Multi-Stage Training Optimization for Pilot Compression and Channel Estimation in Massive MIMO Systems Under Quasi-Sparse Channel Environment

Recently, in a massive multiple-input multipleoutput (MIMO) system, deep learning (DL)-based beamforming method has been proposed for reducing the overhead associated with downlink training and uplink feedback. However, the DL-based approach is sensitive to the variation of the communication environment and requires a huge number of training data to ensure a certain level of performance. To reduce the number of required channel data for training a deep neural network (DNN), we introduce deep transfer learning (DTL), which exploits the information from the pre-trained DNN for training other DNNs to find the beamforming vector in the specific channel. Through DTL, DNN can be trained suitably for the communication environment at each BS with fewer channel data. Moreover, we propose ‘step-by-step’ DTL to flexibly apply DTL considering the uncertainties of the realistic system. Simulation results show that DTL has better performance than the conventional DLapproaches even with a small amount number of channel data. Therefore, the DTL-based approach can be a good framework to train DNN when high overhead occurs or designing the beamformer is complicated such as a massive MIMO system.

In this paper, we develop the channel estimation algorithm for a massive multiple input multiple output (MIMO) system using one-bit analog-to-digital converters (ADCs). Although, one-bit quantization significantly reduces the deployment cost and power consumption of the massive MIMO, the great distortion induced by one-bit quantization makes channel estimation more difficult. Therefore, a channel estimation method by executing a deep neural network (DNN) over multiple signal segments is proposed for the uplink of one-bit massive MIMO. The average of the DNN’s outputs throughout all the segments is the final channel estimate for a transmission block. In order to improve the estimation accuracy without increasing the length of pilot, the data symbols got based on the initial channel estimates are used as the other part of pilots to refine the estimation result. Moreover, a sliding window based pilot segment method is adopted to increase the number of signal segments with a same pilot length. The simulation results show that the proposed scheme outperforms least squares (LS) and Bussgang linear minimum mean squared error (BLMMSE) channel estimators in the whole signal-to-noise-ratio (SNR) region.

Massive multiple-input multiple-output (MIMO) systems are a main enabler of the excessive throughput requirements in 5G and future generation wireless networks as they can serve many users simultaneously with high spectral and energy efficiency. To achieve this massive MIMO systems require accurate and timely channel state information (CSI), which is acquired by a training process that involves pilot transmission, CSI estimation, and feedback. This training process incurs a training overhead, which scales with the number of antennas, users, and subcarriers. Reducing the training overhead in massive MIMO systems has been a major topic of research since the emergence of the concept. Recently, deep learning (DL)-based approaches have been proposed and shown to provide significant reduction in the CSI acquisition and feedback overhead in massive MIMO systems compared to traditional techniques. In this paper, we present an overview of the state-of-the-art DL architectures and algorithms used for CSI acquisition and feedback, and provide further research directions.

As a combination of spatial modulation (SM) system and massive multiple-input multiple-output (MIMO) system, massive SM aided MIMO (SM-MIMO) system is recently proposed. In broadband scenarios, single-carrier (SC) modulation is introduced to massive SM-MIMO system, thus massive SC-SM MIMO system is proposed for uplink multi-user transmission over frequency-selective fading channels. In this study, the uplink spectral efficiency (SE) of multi-cell massive SC-SM MIMO system is analysed, meanwhile the pilot contamination effect is taken into consideration. A tight SE lower bound of multi-cell massive SC-SM MIMO system is proposed with maximum ratio (MR) combining, which also takes into account the imperfect channel estimation, transmit antenna correlation and path loss. The tightness of the authors' proposed closed-form SE lower bound is shown via simulation results. The optimal pilot reuse factor can be determined with different system configurations by simulations, and the pilot reuse factor that is larger than one is more suitable for less TAs and user equipments.

Most existing downlink channel estimation methods rely on channel sparsity in the angular domain to reduce pilot overhead for massive multiple-input multiple-output (MIMO) systems. Compared with channel estimation methods based on predefined basis or offline dictionary learning, in this paper, we design an online two-timescale joint uplink/downlink dictionary learning and channel estimation (TTS-JDLCE) algorithm for dynamic scenarios, where the channel sparsity and angle reciprocity between uplink and downlink transmissions are both exploited to reduce the pilot overhead. The downlink channel estimation is constructed as a TTS stochastic optimization problem with a constraint coupled by the long-term dictionary and short-term sparse channel representations. Treating the dictionary as a learnable parameter, the proposed algorithm can capture dynamic spatial information for enhancing performance. By introducing a relaxed TTS primal-dual decomposition (PDD) framework, the original problem is decomposed into a long-term online dictionary learning subproblem and a family of short-term sparse channel estimation subproblems. Besides, the deep unfolding technique is employed to extract gradient information from short-term problems, which circumvents the non-closed form and non-convexity of long-term subproblem by constructing a convex surrogate problem. Finally, simulations show that the proposed method remarkably reduces the pilot overhead and achieves significant performance gains over various baselines.

