Long Term Evolution (LTE) is an emerging 4G wireless access technology. Multiple-Input Multiple-Output (MIMO) systems are a primary enabler of the high data rate sought to be achieved by LTE. Closed-Loop-Spatial-Multiplexing and Open-Loop-Spatial-Multiplexing are the two primary MIMO transmission modes used in the LTE downlink. An LTE Base Station (eNodeB) is expected to select and switch transmission characteristics of these MIMO modes based on channel quality feedbacks: Channel Quality Indicator (CQI), Precoding Matrix Indicator (PMI) and Rank Indicator (RI) reported by the mobile. In this paper, we show analytically as well as through simulations, that is it not optimal to make MIMO transmission mode selection at the eNodeB based solely on mobile feedback. Doing so can result in significant throughput loss. We then describe an algorithm that can prevent such throughput reduction. The algorithm works by enabling the eNodeB to selectively override the mobile's channel feedback and make a better choice of transmission mode. We provide simulation data to show that such an algorithm can improve the downlink throughput by 110% in some cases and by 20–50% on an average. We also show that an eNodeB equipped with this algorithm is able to match a benchmarked “best case” downlink throughput for a wide range of signal quality and channel models. Existing transmission mode selection algorithms described in literature involve the estimation of additional parameters such as mobile speed, channel diversity etc along with the mobile's channel feedback. The algorithm proposed in this paper is computationally much simpler. It does not involve estimation of any of these additional channel parameters. It is also at par or better in performance. This algorithm has been implemented in the Motorola LTE product.
['Shubhodeep Adhikari']
Downlink transmission mode selection and switching algorithm for LTE