A genetic antenna selection algorithm for massive MIMO systems with channel estimation error

In this paper, we compare I/Q imbalance compensation schemes for massive multi-input multi-output (MIMO) system in presence of I/Q imbalance at both transmitter and receiver. Transceiver I/Q imbalance can be compensated jointly by the base station or separately by both base station and user terminals. We show that the achievable sum rate of self-compensation scheme which compensates its I/Q imbalance separately is severely deteriorated in presence of channel estimation error and the I/Q imbalance parameter estimation error. In contrast, the achievable sum rate of the joint compensation scheme with RZF precoding in the base station shows robust performance against channel estimation error and the I/Q imbalance parameter estimation error.

One bottleneck of the massive multi-input multi-output (MIMO) systems is the pilot contamination caused by users sharing the non-orthogonal pilots. The pilot contamination contributes significantly to the channel estimation error at the base station (BS) and finally results in the saturation of user rates. This paper firstly analyzes the mean square error (MSE) of the channel estimation at the BS. Inspired by the results, this paper then proposes a user pilot scheduling algorithm which preferentially minimizes the channel estimation MSE of users with poorer channel qualities. Simulation results show that the algorithm significantly improves the achievable rates of the poorer users as well as the system sum rate compared with the conventional way.

The evolving 5G standards promise green communications with enhanced data services and significant link reliability. Massive multi input multi output (MIMO) techniques are the driving force behind green communications, since they provide better energy efficiency with reduced transmit power. The massive data generated from such mobile communication systems, is a rich data source of great value. Procuring useful analytics from this precious resource, a big data aware 5G mobile communication system can be developed. A particular choice of big analytics brings in the concept of large random matrix models and single ring law. In this paper, first, big data analytics is performed in the context of a mobile user communicating to, either a massive MIMO or a massive MIMO orthogonal frequency division multiplexing (OFDM) system. Constructive insights such as transmitted (source) signal correlation analysis (attributed to certain network events), channel correlation analysis (attributed to user mobility) have been extracted. Ring law also has its roots in signal detection, which suggests that few other signal detection algorithms may be suitable candidates for signal/channel correlation analysis. Therefore, second, a proposed extension of an information theoretic criterion (ITC) based signal detection algorithm, for correlation analysis, is compared with ring law. Using massive MIMO and MIMO-OFDM system simulations, the said correlation analyses have confirmed the prevalence of ring law. Third, it is deduced that integrating big data analytics with massive MIMO system improves spectral efficiency.

This paper investigates the massive multi-input multi-output (MIMO) system in practical deployment scenarios, in which, to balance the economic and energy efficiency with the system performance, the number of radio frequency (RF) chains is smaller than the number of antennas. The base station employs antenna selection (AS) to fully harness the spatial multiplexing gain. Conventional AS techniques require full channel state information (CSI), which is time-consuming as the antennas cannot be simultaneously connected to the RF chains during the channel estimation process. To tackle this issue, we propose a novel joint channel prediction and AS (JCPAS) framework to reduce the CSI acquisition time and improve the system performance under temporally correlated channels. Our proposed JCPAS framework is a fully probabilistic model driven by deep unsupervised learning. The proposed framework is able to predict the current full CSI, while requiring only a historical window of partial observations. Extensive simulation results show that the proposed JCPAS can significantly improve the system performance under temporally correlated channels, especially for very large-scale systems with highly correlated channels. multi-output (MIMO) system in practical deployment scenarios, in which, to balance the economic and energy efficiency with the system performance, the number of radio frequency (RF) chains is smaller than the number of antennas. The base station employs antenna selection (AS) to fully harness the spatial multiplexing gain. Conventional AS techniques require full channel state information (CSI), which is time-consuming as the antennas cannot be simultaneously connected to the RF chains during the channel estimation process. To tackle this issue, we propose a novel joint channel prediction and AS (JCPAS) framework to reduce the CSI acquisition time and improve the system performance under temporally correlated channels. Our proposed JCPAS framework is a fully probabilistic model driven by deep unsupervised learning. The proposed framework is able to predict the current full CSI, while requiring only a historical window of partial observations. Extensive simulation results show that the proposed JCPAS can significantly improve the system performance under temporally correlated channels, especially for very large-scale systems with highly correlated channels.

The topic of anonymous unmanned aerial vehicle (UAV) localizing based on angle estimation has been frequently discussed in the past few years. However, the existing methodologies are inefficient in a massive sensor arrays scenario. To avoid such drawback, a cooperative three-dimensional (3D) positioning methodology is introduced. The critical idea of the proposed localizing method is to estimate the two-dimensional angle of the anonymous UAV via a polarized massive multi-input multi-output (MIMO) system. To reduce the computational burden and explore the nature of the multi-dimensional data, a tensor compressive sampling (TCS) framework is proposed. Moreover, a closed-form estimation strategy is developed for 2D direction finding. Our framework is shown to be more efficient than the existing algorithm in terms of hardware/software complexity. Besides, it is suitable for a polarized MIMO system with an arbitrary array geometry. Several simulation examples are provided to show its improvement of the new methodology.

