Wireless channel estimation is one of the challenging problems in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) wireless systems. The MIMO-OFDM exploits the spatial resources and increases the reliability and capacity of wireless systems. However, the performance of these systems depends on accurate channel estimation since the receivers require perfect Channel State Information (CSI) for coherent signal detection. Thus, wireless channel estimation is a necessary component of OFDM systems. In this article, we have proposed an algorithm for MIMO-OFDM systems that combines pilot symbols with reliable data symbols for channel estimation. The reliable data symbols serve as virtual pilots and enhance the spectral efficiency. The proposed Data Aided Channel Estimation (DACE) algorithm eliminates the requirement of any additional resources such as excessive number of training symbols to attain the desired performance. Also, it outperforms the traditional Least Square (LS) and Linear Minimum Mean Square Error (LMMSE) methods for channel estimation in terms of Mean Square Error (MSE) and Bit Error Rate (BER) performance of the system.

This article presents an optimal pilot pattern for the data‐aided channel estimation (DACE) scheme for both single‐input single‐output (SISO) and multiple‐input multiple‐output orthogonal frequency division multiplexing (MIMO‐OFDM) wireless systems. The research evaluates the performance of the DACE scheme using different comb‐type pilot patterns for both least square (LS) and linear minimum mean square error (LMMSE) channel estimators. In this regard, it is found that pilot spacing significantly influences system performance. Inserting pilot symbols in consecutive subcarriers cannot compensate for increased pilot spacing. Hence, the solution to this problem is to place pilot symbols at appropriate locations within the given spectrum. Moreover, data symbols which are reliably detected at the receiver are used as additional pilot signals to further enhance system performance. However, reliable data symbols need to be determined carefully because wrong detection results in severe performance degradation. In this respect, the proposed comb‐type pilot pattern using a single pilot subcarrier extracts the maximum number of reliable data symbols for the DACE scheme, improves channel estimation accuracy, and provides bandwidth optimization for MIMO‐OFDM systems. Furthermore, it outperforms all other pilot patterns in terms of system mean square error (MSE) and bit‐error‐rate (BER) performance.

Low-complexity channel estimation techniques are key to enabling efficient, reliable, and real-time communication in modern wireless devices operating under resource and energy constraints. This paper presents for the first time a low-complexity peak-power-assisted data-aided channel estimation (DACE) scheme for both single-input single-output (SISO) and multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) wireless systems. In OFDM, high peak-power levels occur when the subcarriers align in phase and constructively interfere with each other. The research proposes a peak-power-assisted channel estimation scheme that accurately selects peak-power carriers at the transmitter of an OFDM system and uses them as reliable carriers for the DACE scheme. By incorporating these reliable carriers with known pilot symbols as additional pilot signals, channel estimation accuracy significantly improves in MIMO-OFDM systems. This eliminates the need to determine reliable data symbols at the receiver, thereby significantly reducing the computational complexity of the system. However, high peak-powers are considered a major drawback in OFDM. In this work, we incorporate a companding technique to mitigate this issue and provide sufficient margin for the DACE scheme. The performance of the proposed DACE scheme is evaluated using both least square (LS) and linear minimum mean square error (LMMSE) channel estimators. In this regard, the proposed technique not only improves channel estimation accuracy but also enhances the spectral efficiency of the wireless system. It outperforms traditional channel estimators in terms of system mean square error (MSE) and bit-error-rate (BER) performance. It also reduces the pilot overhead by 50% compared to traditional channel estimators and provides bandwidth optimization for MIMO-OFDM systems. This makes it a promising solution for enhancing the performance and efficiency of next-generation wireless communication systems across diverse applications.

Electric vehicles (EVs) are evolving from passive charging loads into grid-supporting resources. Conventional smart-charging and vehicle-to-grid (V2G) strategies treat the charger as a grid-following current source that depends on a stiff external voltage, a dependence that becomes a weakness as inverter-based resources displace synchronous inertia. Grid-forming (GFM) control instead operates the bidirectional charger as a voltage source that establishes local voltage and frequency, enabling weak-grid, islanded, and black-start operation. This review synthesizes 113 sources, most identified through a structured keyword-based search, and organizes them along an evolutionary framework running from conventional charging through V2G and GFM EVs to AI-adaptive, fleet-coordinated resources. It covers bidirectional converter topologies; droop, virtual synchronous machine, virtual oscillator, and matching control; AI-enabled control using neural, fuzzy, reinforcement-learning, predictive, and physics-informed methods; fleet aggregation; resilience and black start; battery constraints; and cybersecurity and standardization. Throughout, experimentally demonstrated capability is distinguished from simulation-only or theoretical results. The synthesis shows that documented field pilots still rely on grid-following control and that GFM-EV validation remains largely confined to simulation. Seven research gaps are identified, including experimental validation, AI verifiability, battery–grid objective conflict, and fleet-scale coordination, and a five-generation roadmap toward autonomous, resilient GFM-EV fleets is proposed.