The beamforming weights which can suppress the interfering signal toward out-of-cell mobile stations in downlink are designed for a time division duplexing based OFDMA system when the channel information is not perfect. The derived beamforming weights do not improve the average SINR performance monotonously with the increased transmit SNR if the inverse of the transmit SNR is used as the regularization factor of the beamforming weights and the channel information obtained by the BS to design the BF weights is not perfect. Therefore, we suggest a simple scheme to select the regularization factor. The proposed beamforming weights improve the performance monotonously with the increased transmit SNR and achieve near-optimal performance. The performance achieved by applying the beamforming weights used in uplink to downlink beamforming is also investigated.

A novel method is proposed that can estimate the tag population in Radio Frequency Identification (RFID) systems by using a Hadamard code for the tag response. We formulate the maximum likelihood estimator for the tag population using the number of observed footprints. The lookup table of the estimation algorithm has low complexity. Simulation results show that the proposed estimator performs considerably better than the conventional schemes.

We analyze linear channel estimation for MIMO-OFDM systems and propose a spherical linear interpolator in closed-form for the beamforming codewords. We also suggest a hybrid interpolator using a simplified version of the derived interpolator. Simulation results show that the proposed schemes are efficient and competitive with respect to the feedback overhead and have low complexity.