Showing papers by "Amine Mezghani published in 2016"

MMSE precoder for massive MIMO using 1-bit quantization

Abstract: We propose a novel linear minimum-mean-squared-error (MMSE) precoder design for a downlink (DL) massive multiple-input-multiple-output (MIMO) scenario. For economical and computational efficiency reasons low resolution 1-bit digital-to-analog (DAC) and analog-to-digital (ADC) converters are used. This comes at the cost of performance gain that can be recovered by the large number of antennas deployed at the base station (BS) and an appropiate pre-coder design to mitigate the distortions due to the coarse quantization. The proposed precoder takes the quantization non-linearities into account and is split into a digital precoder and an analog precoder. We formulate the two-stage precoding problem such that the MSE of the users is minimized under the 1-bit constraint. In the simulations, we compare the new optimized precoding scheme with previously proposed linear precoders in terms of uncoded bit error ratio (BER).

Minimum BER precoding in 1-Bit massive MIMO systems

Abstract: 1-bit digital-to-analog (DACs) and analog-to-digital converters (ADCs) are gaining more interest in massive MIMO systems for economical and computational efficiency. We present a new precoding technique to mitigate the inter-user-interference (IUI) and the channel distortions in a 1-bit downlink MU-MISO system with QPSK symbols. The transmit signal vector is optimized taking into account the 1-bit quantization. We develop a sort of mapping based on a look-up table (LUT) between the input signal and the transmit signal. The LUT is updated for each channel realization. Simulation results show a significant gain in terms of the uncoded bit-error-ratio (BER) compared to the existing linear precoding techniques.

Downlink Achievable Rate Analysis in Massive MIMO Systems with One-Bit DACs

Abstract: In this letter, we investigate the downlink performance of massive multiple-input multiple-output (MIMO) systems where the base station is equipped with one-bit analogto-digital/digital-to-analog converters (ADC/DACs). Considering training-based transmission, we assume the base station (BS) employs the linear minimum mean-squared-error (LMMSE) channel estimator and treats the channel estimate as the true channel to precode the data symbols. We derive an expression for the downlink achievable rate for matched-filter (MF) precoding. A detailed analysis of the resulting power efficiency is pursued using our expression of the achievable rate. Numerical results are presented to verify our analysis. In particular it is shown that, compared with conventional massive MIMO systems, the performance loss in one-bit massive MIMO systems can be compensated for by deploying approximately 2.5 times more antennas at the BS.

Channel estimation in massive MIMO systems using 1-bit quantization

Abstract: Massive MIMO plays an important role for future cellular networks since the large number of antennas deployed at the base station (BS) is capable of increasing the spectral efficiency and the amount of usable spectrum. Using 1-bit analog-to-digital converters can drastically reduce the resulting complexity and power consumption. Therefore, we investigate the channel estimation in 1-bit massive MIMO, where several single-antenna mobile stations (MSs) transmit training sequences to the BS, whose antennas acquire only 1-bit measurements. The channels between the MSs and the BS antennas are described by their impulse responses. In particular, we consider sparse channel impulse responses having only a few non-zero taps. By combining the Expectation-Maximization algorithm for Maximum A Posteriori estimation with the sparse recovery method Iterative Hard Thresholding, we exploit the a priori knowledge of this sparsity and take the 1-bit quantization into account. Since the resulting channel estimation methods combine a good channel estimation performance demonstrated by numerical results with a small computational complexity, they are promising methods for channel estimation in 1-bit massive MIMO.

Weighted Sum Rate Maximization for Multi-User MISO Systems with Low Resolution Digital to Analog Converters

Abstract: We study the problem of downlink beamforming for the Weighted Sum Rate maximization (WSR) of Multi-User Multiple-Input-Single-Output systems with low-resolution Digital-to-Analog Converters (DACs) in a single-cell setup. The DACs, modeled as quantizers, are performing a nonlinear operation on the signals and are linearized using Bussgang decomposition and a linear approximation of the covariance of quantized signals. For the maximization of the WSR of the linearized system, we propose a gradient-based solution and a lower-complexity heuristic solution, based on the structure of the globally optimal solution. Through numerical simulations, we show that taking quantization into account in the filter design results in significant performance improvement when the number of transmit antennas is comparable to the number of users. When the number of transmit antennas becomes much larger than the number of users, it is found that the heuristic solution achieves near-optimal performance and that a quantization-aware design becomes less important.