The main objective of this paper is to provide an extensive and complete examination on the effect of practical impairments such as channel estimation errors (CEEs) and feedback delay (FD) on the performance of diversity schemes over Nakagami‐mfading channels. Under erroneous channel estimation and outdated feedback cases, statistical expressions and several performance metrics related to the post‐processing signal‐to‐noise ratio (SNR) are derived for four different diversity schemes: transmit antenna selection (TAS)/orthogonal space–time block coding, TAS/maximal‐ratio transmission (MRT), MRT/receive antenna selection (RAS), and joint transmit and RAS. Exact analytical expressions for outage probability and average error rates ofM‐ary modulations are derived in order to provide insightful perspectives on the capacity and error performance of diversity schemes that experience both CEE and FD. The asymptotic diversity order of the investigated diversity schemes are derived via a high‐SNR approximation approach. In order to assess the real‐world performance of the investigated diversity schemes and to observe their robustness or sensitivities in practical imperfections, various configurations are considered together with several performance comparisons. Also, Monte Carlo simulations are performed in order to validate the theoretical results. Copyright © 2014 John Wiley & Sons, Ltd.

This paper investigates the achievable uplink spectral efficiency (SE) of multi-cell massive multi-input multioutput (MIMO) systems with the low-resolution analog-to-digital converter (ADC) receiver architecture. For the maximum ratio combining detector, we derive an approximate expression for the achievable SE of the system, which enables the characterization of the impact of key system parameters such as transmit power, the number of antennas, and the ADC resolution bits on the achievable SE. Moreover, simple power scaling laws are presented.

Generally, the downlink channel state information (CSI) used for precoding is conveyed back in massive multi-input multi-output (MIMO) systems with frequency division duplex (FDD) mode, which costs too much bandwidth. However, most of CSI compression algorithms including deep learning based schemes show poor performance with high compression ratio (CR) in ourdoor scenery. In this letter, we propose a new neural network that utilizes the deep neural network (DNN) to improve the learning ability of residual network and a novel activation function to adapt the neural network. Simulation results show that the proposed CsiNet+DNN method exhibits better performance than exsiting methods in recovery quality and accuracy, which also achieves remarkable robustness at noise situations.

A generalized uplink cell-free massive multi-input multi-output (MIMO) system assuming access points (APs) are paired with mixed-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) is investigated in this article. Specifically, the mixed-ADC/DAC architecture sacrifices part of performance gains and advocates a few low-precision ADCs/DACs to reduce the power consumption. Based on this antenna structure, we derive closed-form achievable rate expressions for maximal ratio combining (MRC) receiver filters by drawing support from the additive quantization noise model (AQNM) and analyze the impacts of the key design parameters, i.e., the number of total antenna arrays, the precision of coarse ADCs/DACs, the proportion of ideal ADCs/DACs in the mixed ADCs/DACs structure, and the power control strategy. In addition, the energy efficiency (EE) of the considered mixed-ADC/DAC architecture is also explored by recalling a generic power consumption model. Aside from this, to guarantee uniformly good service wherever users are geographically located, a max–min fairness power optimization algorithm is proposed, which can be determined by solving a geometric programming problem. Numerical simulations validate our theoretical analyses and examine the effectiveness of the proposed algorithm.

We propose a new hybrid precoding technique for massive multi-input multi-output (MIMO) systems using spatial channel covariance matrices in the analog precoder design. Applying a regularized zero-forcing precoder for the baseband precoding matrix, we find an unconstrained analog precoder that maximizes signal-to-leakage-plus-noise ratio (SLNR) while ignoring analog phase shifter constraints. Subsequently, we develop a technique to design a constrained analog precoder that mimics the obtained unconstrained analog precoder under phase shifter constraints. The main idea is to adopt an additional baseband precoding matrix, which we call a compensation matrix. We analyze the SLNR loss due to the proposed hybrid precoding compared to fully digital precoding, and determine which factors have a significant impact on this loss. In the simulations, we show that if the channel is spatially correlated and the number of users is smaller than the number of RF chains, the SLNR loss becomes negligible compared to fully digital precoding. The main benefit of our method stems from the use of spatial channel matrices in such a way that not only is each user's desired signal considered, but also the inter-user interference is incorporated in the analog precoder design.

