Capacity Bounds for Dense Massive MIMO in a LineofSight Propagation Environment The use of largescale antenna arrays grants considerable benefits in energy and spectral efficiency to wireless systems due to spatial resolution and array gain techniques. By assuming a dominant lineofsight environment in a massive multipleinput multipleoutput scenario, we derive analytical expressions for the sumcapacity. Then, we show that convenient simplifications on the sumcapacity expressions are possible when working at low and high signaltonoise ratio regimes. Furthermore, in the case of low and high signaltonoise ratio regimes, it is demonstrated that the Gamma probability density function can approximate the probability density function of the instantaneous channel sumcapacity as the number of served devices and base station antennas grows, respectively. A second important demonstration presented in this work is that a Gamma probability density function can also be used to approximate the probability density function of the summation of the channelxe2x80x99s singular values as the number of devices increases. Finally, it is important to highlight that the presented framework is useful for a massive number of Internet of Things devices as we show that the transmit power of each device can be made inversely proportional to the number of base station antennas.

Energy and SpectralEfficiency Tradeoff in Beam Domain Massive MIMO Downlink with Statistical CSIT We investigate energy and spectralefficiency tradeoff by optimizing resource efficiency (RE) for singlecell massive multipleinput multipleoutput (MIMO) downlink transmission with only statistical channel state information available at the transmitter (CSIT). We first show that beam domain transmission is favorable for RE optimization in massive MIMO downlink, by deriving a closedform solution for the eigenvectors of the optimal transmit covariance matrices. With this conclusion, the RE optimization precoding design is reduced to a realvalued power allocation problem. Exploiting sequential optimization and random matrix theory, we further propose a lowcomplexity twolayer waterfillingstructured power allocation algorithm. Numerical results demonstrate the effectiveness of our proposed statistical CSI aided RE optimization approach.