Byungju Lee

Recent trend of multiuser MIMO in LTE-advanced

Recently, the mobile communication industry is moving rapidly toward Long Term Evolution, or LTE, systems. The leading carriers and vendors are committed to launching LTE service in the near future; in fact, a number of major operators such as Verizon have initiated LTE service already. LTE aims to provide improved service quality over 3G systems in terms of throughput, spectral efficiency, latency, and peak data rate, and the MIMO technique is one of the key enablers of the LTE system for achieving these diverse goals. Among several operational modes of MIMO, multiuser MIMO (MU-MIMO), in which the base station transmits multiple streams to multiple users, has received much attention as a way of achieving improvement in performance. From the initial release (Rel. 8) to the recent release (Rel. 10), so called LTE-Advanced, MUMIMO techniques have evolved from their premature form to a more elaborate version. In this article, we provide an overview of design challenges and the specific solutions for MU-MIMO systems developed in the LTE-Advanced standard.

A MMSE Vector Precoding with Block Diagonalization for Multiuser MIMO Downlink

Block diagonalization (BD) algorithm is a generalization of the channel inversion that converts multiuser multi-input multi-output (MIMO) broadcast channel into single-user MIMO channel without inter-user interference. In this paper, we combine the BD technique with a minimum mean square error vector precoding (MMSE-VP) for achieving further gain in performance with minimal computational overhead. Two key ingredients to make our approach effective are the QR decomposition based block diagonalization and joint optimization of transmitter and receiver parameters in the MMSE sense. In fact, by optimizing precoded signal vector and perturbation vector in the transmitter and receiver jointly, we pursue an optimal balance between the residual interference mitigation and the noise enhancement suppression. From the sum rate analysis as well as the bit error rate simulations (both uncoded and coded cases) in realistic multiuser MIMO downlink, we show that the proposed BD-MVP brings substantial performance gain over existing multiuser MIMO algorithms.

Ontogeny and the possible function of a novel epidermal growth factor-like repeat domain-containing protein, NELL2, in the rat brain

In this study we investigated the mRNA expression of NELL2, a neural tissue‐specific epidermal growth factor (EGF)‐like repeat domain‐containing protein, in the developing and adult rat CNS using in situ hybridization histochemistry and northern blot analysis. The possible candidates that interact with or be regulated by NELL2 were screened with a cDNA expression array in antisense (AS) NELL2 oligodeoxynucleotide (ODN)‐injected rat hypothalami. NELL2 mRNA was detected as early as embryonic day 10, and was predominant in the CNS throughout the pre‐natal stages. Its expression gradually increased during embryonic development and its strong expression was observed throughout the CNS until embryonic day 20. It was detected in the ventricular zone of the spinal cord, medulla and pons in 12‐day‐old‐embryos, suggesting that NELL2 plays a role in the neurogenesis of these areas. After birth its expression gradually decreased, but high levels of expression could be observed in the tenia tecta, piriform cortex, hippocampus, dentate gyrus, cerebellar cortex, ambiguus nucleus, and inferior olivary nucleus of adult rat brains. The analysis of cDNA expression arrays revealed that the administration of AS NELL2 ODN markedly decreased the expression of several Ca2+‐binding proteins and those involved in the transport and release of vesicles such as EF‐hand Ca2+‐binding protein p22 and rab7. This finding was confirmed by relative reverse transcription‐polymerase chain reaction. The effect of NELL2 on synaptic vesicle content in median eminence (ME) nerve terminals was determined with synaptophysin levels as a marker protein in the AS NELL2 ODN‐injected rat. It was significantly decreased by the AS ODN. These data suggest that NELL2 may play an important role in the development of the CNS as well as maintenance of neural functions, by regulating the intracellular machinery involving Ca2+ signaling, synaptic transport and/or release of vesicles.

Antenna grouping based feedback reduction for FDD-based massive MIMO systems

Recent works on massive multiple-input multiple-output (MIMO) have shown that a potential breakthrough in capacity gains can be achieved by deploying a very large number of antennas at the basestation. Although transmit-side channel state information (CSI) can be obtained by employing channel reciprocity in time division duplexing (TDD) systems, explicit feedback of CSI from the user to the basestation is required for frequency division duplexing (FDD) systems. In this paper, we propose an antenna grouping based feedback reduction technique for FDD-based massive MIMO systems. The proposed algorithm, dubbed antenna group beamforming (AGB), groups antenna elements using pre-designed patterns. The proposed method introduces the concept of using a header of overall feedback resources to select a suitable group pattern and the payload to quantize the effective channel vector. Simulation results show that the proposed method achieves significant feedback overhead reduction over conventional approach.

