Pilot Overhead Research Articles

Performance Enhancement of OFDM System using Hybrid Channel Estimation with Pseudo Pilot

For high spectral efficiency and robustness against intersymbol interference (ISI), orthogonal frequency division multiplexing (OFDM) is the best suited technology for many applications such as digital audio broadcasting (DAB), wireless networks, digital video broadcasting (DVB) and LAN. For efficient recovery of the original transmitted signal we require the coherent demodulator as the frequency selective fading channels affects the transmitted signal in OFDM. At the receiver end for coherent demodulation we require channel state information (CSI), which is done by channel estimation. To find the channel characteristics which varies with time and frequency, a reference signal is transmitted with payload signal know as pilot or training sequence and technique or process is known as channel estimation. The use of pilots in the OFDM system leads to pilots overhead. So we propose a new technique which will reduces the transmission delay, reduce pilot overhead, increases transmission rate and improves the BER performance without any computational complexity, which is implemented using the Hybrid algorithm for channel based OFDM system. In this proposed technique, for the channel estimation in OFDM system over AWGN fading channel pseudo-pilots are used. At the transmitter end, pseudo-pilots are employed to breed bank of pseudo random symbol (PRS). In this OFDM system with pseudo-pilot the channel estimation is done by the Hybrid (LS + RLS) estimation approach to recover the original transmitted information. The performance of the proposed techniques and the weighted scheme are compared and verified using computerized simulation carried out using Matrix Laboratory (MATLAB) software.

Performance Evaluation of OFDM System using Pseudo-Pilots with Particle Swarm & Moth Flame (Hybrid) Optimization

This paper is focused on the advanced signal processing techniques for the multi-carrier modulation especially on the orthogonal frequency division multiplexing (OFDM). OFDM has high data rate and robust against the frequency selective multi-path channels. Channel estimation is crucial for the receiver design in coherent detection. In this paper we are investigating and examining the pilot aided system for channel estimation and its special effects on the performance of the OFDM based system. In this paper new technique is proposed which make use of conventional Least Square (LS) and Recursive Least Square (RLS) hybridization techniques for the channel estimation afterwards we apply Particle Swarm Optimization (PSO) and Hybrid PSO (PSO+ Moth Flame Optimization(MFO)) optimization for obtaining more optimize techniques. In the proposed system, to estimate channel impulse response we are using pseudo-pilots instead to pilots as it is useful to overcome the pilot overhead and decrease the complexity by using pseudo random symbol (PRS). The performance of the proposed techniques and the weighted scheme are compared and verified using computerized simulation carried out using Matrix Laboratory (MATLAB) software. It is demonstrated on the basis of graph between BER and signal to Noise Ratio (Eb/NO), that the proposed technique reduces the execution time and increase the transmission rate with low or zero overhead.

A Robust Channel Estimation Scheme for 5G Massive MIMO Systems

Channel state information (CSI) feedback in massive MIMO systems is too large due to large pilot overhead. It is due to the large channel matrix dimension which depends on the number of base station (BS) antennas and consumes the majority of scarce radio resources. To solve this problem, we proposed a scheme for efficient CSI acquisition and reduced pilot overhead. It is based on the separation mechanism for the channel matrix. The spatial correlation among multiuser channel matrices in the virtual angular domain is utilized to split the channel matrix. Then, the two parts of the matrix are estimated by deploying the compressed sensing (CS) techniques. This scheme is novel in the sense that the user equipment (UE) directly transmits the received symbols from the BS to the BS, so a joint CSI recovery is performed at the BS. Simulation results show that the proposed channel estimation scheme effectively estimates the channel with reduced pilot overhead and improved performance as compared with the state-of-the-art schemes.

Structured Turbo Compressed Sensing for Downlink Massive MIMO-OFDM Channel Estimation

Compressed sensing has been employed to reduce the pilot overhead for channel estimation in wireless communication systems. Particularly, structured turbo compressed sensing (STCS) provides a generic framework for structured sparse signal recovery with reduced computational complexity and storage requirement. In this paper, we consider the problem of massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channel estimation in a frequency division duplexing (FDD) downlink system. By exploiting the structured sparsity in the angle-frequency domain (AFD) and angle-delay domain (ADD) of the massive MIMO-OFDM channel, we represent the channel by using AFD and ADD probability models and design message-passing based channel estimators under the STCS framework. Several STCS-based algorithms are proposed for massive MIMO-OFDM channel estimation by exploiting the structured sparsity. We show that, compared with other existing algorithms, the proposed algorithms have a much faster convergence speed and achieve competitive error performance under a wide range of simulation settings.

