Ming Ding

MMSE Based Greedy Antenna Selection Scheme for AF MIMO Relay Systems

We propose a greedy minimum mean squared error (MMSE)-based antenna selection algorithm for amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay systems. Assuming equal-power allocation across the multi-stream data, we derive a closed form expression for the mean squared error (MSE) resulted from adding each additional antenna pair. Based on this result, we iteratively select the antenna-pairs at the relay nodes to minimize the MSE. Simulation results show that our algorithm greatly outperforms the existing schemes.

Sequential and Incremental Precoder Design for Joint Transmission Network MIMO Systems With Imperfect Backhaul

In this paper, we propose a sequential and incremental precoder design for downlink joint transmission (JT) network multiple-input-multiple-output (MIMO) systems with imperfect backhaul links. The objective of our design is to minimize the maximum of the substream mean square errors, which dominates the average bit error rate (BER) performance of the system. In the proposed scheme, we first optimize the precoder at the serving base station (BS) and then sequentially optimize the precoders of nonserving BSs in the JT set according to the descending order of their probabilities of participating in the JT. The BS-wise sequential optimization process can improve system performance when some BSs have to temporarily quit the JT operations because of poor instant backhaul conditions. In addition, the precoder of an additional BS is derived in an incremental way, i.e., the sequentially optimized precoders of the previous BSs are fixed, and thus, the additional precoder plays an incremental part in multi-BS JT operations. An iterative algorithm is designed to jointly optimize the substream precoder and substream power allocation for each additional BS in the proposed sequential and incremental optimization scheme. Simulations show that, under the practical backhaul link conditions, our scheme significantly outperforms the autonomous global precoding scheme in terms of BER performance.

Antenna Selection for AF MIMO Relay Networks and the Capacity Scaling Tendency

We propose a novel antenna selection algorithm for amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay networks. Assuming equal-power allocation across the activated antennas, we iteratively select the antenna pairs to greedily maximize the network capacity in closed-form expressions. Simulation results show that our algorithm greatly outperforms the existing schemes, with the gain being more pronounced when the noise at relay nodes is relatively high. Moreover, from the results, we propose a conjecture that the network capacity with antenna selection shall scale logographically with respect to the number of relays, which achieves the capacity upper bound in the sense of asymptotic capacity scaling tendency.