, the resulting optimization problem is a
mixed-Boolean non-convex fractional program, which is difficult to solve. We
follow the standard continuous relaxation of the binary antenna selection
variables, and then reformulate the problem such that it is amendable to
successive convex approximation. Thereby, solving the continuous relaxation
mostly results in near-binary solution. To recover the binary variables from
the continuous relaxation, we switch off all the antennas for which the
continuous values are smaller than a small threshold. Numerical results
illustrate the superior convergence result and significant achievable gains in
terms of energy efficiency with the proposed algorithm.Comment: 6 pages, 5 figures, accepted to IEEE ICC 2017 - International
Workshop on 5G RAN Desig
Energy-efficient precoding in multicell networks with full-duplex base stations
© 2017, The Author(s). This paper considers multi-input multi-output (MIMO) multicell networks, where the base stations (BSs) are full-duplex transceivers, while uplink and downlink users are equipped with multiple antennas and operate in a half-duplex mode. The problem of interest is to design linear precoders for BSs and users to optimize the network’s energy efficiency. Given that the energy efficiency objective is not a ratio of concave and convex functions, the commonly used Dinkelbach-type algorithms are not applicable. We develop a low-complexity path-following algorithm that only invokes one simple convex quadratic program at each iteration, which converges at least to the local optimum. Numerical results demonstrate the performance advantage of our proposed algorithm in terms of energy efficiency
Energy-Efficient Power Control: A Look at 5G Wireless Technologies
This work develops power control algorithms for energy efficiency (EE)
maximization (measured in bit/Joule) in wireless networks. Unlike previous
related works, minimum-rate constraints are imposed and the
signal-to-interference-plus-noise ratio takes a more general expression, which
allows one to encompass some of the most promising 5G candidate technologies.
Both network-centric and user-centric EE maximizations are considered. In the
network-centric scenario, the maximization of the global EE and the minimum EE
of the network are performed. Unlike previous contributions, we develop
centralized algorithms that are guaranteed to converge, with affordable
computational complexity, to a Karush-Kuhn-Tucker point of the considered
non-convex optimization problems. Moreover, closed-form feasibility conditions
are derived. In the user-centric scenario, game theory is used to study the
equilibria of the network and to derive convergent power control algorithms,
which can be implemented in a fully decentralized fashion. Both scenarios above
are studied under the assumption that single or multiple resource blocks are
employed for data transmission. Numerical results assess the performance of the
proposed solutions, analyzing the impact of minimum-rate constraints, and
comparing the network-centric and user-centric approaches.Comment: Accepted for Publication in the IEEE Transactions on Signal
Processin
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