Low-complexity joint channel estimation and symbol detection for OFDMA systems

In this paper, we propose a joint channel estimation and symbol detection (JCESD) algorithm relying on message-passing algorithms (MPA) for orthogonal frequency division multiple access (OFDMA) systems. The channel estimation and symbol detection leverage the framework of expectation propagation (EP) and belief propagation (BP) with the aid of Gaussian approximation, respectively. Furthermore, to reduce the computation complexity involved in channel estimation, the matrix inversion is transformed into a series of diagonal matrix inversions through the Sherman-Morrison formula. Simulation experiments show that the proposed algorithm can reduce the pilot overhead by about 50%, compared with the traditional linear minimum mean square error (LMMSE) algorithm, and can approach to the bit error rate (BER) performance bound of perfectly known channel state information within 0.1 dB.

Post-FEC Performance of Pilot-Aided Carrier Phase Estimation over Cycle Slip

The POST-forward error correction (FEC) bit error rate (BER) performance and the cycle-slip (CS) probability of the carrier phase estimation (CPE) scheme based on Viterbi–Viterbi phase estimation (VVPE) algorithm and the VV cascaded by pilot-aided-phase-unwrap (PAPU) algorithm have been experimentally investigated in a 56 Gbit/s quadrature phase-shift keying (QPSK) coherent communication system. Experimental results show that, with 0.78% pilot overhead, the VVPE + PAPU scheme greatly improves the POST-FEC performance degraded by continuous CS, maintaining a low CS probability with less influence of filter length. Comparing with the VVPE scheme, the VVPE + PAPU scheme can respectively obtain about 3.1 dB, 1.3 dB, 0.6 dB PRE-FEC optical signal noise ratio (OSNR) gains at PRE-BER of 1.8 × 10−2. Meanwhile, the VVPE + PAPU scheme respectively achieves about 3 dB, 1 dB, and 0.5 dB POST-FEC OSNR gain and improves the FEC limit from 2.5 × 10−3 to 1.4 × 10−2, from 8.9 × 10−3 to 1.8 × 10−2, and from 1.6 × 10−2 to 1.9 × 10−2 under the CPE filter length of 8, 16, and 20.

Differentially Modulated Spectrally Efficient Frequency-Division Multiplexing

This letter proposes a differentially modulated non-orthogonal spectrally efficient frequency-division multiplexing (D-SEFDM) architecture, which allows us to dispense with any pilot overhead needed for channel estimation at the receiver, while increasing the bandwidth efficiency in comparison to the orthogonal frequency-division multiplexing counterpart. While it is a challenging task to carry out differential-modulation-assisted non-coherent detection for multi-carrier transmissions in the presence of a severe inter-carrier interference (ICI), in the proposed scheme ICI elimination and symbol demodulation are implemented in a non-coherent manner, by exploiting the fact that ICI imposed by D-SEFDM is deterministically given, once we have a compression factor of non-orthogonal subcarriers. It is verified in our simulations that the proposed D-SEFDM exhibits a higher performance than the conventional coherent SEFDM scheme, especially for the scenarios of a high Doppler frequency.

Compressive RF Training for Massive MIMO With Channel Support Side Information

Hybrid beamforming (BF) is a promising solution for massive MIMO with limited RF chains. To reduce the amount of pilot overhead for channel estimation, compressed sensing techniques that exploit channel sparsity have been proposed. One key issue is how to design the RF (analog) training vectors to achieve higher BF gain with fewer pilots. Specifically, narrow-beam RF training requires large pilot overhead for finding strongest paths, and random RF training suffers from low BF gain. We propose to use a mixture of narrow-beam and random RF training vectors, and optimize the fraction of the two sets of RF training vectors based on channel support side information (CSSI) at the BS. We show that this optimized fraction exhibits a phase transition: when the CSSI accuracy exceeds a certain threshold, the maximum number of narrow-beam RF training vectors should be used to focus beams in all directions indicated by the CSSI. Otherwise, only random RF training vectors should be used to explore the unknown channel support. Moreover, we derive closed-form bounds on the channel estimation error. Both the analysis and simulations show that the proposed method can achieve substantial gains over various baseline methods.

