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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 136 (14 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.
Convex conic formulations of robust downlink precoder designs with quality of service constraints
 IEEE J. Select. Topics Signal Processing
, 2007
"... We consider the design of linear precoders (beamformers) for broadcast channels with Quality of Service (QoS) constraints for each user, in scenarios with uncertain channel state information (CSI) at the transmitter. We consider a deterministicallybounded model for the channel uncertainty of each u ..."
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Cited by 35 (2 self)
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We consider the design of linear precoders (beamformers) for broadcast channels with Quality of Service (QoS) constraints for each user, in scenarios with uncertain channel state information (CSI) at the transmitter. We consider a deterministicallybounded model for the channel uncertainty of each user, and our goal is to design a robust precoder that minimizes the total transmission power required to satisfy the users ’ QoS constraints for all channels within a specified uncertainty region around the transmitter’s estimate of each user’s channel. Since this problem is not known to be computationally tractable, we will derive three conservative design approaches that yield convex and computationallyefficient restrictions of the original design problem. The three approaches yield semidefinite program (SDP) formulations that offer different tradeoffs between the degree of conservatism and the size of the SDP. We will also show how these conservative approaches can be used to derive efficientlysolvable quasiconvex restrictions of some related design problems, including the robust counterpart to the problem of maximizing the minimum signaltointerferenceplusnoiseratio (SINR) subject to a given power constraint. Our simulation results indicate that in the presence of uncertain CSI the proposed approaches can satisfy the users ’ QoS requirements for a significantly larger set of uncertainties than existing methods, and require less transmission power to do so.
TomlinsonHarashima Precoding for Broadcast Channels with Uncertainty
"... Abstract — We consider the design of TomlinsonHarashima (TH) precoders for broadcast channels in the presence of channel uncertainty. For systems in which uplinkdownlink reciprocity is used to obtain a channel estimate at the transmitter, we present a robust design based on a statistical model for ..."
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Cited by 10 (1 self)
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Abstract — We consider the design of TomlinsonHarashima (TH) precoders for broadcast channels in the presence of channel uncertainty. For systems in which uplinkdownlink reciprocity is used to obtain a channel estimate at the transmitter, we present a robust design based on a statistical model for the channel uncertainty. We provide a convex formulation of the design problem subject to two types of power constraints: a set of constraints on the power transmitted from each antenna and a total power constraint. For the case of the total power constraint, we present a closedform solution for the robust TH precoder that incurs essentially the same computational cost as the corresponding designs that assume perfect channel knowledge. For systems in which the receivers feed back quantized channel state information to the transmitter, we present a robust design based on a bounded model for the channel uncertainty. We provide a convex formulation for the TH precoder that maximizes the performance under the worstcase channel uncertainty subject to both types of power constraints. We also present a conservative robust design for this type of channel uncertainty that has reduced computational complexity for the case of power constraints on individual antennas and leads to a closedform solution for the total power constraint case. Simulation studies verify our analytical results and show that the robust TH precoders can significantly reduce the rather high sensitivity of broadcast transmissions to errors in channel state information. Index Terms — TomlinsonHarashima precoding, broadcast channel, channel uncertainty, robust precoding.
Conjugate gradient projection approach for MIMO Gaussian broadcast channels
 in Proc. IEEE International Symposium on Information Theory (ISIT
, 2007
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 9 (4 self)
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Crosslayer optimization for MIMObased wireless ad hoc netoworks: Routing, power allocation, and bandwidth allocation
 IEEE Journal on Selected Areas in Communications
, 2008
"... Abstract—MIMObased communications systems have great potential to improve network capacity for wireless ad hoc networks. Due to unique physical layer characteristics associated with MIMO, network performance is tightly coupled with mechanisms at physical, link, and routing layers. So far, research ..."
