Results 1  10
of
32
Communication over mimo x channels: Interference alignment, decomposition, and performance analysis
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2008
"... In a multipleantenna system with two transmitters and two receivers, a scenario of data communication, known as the X channel, is studied in which each receiver receives data from both transmitters. In this scenario, it is assumed that each transmitter is unaware of the other transmitter’s data (n ..."
Abstract

Cited by 201 (12 self)
 Add to MetaCart
(Show Context)
In a multipleantenna system with two transmitters and two receivers, a scenario of data communication, known as the X channel, is studied in which each receiver receives data from both transmitters. In this scenario, it is assumed that each transmitter is unaware of the other transmitter’s data (noncooperative scenario). This system can be considered as a combination of two broadcast channels (from the transmitters ’ points of view) and two multipleaccess channels (from the receivers ’ points of view). Taking advantage of both perspectives, two signaling schemes for such a scenario are developed. In these schemes, some linear filters are employed at the transmitters and at the receivers which decompose the system into either two noninterfering multipleantenna broadcast subchannels or two noninterfering multipleantenna multipleaccess subchannels. The main objective in the design of the filters is to exploit the structure of the channel matrices to achieve the
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 ..."
Abstract

Cited by 131 (15 self)
 Add to MetaCart
(Show Context)
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.
Exploiting multiantennas for opportunistic spectrum sharing in cognitive radio networks
 IEEE J. Select. Topics in Signal Processing
, 2008
"... ..."
(Show Context)
Optimal Multiuser Spectrum Management for Digital Subscriber Lines,” Submitted to IEEE Trans. Commun., available at www.geocities.com/raphael_cendrillon
, 2003
"... Abstract — Crosstalk is a major issue in modern DSL systems such as ADSL and VDSL. Static spectrum management, the traditional way of ensuring spectral compatibility, employs spectral masks which can be overly conservative and lead to poor performance. In this paper we present a centralized algorith ..."
Abstract

Cited by 57 (20 self)
 Add to MetaCart
(Show Context)
Abstract — Crosstalk is a major issue in modern DSL systems such as ADSL and VDSL. Static spectrum management, the traditional way of ensuring spectral compatibility, employs spectral masks which can be overly conservative and lead to poor performance. In this paper we present a centralized algorithm for optimal spectrum management (OSM) in DSL. The algorithm uses a dual decomposition to solve the spectrum management problem in an efficient and computationally tractable way. The algorithm shows significant performance gains over existing DSM techniques, e.g. in a downstream ADSL scenario the centralized OSM algorithm can outperform a distributed DSM algorithm such as iterative waterfilling by up to 135% I.
Distributed Compression for MIMO Coordinated Networks with a Backhaul Constraint
"... Abstract—We consider the uplink of a backhaulconstrained, MIMO coordinated network. That is, a singlefrequency network with ..."
Abstract

Cited by 17 (1 self)
 Add to MetaCart
(Show Context)
Abstract—We consider the uplink of a backhaulconstrained, MIMO coordinated network. That is, a singlefrequency network with
An Efficient Signaling Scheme for MIMO Broadcast Systems: Design and Performance Evaluation
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2005
"... A simple signaling method for broadcast channels with multiple transmit multiple receive antennas is proposed. In this method, for each user, the direction in which the user has the maximum gain is determined. The best user in terms of the largest gain is selected. The corresponding ..."
Abstract

Cited by 15 (9 self)
 Add to MetaCart
A simple signaling method for broadcast channels with multiple transmit multiple receive antennas is proposed. In this method, for each user, the direction in which the user has the maximum gain is determined. The best user in terms of the largest gain is selected. The corresponding
Optimization of the MIMO compound capacity
 IEEE Transactions on Wireless Communications
, 2007
"... Abstract — In this paper, we consider the optimization of the compound capacity in a rank one Ricean multiple input multiple output channel using partial channel state information at the transmitter side. We model the channel as a deterministic matrix within a known ellipsoid, and address the compou ..."
Abstract

