Distributive estimation of frequency selective channels for massive MIMO systems (original) (raw)
Related papers
Distributed Channel Estimation and Pilot Contamination Analysis for Massive MIMO-OFDM Systems
IEEE Transactions on Communications, 2016
Massive MIMO communication systems, by virtue of utilizing very large number of antennas, have a potential to yield higher spectral and energy efficiency in comparison with the conventional MIMO systems. In this paper, we consider uplink channel estimation in massive MIMO-OFDM systems with frequency selective channels. With increased number of antennas, the channel estimation problem becomes very challenging as exceptionally large number of channel parameters have to be estimated. We propose an efficient distributed linear minimum mean square error (LMMSE) algorithm that can achieve near optimal channel estimates at very low complexity by exploiting the strong spatial correlations and symmetry of large antenna array elements. The proposed method involves solving a (fixed) reduced dimensional LMMSE problem at each antenna followed by a repetitive sharing of information through collaboration among neighboring antenna elements. To further enhance the channel estimates and/or reduce the number of reserved pilot tones, we propose a data-aided estimation technique that relies on finding a set of most reliable data carriers. We also analyse the effect of pilot contamination on the mean square error (MSE) performance of different channel estimation techniques. Unlike the conventional approaches, we use stochastic geometry to obtain analytical expression for interference variance (or power) across OFDM frequency tones and use it to derive the MSE expressions for different algorithms under both noise and pilot contaminated regimes. Simulation results validate our analysis and the near optimal MSE performance of proposed estimation algorithms.
Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems
Symmetry, 2019
Pilot contamination is the reuse of pilot signals, which is a bottleneck in massive multi-input multi-output (MIMO) systems as it varies directly with the numerous antennas, which are utilized by massive MIMO. This adversely impacts the channel state information (CSI) due to too large pilot overhead outdated feedback CSI. To solve this problem, a compressed sensing scheme is used. The existing algorithms based on compressed sensing require that the channel sparsity should be known, which in the real channel environment is not the case. To deal with the unknown channel sparsity of the massive MIMO channel, this paper proposes a structured sparse adaptive coding sampling matching pursuit (SSA-CoSaMP) algorithm that utilizes the space–time common sparsity specific to massive MIMO channels and improves the CoSaMP algorithm from the perspective of dynamic sparsity adaptive and structural sparsity aspects. It has a unique feature of threshold-based iteration control, which in turn depends...
Second-Order Statistics-Aided Channel Estimation for Multipath Massive MIMO-OFDM Systems
IEEE Access
This paper develops an efficient channel estimation algorithm based on second-order statistics of time division duplex (TDD) multiuser massive multiple-input multiple-output (MIMO) systems. The algorithm uses the received signal correlation to determine the most significant lags (MSLs) of the received signal. We first employ these MSLs to propose a novel set containing the channel's four most significant taps (MSTs). Then, by using them, we propose an efficient semi-blind iterative algorithm called enhanced modified-subspace pursuit (EM-SP). It uses the set mentioned above and two theoretical results (Lemma 1 and Theorem 1) to estimate an arbitrary number of MSTs efficiently. Simulation results show that the normalized mean square error (NMSE) of the proposed EM-SP algorithm is much smaller than that of the subspace pursuit (SP) and orthogonal matching pursuit (OMP) algorithms at the cost of 0.3 % and 2 % more computational complexity for the channels with three and six nonzero paths, respectively. Moreover, the NMSE of it is very close to that of the optimal genie-aided least square algorithm. INDEX TERMS Compressive sensing, massive MIMO-OFDM, frequency-selective channel estimation, most significant tap (MST) detection, second-order statistics.
Improved Downlink Channel Estimation in Time-Varying FDD Massive MIMO Systems
arXiv (Cornell University), 2024
In this work, we address the challenge of accurately obtaining channel state information at the transmitter (CSIT) for frequency division duplexing (FDD) multiple input multiple output systems. Although CSIT is vital for maximizing spatial multiplexing gains, traditional CSIT estimation methods often suffer from impracticality due to the substantial training and feedback overhead they require. To address this challenge, we leverage two sources of prior information simultaneously: the presence of limited local scatterers at the base station (BS) and the time-varying characteristics of the channel. The former results in a redundant angular sparsity of users' channels exceeding the spatial dimension (i.e., the number of BS antennas), while the latter provides a prior non-uniform distribution in the angular domain. We propose a weighted optimization framework that simultaneously reflects both of these features. The optimal weights are then obtained by minimizing the expected recovery error of the optimization problem. This establishes an analytical closedform relationship between the optimal weights and the angular domain characteristics. Numerical experiments verify the effectiveness of our proposed approach in reducing the recovery error and consequently resulting in decreased training and feedback overhead.
Efficient Coordinated Recovery of Sparse Channels in Massive MIMO
IEEE Transactions on Signal Processing, 2015
This paper addresses the problem of estimating sparse channels in massive MIMO-OFDM systems. Most wireless channels are sparse in nature with large delay spread. In addition, these channels as observed by multiple antennas in a neighborhood have approximately common support. The sparsity and common support properties are attractive when it comes to the efficient estimation of large number of channels in massive MIMO systems. Moreover, to avoid pilot contamination and to achieve better spectral efficiency, it is important to use a small number of pilots. We present a novel channel estimation approach which utilizes the sparsity and common support properties to estimate sparse channels and require a small number of pilots. Two algorithms based on this approach have been developed which perform Bayesian estimates of sparse channels even when the prior is non-Gaussian or unknown. Neighboring antennas share among each other their beliefs about the locations of active channel taps to perform estimation. The coordinated approach improves channel estimates and also reduces the required number of pilots. Further improvement is achieved by the data-aided version of the algorithm. Extensive simulation results are provided to demonstrate the performance of the proposed algorithms.
