For multi-user interference channels (IC), an altruistic approach based on the zero-forcing (ZF) criterion shows the near-optimal performance at high signal-to-noise ratio (SNR), whereas its performance at low SNR becomes poor compared to a simple egoistic algorithm (selfish beamforming). Thus, balancing between the egoism and the altruism has been an important issue to achieve good sum-rate performance at overall SNR regime. In this paper, we propose a new approach for enhancing the performance by regularizing the ZF based transceivers. To this end, we start with investigating efficient ZF transceivers for 2-user and 3-user ICs. First, coordinated spatial multiplexing (CSM) is proposed for 2-user IC. For the 3-user case, it is shown that the enhanced interference alignment (E-IA) introduced in our previous work is the optimal ZF transceivers in terms of the sum-rate performance. Next, to improve the performance of the CSM and E-IA schemes at low SNR, we propose a non-iterative regularization method under the high SNR approximation. The distributed implementation of the proposed regularization method is also presented where each node is able to compute its own precoding or decoding matrix using local channel state information. From simulations, it is observed that the proposed regularized design outperforms the conventional schemes in overall SNR regime. Also, we confirm that our distributed approach provides a substantial performance gain over the conventional distributed scheme with reduced computational complexity.

In this paper, we develop linear precoding methods for cognitive radio (CR) multi-user multiple-input multiple-output (MU-MIMO) broadcast systems where unlicensed secondary users (SUs) simultaneously use the same spectrum of the licensed primary user (PU). When the zero-forcing block diagonalization (ZF-BD) precoder is extended to the CR network, a noise enhancement problem occurs. Therefore, we propose a regularized BD precoder based on the minimum mean-square error (MMSE) criteria subject to zero interference constraint for the PU. As a result, the proposed MMSE-BD scheme improves the signal-to-interference-plus-noise ratio at each SU's receiver compared to the ZF-BD based method. Simulation results demonstrate that the proposed algorithm outperforms the ZF based technique by more than 5 dB at the sum-rate 10 bps/Hz for CR MU-MIMO downlink systems.

In this paper, we investigate an interference alignment (IA) technique introduced by Cadambe and Jafar in mutually interfering broadcast channels (IFBCs). First, we study the spatial multiplexing gain (SMG) for the 3-cell IFBC where all base stations and mobile users are equipped with multiple antennas. To achieve the derived optimal SMG, we extend the IA algorithm designed for K-user multi-input multi-output (MIMO) interference channels (IFCs) to the IFBC. In this paper, we present the IA scheme in conjunction with user selection which outperforms the time division multiple access (TDMA) technique in the IFBC environment. The optimal scheduling method capitalizes on multiuser diversity to achieve a significant fraction of sum capacity by using an exhaustive search algorithm. Since the computational complexity of the optimal scheduling method is prohibitive, a reduced complexity suboptimal scheduling method is proposed based on a coordinate ascent approach. Simulation results confirm that the reduced complexity scheduling algorithm achieves the sum rate close to the optimal algorithm with much reduced complexity.

In this paper, we provide a precise expression of the spatial multiplexing gain (SMG) for two mutually interfering multiple-input multiple-output (MIMO) broadcast channels using linear transceiver, referred to as MIMO-IBC. The MIMOIBC has two base stations and K1, K2users, each equipped with multiple antennas, where independent messages are transmitted over fixed channels. We observe the variation of the SMG with respect to user antenna distribution, and compare the derived result to the SMG of the interference channel with full cooperation among users. Additionally, we propose a linear preceding and decoding scheme for the MIMO-IBC in terms of maximizing the total sum rate, by extending one designed for single-cell multiple-input single-output broadcast channels. Simulation results confirm the accuracy of our theoretical SMG analysis for the MIMO-IBC.

