Improved Linear Precoding over Block Diagonalization in Multi-cell Cooperative Networks

In downlink multiuser multiple-input multiple-output (MIMO) systems, block diagonalization (BD) is a practical linear precoding scheme which achieves the same degrees of freedom (DoF) as the optimal linear/nonlinear precoding schemes. However, its sum-rate performance is rather poor in the practical SNR regime due to the transmit power boost problem. In this paper, we propose an improved linear precoding scheme over BD with a so-called "effective-SNR-enhancement" technique. The transmit covariance matrices are obtained by firstly solving a power minimization problem subject to the minimum rate constraint achieved by BD, and then properly scaling the solution to satisfy the power constraints. It is proved that such approach equivalently enhances the system SNR, and hence compensates the transmit power boost problem associated with BD. The power minimization problem is in general non-convex. We therefore propose an efficient algorithm that solves the problem heuristically. Simulation results show significant sum rate gains over the optimal BD and the existing minimum mean square error (MMSE) based precoding schemes.Comment: 21 pages, 4 figure

Degrees of Freedom of the 3-User Rank-Deficient MIMO Interference Channel

We provide the degrees of freedom (DoF) characterization for the $3$-user $M_T\times M_R$ multiple-input multiple-output (MIMO) interference channel (IC) with \emph{rank-deficient} channel matrices, where each transmitter is equipped with $M_T$ antennas and each receiver with $M_R$ antennas, and the interfering channel matrices from each transmitter to the other two receivers are of ranks $D_1$ and $D_2$, respectively. One important intermediate step for both the converse and achievability arguments is to convert the fully-connected rank-deficient channel into an equivalent partially-connected full-rank MIMO-IC by invertible linear transformations. As such, existing techniques developed for full-rank MIMO-IC can be incorporated to derive the DoF outer and inner bounds for the rank-deficient case. Our result shows that when the interfering links are weak in terms of the channel ranks, i.e., $D_1+D_2\leq \min(M_T, M_R)$, zero forcing is sufficient to achieve the optimal DoF. On the other hand, when $D_1+D_2> \min(M_T, M_R)$, a combination of zero forcing and interference alignment is in general required for DoF optimality. The DoF characterization obtained in this paper unifies several existing results in the literature.Comment: 28 pages, 7 figures. To appear in IEEE transactions on wireless communication

Dynamic Resource Allocation for Multiple-Antenna Wireless Power Transfer

We consider a point-to-point multiple-input-single-output (MISO) system where a receiver harvests energy from a wireless power transmitter to power itself for various applications. The transmitter performs energy beamforming by using an instantaneous channel state information (CSI). The CSI is estimated at the receiver by training via a preamble, and fed back to the transmitter. The channel estimate is more accurate when longer preamble is used, but less time is left for wireless power transfer before the channel changes. To maximize the harvested energy, in this paper, we address the key challenge of balancing the time resource used for channel estimation and wireless power transfer (WPT), and also investigate the allocation of energy resource used for wireless power transfer. First, we consider the general scenario where the preamble length is allowed to vary dynamically. Taking into account the effects of imperfect CSI, the optimal preamble length is obtained online by solving a dynamic programming (DP) problem. The solution is shown to be a threshold-type policy that depends only on the channel estimate power. Next, we consider the scenario in which the preamble length is fixed. The optimal preamble length is optimized offline. Furthermore, we derive the optimal power allocation schemes for both scenarios. For the scenario of dynamic-length preamble, the power is allocated according to both the optimal preamble length and the channel estimate power; while for the scenario of fixed-length preamble, the power is allocated according to only the channel estimate power. The analysis results are validated by numerical simulations. Encouragingly, with optimal power allocation, the harvested energy by using optimized fixed-length preamble is almost the same as the harvested energy by employing dynamic-length preamble, hence allowing a low-complexity WPT system to be implemented in practice.Comment: 30 pages, 6 figures, Submitted to the IEEE Transactions on Signal Processin