In 3D massive MIMO (multiple-input multiple-output) systems, especially in time division duplex (TDD) mode, a base station (BS) relies on the uplink sounding signals from mobile stations to obtain the spatial information for downlink MIMO processing. Accordingly, multi-dimensional parameter estimation of MIMO channel becomes crucial for such systems to realize the predicted capacity gains. In this paper, we study the joint estimation of elevation and azimuth angles as well as the delay parameters for 3D massive MIMO orthogonal frequency division multiplexing (OFDM) systems under a parametric channel modeling. To be specific, we introduce a matrix-based joint parameter estimation method, and analytically characterize its performance for massive MIMO OFDM systems. Results show that antenna array configuration at the BS plays a critical role in determining the underlying channel estimation performance, and the characterized MSEs match well with the simulated ones. Also, the parametric channel estimation outperforms the MMSE-based channel estimation in terms of the correlation between the estimated channel and the real channel.

This work introduces efficient schemes for adaptive vertical downtilt beamforming in Long Term Evolution Advanced (LTE-A) networks equipped with active antenna systems (AAS). A framework to determine cell-specific downtilt angles is proposed and solutions are provided with affordable complexities for practical applications. Instead of utilizing a single fixed downtilt, a cell-specific downtilt is obtained and adaptively adjusted across the system, which not only increases signal strength for desired users but also provides better control of inter-cell interference in the system. System level simulation shows that the proposed schemes achieve up to 10% cell edge and 5% cell average spectrum efficiency gain compared to the baseline system with fixed downtilt without involving complex coordination among cells.

Small cells have become an integral component in meeting the increased demand for cellular network capacity. Cloud radio access networks (C-RAN) have been proposed as an effective means to harness the capacity benefits of small cells at reduced capital and operational expenses. With the baseband units (BBUs) separated from the radio access units (RAUs) and moved to the cloud for centralized processing, the backhaul between BBUs and RAUs forms a key component of any C-RAN. In this work, we argue that a one-one mapping of BBUs to RAUs is highly sub-optimal, thereby calling for a functional decoupling of the BBU pool from the RAUs. Further, the backhaul architecture must be made re-configurable to allow the mapping between BBUs and RAUs to be flexible and changed dynamically so as to not just optimize RAN performance but also energy consumption in the BBU pool. Towards this end, we design and implement the first OFDMA-based C-RAN test-bed with a reconfigurable backhaul that allows 4 BBUs to connect flexibly with 4 RAUs using radio-over-fiber technology. We demonstrate the feasibility of our system over a 10 km separation between the BBU pool and RAUs. Further, real world experiments with commercial off-the-shelf WiMAX clients reveal the performance benefits of our reconfigurable backhaul in catering effectively to heterogeneous user (static and mobile clients) and traffic profiles, while also delivering energy benefits in the BBU pool.

This work introduces a complete framework for three-dimensional (3D) channel extension based on two-dimensional (2D) channel models. In the 3D channel modeling, besides the azimuth angles, the multipath components of a receive-transmit pair are determined by elevation angles, correlations between elevation and azimuth, and the 3D configurations of a user equipment's (UE) location and velocity. The 3D channel characteristics for an active antenna system (AAS) are studied in terms of aggregated array gain and channel covariance matrix. The 3D channel model is generated and examined through Monte Carlo simulations and a comparison of fixed vertical beamforming and adaptive vertical beamforming is presented. The comparison shows that adaptive vertical beamforming can potentially provide up to 8dB improvement in coupling loss. In addition, it is shown that for line-of-sight (LoS) the 3D channel covariance matrix can be decoupled into two independent covariance matrices with reduced sizes that effectively leads to reduced feedback overhead. For non-line-of-sight (NLoS), the correlation-based Kronecker model technique can be used to generate the 3D channel matrix with reduced complexity compared to the geometry-based technique.

Discrete Fourier transform (DFT) based channel estimation (CE) has been widely studied as a practical CE scheme over the OFDM based wireless systems. The conventional DFT-based channel estimation utilizes a transform domain cut-off filter to suppress the noise in the time domain. However, this method can suffer significant performance loss due to the channel impulse response (CIR) energy leakage, especially when the available pilot sub-carriers are confined to a small portion of the system bandwidth. In this paper, we propose an enhanced DFT-based channel estimation technique for the long term evolution (LTE) based cellular uplink. A sinc-null based noise power estimation method in conjunction with a dynamic noise removal technique is proposed to suppress the noise in the time domain and achieve better performance while keeping the complexity in check. Simulation results show that the proposed scheme not only achieves better mean square error (MSE) and block error rate (BLER) performance but also exhibits robustness to timing offsets compared to existing DFT based CE schemes.

