Yongzhi Li

Channel Estimation and Performance Analysis of One-Bit Massive MIMO Systems

This paper considers channel estimation and system performance for the uplink of a single-cell massive multiple-input multiple-output system. Each receiver antenna of the base station is assumed to be equipped with a pair of one-bit analog-to-digital converters to quantize the real and imaginary part of the received signal. We first propose an approach for channel estimation that is applicable for both flat and frequency-selective fading, based on the Bussgang decomposition that reformulates the nonlinear quantizer as a linear function with identical first- and second-order statistics. The resulting channel estimator outperforms previously proposed approaches across all SNRs. We then derive closed-form expressions for the achievable rate in flat fading channels assuming low SNR and a large number of users for the maximal ratio and zero forcing receivers that takes channel estimation error due to both noise and one-bit quantization into account. The closed-form expressions, in turn, allow us to obtain insight into important system design issues such as optimal resource allocation, maximal sum spectral efficiency, overall energy efficiency, and number of antennas. Numerical results are presented to verify our analytical results and demonstrate the benefit of optimizing system performance accordingly.

Channel estimation and uplink achievable rates in one-bit massive MIMO systems

This paper considers channel estimation and achievable rates for the uplink of a massive multiple-input multiple-output (MIMO) system where the base station is equipped with one-bit analog-to-digital converters (ADCs). By rewriting the nonlinear one-bit quantization using a linear expression, we first derive a simple and insightful expression for the linear minimum mean-square-error (LMMSE) channel estimator. Then employing this channel estimator, we derive a closed-form expression for the lower bound of the achievable rate for the maximum ratio combiner (MRC) receiver. Numerical results are presented to verify our analysis and show that our proposed LMMSE channel estimator outperforms the near maximum likelihood (nML) estimator proposed previously.

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

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 analog-to-digital/digital-to-analog converters (ADC/DACs). We assume that the base station employs the linear minimum mean-squared-error 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 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 capacity investigation of a linear massive MIMO system using spherical wave model in LOS scenarios

Massive multiple-input multiple-output (MIMO) is a key technology for the 5th generation (5G) of wireless communication systems. The traditional plane wave channel model (PWM) is often not suitable for the large antenna structure, and in certain cases should be replaced by the more accurate spherical wave model (SWM). By using the spherical wave characterization method, this paper investigates the channel capacity performance of a linear massive MIMO system in line-of-sight (LOS) scenarios. Two types of access settings, the point to point (PTP) system and multi-user (MU) system, are considered. In the PTP setting, a geometrical optimization is performed to obtain configurations that are able to generate a full rank channel matrix for a linear massive MIMO system, which yields full spatial diversity even in LOS scenarios. Compared with the approximate and commonly applied rank-1 PWM, this is very useful for fixed wireless access and radio relay systems requiring high throughput. For the MU case, we compare the eigenvalue distributions of the LOS channels using the plane wave and spherical wave characterization method, and sum rate results are obtained by Monte Carlo simulations. The results show that MU systems using the more realistic and accurate SWM can achieve a higher sum rate than results from the PWM. This is beneficial and informative when designing massive MIMO wireless networks.摘要摘要大规模多天线是未来第五代移动通信系统的关键技术之一,当天线尺寸较大时,传统的球面波信道模型并不适合用于准确的描述传播环境。本文使用更为准确的球面波信道模型,研究了直射场景下大规模线性多天线系统(点对点接入和多用户接入两种架构)的信道容量特征。在点对点接入架构中,本文提出了一种基于位置的系统优化方式,相对于平面波信道模型获得的信道秩为1的结果,该优化方式可以让多天线系统在直射场景下获得信道满秩,从而获得全部空间分集增益;在多用户接入架构下,本文比较了球面波和平面波信道模型下的信道的特征值分布特征,通过使用蒙特卡洛仿真方法研究了系统的信道和速率,仿真结果表明,使用球面波信道模型的信道和速率高于平面波信道模型的信道和速率。创新点本文使用更为准确的球面波信道模型,研究了直射场景下大规模线性多天线系统(点对点接入和多用户接入两种架构)的信道容量特征。在点对点接入架构中,本文提出了一种基于位置的系统优化方式,相对于平面波信道模型获得的信道秩为1的结果,该优化方式可以让多天线系统在直射场景下获得信道满秩,从而获得全部空间分集增益;在多用户接入架构下,本文比较了球面波和平面波信道模型下的信道的特征值分布特征,通过使用蒙特卡洛仿真方法研究了系统的信道和速率,仿真结果表明,使用球面波信道模型的信道和速率高于平面波信道模型的信道和速率。

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

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.