How Much Training Is Needed in One-Bit Massive MIMO Systems at Low SNR?

Abstract: This paper considers training-based transmissions in massive multi-input multi-output (MIMO) systems with one-bit analog-to-digital converters (ADCs). We assume that each coherent transmission block consists of a pilot training stage and a data transmission stage. The base station (BS) first employs the linear minimum mean-square-error (LMMSE) method to estimate the channel and then uses the maximum-ratio combining (MRC) receiver to detect the data symbols. We first obtain an approximate closed-form expression for the uplink achievable rate in the low SNR region. Then based on the result, we investigate the optimal training length that maximizes the sum spectral efficiency for two cases: i) The training power and the data transmission power are both optimized; ii) The training power and the data transmission power are equal. Numerical results show that, in contrast to conventional massive MIMO systems, the optimal training length in one-bit massive MIMO systems is greater than the number of users and depends on various parameters such as the coherence interval and the average transmit power. Also, unlike conventional systems, it is observed that in terms of sum spectral efficiency, there is relatively little benefit to separately optimizing the training and data power.

How Much Training is Needed in One-Bit Massive MIMO Systems at Low SNR?

Abstract: This paper considers training-based transmissions in massive multi-input multi-output (MIMO) systems with one-bit analog-to-digital converters (ADCs). We assume that each coherent transmission block consists of a pilot training stage and a data transmission stage. The base station (BS) first employs the linear minimum mean-square-error (LMMSE) method to estimate the channel and then uses the maximum-ratio combining (MRC) receiver to detect the data symbols. We first obtain an approximate closed-form expression for the uplink achievable rate in the low SNR region. Then based on the result, we investigate the optimal training length that maximizes the sum spectral efficiency for two cases: i) The training power and the data transmission power are both optimized; ii) The training power and the data transmission power are equal. Numerical results show that, in contrast to conventional massive MIMO systems, the optimal training length in one-bit massive MIMO systems is greater than the number of users and depends on various parameters such as the coherence interval and the average transmit power. Also, unlike conventional systems, it is observed that in terms of sum spectral efficiency, there is relatively little benefit to separately optimizing the training and data power.

Turbo-Like Joint Data-and-Channel Estimation in Quantized Massive MIMO Systems

Abstract: We consider joint channel-and-data estimation for quantized massive MIMO systems. The estimation for both parts follows a turbo-like fashion, where the estimation error of one step is treated as additive Gaussian noise for the other. An approximate belief propagation algorithm is employed to obtain an approximate minimum mean square error estimate of both the data and channel. The performance of our scheme is compared to a Bayes optimal joint channel-and-data estimation approach by Wen et al. (2015). We observe that 10 turbo iterations are enough to achieve similar performance with lower complexity.

Analysis of one-bit quantized ZF precoding for the multiuser massive MIMO downlink

Abstract: We present a mathematical analysis of linear precoders for downlink multiuser massive MIMO. In the scenario we consider, one-bit digital-to-analog converters are employed at the basestation antennas for mitigating power consumption and hardware complexity. Using the Bussgang theorem, a probability-of-error analysis for a general precoder is presented. The special case of zero-forcing (ZF) precoders is considered and simplified error expressions are derived for asymptotic scenarios. Our analysis illustrates that the probability of error for the quantized ZF precoder depends primarily on the ratio of the number of antennas to the number of users. Our simulations also show that the quantized ZF precoder outperforms the high complexity ML encoder for low-to medium-SNRs.

Blind Estimation of Sparse Multi-User Massive MIMO Channels

Abstract: We provide a maximum likelihood formulation for the blind estimation of massive mmWave MIMO channels while taking into account their underlying sparse structure. The main advantage of this approach is the fact that the overhead due to pilot sequences can be reduced dramatically especially when operating at low SNR per antenna. Thereby, the sparsity in the angular domain is exploited as a key property to enable the unambiguous blind separation between user's channels. On the other hand, as only the sparsity is assumed, the proposed method is robust with respect to the statistical properties of the channel and data and allows the estimation in rapidly time-varying scenarios and eventually the separation of interfering users from adjacent base stations. Additionally, a performance limit is derived based on the clairvoyant Cramer Rao lower bound. Simulation results demonstrate that this maximum likelihood formulation yields superior estimation accuracy with reasonable computational complexity and limited model assumptions.

