In an X channel, multiple transmitters transmit independent signals to different receivers. Separate zero-forcing (ZF) precoding is used at transmitters in the two-user X channel with two transmitters and two receivers. A closed-form optimal power allocation is derived under the sum power constraint (SPC) to maximize the squared minimum distance. The ZF strategy with optimal power allocation achieves a significant signal to noise ratio (SNR) improvement. Under the individual power constraint (IPC), a suboptimal power allocation that achieves better performance compared to the existing algorithms is also proposed.

In this paper, we consider power allocation over multiple transmit antennas in a distributed transmit antenna system with delay diversity assuming that the power delay profile (PDP) is available for each transmit antenna. This paper demonstrates that the optimal power allocation for outage minimization greatly depends on the system's operating signal-to-noise ratio (SNR) range. With a low operating SNR, it is required to assign all power to the antenna closest to the receiver. On the other hand, when the operating SNR is sufficiently high, power must be allocated proportional to the number of the non-zero elements in the PDP for each antenna.

The probability of making mistakes on the decoded signals at the relay has been used for the maximum-likelihood (ML) decision at the receiver in the decode-and-forward (DF) relay network. It is well known that deriving the probability is relatively easy for the uncoded single-antenna transmission with M-pulse amplitude modulation (PAM). However, in the multiplexing multiple-input multiple-output (MIMO) transmission, the multi-dimensional decision region is getting too complicated to derive the probability. In this paper, a high-performance near-ML decoder is devised by applying a well-known pairwise error probability (PEP) of two paired-signals at the relay in the MIMO DF relay network. It also proves that the near-ML decoder can achieve the maximum diversity of MSMD+MR min (MS,MD), where MS, MR, and MD are the number of antennas at the source, relay, and destination, respectively. The simulation results show that 1) the near-ML decoder achieves the diversity we derived and 2) the bit error probability of the near-ML decoder is almost the same as that of the ML decoder.