Performance of Orthogonal Beamforming for SDMA with Limited Feedback

On the multi-antenna broadcast channel, the spatial degrees of freedom support simultaneous transmission to multiple users. The optimal multiuser transmission, known as dirty paper coding, is not directly realizable. Moreover, close-to-optimal solutions such as Tomlinson-Harashima precoding are sensitive to CSI inaccuracy. This paper considers a more practical design called per user unitary and rate control (PU2RC), which has been proposed for emerging cellular standards. PU2RC supports multiuser simultaneous transmission, enables limited feedback, and is capable of exploiting multiuser diversity. Its key feature is an orthogonal beamforming (or precoding) constraint, where each user selects a beamformer (or precoder) from a codebook of multiple orthonormal bases. In this paper, the asymptotic throughput scaling laws for PU2RC with a large user pool are derived for different regimes of the signal-to-noise ratio (SNR). In the multiuser-interference-limited regime, the throughput of PU2RC is shown to scale logarithmically with the number of users. In the normal SNR and noise-limited regimes, the throughput is found to scale double logarithmically with the number of users and also linearly with the number of antennas at the base station. In addition, numerical results show that PU2RC achieves higher throughput and is more robust against CSI quantization errors than the popular alternative of zero-forcing beamforming if the number of users is sufficiently large.Comment: 27 pages; to appear in IEEE Transactions on Vehicular Technolog

Joint Wireless Information and Energy Transfer in a K-User MIMO Interference Channel

Recently, joint wireless information and energy transfer (JWIET) methods have been proposed to relieve the battery limitation of wireless devices. However, the JWIET in a general K-user MIMO interference channel (IFC) has been unexplored so far. In this paper, we investigate for the first time the JWIET in K-user MIMO IFC, in which receivers either decode the incoming information data (information decoding, ID) or harvest the RF energy (energy harvesting, EH). In the K-user IFC, we consider three different scenarios according to the receiver mode -- i) multiple EH receivers and a single ID receiver, ii) multiple IDs and a single EH, and iii) multiple IDs and multiple EHs. For all scenarios, we have found a common necessary condition of the optimal transmission strategy and, accordingly, developed the transmission strategy that satisfies the common necessary condition, in which all the transmitters transferring energy exploit a rank-one energy beamforming. Furthermore, we have also proposed an iterative algorithm to optimize the covariance matrices of the transmitters that transfer information and the powers of the energy beamforming transmitters simultaneously, and identified the corresponding achievable rate-energy tradeoff region. Finally, we have shown that by selecting EH receivers according to their signal-to-leakage-and-harvested energy-ratio (SLER), we can improve the achievable rate-energy region further.Comment: arXiv admin note: text overlap with arXiv:1303.169

Receive Combining vs. Multi-Stream Multiplexing in Downlink Systems with Multi-Antenna Users

In downlink multi-antenna systems with many users, the multiplexing gain is strictly limited by the number of transmit antennas $N$ and the use of these antennas. Assuming that the total number of receive antennas at the multi-antenna users is much larger than $N$, the maximal multiplexing gain can be achieved with many different transmission/reception strategies. For example, the excess number of receive antennas can be utilized to schedule users with effective channels that are near-orthogonal, for multi-stream multiplexing to users with well-conditioned channels, and/or to enable interference-aware receive combining. In this paper, we try to answer the question if the $N$ data streams should be divided among few users (many streams per user) or many users (few streams per user, enabling receive combining). Analytic results are derived to show how user selection, spatial correlation, heterogeneous user conditions, and imperfect channel acquisition (quantization or estimation errors) affect the performance when sending the maximal number of streams or one stream per scheduled user---the two extremes in data stream allocation. While contradicting observations on this topic have been reported in prior works, we show that selecting many users and allocating one stream per user (i.e., exploiting receive combining) is the best candidate under realistic conditions. This is explained by the provably stronger resilience towards spatial correlation and the larger benefit from multi-user diversity. This fundamental result has positive implications for the design of downlink systems as it reduces the hardware requirements at the user devices and simplifies the throughput optimization.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 11 figures. The results can be reproduced using the following Matlab code: https://github.com/emilbjornson/one-or-multiple-stream