Detecting signals in a very large multiple-input multiple-output (MIMO) system requires high complexy of implementation. Thus, belief propagation based detection has been studied recently because of its low complexity. When the transmitted signal sequence is encoded using a channel code decodable by a factor-graph-based algorithm, MIMO signal detection and channel decoding can be combined in a single factor graph. In this paper, a low density parity check (LDPC) coded MIMO system is considered, and two types of factor graphs: bipartite and tripartite graphs are compared. The former updates the log-likelihood-ratio (LLR) values at MIMO detection and parity checking simultaneously. On the other hand, the latter performs the updates alternatively. Simulation results show that the tripartite graph achieves faster convergence and slightly better bit error rate performance. In addition, it is confirmed that the LLR damping in LDPC decoding is important for a stable convergence.

Many carriers are introducing multi-media services to satisfy customer demands for these services. In order to provide such services, carrier must increase their system capacity. It is well known that space division multiple access (SDMA) improves system capacity and is compatible with existing access systems. In order to evaluate the performance of SDMA, we developed an SDMA test bed. The test bed maintains the personal handy phone systems (PHS). The PHS adopts time division multiple access (TDMA). Aiming to compare the performance of SDMA and TDMA using the same analog hardware, the SDMA test bed employs a software-defined radio (SDR) technique. This paper shows the outline and performance of the test bed. The results of laboratory tests indicate that the bit error rate (BER) of the test bed operated in the SDMA mode at under 10-3 when the carrier-tointerference ratio (CIR) was larger than approximately -22 dB. Antenna patterns measured in an anechoic chamber show that the SDMA test bed produces correct antenna patterns when there are three desired signals and one interference signal. The results of the four field tests confirm that the test bed operated while two-multiplex SDMA mode doubled of the traffic and decreased the interference level as compared with the TDMA mode. Furthermore, the test bed operated while threemultiplex SDMA mode improves the traffic about 2.4 to 2.7 times. The SDMA test bed decreased the impact of the adjusted TDMA base station (BS). Therefore, we confirmed that the SDMA improves system capacity without any degradation.

Although multi-user multiple-input multiple-output (MI-MO) systems provide high data rate transmission, they may suffer from interference. Block diagonalization and eigenbeam-space division multiplexing (E-SDM) can suppress interference. The transmitter needs to determine beamforming weights from channel state information (CSI) to use these techniques. However, MIMO channels change in time-varying environments during the time intervals between when transmission parameters are determined and actual MIMO transmission occurs. The outdated CSI causes interference and seriously degrades the quality of transmission. Channel prediction schemes have been developed to mitigate the effects of outdated CSI. We evaluated the accuracy of prediction of autoregressive (AR)-model-based prediction and Lagrange extrapolation in the presence of channel estimation error. We found that Lagrange extrapolation was easy to implement and that it provided performance comparable to that obtained with the AR-model-based technique.