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11
Sum power iterative waterfilling for multiantenna Gaussian broadcast channels
 IEEE Trans. Inform. Theory
, 2005
"... In this paper we consider the problem of maximizing sum rate of a multipleantenna Gaussian broadcast channel. It was recently found that dirty paper coding is capacity achieving for this channel. In order to achieve capacity, the optimal transmission policy (i.e. the optimal transmit covariance str ..."
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Cited by 84 (17 self)
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In this paper we consider the problem of maximizing sum rate of a multipleantenna Gaussian broadcast channel. It was recently found that dirty paper coding is capacity achieving for this channel. In order to achieve capacity, the optimal transmission policy (i.e. the optimal transmit covariance structure) given the channel conditions and power constraint must be found. However, obtaining the optimal transmission policy when employing dirty paper coding is a computationally complex nonconvex problem. We use duality to transform this problem into a wellstructured convex multipleaccess channel problem. We exploit the structure of this problem and derive simple and fast iterative algorithms that provide the optimum transmission policies for the multipleaccess channel, which can easily be mapped to the optimal broadcast channel policies.
On downlink beamforming with greedy user selection: performance analysis and a simple new algorithm
 IEEE Trans. Signal Processing
, 2005
"... Abstract—This paper considers the problem of simultaneous multiuser downlink beamforming. The idea is to employ a transmit antenna array to create multiple “beams ” directed toward the individual users, and the aim is to increase throughput, measured by sum capacity. In particular, we are interested ..."
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Cited by 44 (1 self)
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Abstract—This paper considers the problem of simultaneous multiuser downlink beamforming. The idea is to employ a transmit antenna array to create multiple “beams ” directed toward the individual users, and the aim is to increase throughput, measured by sum capacity. In particular, we are interested in the practically important case of more users than transmit antennas, which requires user selection. Optimal solutions to this problem can be prohibitively complex for online implementation at the base station and entail socalled Dirty Paper (DP) precoding for known interference. Suboptimal solutions capitalize on multiuser (selection) diversity to achieve a significant fraction of sum capacity at lower complexity cost. We analyze the throughput performance in Rayleigh fading of a suboptimal greedy DPbased scheme proposed by Tu and Blum. We also propose another userselection method of the same computational complexity based on simple zeroforcing beamforming. Our results indicate that the proposed method attains a significant fraction of sum capacity and throughput of Tu and Blum’s scheme and, thus, offers an attractive alternative to DPbased schemes. Index Terms—Beamforming, downlink, multiuser diversity. I.
Input optimization for multiantenna broadcast channels and perantenna power constraints
 in Proc. IEEE GLOBECOM
, 2004
"... Abstract — This paper considers a Gaussian multiantenna broadcast channel with individual power constraints on each antenna, rather than the usual sum power constraint over all antennas. Perantenna power constraints are more realistic because in practical implementations each antenna has its own p ..."
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Cited by 7 (0 self)
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Abstract — This paper considers a Gaussian multiantenna broadcast channel with individual power constraints on each antenna, rather than the usual sum power constraint over all antennas. Perantenna power constraints are more realistic because in practical implementations each antenna has its own power amplifier. The main contribution of this paper is a new derivation of the duality result for this class of broadcast channels that allows the input optimization problem to be solved efficiently. Specifically, we show that uplinkdownlink duality is equivalent to Lagrangian duality in minimax optimization, and the dual multipleaccess problem has a much lower computational complexity than the original problem. This duality applies to the entire capacity region. Further, we derive a novel application of Newton’s method for the dual minimax problem that finds an optimal search direction for both the minimization and the maximization problems at the same time. This new computational method is much more efficient than the previous iterative waterfillingbased algorithms and it is applicable to the entire capacity region. Finally, we show that the previous QRbased precoding method can be easily modified to accommodate the perantenna constraint. I.
Experimental evaluation of capacity statistics for short VDSL loops
 IEEE SPAWC 2005
, 2005
"... ..."
Subchannel allocation in multiuser multiple input multiple output systems
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2006
"... Assuming perfect channel state information at the transmitter of a Gaussian broadcast channel, strategies are investigated on how to assign subchannels in frequency and space domain to each receiver aiming at a maximization of the sum rate transmitted over the channel. For the general sum capacity m ..."
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Cited by 1 (1 self)
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Assuming perfect channel state information at the transmitter of a Gaussian broadcast channel, strategies are investigated on how to assign subchannels in frequency and space domain to each receiver aiming at a maximization of the sum rate transmitted over the channel. For the general sum capacity maximizing solution, which has recently been found, a method is proposed that transforms each of the resulting vector channels into a set of scalar channels. This makes possible to achieve capacity by simply using scalar coding and detection techniques. The high complexity involved in the computation of this optimum solution motivates the introduction of a novel suboptimum zeroforcing allocation strategy that directly results in a set of virtually decoupled scalar channels. Simulation results show that this technique tightly approaches the performance of the optimum solution, i.e. complexity reduction comes at almost no cost in terms of sum capacity. As the optimum solution, the zeroforcing allocation strategy applies to any number of transmit antennas, receive antennas and users.
REFERENCES
, 2001
"... ����I. Suppose that there exists a vector �� � that meets Conditions 1) and 2) of Theorem 5. It is clear that this vector �� � is dual feasible, and furthermore ‚�����Y ��� � a‚��8 ˜ H ���Y��� � a‚�� ˜ H ..."
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Cited by 1 (0 self)
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����I. Suppose that there exists a vector �� � that meets Conditions 1) and 2) of Theorem 5. It is clear that this vector �� � is dual feasible, and furthermore ‚�����Y ��� � a‚��8 ˜ H ���Y��� � a‚�� ˜ H
Input Multiple Output (MIMO) Cognitive Radio Channel
, 802
"... Abstract—In this paper, the sum capacity of the Gaussian Multiple ..."
MULTIUSER MULTIPLE ANTENNA SYSTEMS: THEORETICAL LIMITS AND PRACTICAL STRATEGIES
, 2003
"... ii ..."
DIMACS Series in Discrete Mathematics and Theoretical Computer Science The Structure of LeastFavorable Noise in Gaussian Vector Broadcast Channels
"... Abstract. The sum capacity of the Gaussian vector broadcast channel is the saddle point of a Gaussian mutual information game in which the transmitter maximizes the mutual information by choosing the best transmit covariance matrix subject to a power constraint, and the receiver minimizes the mutual ..."
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Abstract. The sum capacity of the Gaussian vector broadcast channel is the saddle point of a Gaussian mutual information game in which the transmitter maximizes the mutual information by choosing the best transmit covariance matrix subject to a power constraint, and the receiver minimizes the mutual information by choosing a leastfavorable noise covariance matrix subject to a diagonal constraint. This result has been established using a decisionfeedback equalization approach under the assumption that the leastfavorable noise covariance matrix is nonsingular. This paper generalizes the above result to the case where the leastfavorable noise is singular. In particular, it is shown that the leastfavorable noise is not unique, and different leastfavorable noise covariance matrices are related to each other by a linear estimation relation. 1.