Intelligent Reflecting Surface (IRS) holds promise for enhancing wireless channel propagation, especially in high-frequency bands like millimeter-wave (mmWave) that are prone to significant propagation losses and blockage effects. However, channel estimation poses significant challenges due to passive nature of the IRS, high dimension of channel matrix, and need to consider superposition of direct channel between transceivers and reflective channel through the IRS. In this study, we explore adaptive and robust estimator for time-varying mmWave channels, considering the use of more general arbitrarily shaped IRS, in contrast to classical regular IRS elements arranged on a grid. Firstly, we establish a consistent-sparse-subspace relationship between direct and reflective channels using linearly modified atomic norm minimization. Secondly, we formulate joint estimation as a low-dimensional and robustly sparse matrix reconstruction programming, resolving efficiently through alternating optimization and alternating direction method of multipliers. Additionally, to reduce complexity, we propose a learning-based solver using fixed-point iteration and a cascaded deep-learning network framework. This solver can adjust its network depth according to the time-varying channel, achieving linear convergence and reduced complexity. Simulation results demonstrate that both proposed channel estimation methods outperform current state-of-the-art techniques.

We propose super-resolution multiple-input multiple-output channel estimators for generalized spatial modulation-based millimeter-wave systems. Utilizing the inherent spatial sparsity of millimeter-wave channels, channel estimation problem is formulated using atomic norm minimization that enhances sparsity in the continuous angles of arrival and departure. Both pilot-assisted and data-aided channel estimators are developed, with the former one formulated as a convex problem and the latter one as a non-convex problem. To efficiently solve these formulated channel estimation problems, we develop non-convex factorization-based conjugate gradient descent methods to restrict search space into low-rank matrices. Superior channel estimation performance of the proposed algorithms compared to the state-of-the-art compressed sensing-based estimators is demonstrated by simulation results.

Exploiting the inherent sparsity of mmWave channels, the channel estimation problem is formulated as an atomic norm minimization that enhances sparsity in the continuous angles of arrival and departure. A semi-blind channel estimator is developed to track the time-varying channel dynamics, which is formulated as a non-convex problem. To solve the formulated channel estimation problem, we develop a computationally efficient conjugate gradient descent method based on nonconvex factorization which restricts the search space to lowrank matrices. Simulation results are presented to illustrate the superior channel estimation performance of the proposed algorithms compared to the existing compressed-sensing-based estimators with finely quantized angle grids.

The universal filtered multi-carrier (UFMC) system, which groups and filters subcarriers before transmission, is a potential multi-carrier modulation technique for the emerging Machine-Type Communications. Given the relaxed time synchronization requirement of UFMC, we design a novel joint timing offset and channel estimation method for multi-user UFMC uplink transmission. The joint estimation problem is formulated based on atomic norm minimization (ANM) that enhances the sparsity of timing offsets in the continuous frequency domain. Simulation results show that the proposed method can achieve considerable performance gain, as compared with existing approaches.

Recently, the combination of multiple-input multiple-output technology with orthogonal time frequency space modulation (MIMO-OTFS) has drawn extensive attention with the advantage of exploring the additional transmit diversity or spatial multiplexing of the MIMO scheme. Motivated by the potential spectral efficiency of multi-domain index modulation, in this paper, we propose a generalized index modulation based MIMO-OTFS (GIM-MIMO-OTFS) to convey extra information with the aid of the spatial and delay-Doppler indices. To reduce the supreme complexity of maximum likelihood (ML) detection with a large size of look-up table in GIM-MIMO-OTFS, a two stage index detector based on power information is presented. Furthermore, the analytical expression of average bit error probability (ABEP) is derived to evaluate the performance of the proposed scheme. Simulation results demonstrate the enhanced performance of the proposed scheme in high-mobility scenarios, and highlight the complexity and outperformance of the proposed detector compared to ML detector.

Dynamic cooperation clustering (DCC) becomes a main enabler for cell-free massive MIMO systems since it can improve the energy efficiency and reduce the complexity of signal processing significantly. However, DCC formation for all served users simultaneously is a very complicated mixed binary nonlinear programming problem, a single agent has limited capability to search the optimal schemes. In this paper, we propose a whale swarm reinforcement learning (WSRL) based DCC method. Exploiting multiple searching agent imitated by a group of whales, whale swarm optimization (WOA) algorithm searches the optimal DCC scheme simultaneously and learn the searching experience from each other. Moreover, the reinforcement learning is integrated to select the most efficient hunting action for each whale, which can accelerate the convergence and avoid the local trap. Simulation results demonstrate that the proposed method has better searching ability and higher convergence speed than the existing works.