We consider a downlink multi-user multi-input-multi-output (MU-MIMO) fading channel wherein the base station can schedule several user terminals on the same time-frequency resource. A severe practical problem in MU-MIMO is that when computing its feedback report, a user does not have an accurate estimate of the interference it might see (if scheduled) from the signals intended for the other co-scheduled users. This results in a mismatch between the user reported signal-to-interference-plus-noise-ratio (SINR) and the one it actually observes in the aftermath of scheduling. To alleviate this problem we propose to inform each user (in a slow or semi-static manner) about the rank of the precoding matrix that it should report, along with an estimate of the total number of streams that the base station expects to co-schedule on a time-frequency resource. The suggested rank and the expected total number of streams can be user-specific and together convey the expected total number of co-scheduled interfering streams to the intended user. Each user then computes one or more SINRs for all the precoding matrices having the suggested rank and reports its preferred precoding matrix along with the corresponding SINRs. The SINRs are computed after assuming that the co-scheduled interfering streams will be transmitted along vectors isotropically distributed in the orthogonal complement of the range of the precoding matrix being examined. Alternatively, the SINRs can be computed after assuming that the co-scheduled interfering streams will be transmitted along the worst-case choice of mutually orthogonal vectors that lie in the orthogonal complement. We show that the proposed solutions, while requiring negligible additional signalling overhead, mitigate the mismatch problem to a large extent and result in significant improvements in system throughput.

In this paper, we consider resource allocation in the the fourth generation multi antenna (4G MIMO) cellular uplink. In particular, we consider the two 4G standards, IEEE 802.16m and 3GPP LTE-A, that have recently been approved. We show that the uplink resource allocation problems in both 802.16m and LTE-A networks are NP-hard. We then propose constant-factor polynomial-time approximation algorithms for both these problems. We also provide linear programming (LP) based upper bounds to benchmark the performance of our proposed approximation algorithms. Simulations reveal that the proposed algorithms have excellent performance, much superior to their worst-case guarantees.

In this paper, an efficient closed-loop link adaption scheme consisting of adaptive modulation and coding as well as adaptive precoding is proposed for the 3GPP LTE-A uplink. The uplink envisaged in the LTE-A cellular network will support precoded multi-rank transmission from the users and have base stations with advanced non-linear receivers. Our proposed scheme considers one such advanced receiver, namely, the turbo successive interference cancelation (Turbo-SIC) receiver. The main difficulty in designing link adaptation schemes for these advanced non-linear receivers is that the soft-outputs of such receivers cannot be simply modeled using a scalar Gaussian channel characterized by a signal-to-interference-plus-noise ratio (SINR). To circumvent this difficulty, we employ SINRs corresponding to an ordered hard-decision SIC receiver, that can be explicitly expressed in closed form. The proposed scheme accurately predicts the turbo SIC performance and results in a perfect match between the predicted and actually simulated performance in terms of block error rate (BLER) and the spectrum efficiency. Realistic and extensive simulations reveal that it provides the highest actual spectrum efficiency compared to other competing schemes.

In this paper, we consider a downlink (DL) multiuser (MU) multi-input-multi-output (MIMO) channel with linear precoding where the base station simultaneously schedules several user terminals on the same frequency sub-band. We assume imperfect (or quantized) per-user channel state information at the base station and present two types of channel state information (CSI) reports from user terminals, namely, the CSI report that assumes the single-user (SU) MIMO transmissions and the enhanced CSI feedback report that assumes the MU-MIMO transmissions, and in the latter case we consider both uniform and nonuniform power allocations. To improve the MU-MIMO system performance, we propose signal-to-interference-plus-noise ratio (SINR) approximation techniques that utilize the quantized CSI available at the base station and improve the rate matching. We also introduce user pooling techniques which enable a reduction in feedback signaling overhead via per-user feedback mode selection. The proposed techniques also allow unconstrained user pairing at the base station scheduler and hence enable dynamic switching between SU and MU MIMO transmissions. The simulation results demonstrate the efficiency of the proposed MU-MIMO enhancement techniques.

In this paper, we propose a practical and systematic approach to implement the MIMO transmission with rank adaptation for 60 GHz systems. In the 60 GHz system with multiple antennas, the transmit and receive (Tx-Rx) antenna arrays are grouped into a number of subarrays with a predetermined subarray separation based on the derived geometrical criteria of creating high rank MIMO in LoS environments. We first apply an enhanced blind beamforming technique based on a stochastic gradient algorithm (SGA) for the inner-subarray antennas, which does not require channel state information (CSI) at either the transmitter or the receiver. Secondly, the composite MIMO channel, as a joint effect of Tx-Rx beamforming and the channel impulse response, can be estimated with much reduced complexity. Finally, the MIMO transmission with rank adaptation is performed by adaptively selecting the better scheme out of the high-rank spatial multiplexing and the rank-1 beamforming whichever gives higher system throughput. Simulation results show that high-rank spatial multiplexing and rank-1 beamforming outperform each other at different geometrical placements and transmit power settings. The proposed MIMO transmission with rank adaptation offers significant performance gain especially at high signal-to-noise ratio (SNR) regions.

