On the Achievable Capacity of MIMOOFDM Systems in the CathLab Environment. In the last years, the evolution of digital communications has been harnessed by medical applications. In that context, wireless communications are preferable over wired communications, as they facilitate the work of health technicians by reducing cabling on the stretchers. However, the use of wireless communications is challenging, especially when high data rates and low latencies are required. In those scenarios, multipleinput multipleoutput (MIMO) techniques might have an important role, thanks to the high capacity gains that they can exhibit, which ideally increase with the MIMO size. In this work, we study the propagation scenario of a typical medical laboratory through raytracing techniques. By taking into account the derived channel model, we study the potential of MIMO techniques in an IEEE 802.11ax environment. Through a set of performance results regarding the system capacity, we show that the MIMO gains might not be as high as supposed in the medical laboratory, being far from the ideal scenario. Therefore, the large data rates required by the modern medical imaging applications might only be achieved with a combination of MIMO systems and large bandwidths.

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.