Jianwen Zhang

Hermitian Precoding for Distributed MIMO Systems with Individual Channel State Information

We consider a distributed multiple-input multiple-output (MIMO) system in which multiple transmitters cooperatively serve a common receiver. It is usually very costly to acquire full channel state information at the transmitter (CSIT) in such a scenario, especially for large-scale antenna systems. In this paper, we assume individual CSIT (I-CSIT), i.e., each transmitter has perfect CSI of its own link but only slow fading factors of the others. A linear Hermitian precoding technique is proposed to enhance the system performance. The optimality of the proposed precoding technique is analyzed. Numerical results demonstrate that the performance loss incurred by the I-CSIT assumption is negligible as compared to the full-CSIT case.

Linear precoding in distributed MIMO systems with partial CSIT

We study the transmission problem in a distributed multiple-input multiple-output (MIMO) system consisting of several distributed transmitters and a common receiver. Assuming partial channel state information at the transmitter (CSIT), we propose a low-cost weighted channel matching and scattering (WCMS) linear precoding strategy. The proposed precoder can be decomposed into two parallel modules: channel matching (CM) and energy scattering. The signals generated by the CM modules from different transmitters provide a coherent gain with improved power efficiency. The use of the scattering modules provides robustness against CSIT uncertainty. By properly combining these two modules, WCMS can achieve coherent gain proportional to the accuracy of the available CSIT as well as robustness against CSIT error. WCMS is simple and fully decentralized and thus is highly suitable for a distributed MIMO system. Numerical results demonstrate that WCMS indeed achieves significant gains in distributed MIMO environments with partial CSIT.

Locally Orthogonal Training Design in Cloud-RANs

We consider training-based channel estimation for cloud radio access networks (CRANs), in which a large amount of remote radio heads (RRHs) and users are randomly scattered over a certain service area. In this model, assigning orthogonal training sequences to all users, if possible, will cause a substantial overhead to the overall network. Instead, we introduce the notion of local orthogonality, in which the training sequence of a user is required to be orthogonal to the training sequences of other users in its neighborhood. We model the design of locally orthogonal training sequences as a graph coloring problem. Then, based on the theory of random geometric graph, we show that the minimum training length scales in the order of ln K, where K is the number of users covered by the CRAN. Therefore, the proposed training design yields a scalable solution to sustain the need of large-scale cooperation in CRANs.

Multi-Antenna Constant Envelope Wireless Power Transfer

In this paper, we study wireless power transfer in a multiuser multiple-input single-output (MISO) system, where a base station equipped with N antennas wirelessly transfers power to distributed single-antenna users. To reduce the implementation cost, we propose a constant-envelope analog beamforming scheme to simultaneously transfer power to multiple users which requires only a single radio frequency (RF) chain at the multi-antenna transmitter. We show that the proposed constant- envelope beamforming design only incurs about 1 dB power loss under homogeneous and independent Rayleigh fading, as compared with the optimal variable-envelope digital beamforming design that however requires N RF chains, one for each transmit antenna.

Doubly hermitian precoding for parallel MIMO relay networks

We consider a parallel multiple-input multiple-output (MIMO) relay network, in which a source node communicates with a destination node assisted by multiple parallel relays. In such a network, it is costly to acquire global channel state information (CSI) at every relay node. In this regard, we assume local CSI, i.e., each node in the network knows perfect CSI of the links from and to this node, but only has some statistical information of the other links. We propose an amplify-and-forward relaying strategy, termed doubly Hermitian precoding to efficiently exploit the potential benefit of local CSI. We show that the proposed relaying strategy is asymptotically capacity-approaching as the number of relays tends to infinity. Numerical results demonstrate that the proposed scheme performs close to the performance upper bound obtained by assuming global CSI.

Hermitian precoding for distributed MIMO systems

In this paper, we consider a distributed MIMO communication network in which multiple transmitters cooperatively send common messages to a single receiver. In this scenario, it is usually costly to acquire full channel state information at the transmitters (CSIT), i.e., every transmitter perfectly knows the overall channel state information (CSI) of the network. Hence, we assume individual CSIT (I-CSIT), i.e., each transmitter only knows its own CSI. We propose a novel precoding technique, named Hermitian precoding, to enhance the system performance under the constraint of I-CSIT. We show that the proposed scheme can perform close to the system capacity with full CSIT. This reveals that the amount of CSI required at the transmitters can be significantly reduced without considerably compromising performance.