Experimental demonstration of blind equalization for OFDM/OQAM-VLC system

Orthogonal frequency-division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) in direct-detection system obtains many benefits such as free of cyclic prefix (CP) and high side-lobe suppression ratio in comparison with CP-OFDM signal. Unfortunately, for OFDM/OQAM signal, it needs an extra interference cancellation process to eliminate the imaginary intrinsic interference (IMI) when using one-tap frequency equalization known as interference approximation method (IAM). Blind equalization without a pilot overhead for OFDM/OQAM signal is proposed and experimentally demonstrated in visible light communication (VLC) system with limited modulation bandwidth. Experimental results show that blind equalization for OFDM/OQAM-VLC can effectively eliminate the IMI effect and can exhibit better symbol error rate on different subcarriers. Furthermore, compared with conventional IAM, it exhibits 3.2 and 2.7 dB overall improvement in Q2-factor using blind equalization based on the constant modulus algorithm and constant norm algorithm, respectively. In addition, the system can achieve a total data rate of 3.96 Gb / s over 5-m free space link with a bit error rate lower than hard-decision forward error correction limit of 3.8 × 10 − 3.

Data-Aided Frequency Offset Estimation for CE-OFDM Broadband Satellite Systems

In recent years, the constant-envelope orthogonal frequency-division multiplexing (CE-OFDM) has been considered as a candidate waveform in broadband satellite systems as it has a 0-dB peak-to-average power ratio (PAPR). However, the carrier frequency offset (CFO) estimation scheme for CE-OFDM broadband satellite systems directly affects system performance. In this paper, we analyze the network architecture and the propagation environment of CE-OFDM broadband satellite systems, and we propose a data-aided CFO estimation strategy based on the frequency domain pilot symbols. The Cramer–Rao bound (CRB) of our CFO estimator is given by mathematical analysis, and the effect of the number of pilot symbols on the estimation performance is analyzed. The pilot symbol-based CFO estimator is composed of a phase demodulator and a discrete Fourier transform (DFT) module, and it can obtain a large estimation range under a small pilot overhead. The simulation results show that the CE-OFDM broadband satellite systems can achieve a good bit error rate (BER) performance by using the proposed strategy to estimate and compensate the CFO.

Enhanced Adaptive Polar-Linear Interpolation Aided Channel Estimation

Among the numerous channel estimation (CE) techniques in the literature, pilot-aided CE has been widely employed, where interpolation methods can be used for reducing pilot overhead. The conventional family of linear interpolation (LI)-based algorithms is easy to implement, but naturally results in residual interpolation errors even at high signal-to-noise ratios. In this letter, an enhanced adaptive polar LI method is proposed, which exploits new algorithms of effective origin optimization, adaptive angle gradient, and sparse channel detection. The proposed scheme is capable of outperforming a range of existing methods and adapting to any tap-delay-line type channel model, in real-time, while maintaining a complexity as low as the traditional LI method.

Compressive Sensing Based Channel Estimation for Massive MIMO Communication Systems

Massive multiple-input multiple-output (MIMO) is believed to be a key technology to get 1000x data rates in wireless communication systems. Massive MIMO occupies a large number of antennas at the base station (BS) to serve multiple users at the same time. It has appeared as a promising technique to realize high-throughput green wireless communications. Massive MIMO exploits the higher degree of spatial freedom, to extensively improve the capacity and energy efficiency of the system. Thus, massive MIMO systems have been broadly accepted as an important enabling technology for 5th Generation (5G) systems. In massive MIMO systems, a precise acquisition of the channel state information (CSI) is needed for beamforming, signal detection, resource allocation, etc. Yet, having large antennas at the BS, users have to estimate channels linked with hundreds of transmit antennas. Consequently, pilot overhead gets prohibitively high. Hence, realizing the correct channel estimation with the reasonable pilot overhead has become a challenging issue, particularly for frequency division duplex (FDD) in massive MIMO systems. In this paper, by taking advantage of spatial and temporal common sparsity of massive MIMO channels in delay domain, nonorthogonal pilot design and channel estimation schemes are proposed under the frame work of structured compressive sensing (SCS) theory that considerably reduces the pilot overheads for massive MIMO FDD systems. The proposed pilot design is fundamentally different from conventional orthogonal pilot designs based on Nyquist sampling theorem. Finally, simulations have been performed to verify the performance of the proposed schemes. Compared to its conventional counterparts with fewer pilots overhead, the proposed schemes improve the performance of the system.

Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems

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 on the SNR level. This approach enables us to determine the sparsity in an indirect manner. The proposed algorithm not only optimizes the channel estimation performance but also reduces the pilot overhead, which saves the spectrum and energy resources. Simulation results show that the proposed algorithm has improved channel performance compared with the existing algorithm, in both low SNR and low pilot overhead.

Deterministic pilot design for structured sparse channel estimation in MISO systems

To reduce the pilot overhead, compressive sensing techniques have been widely adopted for sparse channel estimation. In this paper, we explore the spatial common sparsity across different channels and investigate the deterministic pilot design with superimposed pattern for the downlink channel estimation in multi-input single-output systems. Previous works generally allocate pilots randomly when using the superimposed pilots to estimate the jointly sparse channels, which is storage-hungry and time-consuming. For the joint optimization of pilot locations and symbols, this paper proposes two pilot design schemes, named by Algorithms 1 and 2, to minimize the intrablock and interblock coherence simultaneously. Algorithm 1 sequentially optimizes pilot locations and symbols, while Algorithm 2 integrates the allocation of pilot symbols into the optimization of pilot locations and alternately optimizes the pilot locations and symbols. Both schemes can flexibly select the design criteria to ensure the small intrablock and interblock coherence. Simulation results show that the proposed pilot designs outperform the existing ones in terms of channel estimate mean-squared-error and bit-error-rate.

Beyond‐sparsity–based doubly selective channel estimation for high‐mobility multiuser MIMO‐OFDM systems

AbstractWireless communications in high‐mobility situations face with doubly selective (DS) fading. In addition, many practical wireless channels exhibit a sparse multipath structure. This paper investigates the DS multiuser multiple‐input–multiple‐output (MIMO) channel estimation via compressed sensing. The DS channel is characterized by a general sparse basis expansion model (BEM). This model reduces the channel estimation complexity as the channel estimation problem is reduced to estimating the BEM coefficients instead of numerous channel parameters. In addition, a pilot pattern is proposed for multiuser MIMO orthogonal frequency division multiplexing systems, which includes guard and silent pilots to reduce intercarrier interference and interuser interference, respectively. Then, two beyond‐sparsity features (ie, block and joint sparsity) of the BEM coefficients are exploited to propose a beyond‐sparsity–based channel estimation method. Moreover, a pilot design algorithm is proposed to optimize the pilot positions and enhance the estimator performance. Simulation results demonstrate that the proposed method significantly improves the estimation accuracy over the existing approaches, with a much smaller pilot overhead.

Information-Theoretic Pilot Design for Downlink Channel Estimation in FDD Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is one of the most promising techniques for next generation wireless communications due to its superior capability to provide high spectrum and energy efficiency. Considering the very large number of antennas employed at the base station, however, the pilot overhead for downlink channel estimation becomes unaffordable in frequency division duplex (FDD) multiuser massive MIMO systems. In this paper, we propose an information-theoretic metric to design the pilot for downlink channel estimation in FDD multiuser massive MIMO systems. By exploiting the low-rank nature of the channel covariance matrix, we first derive the minimum number of pilot symbols required to ensure perfect channel recovery, which is much less than the number of antennas at the base station. Further, under a general channel model that the channel vector of each user follows a Gaussian mixture distribution, the pilot symbols are designed by maximizing the weighted sum of the Shannon mutual information between the measurements of the users and their corresponding channel vectors on the complex Grassmannian manifold. Simulation results demonstrate the effectiveness of the proposed information-theoretic pilot design for the downlink channel estimation in FDD massive MIMO systems.

