Multi-User MIMO Downlink Precoding with Dynamic Users Selection for Limited Feedback

In modern (5G) and future Multi-User (MU) wireless communication systems Beyond 5G (B5G) using Multiple Input Multiple Output (MIMO) technology, base stations with large number of antennas communicate with many mobile stations with a small number of antennas. MU-MIMO technology is becoming especially relevant in modern multi-user wireless sensor networks in various application scenarios, but the problem of organizing a multi-user mode on the downlink arises. It can be solved using precoding technology at the base station, using full Channel State Information (CSI) for each mobile station. Transmitting this information for Massive MIMO systems normally requires the allocation of high-speed feedback channel. With limited feedback, reduced information (partial CSI) is used, for example, the code word from the codebook that is closest to the estimated channel vector. An incomplete (or inaccurate) CSI information causes interference from signals, transmitted to neighboring mobile stations, that ultimately results in a decrease of the number of active users served. In this paper we propose a new downlink precoding approach with dynamic users selection for MU-MIMO systems, which also uses codebooks to reduce the information transmitted over feedback channel, but unlike in the existing approaches, here new information uncorrelated with the previous one is transmitted on each new transmission cycle. This allows accumulating the received information and restoring the full MIMO channel matrix with greater accuracy without increasing the feedback overhead: as the CSI accuracy improves, the number of active users increases and after several cycles reaches the maximum value, which is determined by the number of base station transmitting antennas. The statistical simulation confirms the effectiveness of the proposed precoding algorithm for modern and future Massive MIMO systems.