Designing Precoding and Receive Matrices for Interference Alignment in MIMO Interference Channels

Interference is a key bottleneck in wireless communication systems. Interference alignment is a management technique that align interference from other transmitters in the least possibly dimension subspace at each receiver and provides the remaining dimensions for free interference signal. An uncoordinated interference is an example of interference which cannot be aligned coordinately with interference from coordinated part; consequently, the performance of interference alignment approaches are degraded. In this paper, we propose a rank minimization method to enhance the performance of interference alignment in the presence of uncoordinated interference sources. Firstly, to obtain higher multiplexing gain, a new rank minimization based optimization problem is proposed; then, a new class of convex relaxation is introduced which can reduce the optimal value of the problem and obtain lower rank solutions by expanding the feasibility set. Simulation results show that our proposed method can obtain considerably higher multiplexing gain and sum rate than other approaches in the interference alignment framework

Capacity Regions and Sum-Rate Capacities of Vector Gaussian Interference Channels

The capacity regions of vector, or multiple-input multiple-output, Gaussian interference channels are established for very strong interference and aligned strong interference. Furthermore, the sum-rate capacities are established for Z interference, noisy interference, and mixed (aligned weak/intermediate and aligned strong) interference. These results generalize known results for scalar Gaussian interference channels.Comment: 33 pages, 1 figure, submitted to IEEE trans. on Information theor

Capacity Region of Vector Gaussian Interference Channels with Generally Strong Interference

An interference channel is said to have strong interference if for all input distributions, the receivers can fully decode the interference. This definition of strong interference applies to discrete memoryless, scalar and vector Gaussian interference channels. However, there exist vector Gaussian interference channels that may not satisfy the strong interference condition but for which the capacity can still be achieved by jointly decoding the signal and the interference. This kind of interference is called generally strong interference. Sufficient conditions for a vector Gaussian interference channel to have generally strong interference are derived. The sum-rate capacity and the boundary points of the capacity region are also determined.Comment: 50 pages, 11 figures, submitted to IEEE trans. on Information Theor

Dynamic Interference Mitigation for Generalized Partially Connected Quasi-static MIMO Interference Channel

Recent works on MIMO interference channels have shown that interference alignment can significantly increase the achievable degrees of freedom (DoF) of the network. However, most of these works have assumed a fully connected interference graph. In this paper, we investigate how the partial connectivity can be exploited to enhance system performance in MIMO interference networks. We propose a novel interference mitigation scheme which introduces constraints for the signal subspaces of the precoders and decorrelators to mitigate "many" interference nulling constraints at a cost of "little" freedoms in precoder and decorrelator design so as to extend the feasibility region of the interference alignment scheme. Our analysis shows that the proposed algorithm can significantly increase system DoF in symmetric partially connected MIMO interference networks. We also compare the performance of the proposed scheme with various baselines and show via simulations that the proposed algorithms could achieve significant gain in the system performance of randomly connected interference networks.Comment: 30 pages, 10 figures, accepted by IEEE Transaction on Signal Processin