Constrained by quality-of-service (QoS), a robust transceiver design is proposed for multiple-input multiple-output (MIMO) interference channels with imperfect channel state information (CSI) under bounded error model. The QoS measurement is represented as the signal-to-interference-plus-noise ratio (SINR) for each user with single data stream. The problem is formulated as sum power minimization to reduce the total power consumption for energy efficiency. In a centralized manner, alternating optimization is performed at each node. For fixed transmitters, closed-form expression for the receive beamforming vectors is deduced. And for fixed receivers, the sum-power minimization problem is recast as a semi-definite program form with linear matrix inequalities constraints. Simulation results demonstrate the convergence and robustness of the proposed algorithm, which is important for practical applications in future wireless networks.

This paper analyzes the performance of various decoders in a two-user interference channel, and some improved decoders based on enhanced utilization of channel state information at the receiver side are presented. Further, new decoders, namely hierarchical constellation based decoders, are proposed. Simulations show that the improved decoders and the proposed decoders have much better performance than existing decoders. Moreover, the proposed decoders have lower decoding complexity than the traditional maximum likelihood decoder.

This letter considers the robust Tomlinson-Harashima Precoding(THP) transceiver design for Multiple-Input Multiple-Output (MIMO) interference channel (IC). Assuming bounded channel state information (CSI) error, we deal with the optimization for minimizing the worst case per-user mean square error (MSE) and sum MSE. We present an approximate approach to derive the upper bound of the constraint leading to less semidefinite. Then the alternate approach is adopted to update the receiver matrix by solving second-order-cone programming (SOCP), and update the transmitter matrix and feedback matrix by solving semidefinite program (SDP), respectively. Simulation results show that the proposed method achieves similar performance of the S-procedure method, whereas the computation complexity is reduced significantly, especially for the system with large number of transmit antennas.

This letter considers the robust transceiver design for multiple-input multiple-output interference channels under channel state information mismatch. According to alternating schemes, an adaptive algorithm is proposed to solve the minimum SINR maximization problem. Simulation results show the convergence and the effectiveness of the proposed algorithm.

In heterogenous networks (HetNets), the deployment of small cells with the reuse of limited frequency resources to improve the spectral efficiency results in cross- and co-tier interference. In addition, the excessive power usage in such networks is also a critical problem. In this paper, we propose precoding and postcoding schemes to tackle interference and energy efficiency (EE) challenges in the two-tier downlink multiple-input-multiple-output (MIMO) HetNets. We propose transmission strategies based on hierarchical partial coordination (HPC) of the macro cell and small cells to reduce channel state information (CSI) exchange and guarantee the quality of service (QoS) in the upper tier with any change of network deployment in the lower tier. We employ the interference alignment (IA) scheme to cancel cross- and co-tier interference. Additionally, to maximize the EE, power allocation schemes in each tier are proposed based on a combination of Dinkelbach's method and the bisection searching approach. To investigate insights on the optimization problem, a theoretical analysis on the relationship between the maximum achievable EE and the transmit power is derived. Simulation results prove the superior EE performance of the proposed EE maximization scheme over the sum rate maximization approach and confirm the validity of our theoretical findings.

In this paper, we investigate the problem of maximizing the weighted sum outage rate in multiuser multiple-input single-output (MISO) interference channels, where the transmitters have no knowledge of the exact values of channel coefficients, only the statistical information. Unfortunately, this problem is nonconvex and very difficult to deal with. We propose a new, provably convergent iterative algorithm where in each iteration, the original problem is approximated as second-order cone programming (SOCP) by introducing slack variables and using convex approximation. Simulation results show that the proposed SOCP algorithm converges in a few steps, and yields a better performance gain with a lower computational complexity than existing algorithms.

Considering worse-case channel uncertainties, we investigate the robust energy efficient (EE) beamforming design problem in a K-user multiple-input-single-output (MISO) interference channel. Our objective is to maximize the worse-case sum EE under individual transmit power constraints. In general, this fractional programming problem is NP-hard for the optimal solution. To obtain an insight into the problem, we first transform the original problem into its lower bound problem with max-min and fractional form by exploiting the relationship between the user rate and the minimum mean square error (MMSE) and using the min-max inequality. To make it tractable, we transform the problem of fractional form into a subtractive form by using the Dinkelbach transformation, and then propose an iterative algorithm using Lagrangian duality, which leads to the locally optimal solution. Simulation results demonstrate that our proposed robust EE beamforming scheme outperforms the conventional algorithm.

In this paper, we propose four different strategies of node pair selection in multiple input multiple output (MIMO) interference channel where interference alignment (IA) is considered as a transceiver design method. In the first scheme, we consider the maximization of the sum rate by selecting node pairs in a brute force way. We also propose a sub-optimal sum rate maximization scheme with lower complexity than the first scheme. In the third scheme, we aim to minimize the number of links among pairs which incurs the outage in MIMO interference channel. In the fourth scheme, we suggest a max-min node pair selection scheme to enhance both the sum rate and the outage probability. Simulation results demonstrate that all our proposed node pair selection schemes can increase the sum rate but also while also reducing the outage probability compared to the scheme with random node pair selection.

In this letter, we propose a non-cooperative limited feedback precoding and subchannel selection scheme for non-reciprocal multiple-input multiple-output (MIMO) interference channels. At each iteration of the proposed scheme, each user updates its precoder selection for each subchannel and then chooses the predetermined number of subchannels in a distributed and non-cooperative way. We present simulation results to verify the performance of the proposed scheme.

All points on the Pareto boundary can be obtained by solving the weighted sum rate maximization problem for some weighted coefficients. Unfortunately, the problem is non-convex and difficult to solve without performing an exhaustive search. In this paper, we propose an optimal distributed beamforming strategy for the two-user multiple-input single-output (MISO) interference channel (IC). Through minimizing the interference signal power leaked to the other receiver for fixed useful signal power received at the intended receiver, the original non-convex optimization problem can be converted into a family of convex optimization problems, each which can be solved in distributed manner with only local channel state information at each transmitter. After some conversion, we derive the closed-form solutions to all Pareto optimal points based on a game-theoretic viewpoint which indicates that linear combinations of the maximum-ratio transmit (MRT) and zero-forcing (ZF) beamforming strategies can achieve any point on the Pareto boundary of the rate region for the two-user MISO interference channel, and the only computation involved is to solve a basic quadratic equation. Finally, the result is validated via numerical simulations.

The transmission of correlated messages over strong interference channels is examined. The result is the proposal of a single-letter characterization of the sum-rate capacity of strong interference channels with correlated messages. It is shown that if the messages are independent, the sum-rate capacity is equal to that of [1] obtained by Costa and El Gamal. However, it can be larger than that of [1] if the messages are correlated. It is also shown that, in terms of the sum-rate, the achievable rate region in [2] is indeed the sum-rate capacity.

Transmission of correlated messages over interference channels with strong interference is considered. As a result, an achievable rate region is presented. It is shown that if the messages are correlated, the achievable rate region can be larger than the capacity region given by Costa and El Gamal. As an example, the Gaussian interference channel is considered.