Efficient Downlink Channel Reconstruction for FDD Multi-Antenna Systems

In this paper, we propose an efficient downlink channel reconstruction scheme for a frequency-division-duplex multi-antenna system by utilizing uplink channel state information combined with limited feedback. Based on the spatial reciprocity in a wireless channel, the downlink channel is reconstructed by using frequency-independent parameters. We first estimate the gains, delays, and angles during uplink sounding. The gains are then refined through downlink training and sent back to the base station (BS). With limited overhead, the refinement can substantially improve the accuracy of the downlink channel reconstruction. The BS can then reconstruct the downlink channel with the uplink-estimated delays and angles and the downlink-refined gains. We also introduce and extend the Newtonized orthogonal matching pursuit (NOMP) algorithm to detect the delays and gains in a multi-antenna multi-subcarrier condition. The results of our analysis show that the extended NOMP algorithm achieves high estimation accuracy. Simulations and over-the-air tests are performed to assess the performance of the efficient downlink channel reconstruction scheme. The results show that the reconstructed channel is close to the practical channel and that the accuracy is enhanced when the number of BS antennas increases, thereby highlighting that the promising application of the proposed scheme in large-scale antenna array systems

QCD radiative correction to color-octet J/ψJ/\psi inclusive production at B Factories

In nonrelativistic Quantum Chromodynamics (NRQCD), we study the next-to-leading order (NLO) QCD radiative correction to the color-octet $J/\psi$ inclusive production at B Factories. Compared with the leading-order (LO) result, the NLO QCD corrections are found to enhance the short-distance coefficients in the color-octet $J/\psi$ production $e^+ e^-\to c \bar c (^3P_0^{(8)} {\rm or} ^3P_0^{(8)})g$ by a factor of about 1.9. Moreover, the peak at the endpoint in the $J/\psi$ energy distribution predicted at LO can be smeared by the NLO corrections, but the major color-octet contribution still comes from the large energy region of $J/\psi$. By fitting the latest data of $\sigma(e^{+}e^{-}\to J/\psi+X_{\mathrm{non-c\bar{c}}})$ observed by Belle, we find that the values of color-octet matrix elements are much smaller than expected earlier by using the naive velocity scaling rules or extracted from fitting experimental data with LO calculations. As the most stringent constraint by setting the color-singlet contribution to be zero in $e^{+}e^{-}\to J/\psi+X_{\mathrm{non-c\bar{c}}}$, we get an upper limit of the color-octet matrix element, $+ 4.0 <0| {\cal O}^{J/\psi} [{}^3P_0^{(8)}]|0>/m_c^2 <(2.0 \pm 0.6)\times 10^{-2} {\rm GeV}^3$ at NLO in $\alpha_s$.Comment: 18 pages, 8 figure

FDD Massive MIMO Based on Efficient Downlink Channel Reconstruction

Massive multiple-input multiple-output (MIMO) systems deploying a large number of antennas at the base station considerably increase the spectrum efficiency by serving multiple users simultaneously without causing severe interference. However, the advantage relies on the availability of the downlink channel state information (CSI) of multiple users, which is still a challenge in frequency-division-duplex transmission systems. This paper aims to solve this problem by developing a full transceiver framework that includes downlink channel training (or estimation), CSI feedback, and channel reconstruction schemes. Our framework provides accurate reconstruction results for multiple users with small amounts of training and feedback overhead. Specifically, we first develop an enhanced Newtonized orthogonal matching pursuit (eNOMP) algorithm to extract the frequency-independent parameters (i.e., downtilts, azimuths, and delays) from the uplink. Then, by leveraging the information from these frequency-independent parameters, we develop an efficient downlink training scheme to estimate the downlink channel gains for multiple users. This training scheme offers an acceptable estimation error rate of the gains with a limited pilot amount. Numerical results verify the precision of the eNOMP algorithm and demonstrate that the sum-rate performance of the system using the reconstructed downlink channel can approach that of the system using perfect CSI

MegDet: A Large Mini-Batch Object Detector

The improvements in recent CNN-based object detection works, from R-CNN [11], Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly come from new network, new framework, or novel loss design. But mini-batch size, a key factor in the training, has not been well studied. In this paper, we propose a Large MiniBatch Object Detector (MegDet) to enable the training with much larger mini-batch size than before (e.g. from 16 to 256), so that we can effectively utilize multiple GPUs (up to 128 in our experiments) to significantly shorten the training time. Technically, we suggest a learning rate policy and Cross-GPU Batch Normalization, which together allow us to successfully train a large mini-batch detector in much less time (e.g., from 33 hours to 4 hours), and achieve even better accuracy. The MegDet is the backbone of our submission (mmAP 52.5%) to COCO 2017 Challenge, where we won the 1st place of Detection task

Robust Affinity Propagation using Preference Estimation

Affinity propagation is a novel unsupervised learning algorithm for exemplar-based clustering without the priori knowledge of the number of clusters (NC). In this article, the influence of the “preference” on the accuracy of AP output is addressed. We present a robust AP clustering method, which estimates what preference value could possibly yield an optimal clustering result. To demonstrate the performance promotion, we apply the robust AP on picture clustering, using local SIFT, global MPEG-7 CLD, and the proposed preference as the input of AP. The experimental results show that over 40% enhancement of ARI accuracy for several image datasets