Large-scale channel matrices in massive multiple-input multiple-output (MIMO) systems increase the difficulty of channel estimation. Conventional best linear unbiased estimator (BLUE) algorithms include multiple inverse operations for large-scale matrices; however this typically reduces algorithm efficiency. Efficiency can be improved by converting inverse operations into a system of linear equations, which are more easily solved for large matrices. This approach is used in the conjugate gradient (CG) method, which exhibits higher efficiency and precision when solving for approximate optimal solutions. As such, we propose a hybrid conjugate gradient (CG BLUE) algorithm, an improved version of the conventional BLUE technique. This algorithm reduces the dimensions of the channel model through a vectorization transform and uses the CG method to improve algorithm process efficiency during channel estimation for massive MIMO systems. Simulated results of NMSE and algorithm processing time show the proposed CG-BLUE algorithm resulted in nearly one order of magnitude higher calculation efficiency while maintaining the same level of accuracy as conventional BLUE.

Accurate beamspace channel estimation is critical in wideband millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) communication systems. However, the beam squint effect caused by the increase in bandwidth and array dimension can bring additional challenges to the wideband channel estimation. In this study, we propose a wideband channel estimation scheme based on compressed sensing to solve the beam squint effect in broadband mmWave massive MIMO systems. First, the channel estimation problem is transformed into a beamspace direction estimation problem by exploiting the special frequency-dependent sparse structure of a broadband beamspace channel. Then, we propose a beam-band function-based orthogonal matching pursuit algorithm, in which a beam-band function is constructed and used to capture the power of each path component to estimate the spatial direction. In addition, the support of each path component at different sub-carriers is estimated to reduce the influence of the beam squint effect. Moreover, the support is estimated adaptively to improve the estimation accuracy further. Simulation results demonstrate that the proposed scheme reduces the pilot overhead under the beam squint effect and improve the estimation accuracy significantly.

In this paper, we investigate the suitability of single carrier frequency domain processing (SC-FDP) as the downlink transmission scheme in a massive multiple-input multiple-output (MIMO) system. By deriving the sum-rate of the SC-FDP massive MIMO system theoretically, we show that this method obtains a sum-rate similar to that of orthogonal frequency division multiplexing (OFDM) massive MIMO. We also derive the theoretical sum-rate of both SC-FDP and OFDM in a non-synchronized massive MIMO scenario and show that the rate of the former is significantly larger than that of the latter. Moreover, we theoretically analyze the sum-rate of both systems in the presence of power amplifier non-linearity. All the sum-rates are derived for both zero forcing and matched filter precoding schemes. The results show that the effect of power amplifier non-linearity on the sum-rate of both systems is similar when the number of users is large. We also compare SC-FDP with OFDM from the peak to average power ratio (PAPR) and complexity viewpoints. Although the PAPR of SC-FDP signals is lower than that of OFDM signals, for MIMO systems, the difference between their PAPR decreases as we increase the number of users. Thus, both techniques can have similar PAPR in massive MIMO systems. The overall complexity of SC-FDP and OFDM is similar. Due to the mentioned facts, SC-FDP can be considered as a promising transmission scheme for the downlink of the massive MIMO systems in the presence of carrier frequency offset and power amplifier non-linearities.

With the large number of antennas and subcarriers the overhead due to pilot transmission for channel estimation can be prohibitive in wideband massive multiple-input multiple-output (MIMO) systems. This can degrade the overall spectral efficiency significantly, and as a result, curtail the potential benefits of massive MIMO. In this paper, we propose a neural network (NN)-based joint pilot design and downlink channel estimation scheme for frequency division duplex (FDD) MIMO orthogonal frequency division multiplex (OFDM) systems. The proposed NN architecture uses fully connected layers for frequency-aware pilot design, and outperforms linear minimum mean square error (LMMSE) estimation by exploiting inherent correlations in MIMO channel matrices utilizing convolutional NN layers. Our proposed NN architecture uses a non-local attention module to learn longer range correlations in the channel matrix to further improve the channel estimation performance.We also propose an effective pilot reduction technique by gradually pruning less significant neurons from the dense NN layers during training. This constitutes a novel application of NN pruning to reduce the pilot transmission overhead. Our pruning-based pilot reduction technique reduces the overhead by allocating pilots across subcarriers non-uniformly and exploiting the inter-frequency and inter-antenna correlations in the channel matrix efficiently through convolutional layers and attention module.

