On the Ergodic Rate Lower Bounds With Applications to Massive MIMO - 2018 PROJECT TITLE :On the Ergodic Rate Lower Bounds With Applications to Massive MIMO - 2018ABSTRACT:A well-known lower sure widely utilized in the large MIMO literature hinges on channel hardening, i.e., the phenomenon for which, because of the big range of antennas, the effective channel coefficients resulting from beamforming tend to deterministic quantities. If the channel hardening effect will not hold sufficiently well, this certain could be quite so much from the particular achievable rate. In recent developments of massive MIMO, many scenarios where channel hardening isn't sufficiently pronounced have emerged. These settings embody, for instance, the case of small scattering angular unfold, yielding highly correlated channel vectors, and also the case of cell-free massive MIMO. In this short contribution, we gift two new bounds on the achievable ergodic rate that provide a complementary behavior with respect to the classical sure: while the previous performs well in the case of channel hardening and/or when the system is interference-restricted (notably, within the case of finite range of antennas and conjugate beamforming transmission), the new bounds perform well when the useful signal coefficient does not harden however the channel coherence block length is giant with respect to the amount of users, and within the case where interference is nearly entirely eliminated by zero-forcing beamforming. Overall, using the foremost applicable bound relying on the system operating conditions yields a higher understanding of the particular performance of systems where channel hardening might not occur, even within the presence of a very giant range of antennas. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest On MIMO Linear Physical-Layer Network Coding: Full-Rate Full-Diversity Design and Optimization - 2018 On User Pairing in Uplink NOMA - 2018