Compression of Channel State Information in Multiple Input Multiple Output Mobile Systems

Abstract

The firsts trials of the Fifth generation of wireless networks (5G) are taking place worldwide. A variety of use cases are envisaged, requiring flexible and scalable technologies to meet their key performance indicators. Massive MIMO is a 5G-enabling technology that improves spectral efficiency. To exploit the advantages of MIMO, the transmitter needs to have information about the channel condition (CSI) of each User equipment (UE). 5G is being standardized in Frequency division duplexing (FDD) and Time division duplexing (TDD) operational modes; hence, MIMO has to be feasible in both duplexing modes. As TDD operates downlink and uplink on the same frequency, it can rely on channel reciprocity to acquire the CSI needed to further design precoding and user scheduling, for instance. However, FDD cannot exploit channel reciprocity; therefore, massive MIMO in FDD mode is more challenging because the increasing number of antennas may turn the feedback of CSI impractical. Hence, compressing CSI in MIMO FDD systems is of interest. Furthermore, the use of vast spectrum ranges, sub-6 GHz and mmWaves bands, leads to different channel characteristics. Moreover, the close packaging of antenna elements increases the spatial correlation among a MIMO array. Consequently, this correlation can be exploited to leverage compression of CSI. This dissertation presents an overview of CSI compression methods, proposes an heuristic transform coding method with low computational cost, and do a systematic evaluation of the transform coding methods based on realistic MIMO channel simulations. Besides, the impact of different 5G use cases and design of transmitter and receiver antennas are also included on the evaluation to chose