Priori-aided channel tracking for millimeter-Wave beamspace massive MIMO systems

Millimeter-wave (mmWave) massive MIMO systems use many antennas. These systems offer high data rates. But using many radio frequency (RF) chains increases cost and power use. To solve this, lens antenna arrays are used. Energy is focused, allowing the use of fewer RF chains. However, this creates a new challenge. With fewer RF chains, it is hard to estimate the wireless channel. Accurate channel estimation is needed for good system performance. In beamspace, the channel is sparse. This shows that only a few values are large. The rest are close to zero. Because of this, the problem is seen as sparse signal recovery. AMP (Approximate Message Passing) is one popular algorithm used for this. A better version named LAMP (Learned AMP) uses deep learning. But it still does not give the best results. This paper proposes a new method GM-LAMP. It improves the channel estimation accuracy. It uses prior knowledge about the channel. It assumes that the beamspace channel follows a Gaussian mixture distribution. First, a new shrinkage function is created based on this distribution. Then, the original function in the LAMP network is replaced with the new one. As a result, a better deep learning model is developed. The final GM-LAMP network estimates the beamspace channel more precisely. It works well with both theoretical models and real-world data. Simulations show that GM-LAMP performs better than earlier methods. This approach combines math knowledge and deep learning. It shows that using prior information helps deep networks make smarter predictions. The proposed method offers better accuracy and is useful for future mmWave systems.

In this paper, we propose a hybridly connected structure for hybrid beamforming in millimeter-wave (mmWave) massive MIMO systems, where the antenna arrays at the transmitter and receiver consist of multiple sub-arrays, each of which connects to multiple radio frequency (RF) chains, and each RF chain connects to all the antennas corresponding to the sub-array. In this structure, through successive interference cancelation, we decompose the precoding matrix optimization problem into multiple precoding sub-matrix optimization problems. Then, near-optimal hybrid digital and analog precoders are designed through factorizing the precoding sub-matrix for each sub-array. Furthermore, we compare the performance of the proposed hybridly connected structure with the existing fully and partially connected structures in terms of spectral efficiency, the required number of phase shifters, and energy efficiency. Finally, simulation results are presented to demonstrate that the spectral efficiency of the hybridly connected structure is better than that of the partially connected structure and that its spectral efficiency can approach that of the fully connected structure with the increase in the number of RF chains. Moreover, the proposed algorithm for the hybridly connected structure is capable of achieving higher energy efficiency than existing algorithms for the fully and partially connected structures.

In this letter, we propose an iterative algorithm for the joint estimation of the channel covariance matrices and the channel vector for the spatially-correlated massive MIMO system with the following main advantages: low pilot overhead, improved mean square error (MSE), and low complexity. The results show that this scheme outperforms the conventional estimation schemes in terms of the required pilot overhead and the resulting MSE. The results also show that the achievable sum rate of the proposed scheme tends to that of the ideal case that the channel covariance matrices are assumed to be perfectly known at the base station.

The compressive sensing (CS)-based sparse channel estimator is recognized as the most effective solution to the excessive pilot overhead in massive MIMO systems. However, due to the complex signal processing in the wireless communication systems, the measurement matrix in the CS-based channel estimation is sometimes “unfriendly” to the channel recovery. To overcome this problem, in this paper, the state-of-the-art sparse Bayesian learning using approximate message passing with unitary transformation (UTAMP-SBL), which is robust to various measurement matrices, is leveraged to address the multi-user uplink channel estimation for hybrid architecture millimeter wave massive MIMO systems. Specifically, the sparsity of channels in the angular domain is exploited to reduce the pilot overhead. Simulation results demonstrate that the UTAMP-SBL is able to achieve effective performance improvement than other competitors with low pilot overhead.

The emerging reconfigurable intelligent surface (RIS) technology is promising for applications in the millimeter wave (mmWave) communication systems to effectively compensate for propagation loss or tackle the blockage issue. Considering the high mobility of users in realistic scenarios, it is essential to adjust the phase shifts in real time to align the beam towards the mobile users, which requires to frequently estimate the channel state information. Hence, it is imperative to design efficient channel tracking schemes to avoid the complex channel estimation procedure. In this paper, we develop a novel channel tracking scheme with two advantages over conventional schemes. First, our tracking scheme is based on the cascaded angles at the RIS instead of the accurate angle values, which is more practical. Second, it can be employed under a more general setting where the noise can be non-Gaussian. Simulation results show the high tracking accuracy of our proposed scheme, and validate the superiority to the existing EKF-based tracking scheme.

