Weighted Sum-Rate Maximization using Weighted MMSE for MIMO-BC Beamforming Design (original) (raw)
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2012 International Symposium on Wireless Communication Systems (ISWCS), 2012
We consider the maximization of Weighted Sum Rate (WSR) for a Noisy Multiple-Input-Multiple-Output (MIMO) K cell Interfering Broadcast Channel (IBC) when only partial channel state information is available at the transmit side (CSIT) while assuming perfect CSI at the receiver (CSIR). In this work, CSIT is modeled by means of a Gaussian prior representing the mean and the covariance of the channel. The expected WSR is then maximized exploiting the relationship between WSR and the Weighted Mean Square Error (WMSE). This leads to an approximate solution but allows to capitalize on a convenient iterative algorithm for the optimization of the transmit beamforming (BF) matrices. The algorithm uses alternating minimization between BF, receiver (RX) filters and WMSE weights by exploiting convexity properties of each sub problem.
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2007
Abstract We study the maximization of weighted sum rate in multi-input multi-output OFDM broadcast channels based on MMSE linear beamforming. The problem at hand is a well-known non-convex problem. Nevertheless, motivated by Yu's" zero duality gap" result in a multicarrier system, we apply dual decomposition to reduce the original problem to N independent subproblems where N denotes the number of subcarriers.
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In this paper, we study a filter design that maximizes the weighted sum rate (WSR) in multiuser multirelay systems equipped with multiple antennas at each node. Since this problem is generally nonconvex, it is quite complicated to analytically find a solution. Hence, we transform the WSR maximization problem to an equivalent weighted sum meansquare-error (WSMSE) minimization problem, which is more amenable. Then, we identify the filters at the base station and the relays for minimizing the WSMSE with a proper weight and propose an alternating computation algorithm that guarantees a local optimum solution. Through simulations, we confirm the effectiveness of our proposed scheme.
Weighted sum rate maximization in the MIMO Interference Channel
21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, 2010
Centralized algorithms for weighted sum rate (WSR) maximization for the K-user frequency-flat MIMO Interference Channel (MIMO IFC) with full channel state information (CSI) are considered. Maximization of WSR is desirable since it allows the system to cover all the rate tuples on the rate region boundary for a given MIMO IFC. First, we propose an iterative algorithm to design optimal linear transmitters and receivers. The transmitters and receivers are optimized to maximize the WSR of the MIMO IFC. Subsequently, we propose a greedy user selection algorithm based on the maximum WSR algorithm that can be applied to select a subset of transmit-receive pairs that cooperate in the interest of maximizing the sum-rate of the resulting cooperative network. To the best of our knowledge this is the first time user selection has been proposed in the context of the MIMO IFC.
2016
The weighted sum rate (WSR) maximizing linear precoding algorithm is studied in large correlated single stream multiple-input multiple-output (MIMO) interference broadcast channels (IBC).We consider an iterative WSR design which exploits the connection with Weighted sum Minimum Mean Squared Error (WMMSE) designs as in [1], [2], focusing on the version in [1]. We propose an large system approximation of the signal-to-interference plus noise ratio (SINR) at every iteration. The large system approximation of the SINR depends only on the slow fading terms or second order statistics of the channels. In this work, the large system approximation is used to establish a property of the Multi-users single stream MIMO communications. Simulations show that the approximations are accurate.
IEEE Transactions on Signal Processing, 2012
This paper considers the joint linear transceiver design problem for the downlink multiuser multiple-input-multiple-output (MIMO) systems with coordinated base stations (BSs). We consider maximization of the weighted sum rate with per BS antenna power constraint problem. We propose novel centralized and computationally efficient distributed iterative algorithms that achieve local optimum to the latter problem. These algorithms are described as follows. First, by introducing additional optimization variables, we reformulate the original problem into a new problem. Second, for the given precoder matrices of all users, the optimal receivers are computed using minimum mean-square-error (MMSE) method and the optimal introduced variables are obtained in closed form expressions. Third, by keeping the introduced variables and receivers constant, the precoder matrices of all users are optimized by using second-order-cone programming (SOCP) and matrix fractional minimization approaches for the centralized and distributed algorithms, respectively. Finally, the second and third steps are repeated until these algorithms converge. We have shown that the proposed algorithms are guaranteed to converge. We also show that the proposed algorithms require less computational cost than that of the existing linear algorithm. All simulation results demonstrate that our distributed algorithm achieves the same performance as that of the centralized algorithm. Moreover, the proposed algorithms outperform the existing linear algorithm. In particular, when each of the users has single antenna, we have observed that the proposed algorithms achieve the global optimum.
Weighted sum rate maximization for the MIMO X channel through MMSE precoding
2011 IEEE International Symposium on Information Theory Proceedings, 2011
Recent results elucidate the optimality of interference alignment concept for attaining the degrees of freedom of the MIMO X channel. This criterion is useful in the high SNR regime, but in the low-medium SNR regime, optimizing the weighted sum-rate is more meaningful. Moreover, MIMO X channel subsumes the MIMO interference channel, MIMO multiple access, dual MIMO point-to-point and MIMO broadcast channel. In this respect it is desirable to have an algorithm to design the proper linear transmitters and receivers in all cases. In this work we have observed that an algorithm based on alternate optimization along with the proper initialization is able to provide MMSE precoders attaining significant gains in terms of SNR offset with respect the conventional interference alignment solution.
Sum-Rate Maximization in the Multicell MIMO Broadcast Channel With Interference Coordination
IEEE Transactions on Signal Processing, 2014
This paper studies the precoding designs to maximize the weighted sum-rate (WSR) in a multicell multiple-input multiple-output (MIMO) broadcast channel (BC). We consider a multicell network under universal frequency reuse with multiple mobile stations (MS) per cell. With interference coordination (IC) between the multiple cells, the base-station (BS) at each cell only transmits information signals to the MSs within its cell using the dirty paper coding (DPC) technique, while coordinating the intercell interference (ICI) induced to other cells. The main focus of this work is to jointly optimize the encoding covariance matrices across the BSs in order to maximize the network-wide WSR. Since this optimization problem is shown to be nonconvex, obtaining its globally optimal solution is highly complicated. To address this problem, we consider two low-complexity solution approaches with distributed implementation to obtain at least locally optimal solutions. In the first approach, by applying a successive convex approximation technique, the original nonconvex problem is decomposed into a sequence of simpler problems, which can be solved optimally and separately at each BS. In the second approach, the WSR problem is solved via an equivalent problem of weighted sum mean squared error minimization. Both solution approaches will unfold the control signaling among the coordinated BSs to allow their distributed implementation. Simulation results confirm the convergence of the proposed algorithms, as well as their superior performances over schemes with linear precoding or no interference coordination among the BSs.