Optimal Design of Energy and Spectral Efficiency Tradeoff in One-Bit Massive MIMO Systems.

Abstract: Author(s): Li, Yongzhi; Tao, Cheng; Mezghani, Amine; Swindlehurst, A Lee; Seco-Granados, Gonzalo; Liu, Liu | Abstract: This paper considers a single-cell massive multiple-input multiple-output (MIMO) system equipped with a base station (BS) that uses one-bit quantization and investigates the energy efficiency (EE) and spectral efficiency (SE) trade-off. We first propose a new precoding scheme and downlink power allocation strategy that results in uplink-downlink SINR duality for one-bit MIMO systems. Taking into account the effect of the imperfect channel state information, we obtain approximate closed-form expressions for the uplink and downlink achievable rates under duality with maximum ratio combining/matched-filter and zero-forcing processing. We then focus on joint optimization of the competing SE and EE objectives over the number of users, pilot training duration and operating power, using the weighted product method to obtain the EE/SE Pareto boundary. Numerical results are presented to verify our analytical resultsand demonstrate the fundamental tradeoff between EE and SE for different parameter settings.

Spatial coding based on minimum BER in 1-bit massive MIMO systems

Abstract: We consider a downlink 1-bit quantized multiuser (MU) multiple-input-multiple-output (MIMO) system, where 1-bit digital-to-analog (DACs) and analog-to-digital converters (ADCs) are used at the transmitter and the receiver for economical and computational efficiency. We end up with a discrete memoryless channel with input and output vectors belonging to the QPSK constellation. In the context of massive (MIMO) systems the number of base station (BS) antennas is much larger than the number of receive antennas. This leads to high input cardinality of the channel. In this work we introduce a method to reduce the input set based on the mimimum bit-error-ratio (BER) criterion combined with a non-linear precoding technique. This method is denoted as spatial coding. Simulations show that this spatial coding improves the BER behavior significantly removing the error floor due to coarse quantization.

Blind Estimation of Sparse Multi-User Massive MIMO Channels

Abstract: Author(s): Mezghani, Amine; Swindlehurst, A Lee | Abstract: We provide a maximum likelihood formulation for the blind estimation of massive mmWave MIMO channels while taking into account their underlying sparse structure. The main advantage of this approach is the fact that the overhead due to pilot sequences can be reduced dramatically especially when operating at low SNR per antenna. Thereby, the sparsity in the angular domain is exploited as a key property to enable the unambiguous blind separation between user's channels. On the other hand, as only the sparsity is assumed, the proposed method is robust with respect to the statistical properties of the channel and data and allows the estimation in rapidly time-varying scenarios and eventually the separation of interfering users from adjacent base stations. Additionally, a performance limit is derived based on the clairvoyant Cram #x27;er Rao lower bound. Simulation results demonstrate that this maximum likelihood formulation yields superior estimation accuracy with reasonable computational complexity and limited model assumptions.

Precoding under Instantaneous Per-Antenna Peak Power Constraint

Abstract: We consider a multi-user (MU) multiple-input-single-output (MISO) downlink system with M single-antenna users and N transmit antennas with a nonlinear power amplifier (PA) at each antenna. Instead of emitting constant envelope (CE) signals from the antennas to have highly power efficient PAs, we relax the CE constraint and allow the transmit signals to have instantaneous power less than or equal to the available power at each PA. The PA power efficiency decreases but simulation results show that the same performance in terms of bit-error-ratio (BER) can be achieved with less transmitted power and less PA power consumption. We propose a linear and a nonlinear precoder design to mitigate the multi-user interference (MUI) under the constraint of a maximal instantaneous per-antenna peak power.

A Bayesian Sparse Reconstruction Framework for Mitigation of Non-Linear Effects in OFDM Systems

Abstract: The mitigation of nonlinear distortion caused by power amplifiers (PA) in Orthogonal Frequency Division Multiplexing (OFDM) systems is an essential issue to enable energy efficient operation. We propose a new algorithm for receiver-based clipping estimation in OFDM systems that combines the existing Iterative Hard Thresholding method with a novel Bayesian framework to estimate clipping parameters at the receiver. We avoid the use of pilots and formulate the recovery problem solely on reliably detected sub-carriers. We also develop a new criterion for selecting these reliable carriers that takes into account the channel code. Through simulations, we show that the proposed technique outperforms the existing methods both in terms of BER and speed.