Optimal Resource Allocation for Massive MIMO over Spatially Correlated Fading Channels

This paper investigates optimal resource allocation scheme for a typical uplink single-cell massive MIMO system over spatially correlated fading channels. To reduce the pilot contamination effect, we first propose the orthogonal user grouping strategy to partition the terminals into serval groups and assume the users in the same group reuse an identical orthogonal pilot sequence. Employing this strategy, we derive the closed-form expression of the achievable rate for the maximum-ratio combining (MRC) receiver. Then the expression is used to pursue a detailed analysis of the optimal resource allocation scheme so that the system energy efficiency can be maximized. It is found that the optimal training duration is identical to the number of user groups and, hence, can be selected dynamically according to the second order statistical information about the user channels. Numerical results are presented to verify our analysis, and show that the optimal resource allocation can provide significant performance gains compared to the traditional orthogonal training scheme in the high SNR regime.

Sum-Rate Capacity Investigation of Multiuser Massive MIMO Uplink Systems in Semi-Correlated Channels

The recent hot research topic of a massive multiple-output (MIMO) has been primarily studied based on the assumption that channel vectors are asymptotically pairwise orthogonal. This condition is not exactly satisfied in practice. Correlation will occur among antenna elements at the base- station antenna array in a dense scattering environment, or in a LOS propagation condition. In this paper, by using the Mellin transform of the eigenvalue distribution, we derive the semi- correlated sum-rate capacity of multi-antenna channels with an arbitrary number of antennas in closed form. Afterwards, we employ two commonly- used correlation models to compare the theoretical closed-form results and the simulated results and the final results show that they match well.

Analysis of an Upper Bound on the Effects of Large Scale Attenuation on Uplink Transmission Performance for Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is a potential candidate key technology for the 5G of wireless communication systems. In research to date, different power loss and shadowing effects on different antenna elements across the large arrays have been neglected. In this paper, based on an idealized propagation model, a new large scale attenuation (LSA) model is proposed, by which the large scale losses (path loss and shadowing effect) over the antenna array can be considered when establishing a massive MIMO channel model. By using this model, the spectral efficiency (in terms of bits/s/Hz sum-rate) of the maximum ratio combining (MRC) detector is derived for the uplink. The spectral efficiency performance of the zero forcing (ZF) detector also can be derived in the same manner. It can be found that the sum-rate performance (MRC and ZF) of our proposed channel model (assuming independent shadowing on all elements of the array) exceeds that of the conventional model (where the LSA effect is not included). Based upon our theoretical and simulation analysis, we have found that the spectral efficiency gap is mainly from the mean value of different shadowing effects across different elements, and the different path losses experienced by different antenna elements provide negligible contribution. This LSA model and the derived performance results could be beneficial and informative for the research, design and evaluation of the next generation of wireless communication system employing a massive MIMO configuration.

Analytical Approximation for Capacity in Massive MIMO Systems

In most existing research on massive multiple-input multiple-output (MIMO) systems, theoretical analysis relies on the assumption that the number of antennas at the base station is infinite. Under this assumption, channel vectors for different users will be asymptotically orthogonal; therefore, the calculation of channel capacity can be greatly simplified. However, in practical systems, the number of antennas is always finite, and the channel vectors for different users cannot be completely orthogonal. In this paper, we propose an analytical approximation for the channel capacity of massive MIMO systems, with a finite number of antennas. Numerical results show that the derived closed-form expression is more accurate than the one assuming that the channel vectors are asymptotically orthogonal.

Investigation of Sphere Decoder and Channel Tracking Algorithms for Media-Based Modulation over Time-Selective Channels

The performance of media-based modulation (MBM) systems, where additional information can be conveyed by the indices of the channel states created by RF mirrors, over time-selective channels is investigated. By transforming the MBM system model into a traditional MIMO system model, we first propose a reduced complexity sphere decoder algorithm. Then two channel tracking algorithms, which are based on least mean square adaptive filter and recursive least-squares adaptive filter, are employed in order to combat the performance loss caused by the time-varying channels. Numerical results show that the proposed sphere decoder and these two channel tracking algorithms perform well in MBM systems.