ADAPTIVE CHANNEL ESTIMATION FOR SUPER-RESOLUTION SPARSE MIMO-OFDM

A multiple-input multiple (MIMO) communication system combined with the orthogonal frequency division multiplexing (OFDM) modulation technique can achieve reliable high data rate transmission over broadband wireless channels. The most important research topic in the wireless communications is the adaptive channel estimation where the channel is rapidly time-varying. In this paper performance analysis of channel estimation through adaptive channel estimation algorithms for estimating channel using different modulation scheme are investigated. a parametric sparse multiple input multiple output (MIMO)-OFDM channel estimation scheme based on the finite rate of innovation (FRI) theory, whereby super-resolution estimates of path delays with arbitrary values can be achieved. Meanwhile, both the spatial and temporal correlations of wireless MIMO channels are exploited to improve the accuracy of the channel estimation. The estimation of channel at pilot frequencies is based on Least Mean Square and Recursive Least Square channel estimation algorithm. I have compared the performances of channel estimation algorithm by measuring bit error rate vs. SNR with BPSK, QPSK 16-PSK and 256- PSK modulation schemes

Performance Analysis of Parametric and Non-Parametric MIMO-OFDM Channel Estimation Schemes

A parametric super resolution sparse Multi Input Multi Output (MIMO)-OFDM channel estimation technique in view of the Finite Rate of Innovation (FRI) theory has been proposed, whereby super-resolution assessments of delays in paths with arbitrary values can be accomplished. In the mean time, for wireless MIMO channels both the spatial and temporal correlations are made use of, to enhance the precision of the channel estimation. For outside communication situations, where wireless channels are meager in nature, path delays of distinctive transmit-receive antenna pairs share a similar sparse pattern because of the spatial correlation of MIMO channels. At the same time, the channel sparse pattern is almost unaltered amid several adjacent OFDM symbols because of the temporal correlation of MIMO channels. Exploiting these MIMO channel attributes simultaneously, the proposed technique performs better than existing highly developed techniques. Moreover, by joint processing of signals integrated with distinctive antennas, the pilot overhead can be decreased under the structure of the FRI theory. DOI: 10.17762/ijritcc2321-8169.15074

A Two-Stage 2D Channel Extrapolation Scheme for TDD 5G NR Systems

Recently, channel extrapolation has been widely investigated in frequency division duplex (FDD) massive MIMO systems. However, in time division duplex (TDD) fifth generation (5G) new radio (NR) systems, the channel extrapolation problem also arises due to the hopping uplink pilot pattern, which has not been fully researched yet. This paper addresses this gap by formulating a channel extrapolation problem in TDD massive MIMO-OFDM systems for 5G NR, incorporating imperfection factors. A novel two-stage two-dimensional (2D) channel extrapolation scheme in both frequency and time domain is proposed, designed to mitigate the negative effects of imperfection factors and ensure high-accuracy channel estimation. Specifically, in the channel estimation stage, we propose a novel multi-band and multi-timeslot based high-resolution parameter estimation algorithm to achieve 2D channel extrapolation in the presence of imperfection factors. Then, to avoid repeated multi-timeslot based channel estimation, a channel tracking stage is designed during the subsequent time instants, in which a sparse Markov channel model is formulated to capture the dynamic sparsity of massive MIMO-OFDM channels under the influence of imperfection factors. Next, an expectation-maximization (EM) based compressive channel tracking algorithm is designed to jointly estimate unknown imperfection and channel parameters by exploiting the high-resolution prior information of the delay/angle parameters from the previous timeslots. Simulation results underscore the superior performance of our proposed channel extrapolation scheme over baselines

Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications

In the emerging high mobility vehicle-to-everything (V2X) communications using millimeter wave (mmWave) and sub-THz, multiple-input multiple-output (MIMO) channel estimation is an extremely challenging task. At mmWaves/sub-THz frequencies, MIMO channels exhibit few leading paths in the space-time (ST) domain (i.e., directions or arrival/departure and delays). Algebraic low-rank (LR) channel estimation exploits ST channel sparsity through the computation of position-dependent MIMO channel eigenmodes leveraging recurrent training vehicle passages in the coverage cell. LR requires vehicles' geographical positions and tens to hundreds of training vehicles' passages for each position, leading to significant complexity and control signaling overhead. Here, we design a deep-learning (DL)-based LR channel estimation method to infer MIMO channel eigenmodes in V2X urban settings, starting from a single least squares (LS) channel estimate and without needing vehicle's position information. Numerical results show that the proposed method attains comparable mean squared error (mse) performance as the position-based LR. Moreover, we show that the proposed model can be trained on a reference scenario and be effectively transferred to urban contexts with different ST channel features, providing comparable mse performance without an explicit transfer learning procedure. This result eases the deployment in arbitrary dense urban scenarios