In this letter, we provide an expression of spatial multiplexing gain (SMG) for two mutually interfering multiple-input multiple-output (MIMO) broadcast channels, referred to as MIMO-IBC, with linear transceiver. We derive the SMG with respect to user antenna distribution, and compare the systems with and without cooperation among receive antennas in each cell. Additionally, we propose a linear precoding and decoding algorithm for the MIMO-IBC which maximizes the sum rate by extending a solution for single-cell multiple-input single-output broadcast channels. Simulation results confirm the accuracy of our theoretical SMG analysis for the MIMO-IBC.

This paper considers both one-way and two-way relaying systems with multiple relays between two terminal nodes where all nodes have multiple-input multiple-output (MIMO) antennas. We propose a unified algorithm which computes the optimal linear transceivers jointly at the source node and the relay nodes for amplify-and-forward (AF) protocols. First, optimization designs based on the sum-rate and the mean-square error (MSE) criteria are formulated for the two-way AF relaying channel. Due to non-convexity of the given problems, the proposed schemes iteratively identify local-optimal source and relay filters by deriving the gradients of the cost functions for a gradient descent algorithm. Then, the proposed algorithm can optimize a one-way multiple relay system as a special case of the two-way channel. Finally, we prove the global optimality of the maximum sum-rate scheme under an asymptotically large antenna assumption. From simulation results, it is confirmed that the proposed methods yield the near optimum result for the MIMO multiple relay channel even with a moderate number of antennas. Consequently, we show that the proposed algorithm outperforms conventional schemes in terms of the sum-rate and the error performance for both one-way and two-way protocols.

This paper studies linear precoding and decoding schemes for K-user interference channel systems. It was shown by Cadambe and Jafar that the interference alignment (IA) algorithm achieves a theoretical bound on degrees of freedom (DOF) for interference channel systems. Based on this, we first introduce a non-iterative solution for the precoding and decoding scheme. To this end, we determine the orthonormal basis vectors of each user's precoding matrix to achieve the maximum DOF, then we optimize precoding matrices in the IA method according to two different decoding schemes with respect to individual rate. Second, an iterative processing algorithm is proposed which maximizes the weighted sum rate. Deriving the gradient of the weighted sum rate and applying the gradient descent method, the proposed scheme identifies a local-optimal solution iteratively. Simulation results show that the proposed iterative algorithm outperforms other existing methods in terms of sum rate. Also, we exhibit that the proposed non-iterative method approaches a local optimal solution at high signal-to-noise ratio with reduced complexity.

In this paper, we propose a linear preceding and decoding scheme maximizing the sum rate of abuser interference channel systems where each node has multiple antennas. With an iterative approach, the preceding matrices are identified by deriving the gradient of the sum rate and applying the gradient descent method. Due to non-convexity of the formulated problem, the proposed precoder cannot guarantee the global optimal solution, and a locally maximized sum rate can be found by the proposed precoding scheme. Then, we obtain the single-symbol decodable receiver from the modified minimum mean-squared error filter. From simulation results, we exhibit a local optimal sum rate of the interference channel systems with the proposed method. Also, we demonstrate that the proposed algorithm outperforms other existing methods in terms of the sum rate.

It was shown by Cadambe and Jafar that the interference alignment (IA) algorithm achieves the theoretical bound on degrees of freedom (DOF) for interference channel systems. However, since this method addresses the precoder design problem from the DOF point of view, some optimization processes are additionally needed in order to improve the sum rate performance. In this paper, based on the IA method, we propose a two-stage optimization of the precoding and decoding matrices in the interference channels. Simulation results show that the proposed method significantly improves the sum rate of the conventional IA scheme while maintaining the optimality of the DOF.

In this paper, we consider a relay system in multiple-input multiple-output (MIMO) multi-user channels where a single MIMO relay is engaged in communication between multiple source-destination terminal pairs. We propose two amplify-and-forward (AF) relaying schemes which maximize the sum-rate for the interference channel with multiple source-destinations. First, we introduce an iterative scheme which provides a local optimal solution. The proposed scheme iteratively searches an optimum relay matrix by deriving the gradient of the sum-rate and applying the gradient descent algorithm. Next, in order to reduce the computational complexity of the iterative scheme, we propose a block diagonalization (BD) method which utilizes the minimum mean-square error (MMSE) criterion with singular value decomposition (SVD) based rate maximization. Simulation results show that the proposed iterative scheme achieves a near-optimal sum-rate for the given channel model, and the MMSE based BD with the rate maximization approaches the local optimal sum-rate at high signal-to-noise ratio (SNR) regime.

