Energy-efficient operation of battery-powered radios is becoming more important to improve device operational times on a battery charge. Future radios will also likely take on a more active role in coordinated monitoring and use of the spectrum in shared spectrum environments, placing additional demands on energy management and requirements to operate with tolerance to interference. Meanwhile, because of increasing demands on spectrum resources, spectral efficiency (SE) is becoming a more crucial design specification of future radio systems. Hence, multiple-input multiple-output (MIMO) technology is expected to be fundamental to future radios.Anticipating the need for energy-efficient, spectrally-efficient, and interference-tolerant MIMO systems, this dissertation analyzes the energy efficiency (EE) and SE of full-multiplexed MIMO systems and adaptive MIMO systems under diverse channel and interference conditions. The analysis considers MIMO systems with space diversity, polarization diversity, and combined space-polarization diversities. Architectures include co-polarized MIMO (CP-MIMO) arrays, dual-polarized MIMO architectures (DP-MIMO), and space-polarization MIMO (SP-MIMO) architectures.The analysis first considers full multiplexing systems in the absence of interference, and assumes that channel state information is not available at the transmitter. Emulation results reveal that the performance of space-based systems degrades in space-correlated channels, but that DP-MIMO systems provide robust performance across the various channel conditions considered in the analysis. Systems that take advantage of channel state information at the transmitter (CSIT) are addressed next, again in the absence of interference. These adaptive transmission systems employ linear precoding and power control to optimize EE while operating at near-maximum SE for the selected number of beams, symbol rate, and constellations. Finally, systems that operate in environments with co-channel interference (CCI) are considered. These MIMO systems employ an interference avoidance (IA) strategy followed by adaptive transmission to optimize EE on the available subcarriers. The minimum energy solution is conditioned on various factors such as channel coding characteristics, linear precoding, IA schemes and channel realizations. A method to identify the optimal transmit power is derived and simulation results show the effectiveness of the IA techniques over MIMO fading channels with different CCI statistics.