Iterative power allocation for throughput maximisation in IA‐based cellular networks: two‐game approach

As a promising interference management technique, interference alignment (IA) was proposed for improving system capacity and spectral efficiency by precoding and filtering design. However, the previous works assumed the equal power allocation among data streams in IA‐based networks, and the sum‐rate may fall short of the theoretical maximum without the energy‐efficient optimisation. In this study, a novel approach (two‐game theoretic) is presented, to solve the rate maximisation problem in the IA‐based uplink multiple‐input multiple‐output cellular networks. First, a two‐game analytical framework is presented, where the IA design and power allocation are modelled as two game processes, respectively, and the authors prove that both games have Nash equilibrium solutions. Second, based on the framework, two iterative algorithms of joint IA and power allocation that achieve the maximum sum‐rate are proposed. In addition, the authors analyse the sum‐rate performance loss under imperfect channel state information (CSI), which depends on the variance of CSI error. Simulation reveals that the sum‐rate performances of the proposed iterative algorithms are higher than that of the previous schemes at low signal‐to‐noise ratio, whose computational complexities are acceptable.