This paper compares the performance and complexity of various MIMO receiver structures for 802.11n WLAN systems, including linear approaches such as MMSE and ZF, and iterative detection and decoding using list sphere decoding. Optimal beam steering with the low-complexity MMSE receiver is shown to outperform the computationally-expensive list sphere decoder receiver with an uninformed transmitter

A method for predicting packet error rates in MIMO-OFDM WLAN systems is presented. The method is based on using post-detection SNRs as an abstraction of the physical layer, and is motivated by the need for a simple and efficient way of modelling the physical layer in system level simulation scenarios involving multiple stations. The physical layer abstraction is sufficient for generating error processes in the system simulations that accurately reflect the interaction between the MIMO-OFDM physical layer and the underlying wireless channel. We validate the abstract model by comparing packet error rates predicted by the model with packet error rates obtained through full link simulations for two different approaches to MIMO processing, referred to as spatial spreading and eigenvector steering.

This paper presents two transmission techniques for MIMO OFDM communication, referred to as spatial spreading and eigenvector steering. Spatial spreading is used when the transmitting station is not presumed to have sufficient channel state information to compute optimum steering vectors. This situation may occur for a variety of reasons, including poor or aged channel estimates or lack of calibration between the transmit and receive antenna chains. Eigenvector steering is used in cases where the transmitting station has sufficient information about the channel to compute optimum transmit steering vectors. Simulation results showing throughput and range achieved by the two transmission strategies are provided.

Statistics and system performance results derived from the outdoor measurements performed with a true multiple-input-multiple-output (MIMO) wideband channel measurement system are presented. These results are applicable to the MIMO channel modeling particularly for a model with paths at different time delays (tapped-delay line model) and an associated correlation matrix for each time delay.

Several key performance issues for the downlink of IS-95 based CDMA systems are explored. Performance data collected from a number of operational systems using CDMA diagnostic monitor equipment and post-processing analysis software is used to aid in the development of improved link and system performance prediction models. The models developed permit the sensitivity of link performance and system capacity to be examined for a wide variety of deployment scenarios. The impact of orthogonality loss on link performance is assessed along with the relative impact on system capacity. Performance limitations resulting from channel induced multipath and soft handoff are also examined based on using a 3-tap RAKE receiver. The results suggest that many systems may not meet the performance and capacity predictions derived from using simplistic downlink radio network planning models.

Issues encountered in the design of reliable narrowband time-division multiple access (TDMA) digital cellular mobile communication systems are considered. In particular, the problem of compensating for the harsh multipath fading environment in systems whose transmission bandwidth is commensurate with the coherence bandwidth of the fading channel is considered. A TDMA channel characterization parameter, the slot-normalized fade rate, is introduced, and an adaptive bidirectional equalization technique, which estimates the location of a deep fade within a time slot, is proposed. The simulation results show that the carrier-to-noise ratio requirement is only 15.5 dB when this equalization technique is used. This is achieved without diversity, and with low complexity. An equivalent equalized land mobile radio channel model and the analytical solution for the optimal bit likelihood calculation for pi /4-shift quadrature differential phase-shift keying (QDPSK) modulation are also derived under certain channel conditions. The results are used as soft decisions for the convolutional decoder.>