In this paper, channel estimation problem for downlink massive multi-input multi-output (MIMO) system is considered. Motivated by the observation that channels in massive MIMO systems may exhibit sparsity and the path delays vary slowly in one uplink-downlink process even though the path gains may be quite different, we propose a novel channel estimation method based on the compressive sensing. Unlike the conventional methods which do not make use of any a priori information, we estimate the probabilities that the paths are nonzero in the downlink channel by exploiting the channel impulse response (CIR) estimated from the uplink channel estimation. Based on these probabilities, we propose the Weighted Structured Subspace Pursuit (WSSP) algorithm to efficiently reconstruct the massive MIMO channel. Simulation results show that the WSSP could reduce the pilots number significantly while maintain decent channel estimation performance.

Pilot contamination (PC) is a stumbling block in theway of realizing massive multi-input multi-output (MIMO) systems. This contribution proposes a location-aware channel estimation-enhanced massive MIMO system employing time-division duplexing protocol, which is capable of significantly reducing the inter-cell interference caused by PC and, therefore, improving the achievable system performance. Specifically, we present a novel location-aware channel estimation algorithm, which utilizes the property of the steering vector to carry out a fast Fourier transform-based post-processing after the conventional pilot-aided channel estimation for mitigating PC. Our asymptotic analysis proves that this post-processing is capable of removing PC from the interfering users with different angle-of-arrivals (AOAs). Since in practice the AOAs of some users may be similar, we further present a location-aware pilot assignment method to ensure that users utilizing the same pilot have distinguishable AOAs, in order to fully benefit from the location-aware channel estimation. Simulation results demonstrate that the proposed scheme can dramatically reduce the inter-cell interference caused by the re-use of the pilot sequence and improve the overall system performance significantly, while only imposing a modest extra computational cost, in comparison with the conventional pilot-aided channel estimation.

Pair-wise error probability (PEP) is analyzed in the presence of channel estimation error (CEE) for orthogonal frequency division multiplexing (OFDM) in a quasi-static Rayleigh fading channel. Subcarriers in OFDM are grouped in an equi-spaced manner with the number of subcarriers in a group equal to the number of channel taps. One group is dedicated for training and the rest are for data transmission. A linear minimum mean square error (LMMSE) based channel estimation for coherent detection is considered in this paper. This enables the signal dependent CEE to be uncorrelated to the data. Since ML decoding employed with perfect CSI becomes suboptimal in the presence of CEE, an optimal ML decoding is derived. It is observed from our training-based PEP expression that the CEE does not reduce the diversity order but contributes to a loss of coding gain. Moreover, a code design criterion is established. To reduce the loss of coding gain, an optimal training scheme is developed, based on the PEP expression. Loss of performance due to an imperfect channel estimate is quantified in terms of bounds on bit error probability (BEP) for high SNR. Our analytical findings are corroborated by simulation examples.

Lower bounds are computed on the throughput of packet-switched space-time coded systems with delay constraints. The delay constraints are accounted for by defining a delay-limited throughput as the instantaneous throughput of each packet that can be maintained with a certain probability. The analysis assumes quasi-static and independent fading across packets, packet retransmissions until success, and negligible overhead for acknowledgment packets and guard times. Upper bounds on the outage probabilities of orthogonal space-time block codes (OSTBC) and spatial multiplexing with horizontal encoding (SM-HE) are computed in the presence of channel estimation errors to obtain lower bounds on the delay-limited throughput. The target physical-layer data rate is selected at each signal-to-noise ratio (SNR) to maximize the lower bounds on the throughput. The results indicate that higher throughput is achieved using OSTBC at low SNR and SM-HE at high SNR. Furthermore, the loss of throughput due to channel estimation errors is quantified.

This paper compares the performance of a MIMO system applying a Mismatched Maximum Likelihood Detector (MMLD) with that applying a Generalized Likelihood Ratio Detector (GLRD) in the presence of channel estimation errors. Two sources of error are considered: errors due to noise and errors due to time variations induced by Doppler effects. The MMLD relies on the available channel estimate whereas the GLRD combines the channel estimate with a block of received data and uses a joint channel-symbol estimation approach to solve for the transmitted symbols. The GLRD is shown to outperform the MMLD whenever errors in channel estimation are incurred and the performance gap depends on the amount of error. Furthermore, the GLRD provides a unified receiver structure which can also be applied to differentially encoded MIMO systems and to situation where no channel estimate is available as in blind MIMO. The improved performance of the GLRD comes with increased computational complexity. This problem is addressed through search efficient algorithms based on the branch-estimate-bound optimization framework. We analyze the performance of the GLRD and provide a tight upper bound on the BER of the system at high SNR. Simulation results are shown for a 2 × 2 MIMO system.