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.

Massive multiple-input multiple-output (MIMO), or large-scale MIMO, is one of the key technologies for future wireless networks to exhibit a large accessible spectrum and throughput. The performance of a massive MIMO system is strongly reliant on the nature of various channels and interference during multipath transmission. Therefore, it is important to compute accurate channel estimation. This paper considers a massive MIMO system with one-bit analog-to-digital converters (ADCs) on each receiver antenna of the base station. Deep learning (DL)-based channel estimation framework has been developed to reduce signal processing complexity. This DL framework uses conditional generative adversarial networks (cGANs) and various convolutional neural networks, namely reverse residual network (reverse ResNet), squeeze-and-excitation ResNet (SE ResNet), ResUNet++, and reverse SE ResNet, as the generator model of cGAN for extracting the features from the quantized received signals. The simulation results of this paper show that the trained residual block-based generator model of cGAN has better channel generation performance than the standard generator model in terms of mean square error.

A novel asymmetrical massive multiple-input multiple-output (MIMO) system has recently emerged as a crucial solution for reducing hardware complexity and alleviating the data processing pressure. However, the absence of channel reciprocity in this system presents unique challenges when directly applying traditional channel estimation methods, inevitably leading to performance loss. Deep learning approaches hold promise for achieving improved channel estimation performance by implicitly learning channel features. Unfortunately, deep learning approaches are often designed empirically. It is critical to utilize prior knowledge to develop an efficient deep neural network (DNN), especially for wireless communication systems. This paper explores a knowledge-driven DNN design approach and introduces a deep learning-based channel estimation framework for asymmetrical transceivers. The channel estimation problem is decoupled into channel denoising and information inference problems. Two novel DNNs are proposed to eliminate noise and exploit correlative features for reconstructing the missing channel information, respectively. Extensive simulations demonstrate that our proposed channel estimation framework can significantly eliminate noise effects, even in low signal-to-noise ratio regimes, and outperform traditional estimators and other deep learning-based methods. Moreover, ablation studies also validate the effectiveness of our knowledge-driven network structure design approach.

Massive multiple-input multiple-output (MIMO) technology for wireless communication is considered a key technology for the future fifth-generation (5G) communication networks. To reduce the propagation loss of channel, MIMO systems with analog or digital hybrid precoders and combiners (HPC) architecture have been used. This review paper presents the various types of analog and digital HPC designs for the massive MIMO communication systems that were investigated within the last decade. The reviewed systems are useful for both single-user (SU) and multi-user MIMO (MU-MIMO) systems. The key parameter of the study was spectral efficiency (SE), signal-to-noise ratio (SNR), signal-to-interference pulse noise ratio (SINR), system sum-rate (SR) performance, computational complexity (CC), co-channel interference (CCI), inter-primary base station interference (IPBSI), multiuser interference (MUI), effective desired channel gain (EDCG), and minimum mean square error (MMSE).

Reliable and high throughput communication in Massive Multiple-Input Multiple-Output (MIMO) systems strongly depends on accurate channel estimation at the Base Station (BS). However, the channel estimation process in massive MIMO systems is vulnerable to pilot contamination attacks, which not only degrade the efficiency of channel estimation, but also increase the probability of information leakage. In this paper, we propose a defence mechanism against pilot contamination attacks using a deep-learning model, namely Generative Adversarial Networks (GAN), to detect invalid uplink connections at the BS. Training of the models is performed via legitimate data, which consists of received signals from valid users and real channel matrices. The simulation results show that the proposed method is able to detect the pilot contamination attack with 98% accuracy in the best scenario.

Massive multiple-input multiple-output (MIMO) systems are expected to achieve substantial gains in frequency efficiency compared to conventional MIMO systems. However, these promising gains are limited by the pilot contamination in multicell scenario, which results from reusing the same set of pilot sequences in neighboring cells. This paper proposes a clustering based pilot allocation (CPA) algorithm to mitigate the effects of pilot contamination. Specifically, we first define the inter-cell interference (ICI) relationship between users, based on which the users are divided into high and low ICI groups. Then, the users in low ICI group reuse pilots randomly, while the users in high ICI group are clustered by affinity propagation (AP) clustering method. Finally, we perform different pilot allocation strategies according to the types of user clusters. Simulation results show that the proposed algorithm can significantly improve the pilot efficiency compared to the existing pilot allocation algorithms using grouping ideas.