Gye-Tae Gil

Joint ML estimation of carrier frequency, channel, I/Q mismatch, and DC offset in communication receivers

This paper proposes a technique that jointly estimates the I/Q mismatch, dc offset, carrier frequency offset, and channel for receivers in frequency selective channels. The joint estimator is a data-aided technique that requires a training sequence and is derived based on the maximum likelihood (ML) criterion. The characteristics of the joint estimator are analyzed. In particular, it is shown that its accuracy almost achieves the Crame/spl acute/r-Rao lower bound (CRLB). The advantage of the proposed estimator over an existing method is demonstrated through computer simulation.

Exploiting spatial sparsity for estimating channels of hybrid MIMO systems in millimeter wave communications

Hybrid multiple input multiple output (MIMO) systems consist of an analog beamformer with large antenna arrays followed by a digital MIMO processor. Channel estimation for hybrid MIMO systems in millimeter wave (mm-wave) communications is challenging because of the large antenna array and the low signal-to-noise ratio (SNR) before beamforming. In this paper, we propose an open-loop channel estimator for mm-wave hybrid MIMO systems exploiting the sparse nature of mm-wave channels. A sparse signal recovery problem is formulated for channel estimation and solved by the orthogonal matching pursuit (OMP) based methods. A modification of the OMP algorithm, called the multi-grid (MG) OMP, is proposed. It is shown that the MG-OMP can significantly reduce the computational load of the OMP method. A process for designing the training beams is also developed. Specifically, given the analog training beams the baseband processor for beam training is designed. Simulation results demonstrate the advantage of the OMP based methods over the conventional least squares (LS) method and the efficiency of the MG-OMP over the original OMP.

A cost-based adaptive handover hysteresis scheme to minimize the handover failure rate in 3GPP LTE system

We deal with a cost-based adaptive handover hysteresis scheme for the horizontal handover decision strategies, as one of the self-optimization techniques that can minimize the handover failure rate (HFR) in the 3rd generation partnership project (3GPP) long-term evolution (LTE) system based on the network-controlled hard handover. Especially, for real-time operation, we propose an adaptive hysteresis scheme with a simplified cost function considering some dominant factors closely related to HFR performance such as the load difference between the target and serving cells, the velocity of user equipment (UE), and the service type. With the proposed scheme, a proper hysteresis value based on the dominant factors is easily obtained, so that the handover parameter optimization for minimizing the HFR can be effectively achieved. Simulation results show that the proposed scheme can support better HFR performance than the conventional schemes.

Channel Estimation via Orthogonal Matching Pursuit for Hybrid MIMO Systems in Millimeter Wave Communications

We propose an efficient open-loop channel estimator for a millimeter-wave (mm-wave) hybrid multiple-input multiple-output (MIMO) system consisting of radio-frequency (RF) beamformers with large antenna arrays followed by a baseband MIMO processor. A sparse signal recovery problem exploiting the sparse nature of mm-wave channels is formulated for channel estimation based on the parametric channel model with quantized angles of departures/arrivals (AoDs/AoAs), called the angle grids. The problem is solved by the orthogonal matching pursuit (OMP) algorithm employing a redundant dictionary consisting of array response vectors with finely quantized angle grids. We suggest the use of non-uniformly quantized angle grids and show that such grids reduce the coherence of the redundant dictionary. The lower and upper bounds of the sum-of-squared errors of the proposed OMP-based estimator are derived analytically: the lower bound is derived by considering the oracle estimator that assumes the knowledge of AoDs/AoAs, and the upper bound is derived based on the results of the OMP performance guarantees. The design of training vectors (or sensing matrix) is particularly important in hybrid MIMO systems, because the RF beamformer prevents the use of independent and identically distributed random training vectors, which are popular in compressed sensing. We design training vectors so that the total coherence of the equivalent sensing matrix is minimized for a given RF beamforming matrix, which is assumed to be unitary. It is observed that the estimation accuracy can be improved significantly by randomly permuting the columns of the RF beamforming matrix. The simulation results demonstrate the advantage of the proposed OMP with a redundant dictionary over the existing methods such as the least squares method and the OMP based on the virtual channel model.

