Weighted sum rate maximization in the MIMO Interference Channel (original) (raw)
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Optimizing the noisy MIMO Interference Channel at High SNR
2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 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 study the problem of WSR maximization in the High SNR regime. Starting from the High SNR approximation of the WSR we observe that the optimization problem in High SNR becomes, in a first instance, an exploration of the (discrete) pre-log region. Once the optimal pre-log distribution if found, for a given set of weights, the WSR optimization becomes the maximization of the High SNR Rate offset. To avoid the many local optima indicated by this analysis, the use of Deterministic Annealing in 1/SNR is suggested.
On the MIMO Interference Channel
We propose an iterative algorithm to design optimal linear transmitters and receivers in a K-user frequency-flat MIMO Interference Channel (MIMO IFC) with full channel state information (CSI). The transmitters and receivers are optimized to maximize the Weighted Sum Rate (WSR) of the MIMO IFC. Maximization of WSR is desirable since it allows the system to cover all the rate tuples on the Pareto-optimal rate region boundary for a given MIMO IFC.
Weighted Sum Rate Maximization in Full-Duplex Multi-User Multi-Cell MIMO Networks
IEEE Transactions on Communications, 2017
In this paper we focus on a multiuser multi-cell scenario with full-duplex (FD) base-stations (BSs) and halfduplex (HD) downlink (DL) and uplink (UL) users, where all nodes are equipped with multiple antennas. Our goal is to design filters for weighted sum rate (WSR) maximization whilst taking into consideration the effect of transmitter and receiver distortion. Since WSR problems are non-convex we exploit the relationship between rate and mean squared error (MSE) in order to propose low complexity alternating optimization algorithms which are guaranteed to converge. While the initial design assumes perfect channel state information (CSI), we also move beyond this assumption and consider WSR problems under imperfect CSI. This is done using two types of error models; the first is a norm-bounded error model, suitable for cases where the CSI error is dominated by quantization issues, and the second is a stochastic error model, suitable for errors that occur during the channel estimation process itself. Results show that rates achieved in FD mode are higher than those achieved by the baseline HD schemes and demonstrate the robust performance of the proposed imperfect CSI designs. Additionally we also extend our original WSR problem to one which maximizes the total DL rate subject to each UL user achieving a desired target rate. This latter design can be used to overcome potential unfairness issues and ensure that all UL users are equally served in every time slot.
Sum-Rate Maximization in the Multicell MIMO Multiple-Access Channel with Interference Coordination
IEEE Transactions on Wireless Communications, 2000
This paper is concerned with the maximization of the weighted sum-rate (WSR) in the multicell MIMO multiple access channel (MAC). We consider a multicell network operating on the same frequency channel with multiple mobile stations (MS) per cell. Assuming the interference coordination mode in the multicell network, each base-station (BS) only decodes the signals for the MSs within its cell, while the inter-cell transmissions are treated as noise. Nonetheless, the uplink precoders are jointly optimized at MSs through the coordination among the cells in order to maximize the network weighted sum-rate (WSR). Since this WSR maximization problem is shown to be nonconvex, obtaining its globally optimal solution is rather computationally complex. Thus, our focus in this work is on low-complexity algorithms to obtain at least locally optimal solutions. Specifically, we propose two iterative algorithms: one is based on successive convex approximation and the other is based on iterative minimization of weighted mean squared error. Both solution approaches shall then reveal the structure of the optimal uplink precoders. In addition, we also show that the proposed algorithms can be implemented in a distributed manner across the coordinated cells. Simulation results show a significant improvement in the network sum-rate by the proposed algorithms, compared to the case with no interference coordination.
Weighted Sum Rate Maximization for Multiuser Multirelay MIMO Systems
IEEE Transactions on Vehicular Technology, 2013
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.
Greedy Algorithm for Stream Selection in a MIMO Interference Channel
Anais de XXXIII Simpósio Brasileiro de Telecomunicações
In this paper, a Greedy stream selection algorithm is proposed for a M × N K-user Multiple Input Multiple Output Interference Channel (MIMO-IC). The proposed algorithm tries to find the "best stream" allocation by removing streams with low signal-to-interference-plus-noise ratio (SINR) until the system capacity cannot be increased. The algorithm runs with the Minimum Mean Square Error Interference Alignment (MMSE-IA) algorithm and its performance is compared with the exhaustive search by Monte Carlo simulations in two different scenarios. The first scenario is refereed as symmetric attenuation scenario which no path loss is considered among the network nodes. In the second one, by its time, the signal strength is function of the distance, thus more streams are allowed to be transmitted. The results show that the Greedy algorithm outperforms, in both scenarios, any fixed solution and it achieves a good Sum Capacity performance when compared with the best stream allocation (exhaustive search), but with less computational complexity.
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.
The Scientific World Journal, 2014
We present in this work a low-complexity algorithm to solve the sum rate maximization problem in multiuser MIMO broadcast channels with downlink beamforming. Our approach decouples the user selection problem from the resource allocation problem and its main goal is to create a set of quasiorthogonal users. The proposed algorithm exploits physical metrics of the wireless channels that can be easily computed in such a way that a null space projection power can be approximated efficiently. Based on the derived metrics we present a mathematical model that describes the dynamics of the user selection process which renders the user selection problem into an integer linear program. Numerical results show that our approach is highly efficient to form groups of quasiorthogonal users when compared to previously proposed algorithms in the literature. Our user selection algorithm achieves a large portion of the optimum user selection sum rate (90%) for a moderate number of active users.