Downlink Multiuser Detection in the Virtual Cell-Based Ultra-Low Latency Vehicular Networks

To achieve ultra-low latency mobile networking, recent efforts to integrate virtual cell with open-loop communications and proactive network association suggest the facilitation of new technological paradigm, but the interference from different co-locating virtual cells is hard to handle. Open-loop transmissions make beam-forming/interference alignment infeasible due to the need of channel state information feedback. Multiuser detection (MUD) is therefore employed to address the downlink interference. We note that the bit error rate (BER) of maximum-likelihood MUD (ML-MUD) is sensitive to the modulation of the interference. As the interferer uses low-order modulation, the BER of desired signal can approach the ideal case without interference. But if the interferer adopts high-order modulation, the resultant BER is significantly degraded. Our study shows that, such modulation sensitivity can be eased by the multi-antenna technique. We also propose two methods to reduce the notorious computational complexity of MUD, particularly involving higher order modulations. The first scheme is termed as the reduced-computation ML-MUD (R-MLMUD) that exploits the characteristic of downlink to shrink the ML solution space, consequently leading to lower detection complexity. The second scheme is a new projection receiver, called generalized linear minimum mean square error equalizer, resulting in notable signal-to-noise ratio gain over the conventional projection method. The simulation results indicate that the proposed schemes and their integration can achieve satisfactory BER performance.