Sparse Approximations of the LMMSE Channel Estimation in OFDM with Transmitter Diversity
Wireless Personal Communications, 2006
The linear minimum mean-squared error (LMMSE) channel estimation for orthogonal frequency-division multiplexing (OFDM) systems requires a large number of complex multiplications. We evaluate a simplified LMMSE channel estimation algorithm in a transmit diversity environment by applying a significant weight catching (SWC) technique to the LMMSE fixed weighting matrix. The SWC technique itself is based on modifying the smoothing matrix by leaving the largest values in each row and turning the rest to zeros. This allows the computational complexity of the full LMMSE processor to be reduced by more than 50%. In the well known LMMSE by singular value decomposition (SVD) technique the sparse approximation is accomplished by zeroing out all but the r largest singular values. LMMSE by SVD is the preferred approximation technique for low delay spread channels. However, in channels with large delay spreads, LMMSE by SWC is a better choice in terms of computational complexity and estimation accuracy
Reduced-Complexity Recursive MMSE Channel Estimator for the Wireless OFDM Systems
2008 IEEE Wireless Communications and Networking Conference, 2008
An accurate estimate of the channel frequency response (CFR) needed for OFDM equalisation can be obtained using the linear minimum mean square error (MMSE) algorithm. However, for an optimal operation such an estimator requires real-time inversion of the CFR correlation matrix that is found to be impractical due to considerable increase of the receiver's complexity. In this article, we develop the ways of design of the MMSE channel estimator yielding near-optimum performance and being computationally efficient at the same time. The proposed technique is based on the transform-domain processing and tracking of the correlation of the channel impulse response (CIR) observation. The former feature allows reducing the size of the correlation matrix, whereas the latter one leads to a design form with no matrix inversions. Simulations show that the developed algorithm achieves performance close to the optimal MMSE estimator and is particularly beneficial for both the sample-spaced and the non-sample-spaced sparse multipath channels.
IEEE Transactions on Wireless Communications, 2000
The number of transmit antennas that can be employed in the context of least-squares (LS) channel estimation contrived for orthogonal frequency division multiplexing (OFDM) systems employing multiple transmit antennas is limited by the ratio of the number of subcarriers and the number of significant channel impulse response (CIR)-related taps. In order to allow for more complex scenarios in terms of the number of transmit antennas and users supported, CIR-related tap prediction-filtering-based parallel interference cancellation (PIC)-assisted decision-directed channel estimation (DDCE) is investigated. New explicit expressions are derived for the estimator's mean-square error (MSE), and a new iterative procedure is devised for the offline optimization of the CIR-related tap predictor coefficients. These new expressions are capable of accounting for the estimator's novel recursive structure. In the context of our performance results, it is demonstrated, for example, that the estimator is capable of supporting L = 16 transmit antennas, when assuming K = 512 subcarriers and K 0 = 64 significant CIR taps, while LS-optimized DDCE would be limited to employing L = 8 transmit antennas. Index Terms-Decision-directed channel estimation (DDCE), multiple transmit antennas, orthogonal frequency division multiplexing (OFDM), parallel interference cancellation (PIC). I. MOTIVATION I N RECENT years, the family of single-and multiuser orthogonal frequency division multiplexing (OFDM) schemes [1] using time-domain, frequency-domain, as well as spatialdomain spreading [2] has enjoyed a renaissance. Hence, OFDM has found its way into numerous wireless systems that require accurate channel estimation. Accordingly, the topic of decisiondirected channel estimation (DDCE) has been addressed in a variety of contributions, notably, for example, in the detailed discussions of [3]-[8], in the context of single-user single-transmit antenna OFDM environments. The basic idea Manuscript
A Low Complexity Channel Estimation and Detection for Massive MIMO Using SC-FDE
Telecom
5G Communications will support millimeter waves (mm-Wave), alongside the conventional centimeter waves, which will enable much higher throughputs and facilitate the employment of hundreds or thousands of antenna elements, commonly referred to as massive Multiple Input–Multiple Output (MIMO) systems. This article proposes and studies an efficient low complexity receiver that jointly performs channel estimation based on superimposed pilots, and data detection, optimized for massive MIMO (m-MIMO). Superimposed pilots suppress the overheads associated with channel estimation based on conventional pilot symbols, which tends to be more demanding in the case of m-MIMO, leading to a reduction in spectral efficiency. On the other hand, MIMO systems tend to be associated with an increase of complexity and increase of signal processing, with an exponential increase with the number of transmit and receive antennas. A reduction of complexity is obtained with the use of the two proposed algorithm...
Reduced complexity signal detection and channel estimation for iterative MIMO-OFDM systems
2016
Multi-Input Multi-Output (MIMO) is a key technology in broadband wireless communications, and it has been used in WiMax, LTE and WiFi (802.11n/ac). As Orthogonal Frequency Division Multiplexing (OFDM) can transform a frequency selective fading channel into a set of parallel frequency flat fading channels and thus greatly reduce the complexity of equalization, MIMO is typically combined with OFDM in practical applications. For a MIMO-OFDM system, the channel estimation and signal detection algorithms based on linear-minimum-mean-square-error (LMMSE) are often employed because of their good performance. But conventional algorithms typically require a matrix inversion with cubic level complexity, which is a major obstacle for practical implementation. To reduce the complexity, in this thesis, we focused on algorithms design by reducing the number of costly operations and the cost of each operation. Due to the law of large numbers, the matrix to be inverted, in both the LMMSE channel es...