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Cited by 6 (0 self)
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Abstract—MIMObased communications systems have great potential to improve network capacity for wireless ad hoc networks. Due to unique physical layer characteristics associated with MIMO, network performance is tightly coupled with mechanisms at physical, link, and routing layers. So far, research on MIMObased wireless ad hoc networks is still in its infancy and few results are available. In this paper, we consider the problem of jointly optimizing power and bandwidth allocation at each node and multihop/multipath routing in a MIMObased wireless ad hoc network. We develop a solution procedure to this crosslayer optimization problem and use simulations to validate the efficacy of this solution. Index Terms—Multipleinput multipleoutput (MIMO), multihop ad hoc network, crosslayer optimization. I.
t III t to of H I of t t REFER
 N ES AND NOTES PAGES 66TO 80 121 See letters 01' Oort to Danjon anel Funke 01' May 30, 1962, in EHA1. C. I.I.e. 131 In EHA1. . I.I.eI. 141 In EIlAI. .2.l.g
"... Pathophysiology of REM sleep behaviour disorder and ..."
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Pathophysiology of REM sleep behaviour disorder and
Maximum weighted sum rate of multiantenna broadcast channels,” 2007
 Online]. Available: http://arXiv:cs/0703111v1. April
"... Abstract — Recently, researchers showed that dirty paper coding (DPC) is the optimal transmission strategy for multipleinput multipleoutput broadcast channels (MIMOBC). In this paper, we study how to determine the maximum weighted sum of DPC rates through solving the maximum weighted sum rate pro ..."
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Abstract — Recently, researchers showed that dirty paper coding (DPC) is the optimal transmission strategy for multipleinput multipleoutput broadcast channels (MIMOBC). In this paper, we study how to determine the maximum weighted sum of DPC rates through solving the maximum weighted sum rate problem of the dual MIMO multiple access channel (MIMOMAC) with a sum power constraint. We first simplify the maximum weighted sum rate problem such that enumerating all possible decoding orders in the dual MIMOMAC is unnecessary. We then design an efficient algorithm based on conjugate gradient projection (CGP) to solve the maximum weighted sum rate problem. Our proposed CGP method utilizes 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 MIMOBC systems. I.
Sum rate maximization of MIMO broadcast channels with coordination of base stations
 in Proc. IEEE Wireless Commun. Net. Conf. (WCNC
, 2008
"... AbstractWe consider cooperative downlink transmission in multiuser, multicell and multipleantenna cellular networks. Recently, it has been shown that multibase coordinated transmission has significant spectral efficiency gains over that without coordination. The capacity limits can be achieved ..."
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Cited by 2 (1 self)
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AbstractWe consider cooperative downlink transmission in multiuser, multicell and multipleantenna cellular networks. Recently, it has been shown that multibase coordinated transmission has significant spectral efficiency gains over that without coordination. The capacity limits can be achieved using a nonlinear precoding technique known as dirty paper coding, which is still infeasible to implement in practice. This motivates investigation of a simpler linear precoding technique based on generalized zeroforcing known as block diagonalization (BD). In this paper, an enhanced form of BD is proposed for multipleinput multipleoutput (MIMO) multibase coordinated network. It involves optimizing the precoding over the entire null space of other users' transmissions. The performance limits of the multipleantenna downlink with multibase coordination are studied using duality of MIMO broadcast channels (BC) and MIMO multipleaccess channels (MAC) under perantenna power constraint, which has been established recently.
On the maximum weighted sumrate of MIMO Gaussian broadcast channels
 in Proc. IEEE ICC
, 2008
"... Abstract—In this paper, we investigate the maximum weighted sumrate problem (MWSR) of MIMO Gaussian broadcast channels (MIMOBC). We propose an efficient algorithm that employs conjugate gradient projections (CGP) to solve the MWSR problem. The proposed CGP offers provable convergence. By deflect ..."
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Abstract—In this paper, we investigate the maximum weighted sumrate problem (MWSR) of MIMO Gaussian broadcast channels (MIMOBC). We propose an efficient algorithm that employs conjugate gradient projections (CGP) to solve the MWSR problem. The proposed CGP offers provable convergence. By deflecting gradient direction to its Hessian conjugate, CGP enjoys a superlinear convergence rate. Also, CGP has a modest memory requirement. It only needs the solution information from the previous step. More importantly, CGP is able to solve the MWSR problem with arbitrary number of antennas on both sides of a MIMOBC. I.