Non-Data-Aided Approach to I/Q Mismatch Compensation in Low-IF Receivers

A digital signal processing (DSP) technique is presented that can compensate for the in-phase/quadrature-phase (I/Q) mismatch in low-intermediate frequency (IF) receivers. In particular, a non-data-aided (NDA) I/Q mismatch estimator is derived by exploiting the statistical independence between desired and image signals. The proposed technique obtains two baseband signals (uncompensated desired and image signals) from a digital IF signal and processes them to estimate and compensate for the I/Q mismatch. The mean-square error (MSE) of the estimate is analyzed. Computer simulation results indicate that the proposed technique can outperform existing adaptive DSP techniques that are based on the use of blind signal separation algorithms. It is observed that the image rejection ratio (IRR) of the proposed technique decreases monotonically with the number of observed samples for estimation, while that of conventional methods exhibits some floor.

Packet-scheduling algorithm by the ratio of transmit power to the transmission bits in 3GPP LTE downlink

Packet scheduler plays the central role in determining the overall performance of the 3GPP long-term evolution (LTE) based on packet-switching operation. In this paper, a novel minimum transmit power-based (MP) packet-scheduling algorithm is proposed that can achieve power-efficient transmission to the UEs while providing both system throughput gain and fairness improvement. The proposed algorithm is based on a new scheduling metric focusing on the ratio of the transmit power per bit and allocates the physical resource block (PRB) to the UE that requires the least ratio of the transmit power per bit. Through computer simulation, the performance of the proposed MP packet-scheduling algorithm is compared with the conventional packet-scheduling algorithms by two primary criteria: fairness and throughput. The simulation results show that the proposed algorithm outperforms the conventional algorithms in terms of the fairness and throughput.

Nondata-Aided I/Q Mismatch and DC Offset Compensation for Direct-Conversion Receivers

A digital-signal-processing technique is presented that can compensate for the frequency-independent inphase/quadrature (I/Q) mismatch and dc offset in zero-intermediate-frequency direct-conversion receivers. The proposed compensator consists of dc offset and I/Q mismatch estimators followed by a dc offset canceller and self-image suppressor. The estimators are nondata-aided (NDA) schemes that are derived by assuming that the desired signal is a random variable having an arbitrary circular symmetric distribution. The Cramer-Rao lower bounds (CRLBs) for NDA estimation are derived, and the characteristics of the estimators are analyzed in terms of mean-square errors. In particular, it is shown that for white Gaussian distributed signals, the accuracy of the estimates approaches the CRLBs as the number of observed samples increases. The characteristics of the proposed method as well as the validity of the analytical results are demonstrated through computer simulation.

Base Station Selection Technique for MMSE Joint Transmission in Downlink Cooperative MIMO System

Cooperative multiple input multiple output (MIMO) system is capable in mitigating co-channel interference (CCI) that limits the downlink capacity of cellular network. Among the joint transmission designs for base station (BS) cooperation, the linear joint transmissions with per-base power constraint is preferred due to its computational simplicity and reality. However, since the transmissions with per-base power constraint sometimes make BSs cannot fully utilize their usable power, it leads considerable capacity loss. In this paper, we propose a BS selection technique for MMSE joint transmitter in downlink cooperative MIMO system to relive the inefficient usage of power under per-base power constraint. To find the BS group which yields the highest capacity, we compare the system capacity of all the possible BS groups. Simulation results show that the BS selection significantly improves the system capacity compared to the conventional linear joint transmissions by overcoming the aforementioned power allocation problem.

The MAI Mitigation Scheme for OFDM-based Asynchronous Networks over Multi-Cell Environments

This paper investigates the problem of MAI (multiple access interference) occurrence when timing misalignment occurs in the downlink channel of OFDM-based networks. The adaptive guard band allocation scheme is mainly designed to achieve timing synchronization while reducing guard carrier redundancy and maintaining high channel throughput in asynchronous OFDM-based networks. Thus, the proposed scheme reduces the effect of MAI for users receiving multiple transmission signals from surrounding BSs (Base Stations). In a simulation, it is noteworthy that this adaptive carrier allocation is very effective in reducing MAI for asynchronous multi-cell network.

Joint ML estimation of I/Q mismatch, DC offset, carrier frequency, and channel for direct-conversion receivers

This paper proposes a technique to jointly estimate and compensate for I/Q mismatch, DC offset and carrier frequency offset in frequency selective channel environments for direct-conversion receivers. It estimates the unknown parameters in a joint maximum-likelihood (ML) sense from a training sequence. In addition, a design method of training sequences is proposed to minimize the mean-square errors (MSEs) of the proposed estimates. It is shown through simulation that the proposed technique can provide significant performance improvement over the existing schemes, and the proposed method of designing training sequences is effective as well.