Cited by 13 (1 self)
 Add to MetaCart
(Show Context)
Abstract — In this paper, we consider the optimization of the compound capacity in a rank one Ricean multiple input multiple output channel using partial channel state information at the transmitter side. We model the channel as a deterministic matrix within a known ellipsoid, and address the compound capacity defined as the maximum worst case mutual information in the set. We find that the optimal transmit strategy is always beamforming, and can be found using a simple one dimensional search. Similar results are derived for the worst case sumrate of a multiple access channel with individual power constraints and a total power constraint. In this multiuser setting we assume equal array response at the receiver for all users. These results motivate the growing use of systems using simple beamforming transmit strategies. Index Terms — MIMO, compound capacity, beamforming. 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 ..."
Abstract

Cited by 10 (0 self)
 Add to MetaCart
(Show Context)
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.
Conjugate Gradient Projection Approach for MultiAntenna Gaussian Broadcast Channels
"... It has been shown recently that the dirtypaper coding is the optimal strategy for maximizing the sum rate of multipleinput multipleoutput Gaussian broadcast channels (MIMO BC). Moreover, by the channel duality, the nonconvex MIMO BC sum rate problem can be transformed to the convex dual MIMO mult ..."
Abstract

Cited by 9 (4 self)
 Add to MetaCart
(Show Context)
It has been shown recently that the dirtypaper coding is the optimal strategy for maximizing the sum rate of multipleinput multipleoutput Gaussian broadcast channels (MIMO BC). Moreover, by the channel duality, the nonconvex MIMO BC sum rate problem can be transformed to the convex dual MIMO multipleaccess channel (MIMO MAC) problem with a sum power constraint. In this paper, we design an efficient algorithm based on conjugate gradient projection (CGP) to solve the MIMO BC maximum sum rate problem. Our proposed CGP algorithm solves the dual sum power MAC problem by utilizing the powerful concept of Hessian conjugacy. We also develop a rigorous algorithm to solve the projection problem. We show that CGP enjoys provable convergence, nice scalability, and great efficiency for large MIMO BC systems. 1
Broadcast in MIMO systems based on a generalized QR decomposition: Signaling and Performance Analysis
 IEEE Trans. Inform. Theory
, 2008
"... Abstract—A simple signaling method for broadcast channels with multipletransmit multiplereceive antennas is proposed. In this method, for each user, the direction in which the user has the maximum gain is determined. The best user in terms of the largest gain is selected. The corresponding directi ..."
Abstract

Cited by 8 (0 self)
 Add to MetaCart
(Show Context)
Abstract—A simple signaling method for broadcast channels with multipletransmit multiplereceive antennas is proposed. In this method, for each user, the direction in which the user has the maximum gain is determined. The best user in terms of the largest gain is selected. The corresponding direction is used as the modulation vector (MV) for the data stream transmitted to the selected user. The algorithm proceeds in a recursive manner where in each step, the search for the best direction is performed in the null space of the previously selected MVs. It is demonstrated that with the proposed method, each selected MV has no interference on the previously selected MVs. Dirtypaper coding is used to cancel the remaining interference. For the case that each receiver has one antenna, the presented scheme coincides with the known scheme based on Gram–Schmidt orthogonalization (QR decomposition). To analyze the performance of the scheme, an upper bound on the cumulative distribution function (CDF) of each subchannel is derived which is used to establish the diversity order and the asymptotic sum–rate of the scheme. It is shown that using fixed rate codebooks, the diversity order of the jth data stream, 1 j M, is equal to N (M 0 j +1)(K 0 j +1), where M, N, and K indicate the number of transmit antennas, the number of receive antennas, and the number of users, respectively. Furthermore, it is proven that the throughput of this scheme scales as M log log(K) and asymptotically (K 0! 1) tends to the sum–capacity of the multipleinput multipleoutput (MIMO) broadcast channel. The simulation results indicate that the achieved sum–rate is close to the sum–capacity of the underlying broadcast channel. Index Terms—Dirtypaper coding, multipleantenna arrays, multipleinput multipleoutput (MIMO) broadcast channels, multiuser diversity, multiuser systems, QR decomposition, spacedivisionmultiple access.