1. Abstract: In many scenarios, wireless presents a tempting "last-mile" alternative to a wired connection for the delivery of internet service. However, the current state of the art in wireless data is represented by wireless LANs that operate at moderate rates over short distances and cellular networks that offer low rates over long distances. Neither system is designed to serve as a viable last hop (or multi-hop) over moderate distances, and both fall far short of our target data rates. Achieving rates on the order of 100 Mbps while staying within FCC limits on radiated power and spectral usage requires a significant paradigm shift in physical layer design. MIMO (multiple input, multiple output) techniques use multiple antennas at both the transmitter and receiver to dramatically increase achievable data rates compared to conventional, single antenna, systems. The impressive throughput gains in MIMO systems are enabled by acquiring and exploiting detailed knowledge of the wireless channel, generally at the expense of increased computational complexity. Our research focuses on developing and implementing MIMO algorithms for practical systems subject to realistic constraints on available channel information and processing power. Transmit Antennas Receive Antennas Physical Channels Transmit Antennas Receive Antennas Virtual Channels Channel State Information Signal Processing Virtual Channels Transmit Antennas Receive Antennas Better Channel Worse Channel Receive Antennas Better Channel Worse Channel Full Power No Power Better Channel Receive Antennas Worse Channel More Power Less Power Better Channel Worse Channel More Data Less Data Matrix Inversion Channel Estimation Eigendecomposition Vector Quantization Water- Filling FeedbackPreamble 2 Virtual Channels Antenna Spacing Too Close! Reduced to Single Virtual Channel Sufficient Separation At some point in the not-so-distant future, multiple antenna wireless systems will become a common sight in our homes, businesses, and neighborhoods. In a multiple input, multiple output (MIMO) channel, each transmit receive antenna pair has its own channel. The MIMO channel can be manipulated as a matrix using linear algebra techniques. Using eigendecomposition of the MIMO channel matrix, we can construct parallel (non-interfering) “virtual channels” along the eigenvectors of the channel. The virtual channels are not all equally good, and the received signal to noise ratios (SNRs) are proportional to the eigenvalues of the channel matrix. A technique called “Beamforming” puts all of the available transmit power into the best channel (transmitting along the eigenvector with the maximum eigenvalue). “Multiplexing” allocates power across all available channels. Optimal power allocation distributes power in proportion to channel quality. In a system with multiplexing, maximum throughput is achieved through “bit-loading”. Similar to water-filling power allocation, bit-loading transmits more data over the better channels and less over worse channels Insufficient antenna spacing can lead to a decreased number of virtual channels. This is caused by correlation between the adjacent antennas which decreases the rank of the channel matrix, reducing the number of eigenvectors. Good channel state information (CSI) is essential to MIMO systems, but learning and acting on that information requires thoughtful system design and significant computational resources. In order to gain and communicate channel state information (CSI), a portion of each packet must be allocated to a preamble (for channel estimation at the receiver) and feedback data (for communicating CSI back to the transmitter). All of this overhead reduces the size of the data payload and thus decreases the overall throughput of the system. Data PayloadFeedbackPreamble Physical Layer Overhead Packet Structure