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 multiuser beam allocation for millimeter wave (mmWave) massive MIMO systems is investigated, based on an improved hybrid precoding design framework. The framework includes two stages. In the first stage, an orthogonal pilot (OP) based beam training scheme is proposed, where all users can simultaneously perform the beam training with the base station (BS) and all the RF chains at the BS are fully utilized. In the second stage, a channel estimation method based on the results from the beam training is presented without transmitting any pilot sequences. Note that users close in geographical may share the same beam from the base station (BS) and cause the beam conflict. To mitigate the multiuser interference caused by beam conflicts, a quality of service (QoS) constrained beam allocation scheme is proposed, with the objective to maximize the equivalent channel gain for the users satisfying QoS constraints as well as maximizing the number of users satisfying QoS constraints on the premise of no beam conflict for all users. Simulation results verify the effectiveness of the proposed schemes and show that the QoS constrained beam allocation scheme can achieve higher spectral efficiency than existing schemes.

Massive multiple input multiple output (MIMO) technology is one of the promising technologies for fifth generation (5G) cellular communications. In this technology, each cell has a base station (BS) with a large number of antennas, allowing the simultaneous use of the same resources (e.g., frequency and/or time slots) by multiple users of a cell. Therefore, massive MIMO systems can bring very high spectral and power efficiencies. However, this technology faces some important issues that need to be addressed. One of these issues is the performance degradation due to hardware impairments, since low-cost RF chains need to be employed. Another issue is the channel estimation and channel aging effects, especially in fast mobility environments. In this paper we will perform a comprehensive study on these two issues considering two of the most promising candidate waveforms for massive MIMO systems: Orthogonal frequency division multiplexing (OFDM) and single-carrier frequency domain processing (SC-FDP). The studies and the results show that hardware impairments and inaccurate channel knowledge can degrade the performance of massive MIMO systems extensively. However, using suitable low complex estimation and compensation techniques and also selecting a suitable waveform can reduce these effects.

In filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) systems, a large pilot overhead is required due to the existence of the imaginary interference. In this paper, we present an approach to reduce the pilot overhead of channel estimation. A part of pilot overhead is used for transmitting data, and compensating symbols are required and designed to remove the imaginary interference. It is worthwhile to point out that the power of compensating symbols can be helpful for data recovery; hence, the proposed approach decreases the overhead of pilots significantly without the cost of additional pilot energy. In addition, the proposed scheme is extended into multiple input multiple output systems without the performance loss. Compared with the conventional preamble consisting of 3 columns symbols, the pilot overhead is equivalent to 2 column symbols in the proposed preamble. To verify the proposed preamble, numerical simulations are carried out with respects to bit error ratio.

Downlink channel estimation with low pilot overhead is an important and challenging problem in massive multiple‐input–multiple‐output (MIMO) systems due to the substantially increased MIMO channel dimension. A block iterative support detection (block‐ISD)‐based algorithm for downlink channel estimation to reduce the pilot overhead is proposed, which is achieved by fully exploiting the block sparsity inherent in the block‐sparse equivalent channel derived from the spatial correlations of MIMO channels. Furthermore, unlike conventional compressive sensing (CS) algorithms that rely on prior knowledge of the sparsity level, block‐ISD relaxes this demanding requirement and is thus more practically appealing. Simulation results demonstrate that block‐ISD yields better normalised mean square error (NMSE) performance than classical CS algorithms, and achieve a reduction of 84% pilot overhead compared with conventional channel estimation techniques.

