353 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
Coordinated Multicast Beamforming in Multicell Networks
We study physical layer multicasting in multicell networks where each base
station, equipped with multiple antennas, transmits a common message using a
single beamformer to multiple users in the same cell. We investigate two
coordinated beamforming designs: the quality-of-service (QoS) beamforming and
the max-min SINR (signal-to-interference-plus-noise ratio) beamforming. The
goal of the QoS beamforming is to minimize the total power consumption while
guaranteeing that received SINR at each user is above a predetermined
threshold. We present a necessary condition for the optimization problem to be
feasible. Then, based on the decomposition theory, we propose a novel
decentralized algorithm to implement the coordinated beamforming with limited
information sharing among different base stations. The algorithm is guaranteed
to converge and in most cases it converges to the optimal solution. The max-min
SINR (MMS) beamforming is to maximize the minimum received SINR among all users
under per-base station power constraints. We show that the MMS problem and a
weighted peak-power minimization (WPPM) problem are inverse problems. Based on
this inversion relationship, we then propose an efficient algorithm to solve
the MMS problem in an approximate manner. Simulation results demonstrate
significant advantages of the proposed multicast beamforming algorithms over
conventional multicasting schemes.Comment: 10pages, 9 figure
Joint Beamforming and Power Control in Coordinated Multicell: Max-Min Duality, Effective Network and Large System Transition
This paper studies joint beamforming and power control in a coordinated
multicell downlink system that serves multiple users per cell to maximize the
minimum weighted signal-to-interference-plus-noise ratio. The optimal solution
and distributed algorithm with geometrically fast convergence rate are derived
by employing the nonlinear Perron-Frobenius theory and the multicell network
duality. The iterative algorithm, though operating in a distributed manner,
still requires instantaneous power update within the coordinated cluster
through the backhaul. The backhaul information exchange and message passing may
become prohibitive with increasing number of transmit antennas and increasing
number of users. In order to derive asymptotically optimal solution, random
matrix theory is leveraged to design a distributed algorithm that only requires
statistical information. The advantage of our approach is that there is no
instantaneous power update through backhaul. Moreover, by using nonlinear
Perron-Frobenius theory and random matrix theory, an effective primal network
and an effective dual network are proposed to characterize and interpret the
asymptotic solution.Comment: Some typos in the version publised in the IEEE Transactions on
Wireless Communications are correcte
Optimality Properties, Distributed Strategies, and Measurement-Based Evaluation of Coordinated Multicell OFDMA Transmission
The throughput of multicell systems is inherently limited by interference and
the available communication resources. Coordinated resource allocation is the
key to efficient performance, but the demand on backhaul signaling and
computational resources grows rapidly with number of cells, terminals, and
subcarriers. To handle this, we propose a novel multicell framework with
dynamic cooperation clusters where each terminal is jointly served by a small
set of base stations. Each base station coordinates interference to neighboring
terminals only, thus limiting backhaul signalling and making the framework
scalable. This framework can describe anything from interference channels to
ideal joint multicell transmission.
The resource allocation (i.e., precoding and scheduling) is formulated as an
optimization problem (P1) with performance described by arbitrary monotonic
functions of the signal-to-interference-and-noise ratios (SINRs) and arbitrary
linear power constraints. Although (P1) is non-convex and difficult to solve
optimally, we are able to prove: 1) Optimality of single-stream beamforming; 2)
Conditions for full power usage; and 3) A precoding parametrization based on a
few parameters between zero and one. These optimality properties are used to
propose low-complexity strategies: both a centralized scheme and a distributed
version that only requires local channel knowledge and processing. We evaluate
the performance on measured multicell channels and observe that the proposed
strategies achieve close-to-optimal performance among centralized and
distributed solutions, respectively. In addition, we show that multicell
interference coordination can give substantial improvements in sum performance,
but that joint transmission is very sensitive to synchronization errors and
that some terminals can experience performance degradations.Comment: Published in IEEE Transactions on Signal Processing, 15 pages, 7
figures. This version corrects typos related to Eq. (4) and Eq. (28
Interference pricing mechanism for downlink multicell coordinated beamforming
We consider the downlink coordinated beamforming problem in a cellular network in which the base stations (BSs) are equipped with multiple antennas and each user is equipped with a single antenna. The BSs cooperate in sharing their local interference information, and they aim to maximize the sum-rate of the users in the network. A decentralized interference pricing beamforming (IPBF) algorithm is proposed to identify the coordinated beamformer, where a BS is penalized according to the interference it creates to its peers. We show that the decentralized pricing mechanism converges to an interference equilibrium, which is a KKT point of the sum-rate maximization problem. The proofs rely on the identification of rank-1 solutions of each BSs' interference-penalized rate maximization problem. Numerical results show that the proposed iterative mechanism reduces significantly the exchanged information with respect to other state-of-the-art beamforming algorithms with very little sum-rate loss. The version of the algorithm that limits the coordination to a cluster of base stations (IPBF-L) is shown to have very small sum-rate loss with respect to the full coordinated algorithm with much less backhaul information exchange.The work was partially supported by NSF grant CCF-1017982 and SICCNALS project (TEC2011-28219). The work of A. García was partially supported by NSF grant CCF-1017982. A. García-Armada’s work has been partially funded by research projects COMONSENS (CSD2008-00010) and GRE3N (TEC2011-29006-C03-02)Publicad
Energy Efficient Coordinated Beamforming for Multi-cell MISO Systems
In this paper, we investigate the optimal energy efficient coordinated
beamforming in multi-cell multiple-input single-output (MISO) systems with
multiple-antenna base stations (BS) and single-antenna mobile stations
(MS), where each BS sends information to its own intended MS with cooperatively
designed transmit beamforming. We assume single user detection at the MS by
treating the interference as noise. By taking into account a realistic power
model at the BS, we characterize the Pareto boundary of the achievable energy
efficiency (EE) region of the links, where the EE of each link is defined
as the achievable data rate at the MS divided by the total power consumption at
the BS. Since the EE of each link is non-cancave (which is a non-concave
function over an affine function), characterizing this boundary is difficult.
To meet this challenge, we relate this multi-cell MISO system to cognitive
radio (CR) MISO channels by applying the concept of interference temperature
(IT), and accordingly transform the EE boundary characterization problem into a
set of fractional concave programming problems. Then, we apply the fractional
concave programming technique to solve these fractional concave problems, and
correspondingly give a parametrization for the EE boundary in terms of IT
levels. Based on this characterization, we further present a decentralized
algorithm to implement the multi-cell coordinated beamforming, which is shown
by simulations to achieve the EE Pareto boundary.Comment: 6 pages, 2 figures, to be presented in IEEE GLOBECOM 201
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