Downlink Channel Estimation for Massive MIMO Systems Relying on Vector Approximate Message Passing

To reduce the pilot overhead of downlink channel estimation in massive multiple-input-multiple-output (MIMO) systems, a sparse recovery algorithm relying on the vector approximate message passing (VAMP) technique is proposed. More specifically, an a-priori channel model characterized by a multivariate Bernoulli-Gaussian distribution is invoked for exploiting the common sparsity of massive MIMO channels, and the VAMP technique is used for jointly estimating the spatially correlated channels. Moreover, the hyperparameters of the a-priori model are learned by invoking the expectation maximization algorithm. Our numerical results demonstrate that the proposed algorithm is capable of reducing the pilot overhead by 50% in massive MIMO systems.

Exploiting Dynamic Sparsity for Downlink FDD-Massive MIMO Channel Tracking

Accurate channel tracking with a small pilot overhead is vital for real-time massive multiple-input and multiple-output (MIMO) communication over a dynamic channel. Recently, compressive sensing has been applied to reduce the pilot overheads by exploiting the spatial and/or temporal correlation of massive MIMO channels. However, most existing channel estimation and tracking algorithms are based on oversimplified channel models with restrictive assumptions, and thus perform poorly under realistic channels. In this paper, we consider downlink frequency-division duplexing-massive MIMO system operating with limited scattering around the base station and flat fading channel is considered. We propose a two-dimensional Markov Model (2D-MM) to capture the 2-D dynamic sparsity (i.e., structured sparsity in the spatial domain and probabilistic temporal dependency of channel in the temporal domain) of massive MIMO channels. The 2D-MM has the flexibility to model different propagation environments in practice. We derive an effective message passing algorithm called dynamic turbo orthogonal approximate message passing (D-TOAMP) to recursively track a dynamic massive MIMO channel with a 2D-MM prior. The proposed D-TOAMP algorithm does not require knowledge of the 2D-MM channel parameters, which could be automatically learned through the expectation maximization framework. Extensive simulations show that the proposed D-TOAMP can achieve significant gains over the existing algorithms under realistic channels.

Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems

For millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid processing architecture is usually used to reduce the complexity and cost, which poses a very challenging issue in channel estimation. In this paper, deep convolutional neural network (CNN) is employed to address this problem. We first propose a spatial-frequency CNN (SF-CNN) based channel estimation exploiting both the spatial and frequency correlation, where the corrupted channel matrices at adjacent subcarriers are input into the CNN simultaneously. Then, exploiting the temporal correlation in time-varying channels, a spatial-frequency-temporal CNN (SFT-CNN) based approach is developed to further improve the accuracy. Moreover, we design a spatial pilot-reduced CNN (SPR-CNN) to save spatial pilot overhead for channel estimation, where channels in several successive coherence intervals are grouped and estimated by a channel estimation unit with memory. Numerical results show that the proposed SF-CNN and SFT-CNN based approaches outperform the non-ideal minimum mean-squared error (MMSE) estimator but with reduced complexity, and achieve the performance close to the ideal MMSE estimator that is very difficult to be implemented in practical situations. They are also robust to different propagation scenarios. The SPR-CNN based approach achieves comparable performance to SF-CNN and SFT-CNN based approaches while only requires about one third of spatial pilot overhead at the cost of complexity. Our work clearly shows that deep CNN can efficiently exploit channel correlation to improve the estimation performance for mmWave massive MIMO systems.

Massive MIMO Channel Estimation Over the mmWave Systems Through Parameters Learning

In this letter, we formulate an off-grid channel model to characterize spatial sample mismatching in the discrete Fourier transform (DFT) based massive multiple-input-multiple-output (MIMO) channel estimation over the millimeter-wave (mmWave) band. Then, we decompose the off-grid mmWave massive MIMO channel estimation into the learning of model parameters and virtual channel estimation. Specifically, an expectation maximization (EM) based sparse Bayesian learning framework is first developed to learn the model parameters, such as bias parameters and spatial signatures, with unknown noise. With the learned model parameters, we resort to the linear minimum mean square error method to estimate the instantaneous virtual channel with less pilot overhead. Finally, we corroborate the validity of the proposed method through numerical simulations.