High-speed train (HST) communication has emerged as a prominent area of research, given its critical role in meeting the demanding requirements of high mobility scenarios like auto-driving and mission-critical train control signaling. The provision of these services relies heavily on the accurate acquisition of channel state information (CSI). However, traditional methods of channel estimation are inadequate for massive multiple-input multiple-output (MIMO) HST channels due to their extremely short channel coherence time, typically in the microsecond range. In this paper, we propose a dynamic grid-based joint uplink/downlink (UL/DL) channel tracking scheme in the angular domain, specifically designed to address fast time-varying channels in HST communication. The spatial reciprocity between UL and DL channels in frequency division duplex (FDD) systems and fuzzy spatial information are exploited to ensure the consistency of UL/DL supports and improve the tracking performance of the line-of-sight (LOS) channel, respectively. Then, we formulate a sparse Bayesian learning (SBL) problem and adopt a block majorization-minimization (MM) algorithm, where a significant gradient-based fast search (SGFS) algorithm is devised to solve the non-convex maximum a posteriori (MAP) problem for the angle of arrivals/departures (AoAs/AoDs) estimation. Finally, simulations verify the superiority of joint UL/DL channel tracking and show that the proposed scheme converges quickly with more accurate tracking results.

The massive Multiple-input Multiple-output (MIMO) and Non-Orthogonal Multiple Access (NOMA) are considered as two key techniques for the coming 5G wireless communications. Through beam selection, the massive MIMO systems can reduce the number of required radio-frequency (RF) chains while without distinct performance degradation, which can enhance energy efficiency of the systems. In order to further improve energy efficiency, in this paper, we propose an energy efficient power allocation design for beamspace based multi-user multiple-input single-output (MISO) NOMA systems. First, according to the beamspace channel state information (CSI) matrix obtained by beam selection, we get the precoding matrix through zero-forcing (ZF) method. Then we formulate the energy efficiency maximization (EEmax) optimization problem with power allocation considerations. Through sequential convex approximation (SCA) and second-order cone programming (SOCP), the original non-convex problem can be reformulated as a convex optimization problem. Finally, we choose iterative optimization algorithm to solve the reformulated problem. Numerical results show that the proposed power allocation design is effective in achieving better energy efficiency comparing with the conventional methods.

The base station (BS) density influences the coverage performance of cellular communication system. In massive multiple input multiple output (MIMO) system, each BS is equipped with large scale antenna array. The stochastic geometric model is employed because of its analytical tractability. This paper demonstrates that BS density has to be below a certain bound in order to satisfy the coverage requirement in massive MIMO system. Closed-form expression for the BS density bounded by maximum outage probability is derived using Lambert W function. Moreover, the expression for the upper bound of successful area spectral efficiency (ASE) is also derived. Our study claims that increasing BS density will lead to the deterioration of coverage performance in massive MIMO system.

By moving to millimeter wave (mmWave) frequencies, base stations (BSs) will be densely deployed to provide seamless coverage in sixth generation (6G) mobile communication systems, which, unfortunately, leads to severe cell-edge problem. In addition, with massive multiple-input-multiple-output (MIMO) antenna arrays employed at BSs, the beamspace channel is sparse for each user, and thus there is no need to serve all the users in a cell by all the beams therein jointly. Therefore, it is of paramount importance to develop a flexible clustered cell-free networking scheme that can decompose the whole network into a number of weakly interfered small subnetworks operating independently and in parallel. Given a per-user rate constraint for service quality guarantee, this paper aims to maximize the number of decomposed subnetworks so as to reduce the signaling overhead and system complexity as much as possible. By formulating it as a bipartite graph partitioning problem, a rate-constrained network decomposition (RC-NetDecomp) algorithm is proposed, which can smoothly tune the network structure from the current cellular network with simple beam allocation to a fully cooperative network by increasing the required per-user rate. Simulation results demonstrate that the proposed RC-NetDecomp algorithm outperforms existing baselines in terms of average per-user rate, fairness among users and energy efficiency.

To deal with the estimation problem of non-stationary channel in massive Multiple-Input Multiple-Output (MIMO) up-link, the 2D channels' sparse structure information in temporal-spatial domain is used, to design an iterative channel estimation algorithm based on Dirichlet Process (DP) and Variational Bayesian Inference (VBI), which can improve the accuracy under a lower pilot overhead and computation complexity. On account of that the stationary channel models is not suitable for massive MIMO systems anymore, a non-stationary channel prior model utilizing Dirichlet Process is constructed, which can map the physical spatial correlation channels to a probabilistic channel with the same sparse temporal vector. By applying VBI technology, a channel estimation iteration algorithm with low pilot overhead and complexity is designed. Experiment results show the proposed channel method has a better performance on the estimation accuracy than the state-of-art method, meanwhile it works robustly against the dynamic system key parameters.<br/><br/>