A Vector Perturbation with User Selection for Multiuser MIMO Downlink

Recent works on multiuser MIMO study have shown that the linear growth of capacity in single user MIMO system can be translated to the multiuser MIMO scenario as well. In this paper, we propose a method pursuing performance gain of vector perturbation in multiuser downlink systems. Instead of employing the maximum number of mobile users for communication, we use small part of them as virtual users for improving reliability of users participating communication. By controlling parameters of the virtual users including information and perturbation vector, we obtain considerable improvement in the effective SNR. Simulation results on the realistic multiuser MISO and MIMO downlink systems show that the proposed method brings substantial performance gain over the standard vector perturbation with marginal overhead in computations.

Low complexity detection and precoding for massive MIMO systems

Recently, a variety of low complexity soft-input soft-output detection algorithms have been introduced for iterative detection and decoding (IDD) systems. However, it is still challenging to implement soft-input soft-output detector detector at feasible complexity for massive MIMO systems due to the heavy burden in computational complexity. In this paper, we present a novel soft-input soft-output detector for massive MIMO systems that offers substantial complexity reduction over the existing detectors performance close to the full dimensional symbol detector. The proposed soft-input soft-output successive group (SSG) detector detects a subset of symbols successively with an aid of the deliberately designed preprocessing to suppress the inter-group interference. Simulation results performed on 8×8 MIMO flat fading channels demonstrate that the proposed SSG detector achieves significant complexity reduction over the conventional approaches with negligible performance loss.

A vector perturbation with virtual users for multiuser MIMO downlink

In this paper, we put forth an approach pursuing performance gain of vector perturbation in multiuser MIMO downlink. Instead of achieving maximal sum capacity by employing maximum number of mobile users for communication, we sacrifice small part of them for improving the performance of ones participating communication. By controlling parameters of these sacrificing users, we achieve considerable improvement in the effective SNR, which can be translated to the large gain in bit error performance. From simulation results on 4×4 and 10×10 multiuser MIMO systems, we show that the proposed method brings substantial performance gain over the standard vector perturbation.

An Efficient Feedback Compression for Large-Scale MIMO Systems

Large-scale multiple-input multiple-output(MIMO) systems with a large number of antennas at the basestation have drawn considerable interest because of potential ability to achieve high spectral efficiencies. In order to achieve optimal performance of large-scale MIMO systems, the basestation needs to know channel state information (CSI) perfectly. In terms of CSI acquisition, the basestation estimates the downlink channel through channel reciprocity in time division duplexing (TDD) or requires CSI feedback through the uplink in frequency division duplexing (FDD). Due to the large number of transmit antennas at the basestation, uplink CSI feedback would be a major hurdle in developing FDD large-scale MIMO systems. In this paper, we propose an efficient feedback compression technique for FDD large-scale MIMO systems. The proposed method reduces a dimension of vector quantization by grouping high correlated antenna elements. In fact, the proposed method invests a small portion of feedback resources to generate a grouped channel vector and the rest to quantize the grouped channel vector. Simulation results demonstrate that the proposed method achieves significant feedback overhead reduction over conventional methods.

A vector perturbation based user selection for multi-antenna downlink channels

Recent works on multi-user transmission techniques have shown that the linear growth of capacity in single user MIMO system can be translated to the multi-user MIMO scenario as well. In this paper, we propose a method pursuing performance gain of vector perturbation in multi-user downlink channels. Instead of exploiting the maximum available mobile users for communication, we employ small part of them as sacrifice lambs for improving quality of service (QoS) of active users. By controlling information and perturbation vector of sacrificing users, we get considerable improvement in the effective SNR, resulting in large gain in bit error performance. Simulation results show that the proposed method brings substantial performance gain over the standard vector perturbation.

Low complexity soft-input soft-output group detection for massive MIMO systems

In this paper, we present a novel soft-input soft-output detector for large-scale MIMO systems that offers substantial complexity reduction over the existing detectors and performance close to the full dimensional symbol detector. The proposed algorithm, termed soft-input soft-output successive group (SSG) detector, detects a subset of symbols successively with an aid of the preprocessing designed to suppress the inter-group interference. In fact, the proposed preprocessor mitigates the effect of interfering symbol groups using a priori information of the undetected groups and a posteriori information of the detected groups. Simulation results performed on large-scale multi-input multi-output (MIMO) systems demonstrate that the proposed SSG detector achieves significant complexity reduction over the conventional approaches with negligible performance loss.