  This paper considers the so-called multiple-input-multiple-output
interference channel (MIMO-IC) which has relevance in applications such as
multi-cell coordination in cellular networks as well as spectrum sharing in
cognitive radio networks among others. We consider a beamforming design
framework based on striking a compromise between beamforming gain at the
intended receiver (Egoism) and the mitigation of interference created towards
other receivers (Altruism). Combining egoistic and altruistic beamforming has
been shown previously in several papers to be instrumental to optimizing the
rates in a multiple-input-single-output interference channel MISO-IC (i.e.
where receivers have no interference canceling capability). Here, by using the
framework of Bayesian games, we shed more light on these game-theoretic
concepts in the more general context of MIMO channels and more particularly
when coordinating parties only have channel state information (CSI) of channels
that they can measure directly. This allows us to derive distributed
beamforming techniques. We draw parallels with existing work on the MIMO-IC,
including rate-optimizing and interference-alignment precoding techniques,
showing how such techniques may be improved or re-interpreted through a common
prism based on balancing egoistic and altruistic beamforming. Our analysis and
simulations currently limited to single stream transmission per user attest the
improvements over known interference alignment based methods in terms of sum
rate performance in the case of so-called asymmetric networks.
