Channel Estimation and ICI Cancelation in Vehicular Channels of OFDM Wireless Communication Systems

Orthogonal frequency division multiplexing (OFDM) scheme increases bandwidth efficiency (BE) of data transmission and eliminates inter symbol interference (ISI). As a result, it has been widely used for wideband communication systems that have been developed during the past two decades and it can be a good candidate for the emerging communication systems such as fifth generation (5G) cellular networks with high carrier frequency and communication systems of high speed vehicles such as high speed trains (HSTs) and supersonic unmanned aircraft vehicles (UAVs). However, the employment of OFDM for those upcoming systems is challenging because of high Doppler shifts. High Doppler shift makes the wideband communication channel to be both frequency selective and time selective, doubly selective (DS), causes inter carrier interference (ICI) and destroys the orthogonality between the subcarriers of OFDM signal. In order to demodulate the signal in OFDM systems and mitigate ICIs, channel state information (CSI) is required. In this work, we deal with channel estimation (CE) and ICI cancellation in DS vehicular channels. The digitized model of the DS channels can be short and dense, or long and sparse. CE methods that perform well for short and dense channels are highly inefficient for long and sparse channels. As a result, for the latter type of channels, we proposed the employment of compressed sensing (CS) based schemes for estimating the channel. In addition, we extended our CE methods for multiple input multiple output (MIMO) scenarios. We evaluated the CE accuracy and data demodulation fidelity, along with the BE and computational complexity of our methods and compared the results with the previous CE procedures in different environments. The simulation results indicate that our proposed CE methods perform considerably better than the conventional CE schemes

OTFS vs. OFDM in the Presence of Sparsity: A Fair Comparison

Many recent works in the literature declare that Orthogonal Time-Frequency-Space (OTFS) modulation is a promising candidate technology for high mobility communication scenarios. However, a truly fair comparison with its direct concurrent and widely used Orthogonal Frequency-Division Multiplexing (OFDM) modulation has not yet been provided. In this paper, we present such a fair comparison between the two digital modulation formats in terms of achievable communication rate. In this context, we explicitly address the problem of channel estimation by considering, for each modulation, a pilot scheme and the associated channel estimation algorithm specifically adapted to sparse channels in the Doppler-delay domain, targeting the optimization of the pilot overhead to maximize the overall achievable rate. In our achievable rate analysis we consider also the presence of a guard interval or cyclic prefix. The results are supported by numerical simulations, for different time-frequency selective channels including multiple scattering components and under non-perfect channel state information resulting from the considered pilot schemes. This work does not claim to establish in a fully definitive way which is the best modulation format, since such choice depends on many other features which are outside the scope of this work (e.g., legacy, intellectual property, ease and know-how for implementation, and many other criteria). Nevertheless, we provide the foundations to properly compare multi-carrier communication systems in terms of their information theoretic achievable rate potential, within meaningful and sensible assumptions on the channel models and on the receiver complexity (both in terms of channel estimation and in terms of soft-output symbol detection)

Structured compressive sensing-based spatio-temporal joint channel estimation for FDD massive MIMO

Massive MIMO is a promising technique for future 5G communications due to its high spectrum and energy efficiency. To realize its potential performance gain, accurate channel estimation is essential. However, due to massive number of antennas at the base station (BS), the pilot overhead required by conventional channel estimation schemes will be unaffordable, especially for frequency division duplex (FDD) massive MIMO. To overcome this problem, we propose a structured compressive sensing (SCS)-based spatio-temporal joint channel estimation scheme to reduce the required pilot overhead, whereby the spatio-temporal common sparsity of delay-domain MIMO channels is leveraged. Particularly, we first propose the nonorthogonal pilots at the BS under the framework of CS theory to reduce the required pilot overhead. Then, an adaptive structured subspace pursuit (ASSP) algorithm at the user is proposed to jointly estimate channels associated with multiple OFDM symbols from the limited number of pilots, whereby the spatio-temporal common sparsity of MIMO channels is exploited to improve the channel estimation accuracy. Moreover, by exploiting the temporal channel correlation, we propose a space-time adaptive pilot scheme to further reduce the pilot overhead. Additionally, we discuss the proposed channel estimation scheme in multicell scenario. Simulation results demonstrate that the proposed scheme can accurately estimate channels with the reduced pilot overhead, and it is capable of approaching the optimal oracle least squares estimator

D4.2 Intelligent D-Band wireless systems and networks initial designs

This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project

Channel Estimation for Massive MIMO Systems

Massive multiple input multiple output (MIMO) systems can significantly improve the channel capacity by deploying multiple antennas at the transmitter and receiver. Massive MIMO is considered as one of key technologies of the next generation of wireless communication systems. However, with the increase of the number of antennas at the base station, a large number of unknown channel parameters need to be dealt with, which makes the channel estimation a challenging problem. Hence, the research on the channel estimation for massive MIMO is of great importance to the development of the next generation of communication systems. The wireless multipath channel exhibits sparse characteristics, but the traditional channel estimation techniques do not make use of the sparsity. The channel estimation based on compressive sensing (CS) can make full use of the channel sparsity, while use fewer pilot symbols. In this work, CS channel estimation methods are proposed for massive MIMO systems in complex environments operating in multipath channels with static and time-varying parameters. Firstly, a CS channel estimation algorithm for massive MIMO systems with Orthogonal Frequency Division Multiplexing (OFDM) is proposed. By exploiting the spatially common sparsity in the virtual angular domain of the massive MIMO channels, a dichotomous-coordinate-decent-joint-sparse-recovery (DCD-JSR) algorithm is proposed. More specifically, by considering the channel is static over several OFDM symbols and exhibits common sparsity in the virtual angular domain, the DCD-JSR algorithm can jointly estimate multiple sparse channels with low computational complexity. The simulation results have shown that, compared to existing channel estimation algorithms such as the distributed-sparsity-adaptive-matching-pursuit (DSAMP) algorithm, the proposed DCD-JSR algorithm has significantly lower computational complexity and better performance. Secondly, these results have been extended to the case of multipath channels with time-varying parameters. This has been achieved by employing the basis expansion model to approximate the time variation of the channel, thus the modified DCD-JSR algorithm can estimate the channel in a massive MIMO OFDM system operating over frequency selective and highly mobile wireless channels. Simulation results have shown that, compared to the DCD-JSR algorithm designed for time-invariant channels, the modified DCD-JSR algorithm provides significantly better estimation performance in fast time-varying channels