In this work we design efficient soft output demodulators (also referred to as fast soft demodulators) for the 2 × 2 Golden code, which is a promising candidate space time block code in the evolving IEEE and 3GPP cellular standards. For this code, the naive approach for maximum likelihood (ML) hard decision as well as soft-output demodulation entails a complexity of O(M4), where M denotes the cardinality of the QAM constellation from which the underlying modulated symbols are drawn. In contrast, our efficient demodulator exploits the structure of the code and yields the ML decision and soft outputs in the form of exact max-log log-likelihood ratios (LLRs) with an O(M2.5) complexity. Moreover, we also design a sub-optimal soft output demodulator that has an O(M0.5) complexity comparable to that of the linear minimum mean square error (LMMSE) based demodulator but results in substantial performance gains.

In this paper, the downlink rate quantization and cross-layer scheduling design are investigated in multiuser multiple-input single-output (MISO) systems with imperfect channel state information at transmitter (CSIT). We shall propose a systematic analytical design framework based on information theoretical approach. To capture the effect of the potential packet outage, we introduce the average system goodput, which measures the average b/s/Hz delivered to the mobiles successfully, as the system performance objective. Numerical results demonstrate that, by considering the statistics of CSIT errors into the design, the proposed scheduling scheme provides significant performance enhancement.

Cross-layer design for multi-antenna system has been shown to offer high spectral efficiency for multiuser system due to the multi-user diversity and the spatial multiplexing in wireless fading channels. Yet, most of the existing works assumed homogeneous user type (pure delay-insensitive data application). The effect of source statistics, queueing delay and application level requirements were completely ignored. In this paper, we shall propose an analytical cross layer design framework for multi-user multi-antenna systems for wireless multimedia applications with heterogeneous delay requirements. To take delay sensitive users into consideration, we shall utilize both queueing theory and information theory to model the system dynamics. A novel cross layer scheduler is designed to exploit the spatial multiplexing gain as well as the multi-user selection diversity gain, and at the same time maintain the delay constraints of the delay sensitive users. Simulation results indicate that the proposed scheduling design provides desirable delay performance and high throughput gain compared to other heuristic schemes.

This letter studies the performance of uplink cross layer design for multi-antenna systems with outdated channel state information (CSI). We consider a multi-user system with one base station (with n/sub R/ receive antennas) and K mobile users (each with single transmit antenna). The multi-user physical layer is modeled based on information theoretical framework and the cross layer design can be cast as an optimization problem. In view of the high computation complexity in the optimal solution, we propose a low complexity genetic algorithm as suboptimal solution. We found that with outdated CSI, there is significant degradation in the spatial multiplexing and multi-user diversity gain due to potential packet transmission outage as well as misscheduling. To address the poor performance in the presence of outdated CSI, we propose two simple but effective empirical solutions, namely the rate quantization and rate discounting, to tackle the packet outage problem. For instance, it is well-known that rate quantization imposes system capacity loss in systems with perfect CSI. However, we found that rate quantization can enhance the robustness of system capacity with respect to outdated CSI.

In this paper, we investigate the downlink scheduling and rate adaptation design in multiuser multiple-input single-output (MISO) systems with imperfect channel state information at the transmitter (CSIT). We shall propose a systematic design framework to address the multi-user scheduling and rate adaptation combined design based on information theoretical framework. We assume time division duplex (TDD) systems with quasi-static fading channels where packet outage is an important concern with imperfect CSIT. We introduce the average system goodput as the measure of system performance in the presence of CSIT error and effective albeit suboptimal solutions are proposed to maximize the average system goodput. Given any target outage probability /spl isin/, a Gaussian approximation on the outage probability is derived and the closed-form solution is obtained for the cross-layer scheduling design based on this approximation. Numerical results demonstrate that, by considering the statistic of CSIT errors into the design, the proposed schemes provide significant goodput enhancement.

This paper studies the performance of proportional fair space time scheduling in the presence of imperfect channel state information (CSI). We consider the uplink scheduling in the multiuser single-input-multiple-output (SIMO) system. There are K mobiles (with single antenna) and a base station (with n/sub R/ receive antennas) in the system. The space time scheduling problem is formulated with proportional fair (PF) utility function and a novel heuristic solution is presented. We found that in general, with perfect CSI, the system performance improves as n/sub R/ increases due to the extra spatial dimensions. However, at moderate CSI errors, the system performance saturates with respect to SNR and n/sub R/. This is due to the misscheduling and packet outage problem. To address the poor performance in the presence of CSI error, we proposed a low-complexity enhancement, namely proportional rate discounting, to enhance the scheduler's robustness in the presence of CSI error.