850 research outputs found
Distributed Multicell Beamforming Design Approaching Pareto Boundary with Max-Min Fairness
This paper addresses coordinated downlink beamforming optimization in
multicell time-division duplex (TDD) systems where a small number of parameters
are exchanged between cells but with no data sharing. With the goal to reach
the point on the Pareto boundary with max-min rate fairness, we first develop a
two-step centralized optimization algorithm to design the joint beamforming
vectors. This algorithm can achieve a further sum-rate improvement over the
max-min optimal performance, and is shown to guarantee max-min Pareto
optimality for scenarios with two base stations (BSs) each serving a single
user. To realize a distributed solution with limited intercell communication,
we then propose an iterative algorithm by exploiting an approximate
uplink-downlink duality, in which only a small number of positive scalars are
shared between cells in each iteration. Simulation results show that the
proposed distributed solution achieves a fairness rate performance close to the
centralized algorithm while it has a better sum-rate performance, and
demonstrates a better tradeoff between sum-rate and fairness than the Nash
Bargaining solution especially at high signal-to-noise ratio.Comment: 8 figures. To Appear in IEEE Trans. Wireless Communications, 201
Robust Linear Precoder Design for Multi-cell Downlink Transmission
Coordinated information processing by the base stations of multi-cell
wireless networks enhances the overall quality of communication in the network.
Such coordinations for optimizing any desired network-wide quality of service
(QoS) necessitate the base stations to acquire and share some channel state
information (CSI). With perfect knowledge of channel states, the base stations
can adjust their transmissions for achieving a network-wise QoS optimality. In
practice, however, the CSI can be obtained only imperfectly. As a result, due
to the uncertainties involved, the network is not guaranteed to benefit from a
globally optimal QoS. Nevertheless, if the channel estimation perturbations are
confined within bounded regions, the QoS measure will also lie within a bounded
region. Therefore, by exploiting the notion of robustness in the worst-case
sense some worst-case QoS guarantees for the network can be asserted. We adopt
a popular model for noisy channel estimates that assumes that estimation noise
terms lie within known hyper-spheres. We aim to design linear transceivers that
optimize a worst-case QoS measure in downlink transmissions. In particular, we
focus on maximizing the worst-case weighted sum-rate of the network and the
minimum worst-case rate of the network. For obtaining such transceiver designs,
we offer several centralized (fully cooperative) and distributed (limited
cooperation) algorithms which entail different levels of complexity and
information exchange among the base stations.Comment: 38 Pages, 7 Figures, To appear in the IEEE Transactions on Signal
Processin
Adaptive Multicell 3D Beamforming in Multi-Antenna Cellular Networks
We consider a cellular network with multi-antenna base stations (BSs) and
single-antenna users, multicell cooperation, imperfect channel state
information, and directional antennas each with a vertically adjustable beam.
We investigate the impact of the elevation angle of the BS antenna pattern,
denoted as tilt, on the performance of the considered network when employing
either a conventional single-cell transmission or a fully cooperative multicell
transmission. Using the results of this investigation, we propose a novel
hybrid multicell cooperation technique in which the intercell interference is
controlled via either cooperative beamforming in the horizontal plane or
coordinated beamfroming in the vertical plane of the wireless channel, denoted
as adaptive multicell 3D beamforming. The main idea is to divide the coverage
area into two disjoint vertical regions and adapt the multicell cooperation
strategy at the BSs when serving each region. A fair scheduler is used to share
the time-slots between the vertical regions. It is shown that the proposed
technique can achieve performance comparable to that of a fully cooperative
transmission but with a significantly lower complexity and signaling
requirements. To make the performance analysis computationally efficient,
analytical expressions for the user ergodic rates under different beamforming
strategies are also derived.Comment: Accepted for publication in IEEE Transaction on Vehicular Technolog
Coordinated Multi-cell Beamforming for Massive MIMO: A Random Matrix Approach
We consider the problem of coordinated multi- cell downlink beamforming in
massive multiple input multiple output (MIMO) systems consisting of N cells, Nt
antennas per base station (BS) and K user terminals (UTs) per cell.
Specifically, we formulate a multi-cell beamforming algorithm for massive MIMO
systems which requires limited amount of information exchange between the BSs.
The design objective is to minimize the aggregate transmit power across all the
BSs subject to satisfying the user signal to interference noise ratio (SINR)
constraints. The algorithm requires the BSs to exchange parameters which can be
computed solely based on the channel statistics rather than the instantaneous
CSI. We make use of tools from random matrix theory to formulate the
decentralized algorithm. We also characterize a lower bound on the set of
target SINR values for which the decentralized multi-cell beamforming algorithm
is feasible. We further show that the performance of our algorithm
asymptotically matches the performance of the centralized algorithm with full
CSI sharing. While the original result focuses on minimizing the aggregate
transmit power across all the BSs, we formulate a heuristic extension of this
algorithm to incorporate a practical constraint in multi-cell systems, namely
the individual BS transmit power constraints. Finally, we investigate the
impact of imperfect CSI and pilot contamination effect on the performance of
the decentralized algorithm, and propose a heuristic extension of the algorithm
to accommodate these issues. Simulation results illustrate that our algorithm
closely satisfies the target SINR constraints and achieves minimum power in the
regime of massive MIMO systems. In addition, it also provides substantial power
savings as compared to zero-forcing beamforming when the number of antennas per
BS is of the same orders of magnitude as the number of UTs per cell
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