Maximizing MIMO Capacity in Sparse Multipath With Reconfigurable Antenna Arrays

Emerging advances in reconfigurable radio-frequency (RF) front-ends and antenna arrays are enabling new physical modes for accessing the radio spectrum that extend and complement the notion of waveform diversity in wireless communication systems. However, theory and methods for exploiting the potential of reconfigurable RF front-ends are not fully developed. In this paper, we study the impact of reconfigurable antenna arrays on maximizing the capacity of multiple input multiple output (MIMO) wireless communication links in sparse multipath environments. There is growing experimental evidence that physical wireless channels exhibit a sparse multipath structure, even at relatively low antenna dimensions. We propose a model for sparse multipath channels and show that sparse channels afford a new dimension over which capacity can be optimized: the distribution or configuration of the sparse statistically independent degrees of freedom (DoF) in the available spatial signal space dimensions. Our results show that the configuration of the sparse DoF has a profound impact on capacity and also characterize the optimal capacity-maximizing channel configuration at any operating SNR. We then develop a framework for realizing the optimal channel configuration at any SNR by systematically adapting the antenna spacings at the transmitter and the receiver to the level of sparsity in the physical multipath environment. Surprisingly, three canonical array configurations are sufficient for near-optimum performance over the entire SNR range. In a sparse scattering environment with randomly distributed paths, the capacity gain due to the optimal configuration is directly proportional to the number of antennas. Numerical results based on a realistic physical model are presented to illustrate the implications of our framework.