###### Abstract  
As a novel variant of spatial modulation (SM), enhanced SM (ESM) provides higher spectral efficiency and improved bit error rate (BER) performance compared to SM. In ESM, traditional signal detection methods such as maximum likelihood (ML) have the drawback of high complexity. Therefore, in this paper, we try to solve this problem using a deep neural network (DNN). Specifically, we propose a block DNN (B-DNN) structure, in which smaller B-DNNs are utilized to identify the active antennas along with the constellation symbols they transmit. Simulation outcomes indicate that the BER performance related to the introduced B-DNN method outperforms both the minimum mean-square error (MMSE) and the zero-forcing (ZF) methods, approaching that of the ML method. Furthermore, the proposed method requires less computation time compared to the ML method. enhanced spatial modulation; bit error rate; complexity; deep learning 2025133961  
Deep neural network (DNN) has made profound inroads in the wireless communications, manifesting its prowess in diverse applications such as channel coding [3], antenna selection [4] and modulation classification [5] as well as other applications [6; 7; 8; 9]. Recently, several extensions of DNN research achieved notable results. Several researchers have improved the structure of neural networks, leading to the development of more powerful networks, such as Kolmogorov-Arnold networks (KAN) [10]. Other researchers focus on integrating DNN with other technologies to enhance their performance in complex tasks, such as cognitive computing [11]. These efforts can refine and expand the applications of DNN in wireless communications, representing a promising direction for future research. At the same time, KAN and cognitive computing also have some applications in wireless communications. For instance, cognitive computing can be employed to predict the flow status of rectifiers, allowing for preemptive adjustments of network parameters and optimized power management. Moreover, cognitive computing can be utilized for real-time monitoring and analysis of communication signal characteristics, thereby optimizing signal processing algorithms. In terms of KAN, as a recent development in deep learning, it can be used to predict the performance of flexible electrohydrodynamic (EHD) pumps, which in turn can optimize the allocation of network resources. In terms of signal detection, several investigations have been conducted to improve the detection accuracy and complexity problem. In these works, some researchers attempt to improve the structure of neural network in order to enhance the performance of such neural networks in applications related to wireless communications [12; 13; 14]. If we consider the use of SM, in [15], the author used a block deep neural network (B-DNN) for signal detection in generalized SM (GSM). Experimental results showed that the B-DNN could approach the performance of the maximum likelihood (ML) method in terms of BER while exhibiting lower complexity. In [16], the author considered the use of neural network-assisted partial detection in GSM, with one neural network module for detecting the antenna index and another for detecting the received constellation symbol. This approach further reduced complexity. Authors in [17; 18] explored the possibility of using DNN for signal detection in quadrature SM (QSM) and the results demonstrated the feasibility of DNN. Previous work has demonstrated that employing DNN for signal detection in GSM and QSM can reduce complexity while maintaining a low BER. In ESM, as a new variant of SM, to the best of our knowledge, the DNN method for signal detection in ESM has not yet been utilized. In order to fill this gap, we propose a B-DNN method for signal detection in ESM. As an improved version of DNN, the B-DNN has already demonstrated its superior performance in signal detection for GSM. The B-DNN detector is capable of achieving a lower BER compared to the two linear detectors, minimum mean-square error (MMSE) and zero-forcing (ZF).