This paper considers two-way relaying systems with a multiple-input multiple-output (MIMO) relay between two MIMO terminal nodes. The two-way relaying protocol can enhance the spectral efficiency compared with the one-way protocol by compensating the loss from half-duplex signaling. In this paper, we propose an iterative scheme to find a relay weighting matrix maximizing the sum-rate for two-way relay channels. Due to the non-convexity of the given problem, the proposed scheme iteratively identifies a local optimal solution by deriving the gradient of the sum-rate and applying the gradient descent algorithm. Simulation results show that the proposed iterative scheme with provable convergence achieves a near-optimal sum- rate for the two-way MIMO relay channels. Also, we show that the proposed scheme with a few iterations still outperforms the conventional schemes.

In this paper, we develop a minimum mean-squared error (MMSE) based block diagonalization (BD) algorithm for multiuser multi-input multi-output (MIMO) broadcast systems where each user has more than one antenna in the presence of other cell interference (OCI). Unlike the conventional BD based multiuser MIMO transmission schemes which suffer from the noise enhancement problem as eliminating all multi-user interference (MUI) completely, the proposed scheme attempts to suppress the MUI with a consideration of the OCI plus noise and employs an additional residual interference suppression process based on an MMSE criterion. As a result, the proposed scheme improves the signal-to-interference-plus-noise ratio (SINR) at each user's receiver compared to conventional BD based schemes. Simulation results demonstrate that the sum rate performance of the proposed algorithm is always better than that of the conventional BD based algorithms for various OCI configurations.

Block diagonalization (BD) is a well-known precoding method in multiuser multi-input multi-output (MIMO) broadcast channels. This scheme can be considered as a extension of the zero-forcing (ZF) channel inversion to the case where each receiver is equipped with multiple antennas. One of the limitation of the BD is that the sum rate does not grow linearly with the number of users and transmit antennas at low and medium signal-to-noise ratio regime, since the complete suppression of multi-user interference is achieved at the expense of noise enhancement. Also it performs poorly under imperfect channel state information. In this paper, we propose a generalized minimum mean-squared error (MMSE) channel inversion algorithm for users with multiple antennas to overcome the drawbacks of the BD for multiuser MIMO systems. We first introduce a generalized ZF channel inversion algorithm as a new approach of the conventional BD. Applying this idea to the MMSE channel inversion for identifying orthonormal basis vectors of the precoder, and employing the MMSE criterion for finding its combining matrix, the proposed scheme increases the signal-to-interference-plus-noise ratio at each user's receiver. Simulation results confirm that the proposed scheme exhibits a linear growth of the sum rate, as opposed to the BD scheme. For block fading channels with four transmit antennas, the proposed scheme provides a 3 dB gain over the conventional BD scheme at 1% frame error rate. Also, we present a modified precoding method for systems with channel estimation errors and show that the proposed algorithm is robust to channel estimation errors.

Recently a number of transmission schemes have been introduced to achieve sum capacity for multiuser multi- input multi-output (MIMO) broadcast channels (BC). A block diagonalization (BD) is an attractive method which operates only a few dB away from the sum capacity. This scheme is a generalization of the zero-forcing channel inversion to the case where each receiver is equipped with multiple antennas. One of the limitation of the BD is that the sum rate does not grow linearly with the number of users due to the noise enhancement. In this paper, we propose a generalized minimum mean-squared error (MMSE) channel inversion algorithm for users with multiple antennas to overcome the drawbacks of the BD for multiuser MIMO systems. Simulation results confirm that the proposed scheme achieves performance improvement over the conventional BD scheme. Also, we present a preceding method for systems with channel estimation errors and show that the proposed algorithm is robust to the channel estimation errors.